/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
(function (global, factory) {
    typeof exports === 'object' && typeof module !== 'undefined' ? factory(exports) :
    typeof define === 'function' && define.amd ? define(['exports'], factory) :
    (global = global || self, factory(global.tf = global.tf || {}));
}(this, function (exports) { 'use strict';

    /*! *****************************************************************************
    Copyright (c) Microsoft Corporation. All rights reserved.
    Licensed under the Apache License, Version 2.0 (the "License"); you may not use
    this file except in compliance with the License. You may obtain a copy of the
    License at http://www.apache.org/licenses/LICENSE-2.0

    THIS CODE IS PROVIDED ON AN *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
    KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
    WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
    MERCHANTABLITY OR NON-INFRINGEMENT.

    See the Apache Version 2.0 License for specific language governing permissions
    and limitations under the License.
    ***************************************************************************** */
    /* 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) {
        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) : new P(function (resolve) { resolve(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 2017 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.
     * =============================================================================
     */
    // 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 = {};
            this.populateURLFlags();
        }
        Environment.prototype.setPlatform = function (platformName, platform) {
            if (this.platform != null) {
                console.warn("Platform " + this.platformName + " has already been set. " +
                    ("Overwriting the platform with " + platform + "."));
            }
            this.platformName = platformName;
            this.platform = platform;
        };
        Environment.prototype.registerFlag = function (flagName, evaluationFn, setHook) {
            this.flagRegistry[flagName] = { evaluationFn: evaluationFn, setHook: setHook };
            // Override the flag value from the URL. This has to happen here because the
            // environment is initialized before flags get registered.
            if (this.urlFlags[flagName] != null) {
                var flagValue = this.urlFlags[flagName];
                console.warn("Setting feature override from URL " + flagName + ": " + flagValue + ".");
                this.set(flagName, flagValue);
            }
        };
        Environment.prototype.get = function (flagName) {
            if (flagName in this.flags) {
                return this.flags[flagName];
            }
            this.flags[flagName] = this.evaluateFlag(flagName);
            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 = getQueryParams(this.global.location.search);
            if (TENSORFLOWJS_FLAGS_PREFIX in urlParams) {
                var keyValues = urlParams[TENSORFLOWJS_FLAGS_PREFIX].split(',');
                keyValues.forEach(function (keyValue) {
                    var _a = keyValue.split(':'), key = _a[0], value = _a[1];
                    _this.urlFlags[key] = parseValue(key, value);
                });
            }
        };
        return Environment;
    }());
    function getQueryParams(queryString) {
        var params = {};
        queryString.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, function (s) {
            var t = [];
            for (var _i = 1; _i < arguments.length; _i++) {
                t[_i - 1] = arguments[_i];
            }
            decodeParam(params, t[0], t[1]);
            return t.join('=');
        });
        return params;
    }
    function decodeParam(params, name, value) {
        params[decodeURIComponent(name)] = decodeURIComponent(value || '');
    }
    function parseValue(flagName, value) {
        value = value.toLowerCase();
        if (value === 'true' || value === 'false') {
            return value === 'true';
        }
        else if ("" + +value === value) {
            return +value;
        }
        throw new Error("Could not parse value flag value " + value + " for flag " + flagName + ".");
    }
    /**
     * Returns the current environment (a global singleton).
     *
     * The environment object contains the evaluated feature values as well as the
     * active platform.
     */
    /** @doc {heading: 'Environment'} */
    function env() {
        return exports.ENV;
    }
    exports.ENV = null;
    function setEnvironmentGlobal(environment) {
        exports.ENV = environment;
    }

    /**
     * @license
     * Copyright 2019 Google LLC. All Rights Reserved.
     * Licensed under the Apache License, Version 2.0 (the "License");
     * you may not use this file except in compliance with the License.
     * You may obtain a copy of the License at
     *
     * http://www.apache.org/licenses/LICENSE-2.0
     *
     * Unless required by applicable law or agreed to in writing, software
     * distributed under the License is distributed on an "AS IS" BASIS,
     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
     * See the License for the specific language governing permissions and
     * limitations under the License.
     * =============================================================================
     */
    var kernelRegistry = new Map();
    var gradRegistry = new Map();
    /**
     * Returns the kernel function (code) associated with the provided names.
     *
     * @param kernelName The official name of the kernel.
     * @param backendName The official name of the backend.
     */
    function getKernel(kernelName, backendName) {
        var key = makeKey(kernelName, backendName);
        return kernelRegistry.get(key);
    }
    /**
     * Returns the registered gradient info associated with the provided kernel.
     * @param kernelName The official TF kernel name.
     */
    function getGradient(kernelName) {
        return gradRegistry.get(kernelName);
    }
    function getKernelsForBackend(backendName) {
        var it = kernelRegistry.entries();
        var result = [];
        while (true) {
            var _a = it.next(), done = _a.done, value = _a.value;
            if (done) {
                break;
            }
            var key = value[0], config = value[1];
            var backend = key.split('_')[0];
            if (backend === backendName) {
                result.push(config);
            }
        }
        return result;
    }
    /**
     * Registers the function (forward pass) for the kernel in a global registry.
     *
     * @param config A config object with the following properties:
     * - `kernelName` The official name of the kernel.
     * - `backendName` The official name of the backend.
     * - `kernelFunc` The function to run during the forward pass of the kernel.
     * - `setupFunc` Optional. Gets called once, after the backend initializes.
     * - `disposeFunc` Optional. Gets called once, right before the backend is
     * disposed.
     */
    function registerKernel(config) {
        var kernelName = config.kernelName, backendName = config.backendName;
        var key = makeKey(kernelName, backendName);
        if (kernelRegistry.has(key)) {
            throw new Error("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)) {
            console.warn("Overriding the gradient for '" + kernelName + "'");
        }
        gradRegistry.set(kernelName, config);
    }
    /**
     * Removes the kernel function from the registry.
     *
     * @param kernelName The official name of the kernel.
     * @param backendName The official name of the backend.
     *
     */
    function unregisterKernel(kernelName, backendName) {
        var key = makeKey(kernelName, backendName);
        if (!kernelRegistry.has(key)) {
            throw new Error("The kernel '" + kernelName + "' for backend " +
                ("'" + backendName + "' is not registered"));
        }
        kernelRegistry.delete(key);
    }
    /** Removes the registered gradient from the global registry. */
    function unregisterGradient(kernelName) {
        if (!gradRegistry.has(kernelName)) {
            throw new Error("The gradient '" + kernelName + "' for backend is not registered");
        }
        gradRegistry.delete(kernelName);
    }
    function makeKey(kernelName, backendName) {
        return backendName + "_" + kernelName;
    }

    /**
     * @license
     * Copyright 2017 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.
     * =============================================================================
     */
    /**
     * Shuffles the array in-place using Fisher-Yates algorithm.
     *
     * ```js
     * const a = [1, 2, 3, 4, 5];
     * tf.util.shuffle(a);
     * console.log(a);
     * ```
     *
     * @param array The array to shuffle in-place.
     */
    /** @doc {heading: 'Util', namespace: 'util'} */
    // tslint:disable-next-line:no-any
    function shuffle(array) {
        var counter = array.length;
        var temp = 0;
        var index = 0;
        // While there are elements in the array
        while (counter > 0) {
            // Pick a random index
            index = (Math.random() * counter) | 0;
            // Decrease counter by 1
            counter--;
            // And swap the last element with it
            temp = array[counter];
            array[counter] = array[index];
            array[index] = temp;
        }
    }
    /** Clamps a value to a specified range. */
    function clamp(min, x, max) {
        return Math.max(min, Math.min(x, max));
    }
    function nearestLargerEven(val) {
        return val % 2 === 0 ? val : val + 1;
    }
    function sum(arr) {
        var sum = 0;
        for (var i = 0; i < arr.length; i++) {
            sum += arr[i];
        }
        return sum;
    }
    /**
     * Returns a sample from a uniform [a, b) distribution.
     *
     * @param a The minimum support (inclusive).
     * @param b The maximum support (exclusive).
     * @return A pseudorandom number on the half-open interval [a,b).
     */
    function randUniform(a, b) {
        var r = Math.random();
        return (b * r) + (1 - r) * a;
    }
    /** Returns the squared Euclidean distance between two vectors. */
    function distSquared(a, b) {
        var result = 0;
        for (var i = 0; i < a.length; i++) {
            var diff = Number(a[i]) - Number(b[i]);
            result += diff * diff;
        }
        return result;
    }
    /**
     * Asserts that the expression is true. Otherwise throws an error with the
     * provided message.
     *
     * ```js
     * const x = 2;
     * tf.util.assert(x === 2, 'x is not 2');
     * ```
     *
     * @param expr The expression to assert (as a boolean).
     * @param msg A function that returns the message to report when throwing an
     *     error. We use a function for performance reasons.
     */
    /** @doc {heading: 'Util', namespace: 'util'} */
    function assert(expr, msg) {
        if (!expr) {
            throw new Error(typeof msg === 'string' ? msg : msg());
        }
    }
    function assertShapesMatch(shapeA, shapeB, errorMessagePrefix) {
        if (errorMessagePrefix === void 0) { errorMessagePrefix = ''; }
        assert(arraysEqual(shapeA, shapeB), function () { return errorMessagePrefix + (" Shapes " + shapeA + " and " + shapeB + " must match"); });
    }
    function assertNonNull(a) {
        assert(a != null, function () { return "The input to the tensor constructor must be a non-null value."; });
    }
    // NOTE: We explicitly type out what T extends instead of any so that
    // util.flatten on a nested array of number doesn't try to infer T as a
    // number[][], causing us to explicitly type util.flatten<number>().
    /**
     *  Flattens an arbitrarily nested array.
     *
     * ```js
     * const a = [[1, 2], [3, 4], [5, [6, [7]]]];
     * const flat = tf.util.flatten(a);
     * console.log(flat);
     * ```
     *
     *  @param arr The nested array to flatten.
     *  @param result The destination array which holds the elements.
     *  @param skipTypedArray If true, avoids flattening the typed arrays. Defaults
     *      to false.
     */
    /** @doc {heading: 'Util', namespace: 'util'} */
    function flatten(arr, result, skipTypedArray) {
        if (result === void 0) { result = []; }
        if (skipTypedArray === void 0) { skipTypedArray = false; }
        if (result == null) {
            result = [];
        }
        if (Array.isArray(arr) || isTypedArray(arr) && !skipTypedArray) {
            for (var i = 0; i < arr.length; ++i) {
                flatten(arr[i], result, skipTypedArray);
            }
        }
        else {
            result.push(arr);
        }
        return result;
    }
    /**
     * Returns the size (number of elements) of the tensor given its shape.
     *
     * ```js
     * const shape = [3, 4, 2];
     * const size = tf.util.sizeFromShape(shape);
     * console.log(size);
     * ```
     */
    /** @doc {heading: 'Util', namespace: 'util'} */
    function sizeFromShape(shape) {
        if (shape.length === 0) {
            // Scalar.
            return 1;
        }
        var size = shape[0];
        for (var i = 1; i < shape.length; i++) {
            size *= shape[i];
        }
        return size;
    }
    function isScalarShape(shape) {
        return shape.length === 0;
    }
    function arraysEqual(n1, n2) {
        if (n1 === n2) {
            return true;
        }
        if (n1 == null || n2 == null) {
            return false;
        }
        if (n1.length !== n2.length) {
            return false;
        }
        for (var i = 0; i < n1.length; i++) {
            if (n1[i] !== n2[i]) {
                return false;
            }
        }
        return true;
    }
    function isInt(a) {
        return a % 1 === 0;
    }
    function tanh(x) {
        // tslint:disable-next-line:no-any
        if (Math.tanh != null) {
            // tslint:disable-next-line:no-any
            return Math.tanh(x);
        }
        if (x === Infinity) {
            return 1;
        }
        else if (x === -Infinity) {
            return -1;
        }
        else {
            var e2x = Math.exp(2 * x);
            return (e2x - 1) / (e2x + 1);
        }
    }
    function sizeToSquarishShape(size) {
        var width = Math.ceil(Math.sqrt(size));
        return [width, Math.ceil(size / width)];
    }
    /**
     * Creates a new array with randomized indicies to a given quantity.
     *
     * ```js
     * const randomTen = tf.util.createShuffledIndices(10);
     * console.log(randomTen);
     * ```
     *
     * @param number Quantity of how many shuffled indicies to create.
     */
    /** @doc {heading: 'Util', namespace: 'util'} */
    function createShuffledIndices(n) {
        var shuffledIndices = new Uint32Array(n);
        for (var i = 0; i < n; ++i) {
            shuffledIndices[i] = i;
        }
        shuffle(shuffledIndices);
        return shuffledIndices;
    }
    function rightPad(a, size) {
        if (size <= a.length) {
            return a;
        }
        return a + ' '.repeat(size - a.length);
    }
    function repeatedTry(checkFn, delayFn, maxCounter) {
        if (delayFn === void 0) { delayFn = function (counter) { return 0; }; }
        return new Promise(function (resolve, reject) {
            var tryCount = 0;
            var tryFn = function () {
                if (checkFn()) {
                    resolve();
                    return;
                }
                tryCount++;
                var nextBackoff = delayFn(tryCount);
                if (maxCounter != null && tryCount >= maxCounter) {
                    reject();
                    return;
                }
                setTimeout(tryFn, nextBackoff);
            };
            tryFn();
        });
    }
    /**
     * Given the full size of the array and a shape that may contain -1 as the
     * implicit dimension, returns the inferred shape where -1 is replaced.
     * E.g. For shape=[2, -1, 3] and size=24, it will return [2, 4, 3].
     *
     * @param shape The shape, which may contain -1 in some dimension.
     * @param size The full size (number of elements) of the array.
     * @return The inferred shape where -1 is replaced with the inferred size.
     */
    function inferFromImplicitShape(shape, size) {
        var shapeProd = 1;
        var implicitIdx = -1;
        for (var i = 0; i < shape.length; ++i) {
            if (shape[i] >= 0) {
                shapeProd *= shape[i];
            }
            else if (shape[i] === -1) {
                if (implicitIdx !== -1) {
                    throw Error("Shapes can only have 1 implicit size. " +
                        ("Found -1 at dim " + implicitIdx + " and dim " + i));
                }
                implicitIdx = i;
            }
            else if (shape[i] < 0) {
                throw Error("Shapes can not be < 0. Found " + shape[i] + " at dim " + i);
            }
        }
        if (implicitIdx === -1) {
            if (size > 0 && size !== shapeProd) {
                throw Error("Size(" + size + ") must match the product of shape " + shape);
            }
            return shape;
        }
        if (shapeProd === 0) {
            throw Error("Cannot infer the missing size in [" + shape + "] when " +
                "there are 0 elements");
        }
        if (size % shapeProd !== 0) {
            throw Error("The implicit shape can't be a fractional number. " +
                ("Got " + size + " / " + shapeProd));
        }
        var newShape = shape.slice();
        newShape[implicitIdx] = size / shapeProd;
        return newShape;
    }
    function parseAxisParam(axis, shape) {
        var rank = shape.length;
        // Normalize input
        axis = axis == null ? shape.map(function (s, i) { return i; }) : [].concat(axis);
        // Check for valid range
        assert(axis.every(function (ax) { return ax >= -rank && ax < rank; }), function () {
            return "All values in axis param must be in range [-" + rank + ", " + rank + ") but " +
                ("got axis " + axis);
        });
        // Check for only integers
        assert(axis.every(function (ax) { return isInt(ax); }), function () { return "All values in axis param must be integers but " +
            ("got axis " + axis); });
        // Handle negative axis.
        return axis.map(function (a) { return a < 0 ? rank + a : a; });
    }
    /** Reduces the shape by removing all dimensions of shape 1. */
    function squeezeShape(shape, axis) {
        var newShape = [];
        var keptDims = [];
        var isEmptyArray = axis != null && Array.isArray(axis) && axis.length === 0;
        var axes = (axis == null || isEmptyArray) ?
            null :
            parseAxisParam(axis, shape).sort();
        var j = 0;
        for (var i = 0; i < shape.length; ++i) {
            if (axes != null) {
                if (axes[j] === i && shape[i] !== 1) {
                    throw new Error("Can't squeeze axis " + i + " since its dim '" + shape[i] + "' is not 1");
                }
                if ((axes[j] == null || axes[j] > i) && shape[i] === 1) {
                    newShape.push(shape[i]);
                    keptDims.push(i);
                }
                if (axes[j] <= i) {
                    j++;
                }
            }
            if (shape[i] !== 1) {
                newShape.push(shape[i]);
                keptDims.push(i);
            }
        }
        return { newShape: newShape, keptDims: keptDims };
    }
    function getTypedArrayFromDType(dtype, size) {
        var values = null;
        if (dtype == null || dtype === 'float32') {
            values = new Float32Array(size);
        }
        else if (dtype === 'int32') {
            values = new Int32Array(size);
        }
        else if (dtype === 'bool') {
            values = new Uint8Array(size);
        }
        else {
            throw new Error("Unknown data type " + dtype);
        }
        return values;
    }
    function getArrayFromDType(dtype, size) {
        var values = null;
        if (dtype == null || dtype === 'float32') {
            values = new Float32Array(size);
        }
        else if (dtype === 'int32') {
            values = new Int32Array(size);
        }
        else if (dtype === 'bool') {
            values = new Uint8Array(size);
        }
        else if (dtype === 'string') {
            values = new Array(size);
        }
        else {
            throw new Error("Unknown data type " + dtype);
        }
        return values;
    }
    function checkConversionForErrors(vals, dtype) {
        for (var i = 0; i < vals.length; i++) {
            var num = vals[i];
            if (isNaN(num) || !isFinite(num)) {
                throw Error("A tensor of type " + dtype + " being uploaded contains " + num + ".");
            }
        }
    }
    /** Returns true if the dtype is valid. */
    function isValidDtype(dtype) {
        return dtype === 'bool' || dtype === 'complex64' || dtype === 'float32' ||
            dtype === 'int32' || dtype === 'string';
    }
    /**
     * Returns true if the new type can't encode the old type without loss of
     * precision.
     */
    function hasEncodingLoss(oldType, newType) {
        if (newType === 'complex64') {
            return false;
        }
        if (newType === 'float32' && oldType !== 'complex64') {
            return false;
        }
        if (newType === 'int32' && oldType !== 'float32' && oldType !== 'complex64') {
            return false;
        }
        if (newType === 'bool' && oldType === 'bool') {
            return false;
        }
        return true;
    }
    function isTypedArray(a) {
        return a instanceof Float32Array || a instanceof Int32Array ||
            a instanceof Uint8Array;
    }
    function bytesPerElement(dtype) {
        if (dtype === 'float32' || dtype === 'int32') {
            return 4;
        }
        else if (dtype === 'complex64') {
            return 8;
        }
        else if (dtype === 'bool') {
            return 1;
        }
        else {
            throw new Error("Unknown dtype " + dtype);
        }
    }
    /**
     * Returns the approximate number of bytes allocated in the string array - 2
     * bytes per character. Computing the exact bytes for a native string in JS is
     * not possible since it depends on the encoding of the html page that serves
     * the website.
     */
    function bytesFromStringArray(arr) {
        if (arr == null) {
            return 0;
        }
        var bytes = 0;
        arr.forEach(function (x) { return bytes += x.length; });
        return bytes;
    }
    /** Returns true if the value is a string. */
    function isString(value) {
        return typeof value === 'string' || value instanceof String;
    }
    function isBoolean(value) {
        return typeof value === 'boolean';
    }
    function isNumber(value) {
        return typeof value === 'number';
    }
    function inferDtype(values) {
        if (Array.isArray(values)) {
            return inferDtype(values[0]);
        }
        if (values instanceof Float32Array) {
            return 'float32';
        }
        else if (values instanceof Int32Array || values instanceof Uint8Array) {
            return 'int32';
        }
        else if (isNumber(values)) {
            return 'float32';
        }
        else if (isString(values)) {
            return 'string';
        }
        else if (isBoolean(values)) {
            return 'bool';
        }
        return 'float32';
    }
    function isFunction(f) {
        return !!(f && f.constructor && f.call && f.apply);
    }
    function nearestDivisor(size, start) {
        for (var i = start; i < size; ++i) {
            if (size % i === 0) {
                return i;
            }
        }
        return size;
    }
    function computeStrides(shape) {
        var rank = shape.length;
        if (rank < 2) {
            return [];
        }
        // Last dimension has implicit stride of 1, thus having D-1 (instead of D)
        // strides.
        var strides = new Array(rank - 1);
        strides[rank - 2] = shape[rank - 1];
        for (var i = rank - 3; i >= 0; --i) {
            strides[i] = strides[i + 1] * shape[i + 1];
        }
        return strides;
    }
    function toTypedArray(a, dtype, debugMode) {
        if (dtype === 'string') {
            throw new Error('Cannot convert a string[] to a TypedArray');
        }
        if (Array.isArray(a)) {
            a = flatten(a);
        }
        if (debugMode) {
            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);
        }
    }
    function createNestedArray(offset, shape, a) {
        var ret = new Array();
        if (shape.length === 1) {
            var d = shape[0];
            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; });
            for (var i = 0; i < d; i++) {
                ret[i] = createNestedArray(offset + i * len, rest, a);
            }
        }
        return ret;
    }
    // Provide a nested array of TypedArray in given shape.
    function toNestedArray(shape, a) {
        if (shape.length === 0) {
            // Scalar type should return a single number.
            return a[0];
        }
        var size = shape.reduce(function (acc, c) { return acc * c; });
        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.");
        }
        return createNestedArray(0, shape, a);
    }
    function noConversionNeeded(a, dtype) {
        return (a instanceof Float32Array && dtype === 'float32') ||
            (a instanceof Int32Array && dtype === 'int32') ||
            (a instanceof Uint8Array && dtype === 'bool');
    }
    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);
        }
    }
    /**
     * 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();
    }
    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 + "].");
            });
        });
    }
    /**
     * 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);
    }
    /**
     * 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;
    }

    var util = /*#__PURE__*/Object.freeze({
        shuffle: shuffle,
        clamp: clamp,
        nearestLargerEven: nearestLargerEven,
        sum: sum,
        randUniform: randUniform,
        distSquared: distSquared,
        assert: assert,
        assertShapesMatch: assertShapesMatch,
        assertNonNull: assertNonNull,
        flatten: flatten,
        sizeFromShape: sizeFromShape,
        isScalarShape: isScalarShape,
        arraysEqual: arraysEqual,
        isInt: isInt,
        tanh: tanh,
        sizeToSquarishShape: sizeToSquarishShape,
        createShuffledIndices: createShuffledIndices,
        rightPad: rightPad,
        repeatedTry: repeatedTry,
        inferFromImplicitShape: inferFromImplicitShape,
        parseAxisParam: parseAxisParam,
        squeezeShape: squeezeShape,
        getTypedArrayFromDType: getTypedArrayFromDType,
        getArrayFromDType: getArrayFromDType,
        checkConversionForErrors: checkConversionForErrors,
        isValidDtype: isValidDtype,
        hasEncodingLoss: hasEncodingLoss,
        isTypedArray: isTypedArray,
        bytesPerElement: bytesPerElement,
        bytesFromStringArray: bytesFromStringArray,
        isString: isString,
        isBoolean: isBoolean,
        isNumber: isNumber,
        inferDtype: inferDtype,
        isFunction: isFunction,
        nearestDivisor: nearestDivisor,
        computeStrides: computeStrides,
        toTypedArray: toTypedArray,
        toNestedArray: toNestedArray,
        makeOnesTypedArray: makeOnesTypedArray,
        makeZerosTypedArray: makeZerosTypedArray,
        now: now,
        assertNonNegativeIntegerDimensions: assertNonNegativeIntegerDimensions,
        fetch: fetch$1,
        encodeString: encodeString,
        decodeString: decodeString,
        locToIndex: locToIndex,
        indexToLoc: indexToLoc
    });

    /**
     * @license
     * Copyright 2018 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.
     * =============================================================================
     */
    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 _this = this;
            var outputs;
            var holdResultWrapperFn = function () {
                outputs = f();
            };
            var timer = this.backendTimer.time(holdResultWrapperFn);
            outputs.forEach(function (r) {
                // Dangling promise here because we don't want to propagate up
                // asynchronicity.
                r.data().then(function (vals) {
                    checkComputationForErrors(vals, r.dtype, kernelName);
                    timer.then(function (timing) {
                        var extraInfo = '';
                        if (timing.getExtraProfileInfo != null) {
                            extraInfo = timing.getExtraProfileInfo();
                        }
                        _this.logger.logKernelProfile(kernelName, r, vals, timing.kernelMs, inputs, extraInfo);
                    });
                });
            });
            return outputs;
        };
        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];
                // 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 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 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) {
        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] = curGradient.add(dx);
                    curGradient.dispose();
                }
            };
            for (var inputName in node.inputs) {
                _loop_2(inputName);
            }
        };
        // Walk the tape backward and keep a map of Tensor to its gradient.
        for (var i = filteredTape.length - 1; i >= 0; i--) {
            _loop_1(i);
        }
    }

    /**
     * @license
     * Copyright 2018 Google LLC. All Rights Reserved.
     * Licensed under the Apache License, Version 2.0 (the "License");
     * you may not use this file except in compliance with the License.
     * You may obtain a copy of the License at
     *
     * http://www.apache.org/licenses/LICENSE-2.0
     *
     * Unless required by applicable law or agreed to in writing, software
     * distributed under the License is distributed on an "AS IS" BASIS,
     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
     * See the License for the specific language governing permissions and
     * limitations under the License.
     * =============================================================================
     */
    // Maximum number of values before we decide to show ellipsis.
    var FORMAT_LIMIT_NUM_VALS = 20;
    // Number of first and last values to show when displaying a, b,...,y, z.
    var FORMAT_NUM_FIRST_LAST_VALS = 3;
    // Number of significant digits to show.
    var FORMAT_NUM_SIG_DIGITS = 7;
    function tensorToString(vals, shape, dtype, verbose) {
        var strides = computeStrides(shape);
        var padPerCol = computeMaxSizePerColumn(vals, shape, dtype, strides);
        var rank = shape.length;
        var valsLines = subTensorToString(vals, shape, dtype, strides, padPerCol);
        var lines = ['Tensor'];
        if (verbose) {
            lines.push("  dtype: " + dtype);
            lines.push("  rank: " + rank);
            lines.push("  shape: [" + shape + "]");
            lines.push("  values:");
        }
        lines.push(valsLines.map(function (l) { return '    ' + l; }).join('\n'));
        return lines.join('\n');
    }
    function computeMaxSizePerColumn(vals, shape, dtype, strides) {
        var n = sizeFromShape(shape);
        var numCols = strides[strides.length - 1];
        var padPerCol = new Array(numCols).fill(0);
        var rank = shape.length;
        var valuesOrTuples = dtype === 'complex64' ? createComplexTuples(vals) : vals;
        if (rank > 1) {
            for (var row = 0; row < n / numCols; row++) {
                var offset = row * numCols;
                for (var j = 0; j < numCols; j++) {
                    padPerCol[j] = Math.max(padPerCol[j], valToString(valuesOrTuples[offset + j], 0, dtype).length);
                }
            }
        }
        return padPerCol;
    }
    function valToString(val, pad, dtype) {
        var valStr;
        if (Array.isArray(val)) {
            valStr = parseFloat(val[0].toFixed(FORMAT_NUM_SIG_DIGITS)) + " + " +
                (parseFloat(val[1].toFixed(FORMAT_NUM_SIG_DIGITS)) + "j");
        }
        else if (isString(val)) {
            valStr = "'" + val + "'";
        }
        else if (dtype === 'bool') {
            valStr = boolNumToString(val);
        }
        else {
            valStr = parseFloat(val.toFixed(FORMAT_NUM_SIG_DIGITS)).toString();
        }
        return rightPad(valStr, pad);
    }
    function boolNumToString(v) {
        return v === 0 ? 'false' : 'true';
    }
    function subTensorToString(vals, shape, dtype, strides, padPerCol, isLast) {
        if (isLast === void 0) { isLast = true; }
        var storagePerElement = dtype === 'complex64' ? 2 : 1;
        var size = shape[0];
        var rank = shape.length;
        if (rank === 0) {
            if (dtype === 'complex64') {
                var complexTuple = createComplexTuples(vals);
                return [valToString(complexTuple[0], 0, dtype)];
            }
            if (dtype === 'bool') {
                return [boolNumToString(vals[0])];
            }
            return [vals[0].toString()];
        }
        if (rank === 1) {
            if (size > FORMAT_LIMIT_NUM_VALS) {
                var firstValsSize = FORMAT_NUM_FIRST_LAST_VALS * storagePerElement;
                var firstVals = Array.from(vals.slice(0, firstValsSize));
                var lastVals = Array.from(vals.slice((size - FORMAT_NUM_FIRST_LAST_VALS) * storagePerElement, size * storagePerElement));
                if (dtype === 'complex64') {
                    firstVals = createComplexTuples(firstVals);
                    lastVals = createComplexTuples(lastVals);
                }
                return [
                    '[' +
                        firstVals.map(function (x, i) { return valToString(x, padPerCol[i], dtype); })
                            .join(', ') +
                        ', ..., ' +
                        lastVals
                            .map(function (x, i) { return valToString(x, padPerCol[size - FORMAT_NUM_FIRST_LAST_VALS + i], dtype); })
                            .join(', ') +
                        ']'
                ];
            }
            var displayVals = dtype === 'complex64' ? createComplexTuples(vals) :
                Array.from(vals);
            return [
                '[' +
                    displayVals.map(function (x, i) { return valToString(x, padPerCol[i], dtype); })
                        .join(', ') +
                    ']'
            ];
        }
        // The array is rank 2 or more.
        var subshape = shape.slice(1);
        var substrides = strides.slice(1);
        var stride = strides[0] * storagePerElement;
        var lines = [];
        if (size > FORMAT_LIMIT_NUM_VALS) {
            for (var i = 0; i < FORMAT_NUM_FIRST_LAST_VALS; i++) {
                var start = i * stride;
                var end = start + stride;
                lines.push.apply(lines, subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, false /* isLast */));
            }
            lines.push('...');
            for (var i = size - FORMAT_NUM_FIRST_LAST_VALS; i < size; i++) {
                var start = i * stride;
                var end = start + stride;
                lines.push.apply(lines, subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size - 1 /* isLast */));
            }
        }
        else {
            for (var i = 0; i < size; i++) {
                var start = i * stride;
                var end = start + stride;
                lines.push.apply(lines, subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size - 1 /* isLast */));
            }
        }
        var sep = rank === 2 ? ',' : '';
        lines[0] = '[' + lines[0] + sep;
        for (var i = 1; i < lines.length - 1; i++) {
            lines[i] = ' ' + lines[i] + sep;
        }
        var newLineSep = ',\n';
        for (var i = 2; i < rank; i++) {
            newLineSep += '\n';
        }
        lines[lines.length - 1] =
            ' ' + lines[lines.length - 1] + ']' + (isLast ? '' : newLineSep);
        return lines;
    }
    function createComplexTuples(vals) {
        var complexTuples = [];
        for (var i = 0; i < vals.length; i += 2) {
            complexTuples.push([vals[i], vals[i + 1]]);
        }
        return complexTuples;
    }

    /**
     * @license
     * Copyright 2017 Google 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.
     * =============================================================================
     */
    /**
     * A mutable object, similar to `tf.Tensor`, that allows users to set values
     * at locations before converting to an immutable `tf.Tensor`.
     *
     * See `tf.buffer` for creating a tensor buffer.
     */
    /** @doc {heading: 'Tensors', subheading: 'Classes'} */
    var TensorBuffer = /** @class */ (function () {
        function TensorBuffer(shape, dtype, values) {
            var _this = this;
            this.dtype = dtype;
            this.shape = shape.slice();
            this.size = sizeFromShape(shape);
            if (values != null) {
                var n_1 = values.length;
                assert(n_1 === this.size, function () { return "Length of values '" + n_1 + "' does not match the size " +
                    ("inferred by the shape '" + _this.size + "'."); });
            }
            if (dtype === 'complex64') {
                throw new Error("complex64 dtype TensorBuffers are not supported. Please create " +
                    "a TensorBuffer for the real and imaginary parts separately and " +
                    "call tf.complex(real, imag).");
            }
            this.values = values || getArrayFromDType(dtype, this.size);
            this.strides = computeStrides(shape);
        }
        /**
         * Sets a value in the buffer at a given location.
         *
         * @param value The value to set.
         * @param locs  The location indices.
         */
        /** @doc {heading: 'Tensors', subheading: 'Creation'} */
        TensorBuffer.prototype.set = function (value) {
            var _this = this;
            var locs = [];
            for (var _i = 1; _i < arguments.length; _i++) {
                locs[_i - 1] = arguments[_i];
            }
            if (locs.length === 0) {
                locs = [0];
            }
            assert(locs.length === this.rank, function () { return "The number of provided coordinates (" + locs.length + ") must " +
                ("match the rank (" + _this.rank + ")"); });
            var index = this.locToIndex(locs);
            this.values[index] = value;
        };
        /**
         * Returns the value in the buffer at the provided location.
         *
         * @param locs The location indices.
         */
        /** @doc {heading: 'Tensors', subheading: 'Creation'} */
        TensorBuffer.prototype.get = function () {
            var locs = [];
            for (var _i = 0; _i < arguments.length; _i++) {
                locs[_i] = arguments[_i];
            }
            if (locs.length === 0) {
                locs = [0];
            }
            var i = 0;
            for (var _a = 0, locs_1 = locs; _a < locs_1.length; _a++) {
                var loc = locs_1[_a];
                if (loc < 0 || loc >= this.shape[i]) {
                    var msg = "Requested out of range element at " + locs + ". " +
                        ("  Buffer shape=" + this.shape);
                    throw new Error(msg);
                }
                i++;
            }
            var index = locs[locs.length - 1];
            for (var i_1 = 0; i_1 < locs.length - 1; ++i_1) {
                index += this.strides[i_1] * locs[i_1];
            }
            return this.values[index];
        };
        TensorBuffer.prototype.locToIndex = function (locs) {
            if (this.rank === 0) {
                return 0;
            }
            else if (this.rank === 1) {
                return locs[0];
            }
            var index = locs[locs.length - 1];
            for (var i = 0; i < locs.length - 1; ++i) {
                index += this.strides[i] * locs[i];
            }
            return index;
        };
        TensorBuffer.prototype.indexToLoc = function (index) {
            if (this.rank === 0) {
                return [];
            }
            else if (this.rank === 1) {
                return [index];
            }
            var locs = new Array(this.shape.length);
            for (var i = 0; i < locs.length - 1; ++i) {
                locs[i] = Math.floor(index / this.strides[i]);
                index -= locs[i] * this.strides[i];
            }
            locs[locs.length - 1] = index;
            return locs;
        };
        Object.defineProperty(TensorBuffer.prototype, "rank", {
            get: function () {
                return this.shape.length;
            },
            enumerable: true,
            configurable: true
        });
        /**
         * Creates an immutable `tf.Tensor` object from the buffer.
         */
        /** @doc {heading: 'Tensors', subheading: 'Creation'} */
        TensorBuffer.prototype.toTensor = function () {
            return trackerFn().makeTensor(this.values, this.shape, this.dtype);
        };
        return TensorBuffer;
    }());
    // For tracking tensor creation and disposal.
    var trackerFn = null;
    // Used by chaining methods to call into ops.
    var opHandler = null;
    // Used to warn about deprecated methods.
    var deprecationWarningFn = null;
    /**
     * An external consumer can register itself as the tensor tracker. This way
     * the Tensor class can notify the tracker for every tensor created and
     * disposed.
     */
    function setTensorTracker(fn) {
        trackerFn = fn;
    }
    /**
     * An external consumer can register itself as the op handler. This way the
     * Tensor class can have chaining methods that call into ops via the op
     * handler.
     */
    function setOpHandler(handler) {
        opHandler = handler;
    }
    /**
     * Sets the deprecation warning function to be used by this file. This way the
     * Tensor class can be a leaf but still use the environment.
     */
    function setDeprecationWarningFn(fn) {
        deprecationWarningFn = fn;
    }
    /**
     * A `tf.Tensor` object represents an immutable, multidimensional array of
     * numbers that has a shape and a data type.
     *
     * 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');
        }
        /** Flatten a Tensor to a 1D array. */
        /** @doc {heading: 'Tensors', subheading: 'Classes'} */
        Tensor.prototype.flatten = function () {
            this.throwIfDisposed();
            return this.as1D();
        };
        /** Converts a size-1 `tf.Tensor` to a `tf.Scalar`. */
        /** @doc {heading: 'Tensors', subheading: 'Classes'} */
        Tensor.prototype.asScalar = function () {
            this.throwIfDisposed();
            assert(this.size === 1, function () { return 'The array must have only 1 element.'; });
            return this.reshape([]);
        };
        /** Converts a `tf.Tensor` to a `tf.Tensor1D`. */
        /** @doc {heading: 'Tensors', subheading: 'Classes'} */
        Tensor.prototype.as1D = function () {
            this.throwIfDisposed();
            return this.reshape([this.size]);
        };
        /**
         * Converts a `tf.Tensor` to a `tf.Tensor2D`.
         *
         * @param rows Number of rows in `tf.Tensor2D`.
         * @param columns Number of columns in `tf.Tensor2D`.
         */
        /** @doc {heading: 'Tensors', subheading: 'Classes'} */
        Tensor.prototype.as2D = function (rows, columns) {
            this.throwIfDisposed();
            return this.reshape([rows, columns]);
        };
        /**
         * Converts a `tf.Tensor` to a `tf.Tensor3D`.
         *
         * @param rows Number of rows in `tf.Tensor3D`.
         * @param columns Number of columns in `tf.Tensor3D`.
         * @param depth Depth of `tf.Tensor3D`.
         */
        /** @doc {heading: 'Tensors', subheading: 'Classes'} */
        Tensor.prototype.as3D = function (rows, columns, depth) {
            this.throwIfDisposed();
            return this.reshape([rows, columns, depth]);
        };
        /**
         * Converts a `tf.Tensor` to a `tf.Tensor4D`.
         *
         * @param rows Number of rows in `tf.Tensor4D`.
         * @param columns Number of columns in `tf.Tensor4D`.
         * @param depth Depth of `tf.Tensor4D`.
         * @param depth2 4th dimension of `tf.Tensor4D`.
         */
        /** @doc {heading: 'Tensors', subheading: 'Classes'} */
        Tensor.prototype.as4D = function (rows, columns, depth, depth2) {
            this.throwIfDisposed();
            return this.reshape([rows, columns, depth, depth2]);
        };
        /**
         * Converts a `tf.Tensor` to a `tf.Tensor5D`.
         *
         * @param rows Number of rows in `tf.Tensor5D`.
         * @param columns Number of columns in `tf.Tensor5D`.
         * @param depth Depth of `tf.Tensor5D`.
         * @param depth2 4th dimension of `tf.Tensor5D`.
         * @param depth3 5th dimension of 'tf.Tensor5D'
         */
        /** @doc {heading: 'Tensors', subheading: 'Classes'} */
        Tensor.prototype.as5D = function (rows, columns, depth, depth2, depth3) {
            this.throwIfDisposed();
            return this.reshape([rows, columns, depth, depth2, depth3]);
        };
        /**
         * Casts a `tf.Tensor` to a specified dtype.
         *
         * @param dtype Data-type to cast the tensor to.
         */
        /** @doc {heading: 'Tensors', subheading: 'Classes'} */
        Tensor.prototype.asType = function (dtype) {
            this.throwIfDisposed();
            return opHandler.cast(this, dtype);
        };
        Object.defineProperty(Tensor.prototype, "rank", {
            get: function () {
                return this.shape.length;
            },
            enumerable: true,
            configurable: true
        });
        /**
         * Returns a promise of `tf.TensorBuffer` that holds the underlying data.
         */
        /** @doc {heading: 'Tensors', subheading: 'Classes'} */
        Tensor.prototype.buffer = function () {
            return __awaiter(this, void 0, void 0, function () {
                var vals;
                return __generator(this, function (_a) {
                    switch (_a.label) {
                        case 0: return [4 /*yield*/, this.data()];
                        case 1:
                            vals = _a.sent();
                            return [2 /*return*/, opHandler.buffer(this.shape, this.dtype, vals)];
                    }
                });
            });
        };
        /** Returns a `tf.TensorBuffer` that holds the underlying data. */
        /** @doc {heading: 'Tensors', subheading: 'Classes'} */
        Tensor.prototype.bufferSync = function () {
            return opHandler.buffer(this.shape, this.dtype, this.dataSync());
        };
        /**
         * Returns the tensor data as a nested array. The transfer of data is done
         * asynchronously.
         */
        /** @doc {heading: 'Tensors', subheading: 'Classes'} */
        Tensor.prototype.array = function () {
            return __awaiter(this, void 0, void 0, function () {
                var vals;
                return __generator(this, function (_a) {
                    switch (_a.label) {
                        case 0: return [4 /*yield*/, this.data()];
                        case 1:
                            vals = _a.sent();
                            return [2 /*return*/, toNestedArray(this.shape, vals)];
                    }
                });
            });
        };
        /**
         * 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());
        };
        /**
         * Asynchronously downloads the values from the `tf.Tensor`. Returns a
         * promise of `TypedArray` that resolves when the computation has finished.
         */
        /** @doc {heading: 'Tensors', subheading: 'Classes'} */
        Tensor.prototype.data = function () {
            return __awaiter(this, void 0, void 0, function () {
                var data, bytes;
                return __generator(this, function (_a) {
                    switch (_a.label) {
                        case 0:
                            this.throwIfDisposed();
                            data = trackerFn().read(this.dataId);
                            if (!(this.dtype === 'string')) return [3 /*break*/, 2];
                            return [4 /*yield*/, data];
                        case 1:
                            bytes = _a.sent();
                            try {
                                return [2 /*return*/, bytes.map(function (b) { return decodeString(b); })];
                            }
                            catch (_b) {
                                throw new Error('Failed to decode the string bytes into utf-8. ' +
                                    'To get the original bytes, call tensor.bytes().');
                            }
                            _a.label = 2;
                        case 2: return [2 /*return*/, data];
                    }
                });
            });
        };
        /**
         * Synchronously downloads the values from the `tf.Tensor`. This blocks the
         * UI thread until the values are ready, which can cause performance issues.
         */
        /** @doc {heading: 'Tensors', subheading: 'Classes'} */
        Tensor.prototype.dataSync = function () {
            this.throwIfDisposed();
            var data = trackerFn().readSync(this.dataId);
            if (this.dtype === 'string') {
                try {
                    return data.map(function (b) { return decodeString(b); });
                }
                catch (_a) {
                    throw new Error('Failed to decode the string bytes into utf-8. ' +
                        'To get the original bytes, call tensor.bytes().');
                }
            }
            return data;
        };
        /** Returns the underlying bytes of the tensor's data. */
        Tensor.prototype.bytes = function () {
            return __awaiter(this, void 0, void 0, function () {
                var data;
                return __generator(this, function (_a) {
                    switch (_a.label) {
                        case 0:
                            this.throwIfDisposed();
                            return [4 /*yield*/, trackerFn().read(this.dataId)];
                        case 1:
                            data = _a.sent();
                            if (this.dtype === 'string') {
                                return [2 /*return*/, data];
                            }
                            else {
                                return [2 /*return*/, new Uint8Array(data.buffer)];
                            }
                            return [2 /*return*/];
                    }
                });
            });
        };
        /**
         * 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.");
            }
        };
        /** Casts the array to type `float32` */
        /** @doc {heading: 'Tensors', subheading: 'Classes'} */
        Tensor.prototype.toFloat = function () {
            return this.asType('float32');
        };
        /** Casts the array to type `int32` */
        /** @doc {heading: 'Tensors', subheading: 'Classes'} */
        Tensor.prototype.toInt = function () {
            return this.asType('int32');
        };
        /** Casts the array to type `bool` */
        /** @doc {heading: 'Tensors', subheading: 'Classes'} */
        Tensor.prototype.toBool = function () {
            return this.asType('bool');
        };
        /**
         * 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);
        };
        /**
         * Reshapes the tensor into the provided shape.
         * See `tf.reshape` for more details.
         *
         * @param newShape An array of integers defining the output tensor shape.
         */
        /** @doc {heading: 'Tensors', subheading: 'Classes'} */
        Tensor.prototype.reshape = function (newShape) {
            this.throwIfDisposed();
            return opHandler.reshape(this, newShape);
        };
        /**
         * Reshapes the tensor into the shape of the provided tensor.
         *
         * @param x The tensor of required shape.
         */
        /** @doc {heading: 'Tensors', subheading: 'Classes'} */
        Tensor.prototype.reshapeAs = function (x) {
            this.throwIfDisposed();
            return this.reshape(x.shape);
        };
        /**
         * Returns a `tf.Tensor` that has expanded rank, by inserting a dimension
         * into the tensor's shape. See `tf.expandDims` for details.
         *
         * @param axis The dimension index at which to insert shape of 1. Defaults to
         *     0 (the first dimension).
         */
        /** @doc {heading: 'Tensors', subheading: 'Classes'} */
        Tensor.prototype.expandDims = function (axis) {
            if (axis === void 0) { axis = 0; }
            return opHandler.expandDims(this, axis);
        };
        /**
         * Returns the cumulative sum of the `tf.Tensor` along `axis`.
         *
         * @param axis The axis along which to sum. Optional. Defaults to 0.
         * @param exclusive Whether to perform exclusive cumulative sum. 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. Defaults to
         *    false.
         */
        /** @doc {heading: 'Tensors', subheading: 'Classes'} */
        Tensor.prototype.cumsum = function (axis, exclusive, reverse) {
            if (axis === void 0) { axis = 0; }
            if (exclusive === void 0) { exclusive = false; }
            if (reverse === void 0) { reverse = false; }
            return opHandler.cumsum(this, axis, exclusive, reverse);
        };
        /**
         * Returns a `tf.Tensor` with dimensions of size 1 removed from the shape.
         * See `tf.squeeze` for more details.
         *
         * @param axis A 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: 'Classes'} */
        Tensor.prototype.squeeze = function (axis) {
            this.throwIfDisposed();
            return opHandler.squeeze(this, axis);
        };
        /** 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);
        };
        Tensor.prototype.oneHot = function (depth, onValue, offValue) {
            this.throwIfDisposed();
            return opHandler.oneHot(this, depth, onValue, offValue);
        };
        /**
         * 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);
        };
        // Below is chain API that is not exposed to docs to avoid repetition. To
        // expose a method, move it above this comment and add @doc and jsdoc.
        Tensor.prototype.tile = function (reps) {
            this.throwIfDisposed();
            return opHandler.tile(this, reps);
        };
        Tensor.prototype.gather = function (indices, axis) {
            if (axis === void 0) { axis = 0; }
            this.throwIfDisposed();
            return opHandler.gather(this, indices, axis);
        };
        Tensor.prototype.matMul = function (b, transposeA, transposeB) {
            if (transposeA === void 0) { transposeA = false; }
            if (transposeB === void 0) { transposeB = false; }
            this.throwIfDisposed();
            return opHandler.matMul(this, b, transposeA, transposeB);
        };
        Tensor.prototype.dot = function (b) {
            this.throwIfDisposed();
            return opHandler.dot(this, b);
        };
        Tensor.prototype.norm = function (ord, axis, keepDims) {
            if (ord === void 0) { ord = 'euclidean'; }
            if (axis === void 0) { axis = null; }
            if (keepDims === void 0) { keepDims = false; }
            this.throwIfDisposed();
            return opHandler.norm(this, ord, axis, keepDims);
        };
        Tensor.prototype.slice = function (begin, size) {
            this.throwIfDisposed();
            return opHandler.slice(this, begin, size);
        };
        Tensor.prototype.reverse = function (axis) {
            this.throwIfDisposed();
            return opHandler.reverse(this, axis);
        };
        Tensor.prototype.concat = function (x, axis) {
            if (axis === void 0) { axis = 0; }
            this.throwIfDisposed();
            if (x instanceof Tensor) {
                x = [x];
            }
            return opHandler.concat([this].concat(x), axis);
        };
        Tensor.prototype.split = function (numOrSizeSplits, axis) {
            if (axis === void 0) { axis = 0; }
            this.throwIfDisposed();
            return opHandler.split(this, numOrSizeSplits, axis);
        };
        Tensor.prototype.stack = function (x, axis) {
            if (axis === void 0) { axis = 0; }
            return opHandler.stack([this, x], axis);
        };
        Tensor.prototype.unstack = function (axis) {
            if (axis === void 0) { axis = 0; }
            return opHandler.unstack(this, axis);
        };
        Tensor.prototype.pad = function (paddings, constantValue) {
            if (constantValue === void 0) { constantValue = 0; }
            return opHandler.pad(this, paddings, constantValue);
        };
        /**
         * @deprecated Use `tf.batchNorm` instead, and note the positional argument
         *     change of scale, offset, and varianceEpsilon.
         */
        Tensor.prototype.batchNormalization = function (mean, variance, varianceEpsilon, scale, offset) {
            if (varianceEpsilon === void 0) { varianceEpsilon = .001; }
            deprecationWarningFn('tf.batchNormalization() is going away. ' +
                'Use tf.batchNorm() instead, and note the positional argument change ' +
                'of scale, offset, and varianceEpsilon');
            return this.batchNorm(mean, variance, offset, scale, varianceEpsilon);
        };
        Tensor.prototype.batchNorm = function (mean, variance, offset, scale, varianceEpsilon) {
            if (varianceEpsilon === void 0) { varianceEpsilon = .001; }
            this.throwIfDisposed();
            return opHandler.batchNorm(this, mean, variance, offset, scale, varianceEpsilon);
        };
        // Reduction ops.
        Tensor.prototype.all = function (axis, keepDims) {
            if (axis === void 0) { axis = null; }
            if (keepDims === void 0) { keepDims = false; }
            this.throwIfDisposed();
            return opHandler.all(this, axis, keepDims);
        };
        Tensor.prototype.any = function (axis, keepDims) {
            if (axis === void 0) { axis = null; }
            if (keepDims === void 0) { keepDims = false; }
            this.throwIfDisposed();
            return opHandler.any(this, axis, keepDims);
        };
        Tensor.prototype.logSumExp = function (axis, keepDims) {
            if (axis === void 0) { axis = null; }
            if (keepDims === void 0) { keepDims = false; }
            this.throwIfDisposed();
            return opHandler.logSumExp(this, axis, keepDims);
        };
        Tensor.prototype.sum = function (axis, keepDims) {
            if (axis === void 0) { axis = null; }
            if (keepDims === void 0) { keepDims = false; }
            this.throwIfDisposed();
            return opHandler.sum(this, axis, keepDims);
        };
        Tensor.prototype.prod = function (axis, keepDims) {
            if (axis === void 0) { axis = null; }
            if (keepDims === void 0) { keepDims = false; }
            this.throwIfDisposed();
            return opHandler.prod(this, axis, keepDims);
        };
        Tensor.prototype.mean = function (axis, keepDims) {
            if (axis === void 0) { axis = null; }
            if (keepDims === void 0) { keepDims = false; }
            this.throwIfDisposed();
            return opHandler.mean(this, axis, keepDims);
        };
        Tensor.prototype.min = function (axis, keepDims) {
            if (axis === void 0) { axis = null; }
            if (keepDims === void 0) { keepDims = false; }
            this.throwIfDisposed();
            return opHandler.min(this, axis, keepDims);
        };
        Tensor.prototype.max = function (axis, keepDims) {
            if (axis === void 0) { axis = null; }
            if (keepDims === void 0) { keepDims = false; }
            this.throwIfDisposed();
            return opHandler.max(this, axis, keepDims);
        };
        Tensor.prototype.argMin = function (axis) {
            if (axis === void 0) { axis = null; }
            this.throwIfDisposed();
            return opHandler.argMin(this, axis);
        };
        Tensor.prototype.argMax = function (axis) {
            if (axis === void 0) { axis = null; }
            this.throwIfDisposed();
            return opHandler.argMax(this, axis);
        };
        // Transformations
        Tensor.prototype.cast = function (dtype) {
            this.throwIfDisposed();
            return opHandler.cast(this, dtype);
        };
        // Binary ops.
        Tensor.prototype.add = function (x) {
            this.throwIfDisposed();
            return opHandler.add(this, x);
        };
        Tensor.prototype.addStrict = function (x) {
            this.throwIfDisposed();
            return opHandler.addStrict(this, x);
        };
        Tensor.prototype.atan2 = function (x) {
            this.throwIfDisposed();
            return opHandler.atan2(this, x);
        };
        Tensor.prototype.sub = function (x) {
            this.throwIfDisposed();
            return opHandler.sub(this, x);
        };
        Tensor.prototype.subStrict = function (x) {
            this.throwIfDisposed();
            return opHandler.subStrict(this, x);
        };
        Tensor.prototype.pow = function (exp) {
            this.throwIfDisposed();
            return opHandler.pow(this, exp);
        };
        Tensor.prototype.powStrict = function (exp) {
            this.throwIfDisposed();
            return opHandler.powStrict(this, exp);
        };
        Tensor.prototype.mul = function (x) {
            this.throwIfDisposed();
            return opHandler.mul(this, x);
        };
        Tensor.prototype.mulStrict = function (x) {
            this.throwIfDisposed();
            return opHandler.mulStrict(this, x);
        };
        Tensor.prototype.div = function (x) {
            this.throwIfDisposed();
            return opHandler.div(this, x);
        };
        Tensor.prototype.divNoNan = function (x) {
            this.throwIfDisposed();
            return opHandler.divNoNan(this, x);
        };
        Tensor.prototype.floorDiv = function (x) {
            this.throwIfDisposed();
            return opHandler.floorDiv(this, x);
        };
        Tensor.prototype.divStrict = function (x) {
            this.throwIfDisposed();
            return opHandler.divStrict(this, x);
        };
        Tensor.prototype.minimum = function (x) {
            this.throwIfDisposed();
            return opHandler.minimum(this, x);
        };
        Tensor.prototype.minimumStrict = function (x) {
            this.throwIfDisposed();
            return opHandler.minimumStrict(this, x);
        };
        Tensor.prototype.maximum = function (x) {
            this.throwIfDisposed();
            return opHandler.maximum(this, x);
        };
        Tensor.prototype.maximumStrict = function (x) {
            this.throwIfDisposed();
            return opHandler.maximumStrict(this, x);
        };
        Tensor.prototype.mod = function (x) {
            this.throwIfDisposed();
            return opHandler.mod(this, x);
        };
        Tensor.prototype.modStrict = function (x) {
            this.throwIfDisposed();
            return opHandler.modStrict(this, x);
        };
        Tensor.prototype.squaredDifferenceStrict = function (x) {
            this.throwIfDisposed();
            return opHandler.squaredDifferenceStrict(this, x);
        };
        Tensor.prototype.transpose = function (perm) {
            this.throwIfDisposed();
            return opHandler.transpose(this, perm);
        };
        // Compare ops.
        Tensor.prototype.notEqual = function (x) {
            this.throwIfDisposed();
            return opHandler.notEqual(this, x);
        };
        Tensor.prototype.notEqualStrict = function (x) {
            this.throwIfDisposed();
            return opHandler.notEqualStrict(this, x);
        };
        Tensor.prototype.less = function (x) {
            this.throwIfDisposed();
            return opHandler.less(this, x);
        };
        Tensor.prototype.lessStrict = function (x) {
            this.throwIfDisposed();
            return opHandler.lessStrict(this, x);
        };
        Tensor.prototype.equal = function (x) {
            this.throwIfDisposed();
            return opHandler.equal(this, x);
        };
        Tensor.prototype.equalStrict = function (x) {
            this.throwIfDisposed();
            return opHandler.equalStrict(this, x);
        };
        Tensor.prototype.lessEqual = function (x) {
            this.throwIfDisposed();
            return opHandler.lessEqual(this, x);
        };
        Tensor.prototype.lessEqualStrict = function (x) {
            this.throwIfDisposed();
            return opHandler.lessEqualStrict(this, x);
        };
        Tensor.prototype.greater = function (x) {
            this.throwIfDisposed();
            return opHandler.greater(this, x);
        };
        Tensor.prototype.greaterStrict = function (x) {
            this.throwIfDisposed();
            return opHandler.greaterStrict(this, x);
        };
        Tensor.prototype.greaterEqual = function (x) {
            this.throwIfDisposed();
            return opHandler.greaterEqual(this, x);
        };
        Tensor.prototype.greaterEqualStrict = function (x) {
            this.throwIfDisposed();
            return opHandler.greaterEqualStrict(this, x);
        };
        // Compare ops.
        Tensor.prototype.logicalAnd = function (x) {
            this.throwIfDisposed();
            return opHandler.logicalAnd(this, x);
        };
        Tensor.prototype.logicalOr = function (x) {
            this.throwIfDisposed();
            return opHandler.logicalOr(this, x);
        };
        Tensor.prototype.logicalNot = function () {
            this.throwIfDisposed();
            return opHandler.logicalNot(this);
        };
        Tensor.prototype.logicalXor = function (x) {
            this.throwIfDisposed();
            return opHandler.logicalXor(this, x);
        };
        Tensor.prototype.where = function (condition, x) {
            this.throwIfDisposed();
            return opHandler.where(condition, this, x);
        };
        // Unary ops.
        Tensor.prototype.neg = function () {
            this.throwIfDisposed();
            return opHandler.neg(this);
        };
        Tensor.prototype.ceil = function () {
            this.throwIfDisposed();
            return opHandler.ceil(this);
        };
        Tensor.prototype.floor = function () {
            this.throwIfDisposed();
            return opHandler.floor(this);
        };
        Tensor.prototype.sign = function () {
            this.throwIfDisposed();
            return opHandler.sign(this);
        };
        Tensor.prototype.isNaN = function () {
            this.throwIfDisposed();
            return opHandler.isNaN(this);
        };
        Tensor.prototype.isInf = function () {
            this.throwIfDisposed();
            return opHandler.isInf(this);
        };
        Tensor.prototype.isFinite = function () {
            this.throwIfDisposed();
            return opHandler.isFinite(this);
        };
        Tensor.prototype.exp = function () {
            this.throwIfDisposed();
            return opHandler.exp(this);
        };
        Tensor.prototype.expm1 = function () {
            this.throwIfDisposed();
            return opHandler.expm1(this);
        };
        Tensor.prototype.log = function () {
            this.throwIfDisposed();
            return opHandler.log(this);
        };
        Tensor.prototype.log1p = function () {
            this.throwIfDisposed();
            return opHandler.log1p(this);
        };
        Tensor.prototype.sqrt = function () {
            this.throwIfDisposed();
            return opHandler.sqrt(this);
        };
        Tensor.prototype.rsqrt = function () {
            this.throwIfDisposed();
            return opHandler.rsqrt(this);
        };
        Tensor.prototype.square = function () {
            this.throwIfDisposed();
            return opHandler.square(this);
        };
        Tensor.prototype.reciprocal = function () {
            this.throwIfDisposed();
            return opHandler.reciprocal(this);
        };
        Tensor.prototype.abs = function () {
            this.throwIfDisposed();
            return opHandler.abs(this);
        };
        Tensor.prototype.clipByValue = function (min, max) {
            this.throwIfDisposed();
            return opHandler.clipByValue(this, min, max);
        };
        Tensor.prototype.relu = function () {
            this.throwIfDisposed();
            return opHandler.relu(this);
        };
        Tensor.prototype.relu6 = function () {
            this.throwIfDisposed();
            return opHandler.relu6(this);
        };
        Tensor.prototype.elu = function () {
            this.throwIfDisposed();
            return opHandler.elu(this);
        };
        Tensor.prototype.selu = function () {
            this.throwIfDisposed();
            return opHandler.selu(this);
        };
        Tensor.prototype.leakyRelu = function (alpha) {
            if (alpha === void 0) { alpha = 0.2; }
            this.throwIfDisposed();
            return opHandler.leakyRelu(this, alpha);
        };
        Tensor.prototype.prelu = function (alpha) {
            this.throwIfDisposed();
            return opHandler.prelu(this, alpha);
        };
        Tensor.prototype.sigmoid = function () {
            this.throwIfDisposed();
            return opHandler.sigmoid(this);
        };
        Tensor.prototype.logSigmoid = function () {
            this.throwIfDisposed();
            return opHandler.logSigmoid(this);
        };
        Tensor.prototype.softplus = function () {
            this.throwIfDisposed();
            return opHandler.softplus(this);
        };
        Tensor.prototype.zerosLike = function () {
            this.throwIfDisposed();
            return opHandler.zerosLike(this);
        };
        Tensor.prototype.onesLike = function () {
            this.throwIfDisposed();
            return opHandler.onesLike(this);
        };
        Tensor.prototype.sin = function () {
            this.throwIfDisposed();
            return opHandler.sin(this);
        };
        Tensor.prototype.cos = function () {
            this.throwIfDisposed();
            return opHandler.cos(this);
        };
        Tensor.prototype.tan = function () {
            this.throwIfDisposed();
            return opHandler.tan(this);
        };
        Tensor.prototype.asin = function () {
            this.throwIfDisposed();
            return opHandler.asin(this);
        };
        Tensor.prototype.acos = function () {
            this.throwIfDisposed();
            return opHandler.acos(this);
        };
        Tensor.prototype.atan = function () {
            this.throwIfDisposed();
            return opHandler.atan(this);
        };
        Tensor.prototype.sinh = function () {
            this.throwIfDisposed();
            return opHandler.sinh(this);
        };
        Tensor.prototype.cosh = function () {
            this.throwIfDisposed();
            return opHandler.cosh(this);
        };
        Tensor.prototype.tanh = function () {
            this.throwIfDisposed();
            return opHandler.tanh(this);
        };
        Tensor.prototype.asinh = function () {
            this.throwIfDisposed();
            return opHandler.asinh(this);
        };
        Tensor.prototype.acosh = function () {
            this.throwIfDisposed();
            return opHandler.acosh(this);
        };
        Tensor.prototype.atanh = function () {
            this.throwIfDisposed();
            return opHandler.atanh(this);
        };
        Tensor.prototype.erf = function () {
            this.throwIfDisposed();
            return opHandler.erf(this);
        };
        Tensor.prototype.round = function () {
            this.throwIfDisposed();
            return opHandler.round(this);
        };
        Tensor.prototype.step = function (alpha) {
            if (alpha === void 0) { alpha = 0.0; }
            this.throwIfDisposed();
            return opHandler.step(this, alpha);
        };
        Tensor.prototype.softmax = function (dim) {
            if (dim === void 0) { dim = -1; }
            this.throwIfDisposed();
            return opHandler.softmax(this, dim);
        };
        Tensor.prototype.logSoftmax = function (axis) {
            if (axis === void 0) { axis = -1; }
            this.throwIfDisposed();
            return opHandler.logSoftmax(this, axis);
        };
        // Image ops.
        Tensor.prototype.resizeBilinear = function (newShape2D, alignCorners) {
            if (alignCorners === void 0) { alignCorners = false; }
            this.throwIfDisposed();
            return opHandler.image.resizeBilinear(this, newShape2D, alignCorners);
        };
        Tensor.prototype.resizeNearestNeighbor = function (newShape2D, alignCorners) {
            if (alignCorners === void 0) { alignCorners = false; }
            this.throwIfDisposed();
            return opHandler.image.resizeNearestNeighbor(this, newShape2D, alignCorners);
        };
        // Convolutions.
        Tensor.prototype.conv1d = function (filter, stride, pad, dataFormat, dilation, dimRoundingMode) {
            if (dataFormat === void 0) { dataFormat = 'NWC'; }
            if (dilation === void 0) { dilation = 1; }
            this.throwIfDisposed();
            return opHandler.conv1d(this, filter, stride, pad, dataFormat, dilation, dimRoundingMode);
        };
        Tensor.prototype.conv2d = function (filter, strides, pad, dataFormat, dilations, dimRoundingMode) {
            if (dataFormat === void 0) { dataFormat = 'NHWC'; }
            if (dilations === void 0) { dilations = [1, 1]; }
            this.throwIfDisposed();
            return opHandler.conv2d(this, filter, strides, pad, dataFormat, dilations, dimRoundingMode);
        };
        Tensor.prototype.conv2dTranspose = function (filter, outputShape, strides, pad, dimRoundingMode) {
            this.throwIfDisposed();
            return opHandler.conv2dTranspose(this, filter, outputShape, strides, pad, dimRoundingMode);
        };
        Tensor.prototype.depthwiseConv2D = function (filter, strides, pad, dataFormat, dilations, dimRoundingMode) {
            if (dataFormat === void 0) { dataFormat = 'NHWC'; }
            if (dilations === void 0) { dilations = [1, 1]; }
            this.throwIfDisposed();
            return opHandler.depthwiseConv2d(this, filter, strides, pad, dataFormat, dilations, dimRoundingMode);
        };
        Tensor.prototype.separableConv2d = function (depthwiseFilter, pointwiseFilter, strides, pad, dilation, dataFormat) {
            if (dilation === void 0) { dilation = [1, 1]; }
            if (dataFormat === void 0) { dataFormat = 'NHWC'; }
            this.throwIfDisposed();
            return opHandler.separableConv2d(this, depthwiseFilter, pointwiseFilter, strides, pad, dilation, dataFormat);
        };
        // Pooling.
        Tensor.prototype.avgPool = function (filterSize, strides, pad, dimRoundingMode) {
            this.throwIfDisposed();
            return opHandler.avgPool(this, filterSize, strides, pad, dimRoundingMode);
        };
        Tensor.prototype.maxPool = function (filterSize, strides, pad, dimRoundingMode) {
            this.throwIfDisposed();
            return opHandler.maxPool(this, filterSize, strides, pad, dimRoundingMode);
        };
        Tensor.prototype.localResponseNormalization = function (radius, bias, alpha, beta) {
            if (radius === void 0) { radius = 5; }
            if (bias === void 0) { bias = 1; }
            if (alpha === void 0) { alpha = 1; }
            if (beta === void 0) { beta = 0.5; }
            return opHandler.localResponseNormalization(this, radius, bias, alpha, beta);
        };
        Tensor.prototype.pool = function (windowShape, poolingType, padding, dilationRate, strides) {
            this.throwIfDisposed();
            return opHandler.pool(this, windowShape, poolingType, padding, dilationRate, strides);
        };
        Tensor.prototype.variable = function (trainable, name, dtype) {
            if (trainable === void 0) { trainable = true; }
            this.throwIfDisposed();
            return trackerFn().makeVariable(this, trainable, name, dtype);
        };
        Tensor.prototype.unsortedSegmentSum = function (segmentIds, numSegments) {
            this.throwIfDisposed();
            return opHandler.unsortedSegmentSum(this, segmentIds, numSegments);
        };
        Tensor.prototype.batchToSpaceND = function (blockShape, crops) {
            this.throwIfDisposed();
            return opHandler.batchToSpaceND(this, blockShape, crops);
        };
        Tensor.prototype.spaceToBatchND = function (blockShape, paddings) {
            this.throwIfDisposed();
            return opHandler.spaceToBatchND(this, blockShape, paddings);
        };
        Tensor.prototype.topk = function (k, sorted) {
            if (k === void 0) { k = 1; }
            if (sorted === void 0) { sorted = true; }
            this.throwIfDisposed();
            return opHandler.topk(this, k, sorted);
        };
        Tensor.prototype.stridedSlice = function (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; }
            this.throwIfDisposed();
            return opHandler.stridedSlice(this, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask);
        };
        Tensor.prototype.depthToSpace = function (blockSize, dataFormat) {
            this.throwIfDisposed();
            return opHandler.depthToSpace(this, blockSize, dataFormat);
        };
        Tensor.prototype.fft = function () {
            this.throwIfDisposed();
            return opHandler.spectral.fft(this);
        };
        Tensor.prototype.ifft = function () {
            this.throwIfDisposed();
            return opHandler.spectral.ifft(this);
        };
        Tensor.prototype.rfft = function () {
            this.throwIfDisposed();
            return opHandler.spectral.rfft(this);
        };
        Tensor.prototype.irfft = function () {
            this.throwIfDisposed();
            return opHandler.spectral.irfft(this);
        };
        return Tensor;
    }());
    Object.defineProperty(Tensor, Symbol.hasInstance, {
        value: function (instance) {
            return !!instance && instance.dataId != null && instance.shape != null &&
                instance.dtype != null;
        }
    });
    /**
     * 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 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.
     * =============================================================================
     */
    (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 = /*#__PURE__*/Object.freeze({
        makeTypesMatch: makeTypesMatch,
        assertTypesMatch: assertTypesMatch,
        isTensorInList: isTensorInList,
        getTensorsInContainer: getTensorsInContainer
    });

    /**
     * @license
     * Copyright 2018 Google LLC. All Rights Reserved.
     * Licensed under the Apache License, Version 2.0 (the "License");
     * you may not use this file except in compliance with the License.
     * You may obtain a copy of the License at
     *
     * http://www.apache.org/licenses/LICENSE-2.0
     *
     * Unless required by applicable law or agreed to in writing, software
     * distributed under the License is distributed on an "AS IS" BASIS,
     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
     * See the License for the specific language governing permissions and
     * limitations under the License.
     * =============================================================================
     */
    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 };
        }
        EngineState.prototype.dispose = function () {
            for (var variableName in this.registeredVariables) {
                this.registeredVariables[variableName].dispose();
            }
        };
        return EngineState;
    }());
    var Engine = /** @class */ (function () {
        function Engine(ENV) {
            this.ENV = ENV;
            this.registry = {};
            this.registryFactory = {};
            this.pendingBackendInitId = 0;
            this.state = new EngineState();
        }
        Engine.prototype.ready = function () {
            return __awaiter(this, void 0, void 0, function () {
                var sortedBackends, i, backendName, success;
                return __generator(this, function (_a) {
                    switch (_a.label) {
                        case 0:
                            if (this.pendingBackendInit != null) {
                                return [2 /*return*/, this.pendingBackendInit.then(function () { })];
                            }
                            if (this.backendInstance != null) {
                                return [2 /*return*/];
                            }
                            sortedBackends = this.getSortedBackends();
                            i = 0;
                            _a.label = 1;
                        case 1:
                            if (!(i < sortedBackends.length)) return [3 /*break*/, 5];
                            backendName = sortedBackends[i];
                            return [4 /*yield*/, this.initializeBackend(backendName).success];
                        case 2:
                            success = _a.sent();
                            if (!success) return [3 /*break*/, 4];
                            return [4 /*yield*/, this.setBackend(backendName)];
                        case 3:
                            _a.sent();
                            return [2 /*return*/];
                        case 4:
                            i++;
                            return [3 /*break*/, 1];
                        case 5: throw new Error("Could not initialize any backends, all backend initializations " +
                            "failed.");
                    }
                });
            });
        };
        Object.defineProperty(Engine.prototype, "backend", {
            get: function () {
                if (this.pendingBackendInit != null) {
                    throw new Error("Backend '" + this.backendName + "' has not yet been initialized. Make " +
                        "sure to await tf.ready() or await tf.setBackend() before calling " +
                        "other methods");
                }
                if (this.backendInstance == null) {
                    var _a = this.initializeBackendsAndReturnBest(), name_1 = _a.name, asyncInit = _a.asyncInit;
                    if (asyncInit) {
                        throw new Error("The highest priority backend '" + name_1 + "' has not yet been " +
                            "initialized. Make sure to await tf.ready() or " +
                            "await tf.setBackend() before calling other methods");
                    }
                    this.setBackend(name_1);
                }
                return this.backendInstance;
            },
            enumerable: true,
            configurable: true
        });
        Engine.prototype.backendNames = function () {
            return Object.keys(this.registryFactory);
        };
        Engine.prototype.findBackend = function (backendName) {
            if (!(backendName in this.registry)) {
                // If the backend hasn't been initialized but we have a registry entry for
                // it, initialize it and return it.
                if (backendName in this.registryFactory) {
                    var asyncInit = this.initializeBackend(backendName).asyncInit;
                    if (asyncInit) {
                        // Backend is not ready yet.
                        return null;
                    }
                }
                else {
                    return null;
                }
            }
            return this.registry[backendName];
        };
        Engine.prototype.findBackendFactory = function (backendName) {
            if (!(backendName in this.registryFactory)) {
                return null;
            }
            return this.registryFactory[backendName].factory;
        };
        Engine.prototype.registerBackend = function (backendName, factory, priority) {
            if (priority === void 0) { priority = 1; }
            if (backendName in this.registryFactory) {
                console.warn(backendName + " backend was already registered. " +
                    "Reusing existing backend factory.");
                return false;
            }
            this.registryFactory[backendName] = { factory: factory, priority: priority };
            return true;
        };
        Engine.prototype.setBackend = function (backendName) {
            return __awaiter(this, void 0, void 0, function () {
                var _a, success, asyncInit, result, _b;
                return __generator(this, function (_c) {
                    switch (_c.label) {
                        case 0:
                            if (this.registryFactory[backendName] == null) {
                                throw new Error("Backend name '" + backendName + "' not found in registry");
                            }
                            this.backendName = backendName;
                            if (!(this.registry[backendName] == null)) return [3 /*break*/, 4];
                            this.backendInstance = null;
                            _a = this.initializeBackend(backendName), success = _a.success, asyncInit = _a.asyncInit;
                            if (!asyncInit) return [3 /*break*/, 2];
                            return [4 /*yield*/, success];
                        case 1:
                            _b = _c.sent();
                            return [3 /*break*/, 3];
                        case 2:
                            _b = success;
                            _c.label = 3;
                        case 3:
                            result = _b;
                            if (!result) {
                                return [2 /*return*/, false];
                            }
                            _c.label = 4;
                        case 4:
                            this.backendInstance = this.registry[backendName];
                            this.setupRegisteredKernels();
                            // Reset the profiler.
                            this.profiler = new Profiler(this.backendInstance);
                            return [2 /*return*/, true];
                    }
                });
            });
        };
        Engine.prototype.setupRegisteredKernels = function () {
            var _this = this;
            var kernels = getKernelsForBackend(this.backendName);
            kernels.forEach(function (kernel) {
                if (kernel.setupFunc != null) {
                    kernel.setupFunc(_this.backendInstance);
                }
            });
        };
        Engine.prototype.disposeRegisteredKernels = function (backendName) {
            var _this = this;
            var kernels = getKernelsForBackend(backendName);
            kernels.forEach(function (kernel) {
                if (kernel.disposeFunc != null) {
                    kernel.disposeFunc(_this.registry[backendName]);
                }
            });
        };
        /**
         * Initializes a backend by looking up the backend name in the factory
         * registry and calling the factory method. Returns a boolean representing
         * whether the initialization of the backend suceeded. Throws an error if
         * there is no backend in the factory registry.
         */
        Engine.prototype.initializeBackend = function (backendName) {
            var _this = this;
            var registryFactoryEntry = this.registryFactory[backendName];
            if (registryFactoryEntry == null) {
                throw new Error("Cannot initialize backend " + backendName + ", no registration found.");
            }
            try {
                var backend = registryFactoryEntry.factory();
                // Test if the factory returns a promise.
                if (Promise.resolve(backend) === backend) {
                    var promiseId_1 = ++this.pendingBackendInitId;
                    var success = backend
                        .then(function (backendInstance) {
                        // Outdated promise. Another backend was set in the meantime.
                        if (promiseId_1 < _this.pendingBackendInitId) {
                            return false;
                        }
                        _this.registry[backendName] = backendInstance;
                        _this.pendingBackendInit = null;
                        return true;
                    })
                        .catch(function (err) {
                        // Outdated promise. Another backend was set in the meantime.
                        if (promiseId_1 < _this.pendingBackendInitId) {
                            return false;
                        }
                        _this.pendingBackendInit = null;
                        console.warn("Initialization of backend " + backendName + " failed");
                        console.warn(err.stack || err.message);
                        return false;
                    });
                    this.pendingBackendInit = success;
                    return { success: success, asyncInit: true };
                }
                else {
                    this.registry[backendName] = backend;
                    return { success: true, asyncInit: false };
                }
            }
            catch (err) {
                console.warn("Initialization of backend " + backendName + " failed");
                console.warn(err.stack || err.message);
                return { success: false, asyncInit: false };
            }
        };
        Engine.prototype.removeBackend = function (backendName) {
            if (!(backendName in this.registryFactory)) {
                throw new Error(backendName + " backend not found in registry");
            }
            if (this.backendName === backendName && this.pendingBackendInit != null) {
                // There is a pending promise of the backend we want to remove. Make it
                // obsolete.
                this.pendingBackendInitId++;
            }
            if (backendName in this.registry) {
                this.disposeRegisteredKernels(backendName);
                this.registry[backendName].dispose();
                delete this.registry[backendName];
            }
            delete this.registryFactory[backendName];
            // Unset the backend if it is active.
            if (this.backendName === backendName) {
                this.pendingBackendInit = null;
                this.backendName = null;
                this.backendInstance = null;
            }
        };
        Engine.prototype.getSortedBackends = function () {
            var _this = this;
            if (Object.keys(this.registryFactory).length === 0) {
                throw new Error('No backend found in registry.');
            }
            return Object.keys(this.registryFactory).sort(function (a, b) {
                // Highest priority comes first.
                return _this.registryFactory[b].priority -
                    _this.registryFactory[a].priority;
            });
        };
        Engine.prototype.initializeBackendsAndReturnBest = function () {
            var sortedBackends = this.getSortedBackends();
            for (var i = 0; i < sortedBackends.length; i++) {
                var backendName = sortedBackends[i];
                var _a = this.initializeBackend(backendName), success = _a.success, asyncInit = _a.asyncInit;
                if (asyncInit || success) {
                    return { name: backendName, asyncInit: asyncInit };
                }
            }
            throw new Error("Could not initialize any backends, all backend initializations " +
                "failed.");
        };
        Engine.prototype.moveData = function (destBackend, dataId) {
            var info = this.state.tensorInfo.get(dataId);
            var srcBackend = info.backend;
            var values = this.readSync(dataId);
            // Delete the tensor from the old backend and move it to the new
            // backend.
            srcBackend.disposeData(dataId);
            info.backend = destBackend;
            destBackend.move(dataId, values, info.shape, info.dtype);
            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.
         *
         * This method will go away once all kernels are modularized since we won't
         * need to turn off the tape inside runKernel().
         */
        Engine.prototype.clone = function (x) {
            var y = this.makeTensorFromDataId(x.dataId, x.shape, x.dtype);
            var inputs = { x: x };
            var grad = function (dy) { return ({ x: function () { return dy.toFloat(); } }); };
            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, inputsToSave, outputsToSave) {
            var forwardFunc = null;
            var backwardsFunc = null;
            // Call runKernel as a stop-gap until we modularize all kernels.
            // Once we modularize all kernels, we will remove the existing
            // `runKernelFunc`.
            return this.runKernelFunc(forwardFunc, inputs, backwardsFunc, kernelName, attrs, inputsToSave, outputsToSave);
        };
        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 + "'"));
            }
        };
        /**
         * @deprecated Use `runKernel` for newly added kernels. Keep using this method
         *     only for kernels that are not yet fully modularized.
         */
        Engine.prototype.runKernelFunc = function (forwardFunc, inputs, backwardsFunc, kernelName, attrs, inputsToSave, outputsToSave) {
            var _this = this;
            if (inputsToSave === void 0) { inputsToSave = []; }
            if (outputsToSave === void 0) { outputsToSave = []; }
            var outputs;
            var saved = [];
            var isTapeOn = this.isTapeOn();
            if (kernelName == null) {
                kernelName =
                    this.state.activeScope != null ? this.state.activeScope.name : '';
            }
            var saveFunc = 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)); });
            };
            var startingBytecount = this.state.numBytes;
            var startingNumTensors = this.state.numTensors;
            if (this.shouldCheckForMemLeaks()) {
                this.state.numDataMovesStack.push(0);
            }
            var kernelFunc;
            var kernel = getKernel(kernelName, this.backendName);
            var out;
            if (kernel != null) {
                kernelFunc = function () {
                    var numDataIdsBefore = _this.backend.numDataIds();
                    out = kernel.kernelFunc({ inputs: inputs, attrs: attrs, backend: _this.backend });
                    var outInfos = Array.isArray(out) ? out : [out];
                    if (_this.shouldCheckForMemLeaks()) {
                        _this.checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos);
                    }
                    var outTensors = outInfos.map(function (_a) {
                        var dataId = _a.dataId, shape = _a.shape, dtype = _a.dtype;
                        return _this.makeTensorFromDataId(dataId, shape, dtype);
                    });
                    var outsToSave = outTensors.filter(function (_, i) { return outputsToSave[i]; });
                    // Save the inputs and outputs.
                    saveFunc((inputsToSave || []).slice().concat(outsToSave));
                    return outTensors;
                };
            }
            else {
                kernelFunc = function () {
                    var numDataIdsBefore = _this.backend.numDataIds();
                    out = _this.tidy(function () { return forwardFunc(_this.backend, saveFunc); });
                    var outs = (Array.isArray(out) ? out : [out]);
                    if (_this.shouldCheckForMemLeaks()) {
                        _this.checkKernelForMemLeak(kernelName, numDataIdsBefore, outs);
                    }
                    return outs;
                };
            }
            // Stop recording to a tape when running a kernel.
            this.scopedRun(function () { return _this.state.kernelDepth++; }, function () { return _this.state.kernelDepth--; }, function () {
                if (!_this.ENV.getBool('DEBUG')) {
                    outputs = kernelFunc();
                }
                else {
                    outputs = _this.profiler.profileKernel(kernelName, inputs, function () { return kernelFunc(); });
                }
            });
            if (isTapeOn) {
                this.addTapeNode(kernelName, inputs, outputs, backwardsFunc, saved);
            }
            if (this.state.profiling) {
                this.state.activeProfile.kernels.push({
                    name: kernelName,
                    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].shape; }),
                    outputShapes: outputs.map(function (item) { return item.shape; })
                });
            }
            return (Array.isArray(out) ? outputs : outputs[0]);
        };
        /**
         * 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.incRef(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.incRef(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.asType(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.incRef = function (a, backend) {
            var refCount = this.state.tensorInfo.has(a.dataId) ?
                this.state.tensorInfo.get(a.dataId).refCount :
                0;
            this.state.numTensors++;
            if (a.dtype === 'string') {
                this.state.numStringTensors++;
            }
            if (refCount === 0) {
                this.state.numDataBuffers++;
                // 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.tensorInfo.set(a.dataId, {
                    backend: backend || this.backend,
                    dtype: a.dtype,
                    shape: a.shape,
                    bytes: bytes,
                    refCount: 0
                });
                this.state.numBytes += bytes;
            }
            this.state.tensorInfo.get(a.dataId).refCount++;
            if (!(a instanceof Variable)) {
                this.track(a);
            }
        };
        Engine.prototype.disposeTensor = function (a) {
            if (!this.state.tensorInfo.has(a.dataId)) {
                return;
            }
            this.state.numTensors--;
            if (a.dtype === 'string') {
                this.state.numStringTensors--;
            }
            var info = this.state.tensorInfo.get(a.dataId);
            var refCount = info.refCount;
            if (refCount <= 1) {
                // Don't count bytes for complex numbers as they are counted by their
                // components.
                if (a.dtype !== 'complex64') {
                    this.state.numBytes -= info.bytes;
                }
                this.state.numDataBuffers--;
                info.backend.disposeData(a.dataId);
                this.state.tensorInfo.delete(a.dataId);
            }
            else {
                this.state.tensorInfo.get(a.dataId).refCount--;
            }
            // 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;
                return __generator(this, function (_a) {
                    this.state.profiling = true;
                    startBytes = this.state.numBytes;
                    startNumTensors = this.state.numTensors;
                    this.state.activeProfile.kernels = [];
                    this.state.activeProfile.result = query();
                    this.state.profiling = false;
                    this.state.activeProfile.peakBytes = Math.max.apply(Math, this.state.activeProfile.kernels.map(function (d) { return d.totalBytesSnapshot; }));
                    this.state.activeProfile.newBytes = this.state.numBytes - startBytes;
                    this.state.activeProfile.newTensors =
                        this.state.numTensors - startNumTensors;
                    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) {
            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);
                };
            }
            this.state.activeTape.push(tapeNode);
        };
        Engine.prototype.keep = function (result) {
            result.kept = true;
            return result;
        };
        Engine.prototype.startTape = function () {
            if (this.state.gradientDepth === 0) {
                this.state.activeTape = [];
            }
            this.state.gradientDepth++;
        };
        Engine.prototype.endTape = function () {
            this.state.gradientDepth--;
        };
        /**
         * Start a scope. Use this with endScope() to achieve the same functionality
         * as scope() without the need for a function closure.
         */
        Engine.prototype.startScope = function (name) {
            var scopeInfo = {
                track: [],
                name: 'unnamed scope',
                id: this.state.nextScopeId++
            };
            if (name) {
                scopeInfo.name = name;
            }
            this.state.scopeStack.push(scopeInfo);
            this.state.activeScope = scopeInfo;
        };
        /**
         * End a scope. Use this with startScope() to achieve the same functionality
         * as scope() without the need for a function closure.
         */
        Engine.prototype.endScope = function (result) {
            var _this = this;
            var tensorsToTrackInParent = getTensorsInContainer(result);
            var tensorsToTrackInParentSet = new Set(tensorsToTrackInParent.map(function (t) { return t.id; }));
            // Dispose the arrays tracked in this scope.
            for (var i = 0; i < this.state.activeScope.track.length; i++) {
                var tensor = this.state.activeScope.track[i];
                if (!tensor.kept && !tensorsToTrackInParentSet.has(tensor.id)) {
                    tensor.dispose();
                }
            }
            var oldScope = this.state.scopeStack.pop();
            this.state.activeScope = this.state.scopeStack.length === 0 ?
                null :
                this.state.scopeStack[this.state.scopeStack.length - 1];
            // Track the current result in the parent scope.
            tensorsToTrackInParent.forEach(function (tensor) {
                // Only track the tensor if was allocated in the inner scope and is not
                // globally kept.
                if (!tensor.kept && tensor.scopeId === oldScope.id) {
                    _this.track(tensor);
                }
            });
        };
        /**
         * Returns gradients of `f` with respect to each of the `xs`. The gradients
         * returned are of the same length as `xs`, but some might be null if `f`
         * was not a function of that `x`. It also takes optional dy to multiply the
         * gradient, which defaults to `1`.
         */
        Engine.prototype.gradients = function (f, xs, dy, allowNoGradients) {
            var _this = this;
            if (allowNoGradients === void 0) { allowNoGradients = false; }
            assert(xs.length > 0, function () { return 'gradients() received an empty list of xs.'; });
            if (dy != null && dy.dtype !== 'float32') {
                throw new Error("dy must have 'float32' dtype, but has '" + dy.dtype + "'");
            }
            var y = this.scopedRun(function () { return _this.startTape(); }, function () { return _this.endTape(); }, function () { return _this.tidy('forward', f); });
            assert(y instanceof Tensor, function () { return 'The result y returned by f() must be a tensor.'; });
            // Filter out the nodes that don't connect x => y.
            var filteredTape = getFilteredNodesXToY(this.state.activeTape, xs, y);
            if (!allowNoGradients && filteredTape.length === 0 && xs.length > 0) {
                throw new Error('Cannot compute gradient of y=f(x) with respect to x. Make sure ' +
                    'that the f you passed encloses all operations that lead from x ' +
                    'to y.');
            }
            return this.tidy('backward', function () {
                var accumulatedGradientMap = {};
                accumulatedGradientMap[y.id] = (dy == null) ? ones(y.shape) : dy;
                // Backprop gradients through the filtered nodes.
                backpropagateGradients(accumulatedGradientMap, filteredTape, 
                // Pass the tidy function to avoid circular dep with `tape.ts`.
                function (f) { return _this.tidy(f); });
                var grads = xs.map(function (x) { return accumulatedGradientMap[x.id]; });
                if (_this.state.gradientDepth === 0) {
                    // This means that we are not computing higher-order gradients
                    // and can clean up the tape.
                    _this.state.activeTape.forEach(function (node) {
                        for (var _i = 0, _a = node.saved; _i < _a.length; _i++) {
                            var tensor = _a[_i];
                            tensor.dispose();
                        }
                    });
                    _this.state.activeTape = null;
                }
                return { value: y, grads: grads };
            });
        };
        Engine.prototype.customGrad = function (f) {
            var _this = this;
            assert(isFunction(f), function () { return 'The f passed in customGrad(f) must be a function.'; });
            return function () {
                var inputs = [];
                for (var _i = 0; _i < arguments.length; _i++) {
                    inputs[_i] = arguments[_i];
                }
                assert(inputs.every(function (t) { return t instanceof Tensor; }), function () { return 'The args passed in customGrad(f)(x1, x2,...) must all be ' +
                    'tensors'; });
                var res;
                var inputMap = {};
                inputs.forEach(function (input, i) {
                    inputMap[i] = input;
                });
                return _this.runKernelFunc(function (_, save) {
                    res = f.apply(void 0, inputs.concat([save]));
                    assert(res.value instanceof Tensor, function () { return 'The function f passed in customGrad(f) must return an ' +
                        'object where `obj.value` is a tensor'; });
                    assert(isFunction(res.gradFunc), function () { return 'The function f passed in customGrad(f) must return an ' +
                        'object where `obj.gradFunc` is a function.'; });
                    return res.value;
                }, inputMap, 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;
                });
            };
        };
        Engine.prototype.readSync = function (dataId) {
            // Route the read to the correct backend.
            var info = this.state.tensorInfo.get(dataId);
            return info.backend.readSync(dataId);
        };
        Engine.prototype.read = function (dataId) {
            // Route the read to the correct backend.
            var info = this.state.tensorInfo.get(dataId);
            return info.backend.read(dataId);
        };
        Engine.prototype.time = function (query) {
            return __awaiter(this, void 0, void 0, function () {
                var start, timingInfo;
                return __generator(this, function (_a) {
                    switch (_a.label) {
                        case 0:
                            start = now();
                            return [4 /*yield*/, this.backend.time(query)];
                        case 1:
                            timingInfo = _a.sent();
                            timingInfo.wallMs = now() - start;
                            return [2 /*return*/, timingInfo];
                    }
                });
            });
        };
        /**
         * Tracks a Tensor in the current scope to be automatically cleaned up
         * when the current scope ends, and returns the value.
         *
         * @param result The Tensor to track in the current scope.
         */
        Engine.prototype.track = function (result) {
            if (this.state.activeScope != null) {
                result.scopeId = this.state.activeScope.id;
                this.state.activeScope.track.push(result);
            }
            return result;
        };
        Object.defineProperty(Engine.prototype, "registeredVariables", {
            get: function () {
                return this.state.registeredVariables;
            },
            enumerable: true,
            configurable: true
        });
        /**
         * Resets the engine state. Removes all backends but does not remove
         * registered backend factories.
         */
        Engine.prototype.reset = function () {
            // Make any pending promise obsolete.
            this.pendingBackendInitId++;
            this.state.dispose();
            this.ENV.reset();
            this.state = new EngineState();
            for (var backendName in this.registry) {
                this.disposeRegisteredKernels(backendName);
                this.registry[backendName].dispose();
                delete this.registry[backendName];
            }
            this.backendName = null;
            this.backendInstance = null;
            this.pendingBackendInit = null;
        };
        Engine.nextTensorId = 0;
        Engine.nextVariableId = 0;
        return Engine;
    }());
    function ones(shape) {
        var values = makeOnesTypedArray(sizeFromShape(shape), 'float32');
        return ENGINE.makeTensor(values, shape, 'float32');
    }
    var GLOBAL;
    function getGlobalNamespace() {
        if (GLOBAL == 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');
            }
            GLOBAL = ns;
        }
        return GLOBAL;
    }
    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();

    /**
     * @license
     * Copyright 2017 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.
     * =============================================================================
     */
    function isMobile() {
        // tslint:disable-next-line:no-any
        var a = navigator.userAgent || navigator.vendor || window.opera;
        // tslint:disable-next-line:max-line-length
        return /(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i
            .test(a) ||
            // tslint:disable-next-line:max-line-length
            /1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i
                .test(a.substr(0, 4));
    }
    function isBrowser() {
        return (typeof window !== 'undefined' && window.document != null) ||
            //@ts-ignore
            (typeof WorkerGlobalScope !== 'undefined');
    }

    /**
     * @license
     * Copyright 2019 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.
     * =============================================================================
     */
    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; });

    /**
     * @license
     * Copyright 2018 Google LLC. All Rights Reserved.
     * Licensed under the Apache License, Version 2.0 (the "License");
     * you may not use this file except in compliance with the License.
     * You may obtain a copy of the License at
     *
     * http://www.apache.org/licenses/LICENSE-2.0
     *
     * Unless required by applicable law or agreed to in writing, software
     * distributed under the License is distributed on an "AS IS" BASIS,
     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
     * See the License for the specific language governing permissions and
     * limitations under the License.
     * =============================================================================
     */
    var contexts = {};
    var WEBGL_ATTRIBUTES = {
        alpha: false,
        antialias: false,
        premultipliedAlpha: false,
        preserveDrawingBuffer: false,
        depth: false,
        stencil: false,
        failIfMajorPerformanceCaveat: true
    };
    function setWebGLContext(webGLVersion, gl) {
        contexts[webGLVersion] = gl;
    }
    function getWebGLContext(webGLVersion) {
        if (!(webGLVersion in contexts)) {
            contexts[webGLVersion] = getWebGLRenderingContext(webGLVersion);
        }
        var gl = contexts[webGLVersion];
        if (gl.isContextLost()) {
            delete contexts[webGLVersion];
            return getWebGLContext(webGLVersion);
        }
        gl.disable(gl.DEPTH_TEST);
        gl.disable(gl.STENCIL_TEST);
        gl.disable(gl.BLEND);
        gl.disable(gl.DITHER);
        gl.disable(gl.POLYGON_OFFSET_FILL);
        gl.disable(gl.SAMPLE_COVERAGE);
        gl.enable(gl.SCISSOR_TEST);
        gl.enable(gl.CULL_FACE);
        gl.cullFace(gl.BACK);
        return contexts[webGLVersion];
    }
    function createCanvas(webGLVersion) {
        if (typeof OffscreenCanvas !== 'undefined' && webGLVersion === 2) {
            return new OffscreenCanvas(300, 150);
        }
        else if (typeof document !== 'undefined') {
            return document.createElement('canvas');
        }
        else {
            throw new Error('Cannot create a canvas in this context');
        }
    }
    function getWebGLRenderingContext(webGLVersion) {
        if (webGLVersion !== 1 && webGLVersion !== 2) {
            throw new Error('Cannot get WebGL rendering context, WebGL is disabled.');
        }
        var canvas = createCanvas(webGLVersion);
        canvas.addEventListener('webglcontextlost', function (ev) {
            ev.preventDefault();
            delete contexts[webGLVersion];
        }, false);
        if (webGLVersion === 1) {
            return (canvas.getContext('webgl', WEBGL_ATTRIBUTES) ||
                canvas.getContext('experimental-webgl', WEBGL_ATTRIBUTES));
        }
        return canvas.getContext('webgl2', WEBGL_ATTRIBUTES);
    }

    /**
     * @license
     * Copyright 2017 Google 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.
     * =============================================================================
     */
    var PackingScheme;
    (function (PackingScheme) {
        /**
         * All values in a single texel are densely packed without any constraints.
         *
         * This is how the shader encodes a tensor with shape = [2, 3, 4]
         * (indices are [batch, row, col]).
         *
         * 000|001   010|011   020|021
         * -------   -------   -------
         * 002|003   012|013   022|023
         *
         * 100|101   110|111   120|121
         * -------   -------   -------
         * 102|103   112|113   122|123
         *
         */
        PackingScheme[PackingScheme["DENSE"] = 0] = "DENSE";
        /**
         * Single texels contain only values from the same batch, and from adjacent
         * rows and columns.
         *
         * This is how the shader encodes a tensor with shape = [2, 3, 5]
         * (indices are [batch, row, col]).
         *
         * 000|001   002|003   004|xxx   020|021   022|023   024|xxx
         * -------   -------   -------   -------   -------   -------
         * 010|011   012|013   014|xxx   xxx|xxx   xxx|xxx   xxx|xxx
         *
         * 100|101   102|103   104|xxx   120|121   122|123   124|xxx
         * -------   -------   -------   -------   -------   -------
         * 110|111   112|113   114|xxx   xxx|xxx   xxx|xxx   xxx|xxx
         *
         */
        PackingScheme[PackingScheme["SHARED_BATCH"] = 1] = "SHARED_BATCH";
    })(PackingScheme || (PackingScheme = {}));
    var TextureUsage;
    (function (TextureUsage) {
        TextureUsage[TextureUsage["RENDER"] = 0] = "RENDER";
        TextureUsage[TextureUsage["UPLOAD"] = 1] = "UPLOAD";
        TextureUsage[TextureUsage["PIXELS"] = 2] = "PIXELS";
        TextureUsage[TextureUsage["DOWNLOAD"] = 3] = "DOWNLOAD";
    })(TextureUsage || (TextureUsage = {}));
    var PhysicalTextureType;
    (function (PhysicalTextureType) {
        PhysicalTextureType[PhysicalTextureType["UNPACKED_FLOAT16"] = 0] = "UNPACKED_FLOAT16";
        PhysicalTextureType[PhysicalTextureType["UNPACKED_FLOAT32"] = 1] = "UNPACKED_FLOAT32";
        PhysicalTextureType[PhysicalTextureType["PACKED_4X1_UNSIGNED_BYTE"] = 2] = "PACKED_4X1_UNSIGNED_BYTE";
        PhysicalTextureType[PhysicalTextureType["PACKED_2X2_FLOAT32"] = 3] = "PACKED_2X2_FLOAT32";
        PhysicalTextureType[PhysicalTextureType["PACKED_2X2_FLOAT16"] = 4] = "PACKED_2X2_FLOAT16";
    })(PhysicalTextureType || (PhysicalTextureType = {}));
    function getUnpackedMatrixTextureShapeWidthHeight(rows, columns) {
        return [columns, rows];
    }
    function getUnpackedArraySizeFromMatrixSize(matrixSize, channelsPerTexture) {
        return matrixSize * channelsPerTexture;
    }
    /**
     * Get shape for densely packed RGBA texture.
     */
    function getDenseTexShape(shape) {
        var size = sizeFromShape(shape);
        var texelsNeeded = Math.ceil(size / 4);
        return sizeToSquarishShape(texelsNeeded);
    }
    function getPackedMatrixTextureShapeWidthHeight(rows, columns) {
        return [
            Math.max(1, Math.ceil(columns / 2)), Math.max(1, Math.ceil(rows / 2))
        ];
    }
    function getPackedRGBAArraySizeFromMatrixShape(rows, columns) {
        var _a = getPackedMatrixTextureShapeWidthHeight(rows, columns), w = _a[0], h = _a[1];
        return w * h * 4;
    }
    function getTextureConfig(
    // tslint:disable-next-line:no-any
    gl, textureHalfFloatExtension) {
        // tslint:disable-next-line:no-any
        var glany = gl;
        var internalFormatFloat;
        var internalFormatHalfFloat;
        var internalFormatPackedHalfFloat;
        var internalFormatPackedFloat;
        var textureFormatFloat;
        var downloadTextureFormat;
        var downloadUnpackNumChannels;
        var defaultNumChannels;
        var textureTypeHalfFloat;
        var textureTypeFloat;
        if (env().getNumber('WEBGL_VERSION') === 2) {
            internalFormatFloat = glany.R32F;
            internalFormatHalfFloat = glany.R16F;
            internalFormatPackedHalfFloat = glany.RGBA16F;
            internalFormatPackedFloat = glany.RGBA32F;
            textureFormatFloat = glany.RED;
            downloadUnpackNumChannels = 4;
            defaultNumChannels = 1;
            textureTypeHalfFloat = glany.HALF_FLOAT;
            textureTypeFloat = glany.FLOAT;
        }
        else {
            internalFormatFloat = gl.RGBA;
            internalFormatHalfFloat = gl.RGBA;
            internalFormatPackedHalfFloat = gl.RGBA;
            internalFormatPackedFloat = glany.RGBA;
            textureFormatFloat = gl.RGBA;
            downloadUnpackNumChannels = 4;
            defaultNumChannels = 4;
            textureTypeHalfFloat = textureHalfFloatExtension != null ?
                textureHalfFloatExtension.HALF_FLOAT_OES :
                null;
            textureTypeFloat = gl.FLOAT;
        }
        downloadTextureFormat = gl.RGBA;
        return {
            internalFormatFloat: internalFormatFloat,
            internalFormatHalfFloat: internalFormatHalfFloat,
            internalFormatPackedHalfFloat: internalFormatPackedHalfFloat,
            internalFormatPackedFloat: internalFormatPackedFloat,
            textureFormatFloat: textureFormatFloat,
            downloadTextureFormat: downloadTextureFormat,
            downloadUnpackNumChannels: downloadUnpackNumChannels,
            defaultNumChannels: defaultNumChannels,
            textureTypeHalfFloat: textureTypeHalfFloat,
            textureTypeFloat: textureTypeFloat
        };
    }

    /**
     * @license
     * Copyright 2017 Google 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.
     * =============================================================================
     */
    function callAndCheck(gl, debugMode, func) {
        var returnValue = func();
        if (debugMode) {
            checkWebGLError(gl);
        }
        return returnValue;
    }
    function checkWebGLError(gl) {
        var error = gl.getError();
        if (error !== gl.NO_ERROR) {
            throw new Error('WebGL Error: ' + getWebGLErrorMessage(gl, error));
        }
    }
    // https://en.wikipedia.org/wiki/Half-precision_floating-point_format
    var MIN_FLOAT16 = 5.96e-8;
    var MAX_FLOAT16 = 65504;
    function canBeRepresented(num) {
        if (env().getBool('WEBGL_RENDER_FLOAT32_ENABLED') || num === 0 ||
            (MIN_FLOAT16 < Math.abs(num) && Math.abs(num) < MAX_FLOAT16)) {
            return true;
        }
        return false;
    }
    function getWebGLErrorMessage(gl, status) {
        switch (status) {
            case gl.NO_ERROR:
                return 'NO_ERROR';
            case gl.INVALID_ENUM:
                return 'INVALID_ENUM';
            case gl.INVALID_VALUE:
                return 'INVALID_VALUE';
            case gl.INVALID_OPERATION:
                return 'INVALID_OPERATION';
            case gl.INVALID_FRAMEBUFFER_OPERATION:
                return 'INVALID_FRAMEBUFFER_OPERATION';
            case gl.OUT_OF_MEMORY:
                return 'OUT_OF_MEMORY';
            case gl.CONTEXT_LOST_WEBGL:
                return 'CONTEXT_LOST_WEBGL';
            default:
                return "Unknown error code " + status;
        }
    }
    function getExtensionOrThrow(gl, debug, extensionName) {
        return throwIfNull(gl, debug, function () { return gl.getExtension(extensionName); }, 'Extension "' + extensionName + '" not supported on this browser.');
    }
    function createVertexShader(gl, debug, vertexShaderSource) {
        var vertexShader = throwIfNull(gl, debug, function () { return gl.createShader(gl.VERTEX_SHADER); }, 'Unable to create vertex WebGLShader.');
        callAndCheck(gl, debug, function () { return gl.shaderSource(vertexShader, vertexShaderSource); });
        callAndCheck(gl, debug, function () { return gl.compileShader(vertexShader); });
        if (gl.getShaderParameter(vertexShader, gl.COMPILE_STATUS) === false) {
            console.log(gl.getShaderInfoLog(vertexShader));
            throw new Error('Failed to compile vertex shader.');
        }
        return vertexShader;
    }
    function createFragmentShader(gl, debug, fragmentShaderSource) {
        var fragmentShader = throwIfNull(gl, debug, function () { return gl.createShader(gl.FRAGMENT_SHADER); }, 'Unable to create fragment WebGLShader.');
        callAndCheck(gl, debug, function () { return gl.shaderSource(fragmentShader, fragmentShaderSource); });
        callAndCheck(gl, debug, function () { return gl.compileShader(fragmentShader); });
        if (gl.getShaderParameter(fragmentShader, gl.COMPILE_STATUS) === false) {
            logShaderSourceAndInfoLog(fragmentShaderSource, gl.getShaderInfoLog(fragmentShader));
            throw new Error('Failed to compile fragment shader.');
        }
        return fragmentShader;
    }
    var lineNumberRegex = /ERROR: [0-9]+:([0-9]+):/g;
    function logShaderSourceAndInfoLog(shaderSource, shaderInfoLog) {
        var lineNumberRegexResult = lineNumberRegex.exec(shaderInfoLog);
        if (lineNumberRegexResult == null) {
            console.log("Couldn't parse line number in error: " + shaderInfoLog);
            console.log(shaderSource);
            return;
        }
        var lineNumber = +lineNumberRegexResult[1];
        var shaderLines = shaderSource.split('\n');
        var pad = shaderLines.length.toString().length + 2;
        var linesWithLineNumbers = shaderLines.map(function (line, lineNumber) {
            return rightPad((lineNumber + 1).toString(), pad) + line;
        });
        var maxLineLength = 0;
        for (var i = 0; i < linesWithLineNumbers.length; i++) {
            maxLineLength = Math.max(linesWithLineNumbers[i].length, maxLineLength);
        }
        var beforeErrorLines = linesWithLineNumbers.slice(0, lineNumber - 1);
        var errorLine = linesWithLineNumbers.slice(lineNumber - 1, lineNumber);
        var afterErrorLines = linesWithLineNumbers.slice(lineNumber);
        console.log(beforeErrorLines.join('\n'));
        console.log(shaderInfoLog.split('\n')[0]);
        console.log("%c " + rightPad(errorLine[0], maxLineLength), 'border:1px solid red; background-color:#e3d2d2; color:#a61717');
        console.log(afterErrorLines.join('\n'));
    }
    function createProgram(gl, debug) {
        return throwIfNull(gl, debug, function () { return gl.createProgram(); }, 'Unable to create WebGLProgram.');
    }
    function linkProgram(gl, debug, program) {
        callAndCheck(gl, debug, function () { return gl.linkProgram(program); });
        if (gl.getProgramParameter(program, gl.LINK_STATUS) === false) {
            console.log(gl.getProgramInfoLog(program));
            throw new Error('Failed to link vertex and fragment shaders.');
        }
    }
    function validateProgram(gl, debug, program) {
        callAndCheck(gl, debug, function () { return gl.validateProgram(program); });
        if (gl.getProgramParameter(program, gl.VALIDATE_STATUS) === false) {
            console.log(gl.getProgramInfoLog(program));
            throw new Error('Shader program validation failed.');
        }
    }
    function createStaticVertexBuffer(gl, debug, data) {
        var buffer = throwIfNull(gl, debug, function () { return gl.createBuffer(); }, 'Unable to create WebGLBuffer');
        callAndCheck(gl, debug, function () { return gl.bindBuffer(gl.ARRAY_BUFFER, buffer); });
        callAndCheck(gl, debug, function () { return gl.bufferData(gl.ARRAY_BUFFER, data, gl.STATIC_DRAW); });
        return buffer;
    }
    function createStaticIndexBuffer(gl, debug, data) {
        var buffer = throwIfNull(gl, debug, function () { return gl.createBuffer(); }, 'Unable to create WebGLBuffer');
        callAndCheck(gl, debug, function () { return gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, buffer); });
        callAndCheck(gl, debug, function () { return gl.bufferData(gl.ELEMENT_ARRAY_BUFFER, data, gl.STATIC_DRAW); });
        return buffer;
    }
    function getNumChannels() {
        if (env().getNumber('WEBGL_VERSION') === 2) {
            return 1;
        }
        return 4;
    }
    function createTexture(gl, debug) {
        return throwIfNull(gl, debug, function () { return gl.createTexture(); }, 'Unable to create WebGLTexture.');
    }
    function validateTextureSize(width, height) {
        var maxTextureSize = env().getNumber('WEBGL_MAX_TEXTURE_SIZE');
        if ((width <= 0) || (height <= 0)) {
            var requested = "[" + width + "x" + height + "]";
            throw new Error('Requested texture size ' + requested + ' is invalid.');
        }
        if ((width > maxTextureSize) || (height > maxTextureSize)) {
            var requested = "[" + width + "x" + height + "]";
            var max = "[" + maxTextureSize + "x" + maxTextureSize + "]";
            throw new Error('Requested texture size ' + requested +
                ' greater than WebGL maximum on this browser / GPU ' + max + '.');
        }
    }
    function createFramebuffer(gl, debug) {
        return throwIfNull(gl, debug, function () { return gl.createFramebuffer(); }, 'Unable to create WebGLFramebuffer.');
    }
    function bindVertexBufferToProgramAttribute(gl, debug, program, attribute, buffer, arrayEntriesPerItem, itemStrideInBytes, itemOffsetInBytes) {
        var loc = gl.getAttribLocation(program, attribute);
        if (loc === -1) {
            // The GPU compiler decided to strip out this attribute because it's unused,
            // thus no need to bind.
            return false;
        }
        callAndCheck(gl, debug, function () { return gl.bindBuffer(gl.ARRAY_BUFFER, buffer); });
        callAndCheck(gl, debug, function () { return gl.vertexAttribPointer(loc, arrayEntriesPerItem, gl.FLOAT, false, itemStrideInBytes, itemOffsetInBytes); });
        callAndCheck(gl, debug, function () { return gl.enableVertexAttribArray(loc); });
        return true;
    }
    function bindTextureUnit(gl, debug, texture, textureUnit) {
        validateTextureUnit(gl, textureUnit);
        callAndCheck(gl, debug, function () { return gl.activeTexture(gl.TEXTURE0 + textureUnit); });
        callAndCheck(gl, debug, function () { return gl.bindTexture(gl.TEXTURE_2D, texture); });
    }
    function unbindTextureUnit(gl, debug, textureUnit) {
        validateTextureUnit(gl, textureUnit);
        callAndCheck(gl, debug, function () { return gl.activeTexture(gl.TEXTURE0 + textureUnit); });
        callAndCheck(gl, debug, function () { return gl.bindTexture(gl.TEXTURE_2D, null); });
    }
    function getProgramUniformLocationOrThrow(gl, debug, program, uniformName) {
        return throwIfNull(gl, debug, function () { return gl.getUniformLocation(program, uniformName); }, 'uniform "' + uniformName + '" not present in program.');
    }
    function getProgramUniformLocation(gl, program, uniformName) {
        return gl.getUniformLocation(program, uniformName);
    }
    function bindTextureToProgramUniformSampler(gl, debug, program, texture, uniformSamplerLocation, textureUnit) {
        callAndCheck(gl, debug, function () { return bindTextureUnit(gl, debug, texture, textureUnit); });
        callAndCheck(gl, debug, function () { return gl.uniform1i(uniformSamplerLocation, textureUnit); });
    }
    function bindCanvasToFramebuffer(gl, debug) {
        callAndCheck(gl, debug, function () { return gl.bindFramebuffer(gl.FRAMEBUFFER, null); });
        callAndCheck(gl, debug, function () { return gl.viewport(0, 0, gl.canvas.width, gl.canvas.height); });
        callAndCheck(gl, debug, function () { return gl.scissor(0, 0, gl.canvas.width, gl.canvas.height); });
    }
    function bindColorTextureToFramebuffer(gl, debug, texture, framebuffer) {
        callAndCheck(gl, debug, function () { return gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer); });
        callAndCheck(gl, debug, function () { return gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0); });
    }
    function unbindColorTextureFromFramebuffer(gl, debug, framebuffer) {
        callAndCheck(gl, debug, function () { return gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer); });
        callAndCheck(gl, debug, function () { return gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, null, 0); });
    }
    function validateFramebuffer(gl) {
        var status = gl.checkFramebufferStatus(gl.FRAMEBUFFER);
        if (status !== gl.FRAMEBUFFER_COMPLETE) {
            throw new Error('Error binding framebuffer: ' + getFramebufferErrorMessage(gl, status));
        }
    }
    function getFramebufferErrorMessage(gl, status) {
        switch (status) {
            case gl.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:
                return 'FRAMEBUFFER_INCOMPLETE_ATTACHMENT';
            case gl.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:
                return 'FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT';
            case gl.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:
                return 'FRAMEBUFFER_INCOMPLETE_DIMENSIONS';
            case gl.FRAMEBUFFER_UNSUPPORTED:
                return 'FRAMEBUFFER_UNSUPPORTED';
            default:
                return "unknown error " + status;
        }
    }
    function throwIfNull(gl, debug, returnTOrNull, failureMessage) {
        var tOrNull = callAndCheck(gl, debug, function () { return returnTOrNull(); });
        if (tOrNull == null) {
            throw new Error(failureMessage);
        }
        return tOrNull;
    }
    function validateTextureUnit(gl, textureUnit) {
        var maxTextureUnit = gl.MAX_COMBINED_TEXTURE_IMAGE_UNITS - 1;
        var glTextureUnit = textureUnit + gl.TEXTURE0;
        if (glTextureUnit < gl.TEXTURE0 || glTextureUnit > maxTextureUnit) {
            var textureUnitRange = "[gl.TEXTURE0, gl.TEXTURE" + maxTextureUnit + "]";
            throw new Error("textureUnit must be in " + textureUnitRange + ".");
        }
    }
    function getBatchDim(shape, dimsToSkip) {
        if (dimsToSkip === void 0) { dimsToSkip = 2; }
        return sizeFromShape(shape.slice(0, shape.length - dimsToSkip));
    }
    function getRowsCols(shape) {
        if (shape.length === 0) {
            throw Error('Cannot get rows and columns of an empty shape array.');
        }
        return [
            shape.length > 1 ? shape[shape.length - 2] : 1, shape[shape.length - 1]
        ];
    }
    function getShapeAs3D(shape) {
        var shapeAs3D = [1, 1, 1];
        var isScalar = shape.length === 0 || (shape.length === 1 && shape[0] === 1);
        if (!isScalar) {
            shapeAs3D =
                [getBatchDim(shape)].concat(getRowsCols(shape));
        }
        return shapeAs3D;
    }
    function getTextureShapeFromLogicalShape(logShape, isPacked) {
        var _a;
        if (isPacked === void 0) { isPacked = false; }
        var maxTexSize = env().getNumber('WEBGL_MAX_TEXTURE_SIZE');
        if (isPacked) {
            maxTexSize = maxTexSize * 2;
            // This logic ensures we accurately count the number of packed texels needed
            // to accommodate the tensor. We can only pack values in the same texel if
            // they are from adjacent pairs of rows/cols within the same batch. So if a
            // tensor has 3 rows, we pretend it has 4 rows in order to account for the
            // fact that the texels containing the third row are half empty.
            logShape = logShape.map(function (d, i) { return i >= logShape.length - 2 ?
                nearestLargerEven(logShape[i]) :
                logShape[i]; });
            // Packed texture height is at least 2 (the channel height of a single
            // texel).
            if (logShape.length === 1) {
                logShape = [2, logShape[0]];
            }
        }
        // If logical shape is 2, we don't squeeze, since we want to match physical.
        if (logShape.length !== 2) {
            var squeezeResult = squeezeShape(logShape);
            logShape = squeezeResult.newShape;
        }
        var size = sizeFromShape(logShape);
        if (logShape.length <= 1 && size <= maxTexSize) {
            return [1, size];
        }
        else if (logShape.length === 2 && logShape[0] <= maxTexSize &&
            logShape[1] <= maxTexSize) {
            return logShape;
        }
        else if (logShape.length === 3 && logShape[0] * logShape[1] <= maxTexSize &&
            logShape[2] <= maxTexSize) {
            return [logShape[0] * logShape[1], logShape[2]];
        }
        else if (logShape.length === 3 && logShape[0] <= maxTexSize &&
            logShape[1] * logShape[2] <= maxTexSize) {
            return [logShape[0], logShape[1] * logShape[2]];
        }
        else if (logShape.length === 4 &&
            logShape[0] * logShape[1] * logShape[2] <= maxTexSize &&
            logShape[3] <= maxTexSize) {
            return [logShape[0] * logShape[1] * logShape[2], logShape[3]];
        }
        else if (logShape.length === 4 && logShape[0] <= maxTexSize &&
            logShape[1] * logShape[2] * logShape[3] <= maxTexSize) {
            return [logShape[0], logShape[1] * logShape[2] * logShape[3]];
        }
        else {
            if (isPacked) {
                // For packed textures size equals the number of channels required to
                // accommodate the texture data. However in order to squarify such that
                // inner dimensions stay even, we rewrite size to equal the number of
                // texels. Then in the return statement we rehydrate the squarified
                // dimensions to channel units.
                var batchDim = getBatchDim(logShape);
                var rows = 2, cols = 2;
                if (logShape.length) {
                    _a = getRowsCols(logShape), rows = _a[0], cols = _a[1];
                }
                size = batchDim * (rows / 2) * (cols / 2);
                return sizeToSquarishShape(size).map(function (d) { return d * 2; });
            }
            return sizeToSquarishShape(size);
        }
    }
    function isEven(n) {
        return n % 2 === 0;
    }
    /**
     * This determines whether reshaping a packed texture requires rearranging
     * the data within the texture, assuming 2x2 packing.
     */
    function isReshapeFree(shape1, shape2) {
        shape1 = shape1.slice(-2);
        shape2 = shape2.slice(-2);
        if (arraysEqual(shape1, shape2)) {
            return true;
        }
        if (!shape1.length || !shape2.length) { // One of the shapes is a scalar.
            return true;
        }
        if (shape1[0] === 0 || shape1[1] === 0 || shape2[0] === 0 ||
            shape2[1] === 0) {
            return true;
        }
        if (shape1.length !== shape2.length) { // One of the shapes is a vector.
            var shape1Cols = shape1.slice(-1)[0];
            var shape2Cols = shape2.slice(-1)[0];
            if (shape1Cols === shape2Cols) {
                return true;
            }
            if (isEven(shape1Cols) && isEven(shape2Cols) &&
                (shape1[0] === 1 || shape2[0] === 1)) {
                return true;
            }
        }
        return shape1[1] === shape2[1] && isEven(shape1[0]) && isEven(shape2[0]);
    }
    // We cache webgl params because the environment gets reset between
    // unit tests and we don't want to constantly query the WebGLContext for
    // MAX_TEXTURE_SIZE.
    var MAX_TEXTURE_SIZE;
    var MAX_TEXTURES_IN_SHADER;
    function getWebGLMaxTextureSize(webGLVersion) {
        if (MAX_TEXTURE_SIZE == null) {
            var gl = getWebGLContext(webGLVersion);
            MAX_TEXTURE_SIZE = gl.getParameter(gl.MAX_TEXTURE_SIZE);
        }
        return MAX_TEXTURE_SIZE;
    }
    function resetMaxTextureSize() {
        MAX_TEXTURE_SIZE = null;
    }
    function resetMaxTexturesInShader() {
        MAX_TEXTURES_IN_SHADER = null;
    }
    function getMaxTexturesInShader(webGLVersion) {
        if (MAX_TEXTURES_IN_SHADER == null) {
            var gl = getWebGLContext(webGLVersion);
            MAX_TEXTURES_IN_SHADER = gl.getParameter(gl.MAX_TEXTURE_IMAGE_UNITS);
        }
        // We cap at 16 to avoid spurious runtime "memory exhausted" error.
        return Math.min(16, MAX_TEXTURES_IN_SHADER);
    }
    function getWebGLDisjointQueryTimerVersion(webGLVersion) {
        if (webGLVersion === 0) {
            return 0;
        }
        var queryTimerVersion;
        var gl = getWebGLContext(webGLVersion);
        if (hasExtension(gl, 'EXT_disjoint_timer_query_webgl2') &&
            webGLVersion === 2) {
            queryTimerVersion = 2;
        }
        else if (hasExtension(gl, 'EXT_disjoint_timer_query')) {
            queryTimerVersion = 1;
        }
        else {
            queryTimerVersion = 0;
        }
        return queryTimerVersion;
    }
    function hasExtension(gl, extensionName) {
        var ext = gl.getExtension(extensionName);
        return ext != null;
    }
    function isWebGLVersionEnabled(webGLVersion) {
        try {
            var gl = getWebGLContext(webGLVersion);
            if (gl != null) {
                return true;
            }
        }
        catch (e) {
            return false;
        }
        return false;
    }
    function isCapableOfRenderingToFloatTexture(webGLVersion) {
        if (webGLVersion === 0) {
            return false;
        }
        var gl = getWebGLContext(webGLVersion);
        if (webGLVersion === 1) {
            if (!hasExtension(gl, 'OES_texture_float')) {
                return false;
            }
        }
        else {
            if (!hasExtension(gl, 'EXT_color_buffer_float')) {
                return false;
            }
        }
        var isFrameBufferComplete = createFloatTextureAndBindToFramebuffer(gl);
        return isFrameBufferComplete;
    }
    /**
     * Check if we can download values from a float/half-float texture.
     *
     * Note that for performance reasons we use binding a texture to a framebuffer
     * as a proxy for ability to download float values later using readPixels. The
     * texture params of this texture will not match those in readPixels exactly
     * but if we are unable to bind some kind of float texture to the frameBuffer
     * then we definitely will not be able to read float values from it.
     */
    function isDownloadFloatTextureEnabled(webGLVersion) {
        if (webGLVersion === 0) {
            return false;
        }
        var gl = getWebGLContext(webGLVersion);
        if (webGLVersion === 1) {
            if (!hasExtension(gl, 'OES_texture_float')) {
                return false;
            }
            if (!hasExtension(gl, 'WEBGL_color_buffer_float')) {
                return false;
            }
        }
        else {
            if (hasExtension(gl, 'EXT_color_buffer_float')) {
                return createFloatTextureAndBindToFramebuffer(gl);
            }
            var COLOR_BUFFER_HALF_FLOAT = 'EXT_color_buffer_half_float';
            if (hasExtension(gl, COLOR_BUFFER_HALF_FLOAT)) {
                var textureHalfFloatExtension = gl.getExtension(COLOR_BUFFER_HALF_FLOAT);
                return createHalfFloatTextureAndBindToFramebuffer(gl, textureHalfFloatExtension);
            }
            return false;
        }
        var isFrameBufferComplete = createFloatTextureAndBindToFramebuffer(gl);
        return isFrameBufferComplete;
    }
    function createFloatTextureAndBindToFramebuffer(gl) {
        var texConfig = getTextureConfig(gl);
        var texture = gl.createTexture();
        gl.bindTexture(gl.TEXTURE_2D, texture);
        var width = 1;
        var height = 1;
        gl.texImage2D(gl.TEXTURE_2D, 0, texConfig.internalFormatFloat, width, height, 0, texConfig.textureFormatFloat, texConfig.textureTypeFloat, null);
        var frameBuffer = gl.createFramebuffer();
        gl.bindFramebuffer(gl.FRAMEBUFFER, frameBuffer);
        gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);
        var isFrameBufferComplete = gl.checkFramebufferStatus(gl.FRAMEBUFFER) === gl.FRAMEBUFFER_COMPLETE;
        gl.bindTexture(gl.TEXTURE_2D, null);
        gl.bindFramebuffer(gl.FRAMEBUFFER, null);
        gl.deleteTexture(texture);
        gl.deleteFramebuffer(frameBuffer);
        return isFrameBufferComplete;
    }
    function createHalfFloatTextureAndBindToFramebuffer(
    // tslint:disable-next-line:no-any
    gl, textureHalfFloatExtension) {
        var texConfig = getTextureConfig(gl, textureHalfFloatExtension);
        var texture = gl.createTexture();
        gl.bindTexture(gl.TEXTURE_2D, texture);
        var width = 1;
        var height = 1;
        gl.texImage2D(gl.TEXTURE_2D, 0, texConfig.internalFormatHalfFloat, width, height, 0, texConfig.textureFormatFloat, texConfig.textureTypeHalfFloat, null);
        var frameBuffer = gl.createFramebuffer();
        gl.bindFramebuffer(gl.FRAMEBUFFER, frameBuffer);
        gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);
        var isFrameBufferComplete = gl.checkFramebufferStatus(gl.FRAMEBUFFER) === gl.FRAMEBUFFER_COMPLETE;
        gl.bindTexture(gl.TEXTURE_2D, null);
        gl.bindFramebuffer(gl.FRAMEBUFFER, null);
        gl.deleteTexture(texture);
        gl.deleteFramebuffer(frameBuffer);
        return isFrameBufferComplete;
    }
    function isWebGLFenceEnabled(webGLVersion) {
        if (webGLVersion !== 2) {
            return false;
        }
        var gl = getWebGLContext(webGLVersion);
        // tslint:disable-next-line:no-any
        var isEnabled = gl.fenceSync != null;
        return isEnabled;
    }

    var webgl_util = /*#__PURE__*/Object.freeze({
        callAndCheck: callAndCheck,
        canBeRepresented: canBeRepresented,
        getWebGLErrorMessage: getWebGLErrorMessage,
        getExtensionOrThrow: getExtensionOrThrow,
        createVertexShader: createVertexShader,
        createFragmentShader: createFragmentShader,
        createProgram: createProgram,
        linkProgram: linkProgram,
        validateProgram: validateProgram,
        createStaticVertexBuffer: createStaticVertexBuffer,
        createStaticIndexBuffer: createStaticIndexBuffer,
        getNumChannels: getNumChannels,
        createTexture: createTexture,
        validateTextureSize: validateTextureSize,
        createFramebuffer: createFramebuffer,
        bindVertexBufferToProgramAttribute: bindVertexBufferToProgramAttribute,
        bindTextureUnit: bindTextureUnit,
        unbindTextureUnit: unbindTextureUnit,
        getProgramUniformLocationOrThrow: getProgramUniformLocationOrThrow,
        getProgramUniformLocation: getProgramUniformLocation,
        bindTextureToProgramUniformSampler: bindTextureToProgramUniformSampler,
        bindCanvasToFramebuffer: bindCanvasToFramebuffer,
        bindColorTextureToFramebuffer: bindColorTextureToFramebuffer,
        unbindColorTextureFromFramebuffer: unbindColorTextureFromFramebuffer,
        validateFramebuffer: validateFramebuffer,
        getFramebufferErrorMessage: getFramebufferErrorMessage,
        getBatchDim: getBatchDim,
        getRowsCols: getRowsCols,
        getShapeAs3D: getShapeAs3D,
        getTextureShapeFromLogicalShape: getTextureShapeFromLogicalShape,
        isReshapeFree: isReshapeFree,
        getWebGLMaxTextureSize: getWebGLMaxTextureSize,
        resetMaxTextureSize: resetMaxTextureSize,
        resetMaxTexturesInShader: resetMaxTexturesInShader,
        getMaxTexturesInShader: getMaxTexturesInShader,
        getWebGLDisjointQueryTimerVersion: getWebGLDisjointQueryTimerVersion,
        hasExtension: hasExtension,
        isWebGLVersionEnabled: isWebGLVersionEnabled,
        isCapableOfRenderingToFloatTexture: isCapableOfRenderingToFloatTexture,
        isDownloadFloatTextureEnabled: isDownloadFloatTextureEnabled,
        isWebGLFenceEnabled: isWebGLFenceEnabled
    });

    /**
     * @license
     * Copyright 2019 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.
     * =============================================================================
     */
    var ENV$1 = env();
    /**
     * This file contains WebGL-specific flag registrations.
     */
    /**
     * True if WebGL is supported.
     */
    ENV$1.registerFlag('HAS_WEBGL', function () { return ENV$1.getNumber('WEBGL_VERSION') > 0; });
    /** 0: No WebGL, 1: WebGL 1.0, 2: WebGL 2.0. */
    ENV$1.registerFlag('WEBGL_VERSION', function () {
        if (isWebGLVersionEnabled(2)) {
            return 2;
        }
        else if (isWebGLVersionEnabled(1)) {
            return 1;
        }
        return 0;
    });
    ENV$1.registerFlag('WEBGL_BUFFER_SUPPORTED', function () { return ENV$1.get('WEBGL_VERSION') === 2; });
    /** Whether the WebGL backend will sometimes forward ops to the CPU. */
    ENV$1.registerFlag('WEBGL_CPU_FORWARD', function () { return true; });
    /** Whether the WebGL backend will always use f16 textures for rendering. */
    ENV$1.registerFlag('WEBGL_FORCE_F16_TEXTURES', function () { return false; });
    /** Whether to turn all packing related flags on. */
    ENV$1.registerFlag('WEBGL_PACK', function () { return ENV$1.getBool('HAS_WEBGL'); });
    /** Whether we will pack the batchnormalization op. */
    ENV$1.registerFlag('WEBGL_PACK_NORMALIZATION', function () { return ENV$1.getBool('WEBGL_PACK'); });
    /** Whether we will pack the clip op. */
    ENV$1.registerFlag('WEBGL_PACK_CLIP', function () { return ENV$1.getBool('WEBGL_PACK'); });
    /** Whether we will pack the depthwise conv op. */
    // TODO: https://github.com/tensorflow/tfjs/issues/1679
    ENV$1.registerFlag('WEBGL_PACK_DEPTHWISECONV', function () { return false; });
    /** Whether we will pack binary ops. */
    ENV$1.registerFlag('WEBGL_PACK_BINARY_OPERATIONS', function () { return ENV$1.getBool('WEBGL_PACK'); });
    /** Whether we will pack unary ops. */
    ENV$1.registerFlag('WEBGL_PACK_UNARY_OPERATIONS', function () { return ENV$1.getBool('WEBGL_PACK'); });
    /** Whether we will pack array ops. */
    ENV$1.registerFlag('WEBGL_PACK_ARRAY_OPERATIONS', function () { return ENV$1.getBool('WEBGL_PACK'); });
    /** Whether we will pack image ops. */
    ENV$1.registerFlag('WEBGL_PACK_IMAGE_OPERATIONS', function () { return ENV$1.getBool('WEBGL_PACK'); });
    /** Whether we will pack reduce ops. */
    ENV$1.registerFlag('WEBGL_PACK_REDUCE', function () { return ENV$1.getBool('WEBGL_PACK'); });
    /** Whether packed WebGL kernels lazily unpack their outputs. */
    ENV$1.registerFlag('WEBGL_LAZILY_UNPACK', function () { return ENV$1.getBool('WEBGL_PACK'); });
    /** Whether we will use the im2col algorithm to speed up convolutions. */
    ENV$1.registerFlag('WEBGL_CONV_IM2COL', function () { return ENV$1.getBool('WEBGL_PACK'); });
    /** The maximum texture dimension. */
    ENV$1.registerFlag('WEBGL_MAX_TEXTURE_SIZE', function () { return getWebGLMaxTextureSize(ENV$1.getNumber('WEBGL_VERSION')); });
    /** The maximum texture dimension. */
    ENV$1.registerFlag('WEBGL_MAX_TEXTURES_IN_SHADER', function () { return getMaxTexturesInShader(ENV$1.getNumber('WEBGL_VERSION')); });
    /**
     * The disjoint_query_timer extension version.
     * 0: disabled, 1: EXT_disjoint_timer_query, 2:
     * EXT_disjoint_timer_query_webgl2.
     * In Firefox with WebGL 2.0,
     * EXT_disjoint_timer_query_webgl2 is not available, so we must use the
     * WebGL 1.0 extension.
     */
    ENV$1.registerFlag('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION', function () {
        var webGLVersion = ENV$1.getNumber('WEBGL_VERSION');
        if (webGLVersion === 0) {
            return 0;
        }
        return getWebGLDisjointQueryTimerVersion(webGLVersion);
    });
    /**
     * Whether the timer object from the disjoint_query_timer extension gives
     * timing information that is reliable.
     */
    ENV$1.registerFlag('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE', function () { return ENV$1.getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') > 0 &&
        !isMobile(); });
    /**
     * Whether the device is physically capable of rendering to float32 textures.
     */
    ENV$1.registerFlag('WEBGL_RENDER_FLOAT32_CAPABLE', function () { return isCapableOfRenderingToFloatTexture(ENV$1.getNumber('WEBGL_VERSION')); });
    /**
     * Whether rendering to float32 textures is enabled. If disabled, renders to
     * float16 textures.
     */
    ENV$1.registerFlag('WEBGL_RENDER_FLOAT32_ENABLED', function () {
        return ENV$1.getBool('WEBGL_FORCE_F16_TEXTURES') ?
            false :
            ENV$1.getBool('WEBGL_RENDER_FLOAT32_CAPABLE');
    });
    /**
     * Whether downloading float textures is enabled (16 or 32 bit). If disabled,
     * uses IEEE 754 encoding of the float32 values to 4 uint8 when downloading.
     */
    ENV$1.registerFlag('WEBGL_DOWNLOAD_FLOAT_ENABLED', function () { return isDownloadFloatTextureEnabled(ENV$1.getNumber('WEBGL_VERSION')); });
    /** Whether the fence API is available. */
    ENV$1.registerFlag('WEBGL_FENCE_API_ENABLED', function () { return isWebGLFenceEnabled(ENV$1.getNumber('WEBGL_VERSION')); });
    /**
     * Tensors with size <= than this will be uploaded as uniforms, not textures.
     */
    ENV$1.registerFlag('WEBGL_SIZE_UPLOAD_UNIFORM', function () {
        // Use uniform uploads only when 32bit floats are supported. In
        // 16bit
        // environments there are problems with comparing a 16bit texture value
        // with a 32bit uniform value.
        var useUniforms = ENV$1.getBool('WEBGL_RENDER_FLOAT32_ENABLED');
        return useUniforms ? 4 : 0;
    });

    /**
     * @license
     * Copyright 2018 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.
     * =============================================================================
     */
    /**
     * 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().');
        }
    }
    setDeprecationWarningFn(deprecationWarn);
    /**
     * 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.
     *
     * ```js
     * const profile = await tf.profile(() => {
     *   const x = tf.tensor1d([1, 2, 3]);
     *   let x2 = x.square();
     *   x2.dispose();
     *   x2 = x.square();
     *   x2.dispose();
     *   return x;
     * });
     *
     * console.log(`newBytes: ${profile.newBytes}`);
     * console.log(`newTensors: ${profile.newTensors}`);
     * console.log(`byte usage over all kernels: ${profile.kernels.map(k =>
     * k.totalBytesSnapshot)}`);
     * ```
     *
     */
    /** @doc {heading: 'Performance', subheading: 'Profile'} */
    function profile(f) {
        return ENGINE.profile(f);
    }
    /**
     * Executes the provided function `fn` and after it is executed, cleans up all
     * intermediate tensors allocated by `fn` except those returned by `fn`.
     * `fn` must not return a Promise (async functions not allowed). The returned
     * result can be a complex object.
     *
     * Using this method helps avoid memory leaks. In general, wrap calls to
     * operations in `tf.tidy` for automatic memory cleanup.
     *
     * NOTE: Variables do *not* get cleaned up when inside a tidy(). If you want to
     * dispose variables, please use `tf.disposeVariables` or call dispose()
     * directly on variables.
     *
     * ```js
     * // y = 2 ^ 2 + 1
     * const y = tf.tidy(() => {
     *   // a, b, and one will be cleaned up when the tidy ends.
     *   const one = tf.scalar(1);
     *   const a = tf.scalar(2);
     *   const b = a.square();
     *
     *   console.log('numTensors (in tidy): ' + tf.memory().numTensors);
     *
     *   // The value returned inside the tidy function will return
     *   // through the tidy, in this case to the variable y.
     *   return b.add(one);
     * });
     *
     * console.log('numTensors (outside tidy): ' + tf.memory().numTensors);
     * y.print();
     * ```
     *
     * @param nameOrFn The name of the closure, or the function to execute.
     *     If a name is provided, the 2nd argument should be the function.
     *     If debug mode is on, the timing and the memory usage of the function
     *     will be tracked and displayed on the console using the provided name.
     * @param fn The function to execute.
     */
    /** @doc {heading: 'Performance', subheading: 'Memory'} */
    function tidy(nameOrFn, fn) {
        return ENGINE.tidy(nameOrFn, fn);
    }
    /**
     * Disposes any `tf.Tensor`s found within the provided object.
     *
     * @param container an object that may be a `tf.Tensor` or may directly
     *     contain `tf.Tensor`s, such as a `Tensor[]` or `{key: Tensor, ...}`. If
     *     the object is not a `tf.Tensor` or does not contain `Tensors`, nothing
     *     happens. In general it is safe to pass any object here, except that
     *     `Promise`s are not supported.
     */
    /** @doc {heading: 'Performance', subheading: 'Memory'} */
    function dispose(container) {
        var tensors = getTensorsInContainer(container);
        tensors.forEach(function (tensor) { return tensor.dispose(); });
    }
    /**
     * Keeps a `tf.Tensor` generated inside a `tf.tidy` from being disposed
     * automatically.
     *
     * ```js
     * let b;
     * const y = tf.tidy(() => {
     *   const one = tf.scalar(1);
     *   const a = tf.scalar(2);
     *
     *   // b will not be cleaned up by the tidy. a and one will be cleaned up
     *   // when the tidy ends.
     *   b = tf.keep(a.square());
     *
     *   console.log('numTensors (in tidy): ' + tf.memory().numTensors);
     *
     *   // The value returned inside the tidy function will return
     *   // through the tidy, in this case to the variable y.
     *   return b.add(one);
     * });
     *
     * console.log('numTensors (outside tidy): ' + tf.memory().numTensors);
     * console.log('y:');
     * y.print();
     * console.log('b:');
     * b.print();
     * ```
     *
     * @param result The tensor to keep from being disposed.
     */
    /** @doc {heading: 'Performance', subheading: 'Memory'} */
    function keep(result) {
        return ENGINE.keep(result);
    }
    /**
     * Executes `f()` and returns a promise that resolves with timing
     * information.
     *
     * The result is an object with the following properties:
     *
     * - `wallMs`: Wall execution time.
     * - `kernelMs`: Kernel execution time, ignoring data transfer. If using the
     * WebGL backend and the query timer extension is not available, this will
     * return an error object.
     * - On `WebGL` The following additional properties exist:
     *   - `uploadWaitMs`: CPU blocking time on texture uploads.
     *   - `downloadWaitMs`: CPU blocking time on texture downloads (readPixels).
     *
     * ```js
     * const x = tf.randomNormal([20, 20]);
     * const time = await tf.time(() => x.matMul(x));
     *
     * console.log(`kernelMs: ${time.kernelMs}, wallTimeMs: ${time.wallMs}`);
     * ```
     *
     * @param f The function to execute and time.
     */
    /** @doc {heading: 'Performance', subheading: 'Timing'} */
    function time(f) {
        return ENGINE.time(f);
    }
    /**
     * Sets the backend (cpu, webgl, wasm, etc) responsible for creating tensors and
     * executing operations on those tensors. Returns a promise that resolves
     * to a boolean if the backend initialization was successful.
     *
     * Note this disposes the current backend, if any, as well as any tensors
     * associated with it. A new backend is initialized, even if it is of the
     * same type as the previous one.
     *
     * @param backendName The name of the backend. Currently supports
     *     `'webgl'|'cpu'` in the browser, `'tensorflow'` under node.js
     *     (requires tfjs-node), and `'wasm'` (requires tfjs-backend-wasm).
     */
    /** @doc {heading: 'Backends'} */
    function setBackend(backendName) {
        return ENGINE.setBackend(backendName);
    }
    /**
     * Returns a promise that resolves when the currently selected backend (or the
     * highest priority one) has initialized. Await this promise when you are using
     * a backend that has async initialization.
     */
    /** @doc {heading: 'Backends'} */
    function ready() {
        return ENGINE.ready();
    }
    /**
     * Returns the current backend name (cpu, webgl, etc). The backend is
     * responsible for creating tensors and executing operations on those tensors.
     */
    /** @doc {heading: 'Backends'} */
    function getBackend() {
        return ENGINE.backendName;
    }
    /**
     * Removes a backend and the registered factory.
     */
    /** @doc {heading: 'Backends'} */
    function removeBackend(name) {
        ENGINE.removeBackend(name);
    }
    /**
     * Finds the backend registered under the provided name. Returns null if the
     * name is not in the registry, or the registration hasn't finished yet.
     */
    function findBackend(name) {
        return ENGINE.findBackend(name);
    }
    /**
     * Finds the backend factory registered under the provided name. Returns a
     * function that produces a new backend when called. Returns null if the name
     * is not in the registry.
     */
    function findBackendFactory(name) {
        return ENGINE.findBackendFactory(name);
    }
    /**
     * Registers a global backend. The registration should happen when importing
     * a module file (e.g. when importing `backend_webgl.ts`), and is used for
     * modular builds (e.g. custom tfjs bundle with only webgl support).
     *
     * @param factory The backend factory function. When called, it should
     * return a backend instance, or a promise of an instance.
     * @param priority The priority of the backend (higher = more important).
     *     In case multiple backends are registered, the priority is used to find
     *     the best backend. Defaults to 1.
     * @return False if there is already a registered backend under this name, true
     *     if not.
     */
    /** @doc {heading: 'Backends'} */
    function registerBackend(name, factory, priority) {
        if (priority === void 0) { priority = 1; }
        return ENGINE.registerBackend(name, factory, priority);
    }
    /**
     * Gets the current backend. If no backends have been initialized, this will
     * attempt to initialize the best backend. Will throw an error if the highest
     * priority backend has async initialization, in which case, you should call
     * 'await tf.ready()' before running other code.
     */
    /** @doc {heading: 'Backends'} */
    function backend() {
        return ENGINE.backend;
    }
    /**
     * Sets the global platform.
     *
     * @param platformName The name of this platform.
     * @param platform A platform implementation.
     */
    function setPlatform(platformName, platform) {
        env().setPlatform(platformName, platform);
    }

    /**
     * @license
     * Copyright 2018 Google LLC. All Rights Reserved.
     * Licensed under the Apache License, Version 2.0 (the "License");
     * you may not use this file except in compliance with the License.
     * You may obtain a copy of the License at
     *
     * http://www.apache.org/licenses/LICENSE-2.0
     *
     * Unless required by applicable law or agreed to in writing, software
     * distributed under the License is distributed on an "AS IS" BASIS,
     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
     * See the License for the specific language governing permissions and
     * limitations under the License.
     * =============================================================================
     */
    function warn() {
        var msg = [];
        for (var _i = 0; _i < arguments.length; _i++) {
            msg[_i] = arguments[_i];
        }
        if (!env().getBool('IS_TEST')) {
            console.warn.apply(console, msg);
        }
    }

    /**
     * @license
     * Copyright 2018 Google LLC. All Rights Reserved.
     * Licensed under the Apache License, Version 2.0 (the "License");
     * you may not use this file except in compliance with the License.
     * You may obtain a copy of the License at
     *
     * http://www.apache.org/licenses/LICENSE-2.0
     *
     * Unless required by applicable law or agreed to in writing, software
     * distributed under the License is distributed on an "AS IS" BASIS,
     * WITHOUT 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 == null) {
            return;
        }
        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, env().getBool('DEBUG')) :
            flatten(x, [], skipTypedArray);
        return ENGINE.makeTensor(values, inferredShape, inferredDtype);
    }
    function convertToTensorArray(arg, argName, functionName, parseAsDtype) {
        if (parseAsDtype === void 0) { parseAsDtype = 'numeric'; }
        if (!Array.isArray(arg)) {
            throw new Error("Argument " + argName + " passed to " + functionName + " must be a " +
                '`Tensor[]` or `TensorLike[]`');
        }
        var tensors = arg;
        return tensors.map(function (t, i) { return convertToTensor(t, argName + "[" + i + "]", functionName); }, parseAsDtype);
    }

    /**
     * @license
     * Copyright 2017 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 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 2017 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.
     * =============================================================================
     */
    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(shapes, axis) {
        var outputShape = shapes[0].slice();
        for (var i = 1; i < shapes.length; i++) {
            outputShape[axis] += shapes[i][axis];
        }
        return outputShape;
    }

    /**
     * @license
     * Copyright 2018 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.
     * =============================================================================
     */
    /**
     * 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);
        }
        // tslint:disable-next-line:no-any
        var f2 = function () {
            var args = [];
            for (var _i = 0; _i < arguments.length; _i++) {
                args[_i] = arguments[_i];
            }
            ENGINE.startScope(opName);
            try {
                var result = fn.apply(void 0, args);
                if (result instanceof Promise) {
                    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.
     * =============================================================================
     */
    /**
     * 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 " + $imag.shape + ", " +
            "must match in call to tf.complex().");
        return ENGINE.runKernelFunc(function (backend) { return backend.complex($real, $imag); }, { $real: $real, $imag: $imag });
    }
    /**
     * 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 $input = convertToTensor(input, 'input', 'real');
        return ENGINE.runKernelFunc(function (backend) { return backend.real($input); }, { $input: $input });
    }
    /**
     * 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 $input = convertToTensor(input, 'input', 'imag');
        return ENGINE.runKernelFunc(function (backend) { return backend.imag($input); }, { $input: $input });
    }
    var complex = op({ complex_: complex_ });
    var real = op({ real_: real_ });
    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.
     * =============================================================================
     */
    /**
     * 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);
    }
    /** 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, env().getBool('DEBUG')) :
            flatten(values, [], true);
        return ENGINE.makeTensor(values, shape, dtype);
    }
    /**
     * 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);
    }
    /**
     * 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);
    }
    /**
     * 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);
    }
    /**
     * 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);
    }
    /**
     * 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);
    }
    /**
     * 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]]]]]).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);
    }
    /**
     * 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);
    }
    /**
     * 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);
    }
    /**
     * Creates a `tf.Tensor` with all elements set to 1.
     *
     * ```js
     * tf.ones([2, 2]).print();
     * ```
     *
     * @param shape An array of integers defining the output tensor shape.
     * @param dtype The type of an element in the resulting tensor. Defaults to
     *     'float'.
     */
    /** @doc {heading: 'Tensors', subheading: 'Creation'} */
    function ones$1(shape, dtype) {
        if (dtype === void 0) { dtype = 'float32'; }
        if (dtype === 'complex64') {
            var real_1 = ones$1(shape, 'float32');
            var imag_1 = zeros(shape, 'float32');
            return complex(real_1, imag_1);
        }
        var values = makeOnesTypedArray(sizeFromShape(shape), dtype);
        return ENGINE.makeTensor(values, shape, dtype);
    }
    /**
     * 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_2 = zeros(shape, 'float32');
            var imag_2 = zeros(shape, 'float32');
            return complex(real_2, imag_2);
        }
        var values = makeZerosTypedArray(sizeFromShape(shape), dtype);
        return ENGINE.makeTensor(values, shape, dtype);
    }
    /**
     * 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) {
        return ENGINE.runKernelFunc(function (backend) { return backend.fill(shape, value, dtype); }, {});
    }
    /**
     * 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 $x = convertToTensor(x, 'x', 'onesLike');
        if ($x.dtype === 'complex64') {
            var r = onesLike(real($x));
            var i = zerosLike(imag($x));
            return complex(r, i);
        }
        var der = function (dy, saved) { return ({ $x: function () { return zerosLike(dy); } }); };
        return ENGINE.runKernelFunc(function (backend) { return backend.onesLike($x); }, { $x: $x }, der);
    }
    /**
     * 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 der = function (dy, saved) { return ({ $x: function () { return zerosLike(dy); } }); };
        return ENGINE.runKernelFunc(function (backend) { return backend.zerosLike($x); }, { $x: $x }, der);
    }
    /**
     * 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.');
        }
        return ENGINE.runKernelFunc(function (backend) { return backend.linspace(start, stop, num); }, {});
    }
    /**
     * 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.
     *
     * ```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 sameStartStop = start === stop;
        var increasingRangeNegativeStep = start < stop && step < 0;
        var decreasingRangePositiveStep = stop < start && step > 1;
        if (sameStartStop || increasingRangeNegativeStep ||
            decreasingRangePositiveStep) {
            return zeros([0], dtype);
        }
        var numElements = Math.abs(Math.ceil((stop - start) / step));
        var values = 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 tensor1d(values, dtype);
    }
    var onesLike = op({ onesLike_: onesLike_ });
    var zerosLike = op({ zerosLike_: zerosLike_ });

    /**
     * @license
     * Copyright 2018 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.
     * =============================================================================
     */
    /**
     * 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 */);
    }
    /**
     * 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);
    }
    /**
     * 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);
    }
    /**
     * 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);
    }
    /**
     * 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 $tensors = convertToTensorArray(tensors, 'tensors', 'concat');
        if ($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 + ". ");
                }
            });
        }
        axis = parseAxisParam(axis, $tensors[0].shape)[0];
        var outShape = computeOutShape($tensors.map(function (t) { return t.shape; }), axis);
        if (sizeFromShape(outShape) === 0) {
            return tensor([], outShape);
        }
        // Keep only non-empty tensors (ignore tensors with 0 in their shape).
        $tensors = $tensors.filter(function (t) { return t.size > 0; });
        if ($tensors.length === 1) {
            return $tensors[0];
        }
        var shapes = $tensors.map(function (t) { return t.shape; });
        assertParamsConsistent(shapes, axis);
        var der = function (dy) {
            var sizeSplits = shapes.map(function (s) { return s[axis]; });
            var derTensors = split(dy, sizeSplits, axis);
            return derTensors.map(function (t) { return function () { return t; }; });
        };
        var inputs = $tensors;
        var attr = { axis: axis };
        return ENGINE.runKernelFunc(function (backend) { return backend.concat($tensors, axis); }, inputs, der, 'Concat', attr);
    }
    /**
     * 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]`.
     * @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');
        axis = parseAxisParam(axis, $x.shape)[0];
        var splitSizes;
        if (typeof (numOrSizeSplits) === 'number') {
            assert($x.shape[axis] % numOrSizeSplits === 0, function () { return 'Number of splits must evenly divide the axis.'; });
            splitSizes =
                new Array(numOrSizeSplits).fill($x.shape[axis] / numOrSizeSplits);
        }
        else {
            assert($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;
        }
        var der = function (dy) { return ({ $x: function () { return concat(dy, axis); } }); };
        return ENGINE.runKernelFunc(function (backend) { return backend.split($x, splitSizes, axis); }, { $x: $x }, der);
    }
    var concat = op({ concat_: concat_ });
    var concat1d = op({ concat1d_: concat1d_ });
    var concat2d = op({ concat2d_: concat2d_ });
    var concat3d = op({ concat3d_: concat3d_ });
    var concat4d = op({ concat4d_: concat4d_ });
    var split = op({ split_: split_ });

    var commonjsGlobal = typeof globalThis !== 'undefined' ? globalThis : typeof window !== 'undefined' ? window : typeof global !== 'undefined' ? global : typeof self !== 'undefined' ? self : {};

    function createCommonjsModule(fn, module) {
    	return module = { exports: {} }, fn(module, module.exports), module.exports;
    }

    var alea = createCommonjsModule(function (module) {
    // A port of an algorithm by Johannes Baagøe <baagoe@baagoe.com>, 2010
    // http://baagoe.com/en/RandomMusings/javascript/
    // https://github.com/nquinlan/better-random-numbers-for-javascript-mirror
    // Original work is under MIT license -

    // Copyright (C) 2010 by Johannes Baagøe <baagoe@baagoe.org>
    //
    // Permission is hereby granted, free of charge, to any person obtaining a copy
    // of this software and associated documentation files (the "Software"), to deal
    // in the Software without restriction, including without limitation the rights
    // to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
    // copies of the Software, and to permit persons to whom the Software is
    // furnished to do so, subject to the following conditions:
    // 
    // The above copyright notice and this permission notice shall be included in
    // all copies or substantial portions of the Software.
    // 
    // THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
    // IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
    // FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
    // AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
    // LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
    // OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
    // THE SOFTWARE.



    (function(global, module, define) {

    function Alea(seed) {
      var me = this, mash = Mash();

      me.next = function() {
        var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32
        me.s0 = me.s1;
        me.s1 = me.s2;
        return me.s2 = t - (me.c = t | 0);
      };

      // Apply the seeding algorithm from Baagoe.
      me.c = 1;
      me.s0 = mash(' ');
      me.s1 = mash(' ');
      me.s2 = mash(' ');
      me.s0 -= mash(seed);
      if (me.s0 < 0) { me.s0 += 1; }
      me.s1 -= mash(seed);
      if (me.s1 < 0) { me.s1 += 1; }
      me.s2 -= mash(seed);
      if (me.s2 < 0) { me.s2 += 1; }
      mash = null;
    }

    function copy(f, t) {
      t.c = f.c;
      t.s0 = f.s0;
      t.s1 = f.s1;
      t.s2 = f.s2;
      return t;
    }

    function impl(seed, opts) {
      var xg = new Alea(seed),
          state = opts && opts.state,
          prng = xg.next;
      prng.int32 = function() { return (xg.next() * 0x100000000) | 0; };
      prng.double = function() {
        return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53
      };
      prng.quick = prng;
      if (state) {
        if (typeof(state) == 'object') copy(state, xg);
        prng.state = function() { return copy(xg, {}); };
      }
      return prng;
    }

    function Mash() {
      var n = 0xefc8249d;

      var mash = function(data) {
        data = data.toString();
        for (var i = 0; i < data.length; i++) {
          n += data.charCodeAt(i);
          var h = 0.02519603282416938 * n;
          n = h >>> 0;
          h -= n;
          h *= n;
          n = h >>> 0;
          h -= n;
          n += h * 0x100000000; // 2^32
        }
        return (n >>> 0) * 2.3283064365386963e-10; // 2^-32
      };

      return mash;
    }


    if (module && module.exports) {
      module.exports = impl;
    } else if (define && define.amd) {
      define(function() { return impl; });
    } else {
      this.alea = impl;
    }

    })(
      commonjsGlobal,
      module,    // present in node.js
      (typeof undefined) == 'function'   // present with an AMD loader
    );
    });

    var xor128 = createCommonjsModule(function (module) {
    // A Javascript implementaion of the "xor128" prng algorithm by
    // George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper

    (function(global, module, define) {

    function XorGen(seed) {
      var me = this, strseed = '';

      me.x = 0;
      me.y = 0;
      me.z = 0;
      me.w = 0;

      // Set up generator function.
      me.next = function() {
        var t = me.x ^ (me.x << 11);
        me.x = me.y;
        me.y = me.z;
        me.z = me.w;
        return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);
      };

      if (seed === (seed | 0)) {
        // Integer seed.
        me.x = seed;
      } else {
        // String seed.
        strseed += seed;
      }

      // Mix in string seed, then discard an initial batch of 64 values.
      for (var k = 0; k < strseed.length + 64; k++) {
        me.x ^= strseed.charCodeAt(k) | 0;
        me.next();
      }
    }

    function copy(f, t) {
      t.x = f.x;
      t.y = f.y;
      t.z = f.z;
      t.w = f.w;
      return t;
    }

    function impl(seed, opts) {
      var xg = new XorGen(seed),
          state = opts && opts.state,
          prng = function() { return (xg.next() >>> 0) / 0x100000000; };
      prng.double = function() {
        do {
          var top = xg.next() >>> 11,
              bot = (xg.next() >>> 0) / 0x100000000,
              result = (top + bot) / (1 << 21);
        } while (result === 0);
        return result;
      };
      prng.int32 = xg.next;
      prng.quick = prng;
      if (state) {
        if (typeof(state) == 'object') copy(state, xg);
        prng.state = function() { return copy(xg, {}); };
      }
      return prng;
    }

    if (module && module.exports) {
      module.exports = impl;
    } else if (define && define.amd) {
      define(function() { return impl; });
    } else {
      this.xor128 = impl;
    }

    })(
      commonjsGlobal,
      module,    // present in node.js
      (typeof undefined) == 'function'   // present with an AMD loader
    );
    });

    var xorwow = createCommonjsModule(function (module) {
    // A Javascript implementaion of the "xorwow" prng algorithm by
    // George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper

    (function(global, module, define) {

    function XorGen(seed) {
      var me = this, strseed = '';

      // Set up generator function.
      me.next = function() {
        var t = (me.x ^ (me.x >>> 2));
        me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;
        return (me.d = (me.d + 362437 | 0)) +
           (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;
      };

      me.x = 0;
      me.y = 0;
      me.z = 0;
      me.w = 0;
      me.v = 0;

      if (seed === (seed | 0)) {
        // Integer seed.
        me.x = seed;
      } else {
        // String seed.
        strseed += seed;
      }

      // Mix in string seed, then discard an initial batch of 64 values.
      for (var k = 0; k < strseed.length + 64; k++) {
        me.x ^= strseed.charCodeAt(k) | 0;
        if (k == strseed.length) {
          me.d = me.x << 10 ^ me.x >>> 4;
        }
        me.next();
      }
    }

    function copy(f, t) {
      t.x = f.x;
      t.y = f.y;
      t.z = f.z;
      t.w = f.w;
      t.v = f.v;
      t.d = f.d;
      return t;
    }

    function impl(seed, opts) {
      var xg = new XorGen(seed),
          state = opts && opts.state,
          prng = function() { return (xg.next() >>> 0) / 0x100000000; };
      prng.double = function() {
        do {
          var top = xg.next() >>> 11,
              bot = (xg.next() >>> 0) / 0x100000000,
              result = (top + bot) / (1 << 21);
        } while (result === 0);
        return result;
      };
      prng.int32 = xg.next;
      prng.quick = prng;
      if (state) {
        if (typeof(state) == 'object') copy(state, xg);
        prng.state = function() { return copy(xg, {}); };
      }
      return prng;
    }

    if (module && module.exports) {
      module.exports = impl;
    } else if (define && define.amd) {
      define(function() { return impl; });
    } else {
      this.xorwow = impl;
    }

    })(
      commonjsGlobal,
      module,    // present in node.js
      (typeof undefined) == 'function'   // present with an AMD loader
    );
    });

    var xorshift7 = createCommonjsModule(function (module) {
    // A Javascript implementaion of the "xorshift7" algorithm by
    // François Panneton and Pierre L'ecuyer:
    // "On the Xorgshift Random Number Generators"
    // http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf

    (function(global, module, define) {

    function XorGen(seed) {
      var me = this;

      // Set up generator function.
      me.next = function() {
        // Update xor generator.
        var X = me.x, i = me.i, t, v;
        t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);
        t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);
        t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);
        t = X[(i + 4) & 7]; v ^= t ^ (t << 7);
        t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);
        X[i] = v;
        me.i = (i + 1) & 7;
        return v;
      };

      function init(me, seed) {
        var j, w, X = [];

        if (seed === (seed | 0)) {
          // Seed state array using a 32-bit integer.
          w = X[0] = seed;
        } else {
          // Seed state using a string.
          seed = '' + seed;
          for (j = 0; j < seed.length; ++j) {
            X[j & 7] = (X[j & 7] << 15) ^
                (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);
          }
        }
        // Enforce an array length of 8, not all zeroes.
        while (X.length < 8) X.push(0);
        for (j = 0; j < 8 && X[j] === 0; ++j);
        if (j == 8) w = X[7] = -1; else w = X[j];

        me.x = X;
        me.i = 0;

        // Discard an initial 256 values.
        for (j = 256; j > 0; --j) {
          me.next();
        }
      }

      init(me, seed);
    }

    function copy(f, t) {
      t.x = f.x.slice();
      t.i = f.i;
      return t;
    }

    function impl(seed, opts) {
      if (seed == null) seed = +(new Date);
      var xg = new XorGen(seed),
          state = opts && opts.state,
          prng = function() { return (xg.next() >>> 0) / 0x100000000; };
      prng.double = function() {
        do {
          var top = xg.next() >>> 11,
              bot = (xg.next() >>> 0) / 0x100000000,
              result = (top + bot) / (1 << 21);
        } while (result === 0);
        return result;
      };
      prng.int32 = xg.next;
      prng.quick = prng;
      if (state) {
        if (state.x) copy(state, xg);
        prng.state = function() { return copy(xg, {}); };
      }
      return prng;
    }

    if (module && module.exports) {
      module.exports = impl;
    } else if (define && define.amd) {
      define(function() { return impl; });
    } else {
      this.xorshift7 = impl;
    }

    })(
      commonjsGlobal,
      module,    // present in node.js
      (typeof undefined) == 'function'   // present with an AMD loader
    );
    });

    var xor4096 = createCommonjsModule(function (module) {
    // A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.
    //
    // This fast non-cryptographic random number generator is designed for
    // use in Monte-Carlo algorithms. It combines a long-period xorshift
    // generator with a Weyl generator, and it passes all common batteries
    // of stasticial tests for randomness while consuming only a few nanoseconds
    // for each prng generated.  For background on the generator, see Brent's
    // paper: "Some long-period random number generators using shifts and xors."
    // http://arxiv.org/pdf/1004.3115v1.pdf
    //
    // Usage:
    //
    // var xor4096 = require('xor4096');
    // random = xor4096(1);                        // Seed with int32 or string.
    // assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.
    // assert.equal(random.int32(), 1806534897);   // signed int32, 32 bits.
    //
    // For nonzero numeric keys, this impelementation provides a sequence
    // identical to that by Brent's xorgens 3 implementaion in C.  This
    // implementation also provides for initalizing the generator with
    // string seeds, or for saving and restoring the state of the generator.
    //
    // On Chrome, this prng benchmarks about 2.1 times slower than
    // Javascript's built-in Math.random().

    (function(global, module, define) {

    function XorGen(seed) {
      var me = this;

      // Set up generator function.
      me.next = function() {
        var w = me.w,
            X = me.X, i = me.i, t, v;
        // Update Weyl generator.
        me.w = w = (w + 0x61c88647) | 0;
        // Update xor generator.
        v = X[(i + 34) & 127];
        t = X[i = ((i + 1) & 127)];
        v ^= v << 13;
        t ^= t << 17;
        v ^= v >>> 15;
        t ^= t >>> 12;
        // Update Xor generator array state.
        v = X[i] = v ^ t;
        me.i = i;
        // Result is the combination.
        return (v + (w ^ (w >>> 16))) | 0;
      };

      function init(me, seed) {
        var t, v, i, j, w, X = [], limit = 128;
        if (seed === (seed | 0)) {
          // Numeric seeds initialize v, which is used to generates X.
          v = seed;
          seed = null;
        } else {
          // String seeds are mixed into v and X one character at a time.
          seed = seed + '\0';
          v = 0;
          limit = Math.max(limit, seed.length);
        }
        // Initialize circular array and weyl value.
        for (i = 0, j = -32; j < limit; ++j) {
          // Put the unicode characters into the array, and shuffle them.
          if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);
          // After 32 shuffles, take v as the starting w value.
          if (j === 0) w = v;
          v ^= v << 10;
          v ^= v >>> 15;
          v ^= v << 4;
          v ^= v >>> 13;
          if (j >= 0) {
            w = (w + 0x61c88647) | 0;     // Weyl.
            t = (X[j & 127] ^= (v + w));  // Combine xor and weyl to init array.
            i = (0 == t) ? i + 1 : 0;     // Count zeroes.
          }
        }
        // We have detected all zeroes; make the key nonzero.
        if (i >= 128) {
          X[(seed && seed.length || 0) & 127] = -1;
        }
        // Run the generator 512 times to further mix the state before using it.
        // Factoring this as a function slows the main generator, so it is just
        // unrolled here.  The weyl generator is not advanced while warming up.
        i = 127;
        for (j = 4 * 128; j > 0; --j) {
          v = X[(i + 34) & 127];
          t = X[i = ((i + 1) & 127)];
          v ^= v << 13;
          t ^= t << 17;
          v ^= v >>> 15;
          t ^= t >>> 12;
          X[i] = v ^ t;
        }
        // Storing state as object members is faster than using closure variables.
        me.w = w;
        me.X = X;
        me.i = i;
      }

      init(me, seed);
    }

    function copy(f, t) {
      t.i = f.i;
      t.w = f.w;
      t.X = f.X.slice();
      return t;
    }
    function impl(seed, opts) {
      if (seed == null) seed = +(new Date);
      var xg = new XorGen(seed),
          state = opts && opts.state,
          prng = function() { return (xg.next() >>> 0) / 0x100000000; };
      prng.double = function() {
        do {
          var top = xg.next() >>> 11,
              bot = (xg.next() >>> 0) / 0x100000000,
              result = (top + bot) / (1 << 21);
        } while (result === 0);
        return result;
      };
      prng.int32 = xg.next;
      prng.quick = prng;
      if (state) {
        if (state.X) copy(state, xg);
        prng.state = function() { return copy(xg, {}); };
      }
      return prng;
    }

    if (module && module.exports) {
      module.exports = impl;
    } else if (define && define.amd) {
      define(function() { return impl; });
    } else {
      this.xor4096 = impl;
    }

    })(
      commonjsGlobal,                                     // window object or global
      module,    // present in node.js
      (typeof undefined) == 'function'   // present with an AMD loader
    );
    });

    var tychei = createCommonjsModule(function (module) {
    // A Javascript implementaion of the "Tyche-i" prng algorithm by
    // Samuel Neves and Filipe Araujo.
    // See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf

    (function(global, module, define) {

    function XorGen(seed) {
      var me = this, strseed = '';

      // Set up generator function.
      me.next = function() {
        var b = me.b, c = me.c, d = me.d, a = me.a;
        b = (b << 25) ^ (b >>> 7) ^ c;
        c = (c - d) | 0;
        d = (d << 24) ^ (d >>> 8) ^ a;
        a = (a - b) | 0;
        me.b = b = (b << 20) ^ (b >>> 12) ^ c;
        me.c = c = (c - d) | 0;
        me.d = (d << 16) ^ (c >>> 16) ^ a;
        return me.a = (a - b) | 0;
      };

      /* The following is non-inverted tyche, which has better internal
       * bit diffusion, but which is about 25% slower than tyche-i in JS.
      me.next = function() {
        var a = me.a, b = me.b, c = me.c, d = me.d;
        a = (me.a + me.b | 0) >>> 0;
        d = me.d ^ a; d = d << 16 ^ d >>> 16;
        c = me.c + d | 0;
        b = me.b ^ c; b = b << 12 ^ d >>> 20;
        me.a = a = a + b | 0;
        d = d ^ a; me.d = d = d << 8 ^ d >>> 24;
        me.c = c = c + d | 0;
        b = b ^ c;
        return me.b = (b << 7 ^ b >>> 25);
      }
      */

      me.a = 0;
      me.b = 0;
      me.c = 2654435769 | 0;
      me.d = 1367130551;

      if (seed === Math.floor(seed)) {
        // Integer seed.
        me.a = (seed / 0x100000000) | 0;
        me.b = seed | 0;
      } else {
        // String seed.
        strseed += seed;
      }

      // Mix in string seed, then discard an initial batch of 64 values.
      for (var k = 0; k < strseed.length + 20; k++) {
        me.b ^= strseed.charCodeAt(k) | 0;
        me.next();
      }
    }

    function copy(f, t) {
      t.a = f.a;
      t.b = f.b;
      t.c = f.c;
      t.d = f.d;
      return t;
    }
    function impl(seed, opts) {
      var xg = new XorGen(seed),
          state = opts && opts.state,
          prng = function() { return (xg.next() >>> 0) / 0x100000000; };
      prng.double = function() {
        do {
          var top = xg.next() >>> 11,
              bot = (xg.next() >>> 0) / 0x100000000,
              result = (top + bot) / (1 << 21);
        } while (result === 0);
        return result;
      };
      prng.int32 = xg.next;
      prng.quick = prng;
      if (state) {
        if (typeof(state) == 'object') copy(state, xg);
        prng.state = function() { return copy(xg, {}); };
      }
      return prng;
    }

    if (module && module.exports) {
      module.exports = impl;
    } else if (define && define.amd) {
      define(function() { return impl; });
    } else {
      this.tychei = impl;
    }

    })(
      commonjsGlobal,
      module,    // present in node.js
      (typeof undefined) == 'function'   // present with an AMD loader
    );
    });

    var seedrandom = createCommonjsModule(function (module) {
    /*
    Copyright 2014 David Bau.

    Permission is hereby granted, free of charge, to any person obtaining
    a copy of this software and associated documentation files (the
    "Software"), to deal in the Software without restriction, including
    without limitation the rights to use, copy, modify, merge, publish,
    distribute, sublicense, and/or sell copies of the Software, and to
    permit persons to whom the Software is furnished to do so, subject to
    the following conditions:

    The above copyright notice and this permission notice shall be
    included in all copies or substantial portions of the Software.

    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
    MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
    IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
    CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
    TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
    SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

    */

    (function (pool, math) {
    //
    // The following constants are related to IEEE 754 limits.
    //
    var global = this,
        width = 256,        // each RC4 output is 0 <= x < 256
        chunks = 6,         // at least six RC4 outputs for each double
        digits = 52,        // there are 52 significant digits in a double
        rngname = 'random', // rngname: name for Math.random and Math.seedrandom
        startdenom = math.pow(width, chunks),
        significance = math.pow(2, digits),
        overflow = significance * 2,
        mask = width - 1,
        nodecrypto;         // node.js crypto module, initialized at the bottom.

    //
    // seedrandom()
    // This is the seedrandom function described above.
    //
    function seedrandom(seed, options, callback) {
      var key = [];
      options = (options == true) ? { entropy: true } : (options || {});

      // Flatten the seed string or build one from local entropy if needed.
      var shortseed = mixkey(flatten(
        options.entropy ? [seed, tostring(pool)] :
        (seed == null) ? autoseed() : seed, 3), key);

      // Use the seed to initialize an ARC4 generator.
      var arc4 = new ARC4(key);

      // This function returns a random double in [0, 1) that contains
      // randomness in every bit of the mantissa of the IEEE 754 value.
      var prng = function() {
        var n = arc4.g(chunks),             // Start with a numerator n < 2 ^ 48
            d = startdenom,                 //   and denominator d = 2 ^ 48.
            x = 0;                          //   and no 'extra last byte'.
        while (n < significance) {          // Fill up all significant digits by
          n = (n + x) * width;              //   shifting numerator and
          d *= width;                       //   denominator and generating a
          x = arc4.g(1);                    //   new least-significant-byte.
        }
        while (n >= overflow) {             // To avoid rounding up, before adding
          n /= 2;                           //   last byte, shift everything
          d /= 2;                           //   right using integer math until
          x >>>= 1;                         //   we have exactly the desired bits.
        }
        return (n + x) / d;                 // Form the number within [0, 1).
      };

      prng.int32 = function() { return arc4.g(4) | 0; };
      prng.quick = function() { return arc4.g(4) / 0x100000000; };
      prng.double = prng;

      // Mix the randomness into accumulated entropy.
      mixkey(tostring(arc4.S), pool);

      // Calling convention: what to return as a function of prng, seed, is_math.
      return (options.pass || callback ||
          function(prng, seed, is_math_call, state) {
            if (state) {
              // Load the arc4 state from the given state if it has an S array.
              if (state.S) { copy(state, arc4); }
              // Only provide the .state method if requested via options.state.
              prng.state = function() { return copy(arc4, {}); };
            }

            // If called as a method of Math (Math.seedrandom()), mutate
            // Math.random because that is how seedrandom.js has worked since v1.0.
            if (is_math_call) { math[rngname] = prng; return seed; }

            // Otherwise, it is a newer calling convention, so return the
            // prng directly.
            else return prng;
          })(
      prng,
      shortseed,
      'global' in options ? options.global : (this == math),
      options.state);
    }
    math['seed' + rngname] = seedrandom;

    //
    // ARC4
    //
    // An ARC4 implementation.  The constructor takes a key in the form of
    // an array of at most (width) integers that should be 0 <= x < (width).
    //
    // The g(count) method returns a pseudorandom integer that concatenates
    // the next (count) outputs from ARC4.  Its return value is a number x
    // that is in the range 0 <= x < (width ^ count).
    //
    function ARC4(key) {
      var t, keylen = key.length,
          me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];

      // The empty key [] is treated as [0].
      if (!keylen) { key = [keylen++]; }

      // Set up S using the standard key scheduling algorithm.
      while (i < width) {
        s[i] = i++;
      }
      for (i = 0; i < width; i++) {
        s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];
        s[j] = t;
      }

      // The "g" method returns the next (count) outputs as one number.
      (me.g = function(count) {
        // Using instance members instead of closure state nearly doubles speed.
        var t, r = 0,
            i = me.i, j = me.j, s = me.S;
        while (count--) {
          t = s[i = mask & (i + 1)];
          r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];
        }
        me.i = i; me.j = j;
        return r;
        // For robust unpredictability, the function call below automatically
        // discards an initial batch of values.  This is called RC4-drop[256].
        // See http://google.com/search?q=rsa+fluhrer+response&btnI
      })(width);
    }

    //
    // copy()
    // Copies internal state of ARC4 to or from a plain object.
    //
    function copy(f, t) {
      t.i = f.i;
      t.j = f.j;
      t.S = f.S.slice();
      return t;
    }
    //
    // flatten()
    // Converts an object tree to nested arrays of strings.
    //
    function flatten(obj, depth) {
      var result = [], typ = (typeof obj), prop;
      if (depth && typ == 'object') {
        for (prop in obj) {
          try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}
        }
      }
      return (result.length ? result : typ == 'string' ? obj : obj + '\0');
    }

    //
    // mixkey()
    // Mixes a string seed into a key that is an array of integers, and
    // returns a shortened string seed that is equivalent to the result key.
    //
    function mixkey(seed, key) {
      var stringseed = seed + '', smear, j = 0;
      while (j < stringseed.length) {
        key[mask & j] =
          mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));
      }
      return tostring(key);
    }

    //
    // autoseed()
    // Returns an object for autoseeding, using window.crypto and Node crypto
    // module if available.
    //
    function autoseed() {
      try {
        var out;
        if (nodecrypto && (out = nodecrypto.randomBytes)) {
          // The use of 'out' to remember randomBytes makes tight minified code.
          out = out(width);
        } else {
          out = new Uint8Array(width);
          (global.crypto || global.msCrypto).getRandomValues(out);
        }
        return tostring(out);
      } catch (e) {
        var browser = global.navigator,
            plugins = browser && browser.plugins;
        return [+new Date, global, plugins, global.screen, tostring(pool)];
      }
    }

    //
    // tostring()
    // Converts an array of charcodes to a string
    //
    function tostring(a) {
      return String.fromCharCode.apply(0, a);
    }

    //
    // When seedrandom.js is loaded, we immediately mix a few bits
    // from the built-in RNG into the entropy pool.  Because we do
    // not want to interfere with deterministic PRNG state later,
    // seedrandom will not call math.random on its own again after
    // initialization.
    //
    mixkey(math.random(), pool);

    //
    // Nodejs and AMD support: export the implementation as a module using
    // either convention.
    //
    if (module.exports) {
      module.exports = seedrandom;
      // When in node.js, try using crypto package for autoseeding.
      try {
        nodecrypto = require('crypto');
      } catch (ex) {}
    }

    // End anonymous scope, and pass initial values.
    })(
      [],     // pool: entropy pool starts empty
      Math    // math: package containing random, pow, and seedrandom
    );
    });

    // A library of seedable RNGs implemented in Javascript.
    //
    // Usage:
    //
    // var seedrandom = require('seedrandom');
    // var random = seedrandom(1); // or any seed.
    // var x = random();       // 0 <= x < 1.  Every bit is random.
    // var x = random.quick(); // 0 <= x < 1.  32 bits of randomness.

    // alea, a 53-bit multiply-with-carry generator by Johannes Baagøe.
    // Period: ~2^116
    // Reported to pass all BigCrush tests.


    // xor128, a pure xor-shift generator by George Marsaglia.
    // Period: 2^128-1.
    // Reported to fail: MatrixRank and LinearComp.


    // xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.
    // Period: 2^192-2^32
    // Reported to fail: CollisionOver, SimpPoker, and LinearComp.


    // xorshift7, by François Panneton and Pierre L'ecuyer, takes
    // a different approach: it adds robustness by allowing more shifts
    // than Marsaglia's original three.  It is a 7-shift generator
    // with 256 bits, that passes BigCrush with no systmatic failures.
    // Period 2^256-1.
    // No systematic BigCrush failures reported.


    // xor4096, by Richard Brent, is a 4096-bit xor-shift with a
    // very long period that also adds a Weyl generator. It also passes
    // BigCrush with no systematic failures.  Its long period may
    // be useful if you have many generators and need to avoid
    // collisions.
    // Period: 2^4128-2^32.
    // No systematic BigCrush failures reported.


    // Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random
    // number generator derived from ChaCha, a modern stream cipher.
    // https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf
    // Period: ~2^127
    // No systematic BigCrush failures reported.


    // The original ARC4-based prng included in this library.
    // Period: ~2^1600


    seedrandom.alea = alea;
    seedrandom.xor128 = xor128;
    seedrandom.xorwow = xorwow;
    seedrandom.xorshift7 = xorshift7;
    seedrandom.xor4096 = xor4096;
    seedrandom.tychei = tychei;

    var seedrandom$1 = seedrandom;
    var seedrandom_1 = seedrandom$1.alea;

    /**
     * @license
     * Copyright 2017 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.
     * =============================================================================
     */
    // https://en.wikipedia.org/wiki/Marsaglia_polar_method
    var MPRandGauss = /** @class */ (function () {
        function MPRandGauss(mean, stdDeviation, dtype, truncated, seed) {
            this.mean = mean;
            this.stdDev = stdDeviation;
            this.dtype = dtype;
            this.nextVal = NaN;
            this.truncated = truncated;
            if (this.truncated) {
                this.upper = this.mean + this.stdDev * 2;
                this.lower = this.mean - this.stdDev * 2;
            }
            var seedValue = seed ? seed : Math.random();
            this.random = seedrandom_1(seedValue.toString());
        }
        /** Returns next sample from a Gaussian distribution. */
        MPRandGauss.prototype.nextValue = function () {
            if (!isNaN(this.nextVal)) {
                var value = this.nextVal;
                this.nextVal = NaN;
                return value;
            }
            var resultX, resultY;
            var isValid = false;
            while (!isValid) {
                var v1 = void 0, v2 = void 0, s = void 0;
                do {
                    v1 = 2 * this.random() - 1;
                    v2 = 2 * this.random() - 1;
                    s = v1 * v1 + v2 * v2;
                } while (s >= 1 || s === 0);
                var mul = Math.sqrt(-2.0 * Math.log(s) / s);
                resultX = this.mean + this.stdDev * v1 * mul;
                resultY = this.mean + this.stdDev * v2 * mul;
                if (!this.truncated || this.isValidTruncated(resultX)) {
                    isValid = true;
                }
            }
            if (!this.truncated || this.isValidTruncated(resultY)) {
                this.nextVal = this.convertValue(resultY);
            }
            return this.convertValue(resultX);
        };
        /** Handles proper rounding for non-floating-point numbers. */
        MPRandGauss.prototype.convertValue = function (value) {
            if (this.dtype == null || this.dtype === 'float32') {
                return value;
            }
            return Math.round(value);
        };
        /** Returns true if less than 2-standard-deviations from the mean. */
        MPRandGauss.prototype.isValidTruncated = function (value) {
            return value <= this.upper && value >= this.lower;
        };
        return MPRandGauss;
    }());
    // Marsaglia, George, and Wai Wan Tsang. 2000. "A Simple Method for Generating
    // Gamma Variables."
    var RandGamma = /** @class */ (function () {
        function RandGamma(alpha, beta, dtype, seed) {
            this.alpha = alpha;
            this.beta = 1 / beta; // convert rate to scale parameter
            this.dtype = dtype;
            var seedValue = seed ? seed : Math.random();
            this.randu = seedrandom_1(seedValue.toString());
            this.randn = new MPRandGauss(0, 1, dtype, false, this.randu());
            if (alpha < 1) {
                this.d = alpha + (2 / 3);
            }
            else {
                this.d = alpha - (1 / 3);
            }
            this.c = 1 / Math.sqrt(9 * this.d);
        }
        /** Returns next sample from a gamma distribution. */
        RandGamma.prototype.nextValue = function () {
            var x2, v0, v1, x, u, v;
            while (true) {
                do {
                    x = this.randn.nextValue();
                    v = 1 + (this.c * x);
                } while (v <= 0);
                v *= v * v;
                x2 = x * x;
                v0 = 1 - (0.331 * x2 * x2);
                v1 = (0.5 * x2) + (this.d * (1 - v + Math.log(v)));
                u = this.randu();
                if (u < v0 || Math.log(u) < v1) {
                    break;
                }
            }
            v = (1 / this.beta) * this.d * v;
            if (this.alpha < 1) {
                v *= Math.pow(this.randu(), 1 / this.alpha);
            }
            return this.convertValue(v);
        };
        /** Handles proper rounding for non-floating-point numbers. */
        RandGamma.prototype.convertValue = function (value) {
            if (this.dtype === 'float32') {
                return value;
            }
            return Math.round(value);
        };
        return RandGamma;
    }());
    var UniformRandom = /** @class */ (function () {
        function UniformRandom(min, max, dtype, seed) {
            var _this = this;
            if (min === void 0) { min = 0; }
            if (max === void 0) { max = 1; }
            /** Handles proper rounding for non floating point numbers. */
            this.canReturnFloat = function () {
                return (_this.dtype == null || _this.dtype === 'float32');
            };
            this.min = min;
            this.range = max - min;
            this.dtype = dtype;
            if (seed == null) {
                seed = Math.random();
            }
            if (typeof seed === 'number') {
                seed = seed.toString();
            }
            if (!this.canReturnFloat() && this.range <= 1) {
                throw new Error("The difference between " + min + " - " + max + " <= 1 and dtype is not float");
            }
            this.random = seedrandom_1(seed);
        }
        UniformRandom.prototype.convertValue = function (value) {
            if (this.canReturnFloat()) {
                return value;
            }
            return Math.round(value);
        };
        UniformRandom.prototype.nextValue = function () {
            return this.convertValue(this.min + this.range * this.random());
        };
        return UniformRandom;
    }());

    /**
     * @license
     * Copyright 2018 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.
     * =============================================================================
     */
    /**
     * 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 = input.reshape(newShape);
        }
        var reps = Array.from(shape);
        for (var i = shape.length - 1; i >= 0; i--) {
            if (input.shape[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 input.clone();
        }
        return ENGINE.runKernelFunc(function (backend) { return backend.tile(input, reps); }, { input: input }, function (dy) {
            return ({ input: function () { return dy.sum(axes, /*keepDims=*/ true); } });
        });
    }
    /**
     * 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', null);
        var der = function (dy) {
            return { $x: function () { return dy.toFloat(); } };
        };
        return ENGINE.runKernelFunc(function () { return ENGINE.makeTensorFromDataId($x.dataId, $x.shape, $x.dtype); }, { $x: $x }, der);
    }
    /**
     * 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 = buff.toTensor().as2D(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) {
                return tile(expandDims(expandDims(out, 0), 0), [batchShape[0], batchShape[1], 1, 1]);
            }
            else if (batchShape.length === 3) {
                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."));
            }
        }
    }
    /**
     * 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();
    }
    /**
     * 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();
    }
    /**
     * 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();
    }
    /**
     * 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();
    }
    /**
     * 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'.
     */
    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);
    }
    /**
     * 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);
        }
        seed = seed || Math.random();
        var logits2D = origRank === 1 ? $logits.as2D(1, -1) : $logits;
        var res = ENGINE.runKernelFunc(function (backend) { return backend.multinomial(logits2D, normalized, numSamples, seed); }, { logits2D: logits2D });
        return origRank === 1 ? res.as1D() : res;
    }
    /**
     * 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 outShape = $indices.shape.concat([depth]);
        $indices = $indices.flatten();
        var grad = function (dy) {
            return { $indices: function () { return zeros($indices.shape, 'float32'); } };
        };
        var result = ENGINE.runKernelFunc(function (backend) { return backend.oneHot($indices, depth, onValue, offValue); }, { $indices: $indices }, grad);
        return result.reshape(outShape);
    }
    /**
     * 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', null);
        shape = inferFromImplicitShape(shape, $x.size);
        assert($x.size === sizeFromShape(shape), function () { return 'new shape and old shape must have the same number of elements.'; });
        var grad = function (dy) {
            return { x: function () { return dy.reshape($x.shape); } };
        };
        var attrs = { shape: shape };
        return ENGINE.runKernelFunc(function (backend) { return backend.reshape($x, shape); }, { x: $x }, grad, 'Reshape', attrs);
    }
    /**
     * 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);
    }
    /**
     * 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' && $x.dtype === 'string') {
            throw new Error('Only strings can be casted to strings');
        }
        var grad = function (dy) {
            return { x: function () { return dy.clone(); } };
        };
        var attrs = { dtype: dtype };
        return ENGINE.runKernelFunc(function (backend) { return backend.cast($x, dtype); }, { x: $x }, grad, 'Cast', attrs);
    }
    /**
     * 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 parseAs = null;
        var $x = convertToTensor(x, 'x', 'tile', parseAs);
        assert($x.rank === reps.length, function () { return "Error in transpose: rank of input " + $x.rank + " " +
            ("must match length of reps " + reps + "."); });
        var grad = function (dy, saved) {
            var $x = saved[0];
            var derX = function () {
                var xGrad = zerosLike($x);
                // TODO(cais): Maybe reduce memory footprint by avoiding repeated
                // slicing.
                if ($x.rank === 1) {
                    for (var i = 0; i < reps[0]; ++i) {
                        xGrad = xGrad.add(dy.slice([i * $x.shape[0]], [$x.shape[0]]));
                    }
                }
                else if ($x.rank === 2) {
                    for (var i = 0; i < reps[0]; ++i) {
                        for (var j = 0; j < reps[1]; ++j) {
                            xGrad = xGrad.add(dy.slice([i * $x.shape[0], j * $x.shape[1]], [$x.shape[0], $x.shape[1]]));
                        }
                    }
                }
                else if ($x.rank === 3) {
                    for (var i = 0; i < reps[0]; ++i) {
                        for (var j = 0; j < reps[1]; ++j) {
                            for (var k = 0; k < reps[2]; ++k) {
                                xGrad = xGrad.add(dy.slice([i * $x.shape[0], j * $x.shape[1], k * $x.shape[2]], [$x.shape[0], $x.shape[1], $x.shape[2]]));
                            }
                        }
                    }
                }
                else if ($x.rank === 4) {
                    for (var i = 0; i < reps[0]; ++i) {
                        for (var j = 0; j < reps[1]; ++j) {
                            for (var k = 0; k < reps[2]; ++k) {
                                for (var l = 0; l < reps[3]; ++l) {
                                    xGrad = xGrad.add(dy.slice([
                                        i * $x.shape[0], j * $x.shape[1], k * $x.shape[2],
                                        l * $x.shape[3]
                                    ], [$x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]));
                                }
                            }
                        }
                    }
                }
                else {
                    throw new Error("Gradient for tile operation is not implemented for rank-" +
                        ($x.rank + " tensors yet."));
                }
                return xGrad;
            };
            return { x: derX };
        };
        var inputsToSave = [$x];
        var attrs = { reps: reps };
        return ENGINE.runKernelFunc(function (backend, save) {
            var res = backend.tile($x, reps);
            save([$x]);
            return res;
        }, { x: $x }, grad, 'Tile', attrs, inputsToSave);
    }
    /**
     * 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);
    }
    /**
     * 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);
    }
    /**
     * 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);
    }
    /**
     * 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);
    }
    /**
     * Pads a `tf.Tensor` with a given value and paddings.
     *
     * This operation currently only implements the `CONSTANT` mode.
     *
     * 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 grad = function (dy) {
            // Pad introduces values around the original tensor, so the gradient
            // slices the original shape out of the gradient.
            var begin = paddings.map(function (p) { return p[0]; });
            return { x: function () { return dy.slice(begin, $x.shape); } };
        };
        var attrs = { paddings: paddings, constantValue: constantValue };
        return ENGINE.runKernelFunc(function (backend) { return backend.pad($x, paddings, constantValue); }, { x: $x }, grad, 'PadV2', attrs);
    }
    /**
     * 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 $tensors = convertToTensorArray(tensors, 'tensors', 'stack');
        assert($tensors.length >= 1, function () { return 'Pass at least one tensor to tf.stack'; });
        if ($tensors.length === 1) {
            return $tensors[0].expandDims(axis);
        }
        var rank = $tensors[0].rank;
        var shape = $tensors[0].shape;
        var dtype = $tensors[0].dtype;
        assert(axis <= rank, function () { return 'Axis must be <= rank of the tensor'; });
        $tensors.forEach(function (t) {
            assertShapesMatch(shape, t.shape, 'All tensors passed to stack must have matching shapes');
        });
        $tensors.forEach(function (t) {
            assert(dtype === t.dtype, function () { return 'All tensors passed to stack must have matching dtypes'; });
        });
        var expandedTensors = $tensors.map(function (t) { return t.expandDims(axis); });
        return concat(expandedTensors, axis);
    }
    /**
     * 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 grad = function (dy) {
            return { $x: function () { return dy.spaceToBatchND(blockShape, crops); } };
        };
        return ENGINE.runKernelFunc(function (backend) { return backend.batchToSpaceND($x, blockShape, crops); }, { $x: $x }, grad);
    }
    /**
     * 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 grad = function (dy) {
            return { $x: function () { return dy.batchToSpaceND(blockShape, paddings); } };
        };
        return ENGINE.runKernelFunc(function (backend) { return backend.spaceToBatchND($x, blockShape, paddings); }, { $x: $x }, grad);
    }
    /**
     * 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; }
        axis = axis || 0;
        var $x = convertToTensor(x, 'x', 'unstack');
        assert(axis >= -$x.shape.length && axis < $x.shape.length, function () {
            return "Axis = " + axis + " is not in [-" + $x.shape.length + ", " + $x.shape.length + ")";
        });
        if (axis < 0) {
            axis += $x.shape.length;
        }
        var grad = function (dy) {
            return { x: function () { return stack(dy, axis); } };
        };
        var attrs = { axis: axis };
        return ENGINE.runKernelFunc(function (backend) { return backend.unstack($x, axis); }, { x: $x }, grad, 'Unpack', attrs);
    }
    /**
     * 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');
        axis = axis | 0;
        var permutation = getAxesPermutation([axis], $x.rank);
        var permutedX = $x;
        if (permutation != null) {
            permutedX = $x.transpose(permutation);
        }
        var permutedAxis = getInnerMostAxes(1, $x.rank)[0];
        var grad = function (dy) {
            return { permutedX: function () { return dy.cumsum(axis, exclusive, !reverse); } };
        };
        var value = ENGINE.runKernelFunc(function (backend) { return backend.cumsum(permutedX, permutedAxis, exclusive, reverse); }, { permutedX: permutedX }, grad);
        if (permutation != null) {
            value = value.transpose(permutation);
        }
        return value;
    }
    /**
     * 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 parseAs = null;
        var $x = convertToTensor(x, 'x', 'expandDims', parseAs);
        assert(axis <= $x.rank, function () { return 'Axis must be <= rank of the tensor'; });
        var newShape = $x.shape.slice();
        if (axis < 0) {
            // Negative value is counted from the tail of rank.
            assert(-($x.rank + 1) <= axis, function () { return "Axis must be in the interval [" + -($x.rank + 1) + ", " + $x.rank + "]"; });
            axis = $x.rank + axis + 1;
        }
        newShape.splice(axis, 0, 1);
        return reshape($x, newShape);
    }
    /**
     * Rearranges data from depth into blocks of spatial data. More specifically,
     * this op outputs a copy of the input tensor where values from the `depth`
     * dimension are moved in spatial blocks to the `height` and `width` dimensions.
     * The attr `blockSize` indicates the input block size and how the data is
     * moved.
     *
     *  - Chunks of data of size `blockSize * blockSize` from depth are rearranged
     * into non-overlapping blocks of size `blockSize x blockSize`
     *
     *  - The width the output tensor is `inputWidth * blockSize`, whereas the
     * height is `inputHeight * blockSize`
     *
     *  - The Y, X coordinates within each block of the output image are determined
     * by the high order component of the input channel index
     *
     *  - The depth of the input tensor must be divisible by `blockSize *
     * blockSize`
     *
     * The `dataFormat` attr specifies the layout of the input and output tensors
     * with the following options: "NHWC": [ `batch, height, width, channels` ]
     * "NCHW": [ `batch, channels, height, width` ]
     *
     * ```js
     * const x = tf.tensor4d([1, 2, 3, 4], [1, 1, 1, 4]);
     * const blockSize = 2;
     * const dataFormat = "NHWC";
     *
     * tf.depthToSpace(x, blockSize, dataFormat).print();
     * ```
     *
     * @param x The input tensor of rank 4
     * @param blockSIze  An `int` that is `>= 2`. The size of the spatial block
     * @param dataFormat An optional string from: "NHWC", "NCHW". Defaults to "NHWC"
     */
    /** @doc {heading: 'Tensors', subheading: 'Transformations'} */
    function depthToSpace_(x, blockSize, dataFormat) {
        if (dataFormat === void 0) { dataFormat = 'NHWC'; }
        var $x = convertToTensor(x, 'x', 'depthToSpace');
        var inputHeight = (dataFormat === 'NHWC') ? $x.shape[1] : $x.shape[2];
        var inputWidth = (dataFormat === 'NHWC') ? $x.shape[2] : $x.shape[3];
        var inputDepth = (dataFormat === 'NHWC') ? $x.shape[3] : $x.shape[1];
        assert(inputHeight * blockSize >= 0, function () { return "Negative dimension size caused by overflow when multiplying\n      " + inputHeight + " and " + blockSize + "  for depthToSpace with input shape\n      " + $x.shape; });
        assert(inputWidth * blockSize >= 0, function () { return "Negative dimension size caused by overflow when multiplying\n      " + inputWidth + " and " + blockSize + " for depthToSpace with input shape\n          " + $x.shape; });
        assert((inputDepth % (blockSize * blockSize) === 0), function () { return "Dimension size must be evenly divisible by " + blockSize * blockSize + " but is " + inputDepth + " for depthToSpace with input shape " + $x.shape; });
        return ENGINE.runKernelFunc(function (backend) { return backend.depthToSpace($x, blockSize, dataFormat); }, { $x: $x });
    }
    /**
     * 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()]];
                }
            });
        });
    }
    /**
     * 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);
    }
    /**
     * 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));
    }
    var batchToSpaceND = op({ batchToSpaceND_: batchToSpaceND_ });
    var broadcastTo = op({ broadcastTo_: broadcastTo_ });
    var cast = op({ cast_: cast_ });
    var clone = op({ clone_: clone_ });
    var cumsum = op({ cumsum_: cumsum_ });
    var depthToSpace = op({ depthToSpace_: depthToSpace_ });
    var expandDims = op({ expandDims_: expandDims_ });
    var eye = op({ eye_: eye_ });
    var multinomial = op({ multinomial_: multinomial_ });
    var oneHot = op({ oneHot_: oneHot_ });
    var pad = op({ pad_: pad_ });
    var pad1d = op({ pad1d_: pad1d_ });
    var pad2d = op({ pad2d_: pad2d_ });
    var pad3d = op({ pad3d_: pad3d_ });
    var pad4d = op({ pad4d_: pad4d_ });
    var rand = op({ rand_: rand_ });
    var randomNormal = op({ randomNormal_: randomNormal_ });
    var randomGamma = op({ randomGamma_: randomGamma_ });
    var randomUniform = op({ randomUniform_: randomUniform_ });
    var reshape = op({ reshape_: reshape_ });
    var spaceToBatchND = op({ spaceToBatchND_: spaceToBatchND_ });
    var squeeze = op({ squeeze_: squeeze_ });
    var stack = op({ stack_: stack_ });
    var tile = op({ tile_: tile_ });
    var truncatedNormal = op({ truncatedNormal_: truncatedNormal_ });
    var unstack = op({ unstack_: unstack_ });
    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.
     * =============================================================================
     */
    /**
     * Gets the new shape of the input Tensor after it's been reshaped
     * to:
     * [blockShape[0], ..., blockShape[M-1], batch / prod(blockShape),
     * inputShape[1], ..., inputShape[N-1]]
     *
     * See step 1: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd
     */
    function getReshaped(inputShape, blockShape, prod, batchToSpace) {
        if (batchToSpace === void 0) { batchToSpace = true; }
        var reshaped = [];
        if (batchToSpace) {
            reshaped = reshaped.concat(blockShape.slice(0));
            reshaped.push(inputShape[0] / prod);
            reshaped = reshaped.concat(inputShape.slice(1));
        }
        else {
            reshaped = reshaped.concat(inputShape[0]);
            var spatialLength = blockShape.length;
            for (var i = 0; i < spatialLength; ++i) {
                reshaped =
                    reshaped.concat([inputShape[i + 1] / blockShape[i], blockShape[i]]);
            }
            reshaped = reshaped.concat(inputShape.slice(spatialLength + 1));
        }
        return reshaped;
    }
    /**
     * Gets the permutation that will transpose the dimensions of the
     * reshaped tensor to shape:
     *
     * [batch / prod(block_shape),inputShape[1], blockShape[0], ...,
     * inputShape[M], blockShape[M-1],inputShape[M+1], ..., inputShape[N-1]]
     *
     * see step 2: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd
     */
    function getPermuted(reshapedRank, blockShapeRank, batchToSpace) {
        if (batchToSpace === void 0) { batchToSpace = true; }
        var permuted = [];
        if (batchToSpace) {
            permuted.push(blockShapeRank);
            for (var i = blockShapeRank + 1; i < reshapedRank; ++i) {
                if (i <= 2 * blockShapeRank) {
                    permuted.push(i);
                    permuted.push(i - (blockShapeRank + 1));
                }
                else {
                    permuted.push(i);
                }
            }
        }
        else {
            var permutedBeforeBatch = [];
            var permutedAfterBatch = [];
            for (var i = 1; i < reshapedRank; ++i) {
                if (i >= blockShapeRank * 2 + 1 || i % 2 === 1) {
                    permutedAfterBatch.push(i);
                }
                else {
                    permutedBeforeBatch.push(i);
                }
            }
            permuted.push.apply(permuted, permutedBeforeBatch);
            permuted.push(0);
            permuted.push.apply(permuted, permutedAfterBatch);
        }
        return permuted;
    }
    /**
     * Gets the shape of the reshaped and permuted input Tensor before any cropping
     * is applied.  The new shape will be:
     *
     * [batch / prod(blockShape),inputShape[1] * blockShape[0], ...,
     * inputShape[M] * blockShape[M-1],inputShape[M+1], ..., inputShape[N-1]]
     *
     * See step 3: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd
     */
    function getReshapedPermuted(inputShape, blockShape, prod, batchToSpace) {
        if (batchToSpace === void 0) { batchToSpace = true; }
        var reshapedPermuted = [];
        if (batchToSpace) {
            reshapedPermuted.push(inputShape[0] / prod);
        }
        else {
            reshapedPermuted.push(inputShape[0] * prod);
        }
        for (var i = 1; i < inputShape.length; ++i) {
            if (i <= blockShape.length) {
                if (batchToSpace) {
                    reshapedPermuted.push(blockShape[i - 1] * inputShape[i]);
                }
                else {
                    reshapedPermuted.push(inputShape[i] / blockShape[i - 1]);
                }
            }
            else {
                reshapedPermuted.push(inputShape[i]);
            }
        }
        return reshapedPermuted;
    }
    /**
     * Converts the crops argument into the beginning coordinates of a slice
     * operation.
     */
    function getSliceBeginCoords(crops, blockShape) {
        var sliceBeginCoords = [0];
        for (var i = 0; i < blockShape; ++i) {
            sliceBeginCoords.push(crops[i][0]);
        }
        return sliceBeginCoords;
    }
    /**
     * Converts the crops argument into the size of a slice operation.  When
     * combined with getSliceBeginCoords this function allows the reshaped and
     * permuted Tensor to be cropped to its final output shape of:
     *
     * inputShape[1] * blockShape[0] - crops[0,0] - crops[0,1], ...,
     * inputShape[M] * blockShape[M-1] -crops[M-1,0] -
     * crops[M-1,1],inputShape[M+1], ..., inputShape[N-1]]
     *
     * See step 4: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd
     */
    function getSliceSize(uncroppedShape, crops, blockShape) {
        var sliceSize = uncroppedShape.slice(0, 1);
        for (var i = 0; i < blockShape; ++i) {
            sliceSize.push(uncroppedShape[i + 1] - crops[i][0] - crops[i][1]);
        }
        return sliceSize;
    }

    /**
     * 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) {
        if (tensor.rank < 1) {
            throw new Error('tf.gatherND() expects the input to be rank 1 or higher,' +
                (" but the rank was " + tensor.rank + "."));
        }
        if (indices.rank < 1) {
            throw new Error('tf.gatherND() expects the indices to be rank 1 or higher,' +
                (" but the rank was " + indices.rank + "."));
        }
        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[indices.rank - 1] > tensor.rank) {
            throw new Error('index innermost dimension length must be <= tensor rank; saw: ' +
                (indices.shape[indices.rank - 1] + " vs. " + tensor.rank));
        }
        if (tensor.size === 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 < tensor.rank; ++i) {
            sliceSize *= inputShape[i];
            resultShape.push(inputShape[i]);
        }
        var strides = computeStrides(tensor.shape).map(function (stride) { return stride / sliceSize; }).concat([1]).slice(0, sliceRank);
        return [resultShape, nResult, sliceSize, strides];
    }

    var gather_nd_util = /*#__PURE__*/Object.freeze({
        prepareAndValidate: prepareAndValidate
    });

    /**
     * @license
     * Copyright 2017 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.
     * =============================================================================
     */
    var PARALLELIZE_THRESHOLD = 30;
    function computeOptimalWindowSize(inSize) {
        if (inSize <= PARALLELIZE_THRESHOLD) {
            return inSize;
        }
        return nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));
    }

    /**
     * Check whether updates.shape = indices.shape[:batchDim] +
     * shape[sliceDim:]
     *
     * @param x The input tensor.
     */
    function validateUpdateShape(shape, indices, updates) {
        var sliceDim = (indices.rank > 1) ? indices.shape[indices.rank - 1] : 1;
        var batchDim = (indices.rank > 1) ? indices.rank - 1 : 1;
        var shapeError = 'Must have updates.shape = indices.shape[:batchDim] + ' +
            ("shape[sliceDim:], got updates.shape: " + updates.shape) +
            (", indices.shape: " + indices.shape + ", shape: " + shape) +
            (", sliceDim: " + sliceDim + ", and batchDim: " + batchDim + ".");
        if (updates.rank < batchDim) {
            throw new Error(shapeError + (" update.rank < " + batchDim + ". "));
        }
        if (shape.length < sliceDim + (updates.rank - batchDim)) {
            throw new Error(shapeError +
                (" Output shape length < " + (sliceDim + (updates.rank - batchDim))));
        }
        if (updates.rank !== batchDim + shape.length - sliceDim) {
            throw new Error(shapeError + (" update.rank != " + (batchDim + shape.length - sliceDim)));
        }
        for (var d = 0; d < batchDim; ++d) {
            if (updates.shape[d] !== indices.shape[d]) {
                throw new Error(shapeError +
                    (" updates.shape[" + d + "] (" + updates.shape[d] + ") != indices.shape[" + d + "] (" + indices.shape[d] + ")."));
            }
        }
        for (var d = 0; d < updates.rank - batchDim; ++d) {
            if (updates.shape[d + batchDim] !== shape[d + sliceDim]) {
                throw new Error(shapeError +
                    (" updates.shape[" + (d + batchDim) + "] (" + updates.shape[d + batchDim] + ") != shape[" + (d + batchDim) + "] (" + shape[d + batchDim] + ")"));
            }
        }
    }
    /**
     * Validate scatter nd inputs.
     *
     * @param update The tensor contains the update values.
     * @param indices The tensor contains the indices for the update values.
     * @param shape The shape of the output tensor.
     */
    function validateInput(updates, indices, shape) {
        if (indices.rank < 1) {
            throw new Error('tf.scatterND() expects the indices to be rank 1 or higher,' +
                (" but the rank was " + indices.rank + "."));
        }
        if (updates.rank < 1) {
            throw new Error('tf.scatterND() expects the updates to be rank 1 or higher,' +
                (" but the rank was " + updates.rank + "."));
        }
        if (indices.dtype !== 'int32') {
            throw new Error("The dtype of 'indices' should be int32, but got dtype: " + indices.dtype);
        }
        if (shape.length < 1) {
            throw new Error("Output rank must be greater or equal to 1, but got shape: " + shape);
        }
        if (shape.length === 0) {
            if (indices.size === 0) {
                throw new Error("Indices specified for empty output. indices shape: " + indices.shape);
            }
            if (updates.size === 0) {
                throw new Error("Updates specified for empty output. updates shape: " + updates.shape);
            }
        }
        validateUpdateShape(shape, indices, updates);
    }
    /**
     * Calculate the shape information for the output.
     *
     * @param update The tensor contains the update values.
     * @param indices The tensor contains the indices for the update values.
     * @param shape The shape of the output tensor.
     *
     * @returns ScatterShapeInfo
     */
    function calculateShapes(updates, indices, shape) {
        // Calculate the number of dimensions in indices
        var indicesRank = indices.shape.length;
        var sliceRank = (indicesRank > 1) ? indices.shape[indicesRank - 1] : 1;
        // Calculate the number of elements that make up each slice of our updated
        // tensor. This allows us to work with flattened tensors and copy over whole
        // slices at a time.
        var totalNd = shape.length;
        var sliceSize = 1;
        for (var i = sliceRank; i < totalNd; ++i) {
            sliceSize *= shape[i];
        }
        var safeSliceDim = (sliceRank < 1) ? 1 : sliceRank;
        var numUpdates = sizeFromShape(indices.shape) / safeSliceDim;
        var strides = computeStrides(shape.slice(0, sliceRank)).concat([1]);
        var outputSize = sizeFromShape(shape);
        return { sliceRank: sliceRank, numUpdates: numUpdates, sliceSize: sliceSize, strides: strides, outputSize: outputSize };
    }

    var scatter_nd_util = /*#__PURE__*/Object.freeze({
        validateUpdateShape: validateUpdateShape,
        validateInput: validateInput,
        calculateShapes: calculateShapes
    });

    /**
     * @license
     * Copyright 2018 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.
     * =============================================================================
     */
    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$1(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) {
        var dimSize = x.shape[axis];
        var outputShape = [];
        var batchSize = 1;
        var sliceSize = 1;
        for (var i = 0; i < axis; i++) {
            outputShape.push(x.shape[i]);
            batchSize *= x.shape[i];
        }
        for (var i = 0; i < indices.rank; i++) {
            outputShape.push(indices.shape[i]);
        }
        for (var i = axis + 1; i < x.rank; i++) {
            outputShape.push(x.shape[i]);
            sliceSize *= x.shape[i];
        }
        return { batchSize: batchSize, sliceSize: sliceSize, dimSize: dimSize, outputShape: outputShape };
    }

    /**
     * @license
     * Copyright 2017 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.
     * =============================================================================
     */
    function assertParamsValid(input, begin, size) {
        assert(input.rank === begin.length, function () { return "Error in slice" + input.rank + "D: Length of begin " + begin + " must " +
            ("match the rank of the array (" + input.rank + ")."); });
        assert(input.rank === size.length, function () { return "Error in slice" + input.rank + "D: Length of size " + size + " must " +
            ("match the rank of the array (" + input.rank + ")."); });
        var _loop_1 = function (i) {
            assert(begin[i] + size[i] <= input.shape[i], function () { return "Error in slice" + input.rank + "D: begin[" + i + "] + size[" + i + "] " +
                ("(" + (begin[i] + size[i]) + ") would overflow input.shape[" + i + "] (" + input.shape[i] + ")"); });
        };
        for (var i = 0; i < input.rank; ++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;
    }
    function startForAxis(beginMask, startIndices, strides, inputShape, axis) {
        // Begin with the specified index
        var start = startIndices[axis];
        var stride = strides[axis] || 1;
        // Check the axis bit from right of beginMask or the begin index is not set
        // for the axis.
        if (beginMask & 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) {
        // Begin with the specified index
        var stop = stopIndices[axis];
        var stride = strides[axis] || 1;
        // Check the axis bit from right of endMask or if the stop index is not set
        // for this axis.
        if (endMask & (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;
    }

    var slice_util = /*#__PURE__*/Object.freeze({
        assertParamsValid: assertParamsValid,
        maskToAxes: maskToAxes,
        computeOutShape: computeOutShape$2,
        startForAxis: startForAxis,
        stopForAxis: stopForAxis,
        isSliceContinous: isSliceContinous,
        computeFlatOffset: computeFlatOffset
    });

    /**
     * @license
     * Copyright 2018 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.
     * =============================================================================
     */
    /**
     * 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', null);
            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', null);
            var $dy = (dy != null) ? convertToTensor(dy, 'dy', 'tf.grads') : null;
            return ENGINE.tidy(function () {
                var _a = ENGINE.gradients(function () { return f.apply(void 0, $args); }, $args, $dy), value = _a.value, grads = _a.grads;
                if ($dy != null) {
                    assertShapesMatch(value.shape, $dy.shape, 'The shape of dy passed in grads(f)([x1,...], dy) must ' +
                        'match the shape returned by f([x1,...])');
                }
                checkGrads(grads);
                return grads;
            });
        };
    }
    /**
     * Like `tf.grad`, but also returns the value of `f()`. Useful when `f()`
     * returns a metric you want to show.
     *
     * The result is a rich object with the following properties:
     * - grad: The gradient of `f(x)` w.r.t `x` (result of `tf.grad`).
     * - value: The value returned by `f(x)`.
     *
     * ```js
     * // f(x) = x ^ 2
     * const f = x => x.square();
     * // f'(x) = 2x
     * const g = tf.valueAndGrad(f);
     *
     * const x = tf.tensor1d([2, 3]);
     * const {value, grad} = g(x);
     *
     * console.log('value');
     * value.print();
     * console.log('grad');
     * grad.print();
     * ```
     */
    /** @doc {heading: 'Training', subheading: 'Gradients'} */
    function valueAndGrad(f) {
        assert(isFunction(f), function () { return 'The f passed in valueAndGrad(f) must be a function'; });
        return function (x, dy) {
            assert(x instanceof Tensor, function () { return 'The x passed in valueAndGrad(f)(x) must be a tensor'; });
            assert(dy == null || dy instanceof Tensor, function () { return 'The dy passed in valueAndGrad(f)(x, dy) must be a tensor'; });
            var _a = ENGINE.gradients(function () { return f(x); }, [x], dy), grads = _a.grads, value = _a.value;
            checkGrads(grads);
            return { grad: grads[0], value: value };
        };
    }
    /**
     * Like `tf.grads`, but returns also the value of `f()`. Useful when `f()`
     * returns a metric you want to show.
     *
     * The result is a rich object with the following properties:
     * - grads: The gradients of `f()` w.r.t each input (result of `tf.grads`).
     * - value: The value returned by `f(x)`.
     *
     * ```js
     * // f(a, b) = a * b
     * const f = (a, b) => a.mul(b);
     * // df/da = b, df/db = a
     * const g = tf.valueAndGrads(f);
     *
     * const a = tf.tensor1d([2, 3]);
     * const b = tf.tensor1d([-2, -3]);
     * const {value, grads} = g([a, b]);
     *
     * const [da, db] = grads;
     *
     * console.log('value');
     * value.print();
     *
     * console.log('da');
     * da.print();
     * console.log('db');
     * db.print();
     * ```
     */
    /** @doc {heading: 'Training', subheading: 'Gradients'} */
    function valueAndGrads(f) {
        assert(isFunction(f), function () { return 'The f passed in valueAndGrads(f) must be a function'; });
        return function (args, dy) {
            assert(Array.isArray(args) && args.every(function (arg) { return arg instanceof Tensor; }), function () { return 'The args passed in valueAndGrads(f)(args) must be array of ' +
                'tensors'; });
            assert(dy == null || dy instanceof Tensor, function () { return 'The dy passed in valueAndGrads(f)(args, dy) must be a tensor'; });
            var res = ENGINE.gradients(function () { return f.apply(void 0, args); }, args, dy);
            if (dy != null) {
                assertShapesMatch(res.value.shape, dy.shape, 'The shape of dy passed in valueAndGrads(f)([x1,...], dy) must ' +
                    'match the shape returned by f([x1,...])');
            }
            checkGrads(res.grads);
            return res;
        };
    }
    /**
     * Computes and returns the gradient of f(x) with respect to the list of
     * trainable variables provided by `varList`. If no list is provided, it
     * defaults to all trainable variables.
     *
     * ```js
     * const a = tf.variable(tf.tensor1d([3, 4]));
     * const b = tf.variable(tf.tensor1d([5, 6]));
     * const x = tf.tensor1d([1, 2]);
     *
     * // f(a, b) = a * x ^ 2 + b * x
     * const f = () => a.mul(x.square()).add(b.mul(x)).sum();
     * // df/da = x ^ 2, df/db = x
     * const {value, grads} = tf.variableGrads(f);
     *
     * Object.keys(grads).forEach(varName => grads[varName].print());
     * ```
     *
     * @param f The function to execute. f() should return a scalar.
     * @param varList The list of variables to compute the gradients with respect
     *     to. Defaults to all trainable variables.
     * @returns An object with the following keys and values:
     *   - `value`: The value of the function `f`.
     *   - `grads`: A map from the names of the variables to the gradients.
     *     If the `varList` argument is provided explicitly and contains a subset of
     *     non-trainable variables, this map in the return value will contain keys
     *     that map the names of the non-trainable variables to `null`.
     */
    /** @doc {heading: 'Training', subheading: 'Gradients'} */
    function variableGrads(f, varList) {
        assert(isFunction(f), function () { return 'The f passed in variableGrads(f) must be a function'; });
        assert(varList == null ||
            Array.isArray(varList) && varList.every(function (v) { return v instanceof Variable; }), function () {
            return 'The varList passed in variableGrads(f, varList) must be an array ' +
                'of variables';
        });
        var specifiedVarList = varList != null;
        if (!specifiedVarList) {
            // Get all of the trainable variables.
            varList = [];
            for (var varName in ENGINE.registeredVariables) {
                varList.push(ENGINE.registeredVariables[varName]);
            }
        }
        var specifiedNonTrainable = specifiedVarList ? varList.filter(function (variable) { return !variable.trainable; }) : null;
        // Prune non-trainable variables.
        var originalVarCount = varList.length;
        varList = varList.filter(function (variable) { return variable.trainable; });
        assert(varList.length > 0, function () { return "variableGrads() expects at least one of the input variables to " +
            ("be trainable, but none of the " + originalVarCount + " variables is ") +
            "trainable."; });
        var allowNoGradients = true;
        var _a = ENGINE.gradients(f, varList, null, allowNoGradients), value = _a.value, grads = _a.grads;
        assert(grads.some(function (g) { return g != null; }), function () { return 'Cannot find a connection between any variable and the result of ' +
            'the loss function y=f(x). Please make sure the operations that ' +
            'use variables are inside the function f passed to minimize().'; });
        assert(value.rank === 0, function () { return "The f passed in variableGrads(f) must return a scalar, but it " +
            ("returned a rank-" + value.rank + " tensor"); });
        var namedGrads = {};
        varList.forEach(function (v, i) {
            if (grads[i] != null) {
                namedGrads[v.name] = grads[i];
            }
        });
        if (specifiedNonTrainable != null) {
            // If varList is explicitly provided and contains non-trainable values,
            // add them to the returned gradients with `null` values.
            specifiedNonTrainable.forEach(function (v) { return namedGrads[v.name] = null; });
        }
        return { value: value, grads: namedGrads };
    }
    /**
     * Overrides the gradient computation of a function `f`.
     *
     * Takes a function
     * `f(...inputs, save) => {value: Tensor, gradFunc: (dy, saved) => Tensor[]}`
     * and returns another function `g(...inputs)` which takes the same inputs as
     * `f`. When called, `g` returns `f().value`. In backward mode, custom gradients
     * with respect to each input of `f` are computed using `f().gradFunc`.
     *
     * The `save` function passsed to `f` should be used for saving tensors needed
     * in the gradient. And the `saved` passed to the `gradFunc` is a
     * `NamedTensorMap`, which contains those saved tensor.
     *
     * ```js
     * const customOp = tf.customGrad((x, save) => {
     *   // Save x to make sure it's available later for the gradient.
     *   save([x]);
     *   // Override gradient of our custom x ^ 2 op to be dy * abs(x);
     *   return {
     *     value: x.square(),
     *     // Note `saved.x` which points to the `x` we saved earlier.
     *     gradFunc: (dy, saved) => [dy.mul(saved[0].abs())]
     *   };
     * });
     *
     * const x = tf.tensor1d([-1, -2, 3]);
     * const dx = tf.grad(x => customOp(x));
     *
     * console.log(`f(x):`);
     * customOp(x).print();
     * console.log(`f'(x):`);
     * dx(x).print();
     * ```
     *
     * @param f The function to evaluate in forward mode, which should return
     *     `{value: Tensor, gradFunc: (dy, saved) => Tensor[]}`, where `gradFunc`
     *     returns the custom gradients of `f` with respect to its inputs.
     */
    /** @doc {heading: 'Training', subheading: 'Gradients'} */
    function customGrad(f) {
        return ENGINE.customGrad(f);
    }
    function checkGrads(grads) {
        var numNullGradients = grads.filter(function (g) { return g == null; }).length;
        if (numNullGradients > 0) {
            throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that\n    the f you passed encloses all operations that lead from x to y.");
        }
    }

    /**
     * @license
     * Copyright 2018 Google 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 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 " + $logits.rank + " and dim was " + dim));
        }
        var inputsToSave = [];
        var outputsToSave = [true];
        return ENGINE.runKernelFunc(function (backend, save) {
            var y = backend.softmax($logits, dim);
            save([y]);
            return y;
        }, { logits: $logits }, function (dy, saved) {
            var y = saved[0];
            var dyTimesY = dy.mul(y);
            var keepDims = true;
            return {
                logits: function () { return dyTimesY.sub(dyTimesY.sum([dim], keepDims).mul(y)); }
            };
        }, 'Softmax', { dim: dim }, inputsToSave, outputsToSave);
    }
    /**
     * 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 " + $logits.rank + " and axis was " + axis));
        }
        var customOp = customGrad(function (logits, save) {
            var keepDims = true;
            var xMax = logits.max(axis, true);
            var shifted = logits.sub(xMax);
            var value = shifted.toFloat().sub(shifted.exp().sum(axis, keepDims).log());
            save([value]);
            var gradFunc = function (dy, saved) {
                var value = saved[0];
                var softmax = value.exp();
                return dy.sub(dy.sum(axis, keepDims).mul(softmax));
            };
            return { value: value, gradFunc: gradFunc };
        });
        return customOp($logits);
    }
    var softmax = op({ softmax_: softmax_ });
    var logSoftmax = op({ logSoftmax_: logSoftmax_ });

    /**
     * @license
     * Copyright 2018 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.
     * =============================================================================
     */
    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.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) {
            return notYetImplemented('disposeData');
        };
        KernelBackend.prototype.write = function (values, shape, dtype) {
            return notYetImplemented('write');
        };
        KernelBackend.prototype.move = function (dataId, values, shape, dtype) {
            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.batchMatMul = function (a, b, transposeA, transposeB) {
            return notYetImplemented('batchMatMul');
        };
        KernelBackend.prototype.fusedBatchMatMul = function (_a) {
            var a = _a.a, b = _a.b, transposeA = _a.transposeA, transposeB = _a.transposeB, bias = _a.bias, activation = _a.activation, preluActivationWeights = _a.preluActivationWeights;
            return notYetImplemented('fusedBatchMatMul');
        };
        KernelBackend.prototype.slice = function (x, begin, size) {
            return notYetImplemented('slice');
        };
        KernelBackend.prototype.stridedSlice = function (x, begin, end, strides) {
            return notYetImplemented('stridedSlice');
        };
        KernelBackend.prototype.unstack = function (x, axis) {
            return notYetImplemented('unstack');
        };
        KernelBackend.prototype.reverse = function (a, axis) {
            return notYetImplemented('reverse');
        };
        KernelBackend.prototype.concat = function (tensors, axis) {
            return notYetImplemented('concat');
        };
        KernelBackend.prototype.neg = function (a) {
            return notYetImplemented('neg');
        };
        KernelBackend.prototype.add = function (a, b) {
            return notYetImplemented('add');
        };
        KernelBackend.prototype.addN = function (tensors) {
            return notYetImplemented('addN');
        };
        KernelBackend.prototype.subtract = function (a, b) {
            return notYetImplemented('subtract');
        };
        KernelBackend.prototype.multiply = function (a, b) {
            return notYetImplemented('multiply');
        };
        KernelBackend.prototype.realDivide = function (a, b) {
            return notYetImplemented('realDivide');
        };
        KernelBackend.prototype.floorDiv = function (a, b) {
            return notYetImplemented('floorDiv');
        };
        KernelBackend.prototype.sum = function (x, axes) {
            return notYetImplemented('sum');
        };
        KernelBackend.prototype.prod = function (x, axes) {
            return notYetImplemented('prod');
        };
        KernelBackend.prototype.unsortedSegmentSum = function (x, segmentIds, numSegments) {
            return notYetImplemented('unsortedSegmentSum');
        };
        KernelBackend.prototype.argMin = function (x, axis) {
            return notYetImplemented('argMin');
        };
        KernelBackend.prototype.argMax = function (x, axis) {
            return notYetImplemented('argMax');
        };
        KernelBackend.prototype.equal = function (a, b) {
            return notYetImplemented('equal');
        };
        KernelBackend.prototype.notEqual = function (a, b) {
            return notYetImplemented('notEqual');
        };
        KernelBackend.prototype.less = function (a, b) {
            return notYetImplemented('less');
        };
        KernelBackend.prototype.lessEqual = function (a, b) {
            return notYetImplemented('lessEqual');
        };
        KernelBackend.prototype.greater = function (a, b) {
            return notYetImplemented('greater');
        };
        KernelBackend.prototype.greaterEqual = function (a, b) {
            return notYetImplemented('greaterEqual');
        };
        KernelBackend.prototype.logicalNot = function (a) {
            return notYetImplemented('logicalNot');
        };
        KernelBackend.prototype.logicalAnd = function (a, b) {
            return notYetImplemented('logicalAnd');
        };
        KernelBackend.prototype.logicalOr = function (a, b) {
            return notYetImplemented('logicalOr');
        };
        KernelBackend.prototype.where = function (condition) {
            return notYetImplemented('where');
        };
        KernelBackend.prototype.select = function (condition, a, b) {
            return notYetImplemented('select');
        };
        KernelBackend.prototype.topk = function (x, k, sorted) {
            return notYetImplemented('topk');
        };
        KernelBackend.prototype.min = function (x, axes) {
            return notYetImplemented('min');
        };
        KernelBackend.prototype.minimum = function (a, b) {
            return notYetImplemented('minimum');
        };
        KernelBackend.prototype.mod = function (a, b) {
            return notYetImplemented('mod');
        };
        KernelBackend.prototype.max = function (x, axes) {
            return notYetImplemented('max');
        };
        KernelBackend.prototype.maximum = function (a, b) {
            return notYetImplemented('maximum');
        };
        KernelBackend.prototype.all = function (x, axes) {
            return notYetImplemented('all');
        };
        KernelBackend.prototype.any = function (x, axes) {
            return notYetImplemented('any');
        };
        KernelBackend.prototype.squaredDifference = function (a, b) {
            return notYetImplemented('squaredDifference');
        };
        KernelBackend.prototype.ceil = function (x) {
            return notYetImplemented('ceil');
        };
        KernelBackend.prototype.floor = function (x) {
            return notYetImplemented('floor');
        };
        KernelBackend.prototype.round = function (x) {
            return notYetImplemented('round');
        };
        KernelBackend.prototype.sign = function (x) {
            return notYetImplemented('sign');
        };
        KernelBackend.prototype.isNaN = function (x) {
            return notYetImplemented('isNaN');
        };
        KernelBackend.prototype.isInf = function (x) {
            return notYetImplemented('isInf');
        };
        KernelBackend.prototype.isFinite = function (x) {
            return notYetImplemented('isFinite');
        };
        KernelBackend.prototype.pow = function (a, b) {
            return notYetImplemented('pow');
        };
        KernelBackend.prototype.exp = function (x) {
            return notYetImplemented('exp');
        };
        KernelBackend.prototype.expm1 = function (x) {
            return notYetImplemented('expm1');
        };
        KernelBackend.prototype.softmax = function (x, dim) {
            return notYetImplemented('softmax');
        };
        KernelBackend.prototype.log = function (x) {
            return notYetImplemented('log');
        };
        KernelBackend.prototype.log1p = function (x) {
            return notYetImplemented('log1p');
        };
        KernelBackend.prototype.sqrt = function (x) {
            return notYetImplemented('sqrt');
        };
        KernelBackend.prototype.rsqrt = function (x) {
            return notYetImplemented('rsqrt');
        };
        KernelBackend.prototype.square = function (x) {
            return notYetImplemented('square');
        };
        KernelBackend.prototype.reciprocal = function (x) {
            return notYetImplemented('reciprocal');
        };
        KernelBackend.prototype.relu = function (x) {
            return notYetImplemented('relu');
        };
        KernelBackend.prototype.relu6 = function (x) {
            return notYetImplemented('relu6');
        };
        KernelBackend.prototype.prelu = function (x, a) {
            return notYetImplemented('prelu');
        };
        KernelBackend.prototype.elu = function (x) {
            return notYetImplemented('elu');
        };
        KernelBackend.prototype.eluDer = function (dy, y) {
            return notYetImplemented('eluDer');
        };
        KernelBackend.prototype.selu = function (x) {
            return notYetImplemented('selu');
        };
        KernelBackend.prototype.int = function (x) {
            return notYetImplemented('int');
        };
        KernelBackend.prototype.clip = function (x, min, max) {
            return notYetImplemented('clip');
        };
        KernelBackend.prototype.abs = function (x) {
            return notYetImplemented('abs');
        };
        KernelBackend.prototype.complexAbs = function (x) {
            return notYetImplemented('complexAbs');
        };
        KernelBackend.prototype.sigmoid = function (x) {
            return notYetImplemented('sigmoid');
        };
        KernelBackend.prototype.softplus = function (x) {
            return notYetImplemented('softplus');
        };
        KernelBackend.prototype.sin = function (x) {
            return notYetImplemented('sin');
        };
        KernelBackend.prototype.cos = function (x) {
            return notYetImplemented('cos');
        };
        KernelBackend.prototype.tan = function (x) {
            return notYetImplemented('tan');
        };
        KernelBackend.prototype.asin = function (x) {
            return notYetImplemented('asin');
        };
        KernelBackend.prototype.acos = function (x) {
            return notYetImplemented('acos');
        };
        KernelBackend.prototype.atan = function (x) {
            return notYetImplemented('atan');
        };
        KernelBackend.prototype.atan2 = function (a, b) {
            return notYetImplemented('atan2');
        };
        KernelBackend.prototype.sinh = function (x) {
            return notYetImplemented('sinh');
        };
        KernelBackend.prototype.cosh = function (x) {
            return notYetImplemented('cosh');
        };
        KernelBackend.prototype.tanh = function (x) {
            return notYetImplemented('tanh');
        };
        KernelBackend.prototype.asinh = function (x) {
            return notYetImplemented('asinh');
        };
        KernelBackend.prototype.acosh = function (x) {
            return notYetImplemented('acosh');
        };
        KernelBackend.prototype.atanh = function (x) {
            return notYetImplemented('atanh');
        };
        KernelBackend.prototype.erf = function (x) {
            return notYetImplemented('erf');
        };
        KernelBackend.prototype.step = function (x, alpha) {
            return notYetImplemented('step');
        };
        KernelBackend.prototype.fusedConv2d = function (_a) {
            var input = _a.input, filter = _a.filter, convInfo = _a.convInfo, bias = _a.bias, activation = _a.activation, preluActivationWeights = _a.preluActivationWeights;
            return notYetImplemented('fusedConv2d');
        };
        KernelBackend.prototype.conv2d = function (x, filter, convInfo) {
            return notYetImplemented('conv2d');
        };
        KernelBackend.prototype.conv2dDerInput = function (dy, filter, convInfo) {
            return notYetImplemented('conv2dDerInput');
        };
        KernelBackend.prototype.conv2dDerFilter = function (x, dY, convInfo) {
            return notYetImplemented('conv2dDerFilter');
        };
        KernelBackend.prototype.fusedDepthwiseConv2D = function (_a) {
            var input = _a.input, filter = _a.filter, convInfo = _a.convInfo, bias = _a.bias, activation = _a.activation, preluActivationWeights = _a.preluActivationWeights;
            return notYetImplemented('fusedDepthwiseConv2D');
        };
        KernelBackend.prototype.depthwiseConv2D = function (input, filter, convInfo) {
            return notYetImplemented('depthwiseConv2D');
        };
        KernelBackend.prototype.depthwiseConv2DDerInput = function (dy, filter, convInfo) {
            return notYetImplemented('depthwiseConv2DDerInput');
        };
        KernelBackend.prototype.depthwiseConv2DDerFilter = function (x, dY, convInfo) {
            return notYetImplemented('depthwiseConv2DDerFilter');
        };
        KernelBackend.prototype.conv3d = function (x, filter, convInfo) {
            return notYetImplemented('conv3d');
        };
        KernelBackend.prototype.conv3dDerInput = function (dy, filter, convInfo) {
            return notYetImplemented('conv3dDerInput');
        };
        KernelBackend.prototype.conv3dDerFilter = function (x, dY, convInfo) {
            return notYetImplemented('conv3dDerFilter');
        };
        KernelBackend.prototype.maxPool = function (x, convInfo) {
            return notYetImplemented('maxPool');
        };
        KernelBackend.prototype.maxPoolBackprop = function (dy, x, y, convInfo) {
            return notYetImplemented('maxPoolBackprop');
        };
        KernelBackend.prototype.avgPool = function (x, convInfo) {
            return notYetImplemented('avgPool');
        };
        KernelBackend.prototype.avgPoolBackprop = function (dy, x, convInfo) {
            return notYetImplemented('avgPoolBackprop');
        };
        KernelBackend.prototype.avgPool3d = function (x, convInfo) {
            return notYetImplemented('avgPool3d');
        };
        KernelBackend.prototype.avgPool3dBackprop = function (dy, x, convInfo) {
            return notYetImplemented('avgPool3dBackprop');
        };
        KernelBackend.prototype.maxPool3d = function (x, convInfo) {
            return notYetImplemented('maxPool3d');
        };
        KernelBackend.prototype.maxPool3dBackprop = function (dy, x, y, convInfo) {
            return notYetImplemented('maxPool3dBackprop');
        };
        KernelBackend.prototype.reshape = function (x, shape) {
            return notYetImplemented('reshape');
        };
        KernelBackend.prototype.cast = function (x, dtype) {
            return notYetImplemented('cast');
        };
        KernelBackend.prototype.tile = function (x, reps) {
            return notYetImplemented('tile');
        };
        KernelBackend.prototype.pad = function (x, paddings, constantValue) {
            return notYetImplemented('pad');
        };
        KernelBackend.prototype.transpose = function (x, perm) {
            return notYetImplemented('transpose');
        };
        KernelBackend.prototype.gather = function (x, indices, axis) {
            return notYetImplemented('gather');
        };
        KernelBackend.prototype.gatherND = function (x, indices) {
            return notYetImplemented('gatherND');
        };
        KernelBackend.prototype.scatterND = function (indices, updates, shape) {
            return notYetImplemented('scatterND');
        };
        KernelBackend.prototype.batchToSpaceND = function (x, blockShape, crops) {
            return notYetImplemented('batchToSpaceND');
        };
        KernelBackend.prototype.spaceToBatchND = function (x, blockShape, paddings) {
            return notYetImplemented('spaceToBatchND');
        };
        KernelBackend.prototype.resizeBilinear = function (x, newHeight, newWidth, alignCorners) {
            return notYetImplemented('resizeBilinear');
        };
        KernelBackend.prototype.resizeBilinearBackprop = function (dy, x, alignCorners) {
            return notYetImplemented('resizeBilinearBackprop');
        };
        KernelBackend.prototype.resizeNearestNeighbor = function (x, newHEight, newWidth, alignCorners) {
            return notYetImplemented('resizeNearestNeighbor');
        };
        KernelBackend.prototype.resizeNearestNeighborBackprop = function (dy, x, alignCorners) {
            return notYetImplemented('resizeNearestNeighborBackprop');
        };
        KernelBackend.prototype.batchNormalization = function (x, mean, variance, varianceEpsilon, scale, offset) {
            return notYetImplemented('batchNormalization');
        };
        KernelBackend.prototype.localResponseNormalization4D = function (x, radius, bias, alpha, beta) {
            return notYetImplemented('localResponseNormalization4D');
        };
        KernelBackend.prototype.LRNGrad = function (dy, inputImage, outputImage, radius, bias, alpha, beta) {
            return notYetImplemented('LRNGrad');
        };
        KernelBackend.prototype.multinomial = function (logits, normalized, numSamples, seed) {
            return notYetImplemented('multinomial');
        };
        KernelBackend.prototype.oneHot = function (indices, depth, onValue, offValue) {
            return notYetImplemented('oneHot');
        };
        KernelBackend.prototype.cumsum = function (x, axis, exclusive, reverse) {
            return notYetImplemented('cumsum');
        };
        KernelBackend.prototype.nonMaxSuppression = function (boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
            return notYetImplemented('nonMaxSuppression');
        };
        KernelBackend.prototype.fft = function (x) {
            return notYetImplemented('fft');
        };
        KernelBackend.prototype.ifft = function (x) {
            return notYetImplemented('ifft');
        };
        KernelBackend.prototype.complex = function (real, imag) {
            return notYetImplemented('complex');
        };
        KernelBackend.prototype.real = function (input) {
            return notYetImplemented('real');
        };
        KernelBackend.prototype.imag = function (input) {
            return notYetImplemented('imag');
        };
        KernelBackend.prototype.cropAndResize = function (image, boxes, boxIndex, cropSize, method, extrapolationValue) {
            return notYetImplemented('cropAndResize');
        };
        KernelBackend.prototype.depthToSpace = function (x, blockSize, dataFormat) {
            return notYetImplemented('depthToSpace');
        };
        // Aligns with the "SplitV" kernel in TensorFlow.
        KernelBackend.prototype.split = function (value, sizeSplits, axis) {
            return notYetImplemented('split');
        };
        KernelBackend.prototype.sparseToDense = function (sparseIndices, sparseValues, outputShape, defaultValue) {
            return notYetImplemented('sparseToDense');
        };
        KernelBackend.prototype.diag = function (x) {
            return notYetImplemented('diag');
        };
        KernelBackend.prototype.fill = function (shape, value, dtype) {
            return notYetImplemented('fill');
        };
        KernelBackend.prototype.onesLike = function (x) {
            return notYetImplemented('onesLike');
        };
        KernelBackend.prototype.zerosLike = function (x) {
            return notYetImplemented('zerosLike');
        };
        KernelBackend.prototype.linspace = function (start, stop, num) {
            return notYetImplemented('linspace');
        };
        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. " +
            "Did you forget to import the kernel?");
    }

    /**
     * @license
     * Copyright 2017 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 dimensions in the input shape that are broadcasted to
     * produce the provided output shape.
     *
     * The returned dimensions are 0-indexed and sorted. An example:
     * inShape = [4, 1, 3]
     * outShape = [5, 4, 3, 3]
     * result = [1]. Dimension 1 (2nd dimension of input) gets broadcasted 1 => 3.
     */
    function getBroadcastDims(inShape, outShape) {
        var inRank = inShape.length;
        var dims = [];
        for (var i = 0; i < inRank; i++) {
            var dim = inRank - 1 - i;
            var a = inShape[dim] || 1;
            var b = outShape[outShape.length - 1 - i] || 1;
            if (b > 1 && a === 1) {
                dims.unshift(dim);
            }
        }
        return dims;
    }
    /**
     * Returns the axes in the output space that should be reduced to produce
     * the input space.
     */
    function getReductionAxes(inShape, outShape) {
        var result = [];
        for (var i = 0; i < outShape.length; i++) {
            var inDim = inShape[inShape.length - i - 1];
            var outAxis = outShape.length - i - 1;
            var outDim = outShape[outAxis];
            if (inDim == null || (inDim === 1 && outDim > 1)) {
                result.unshift(outAxis);
            }
        }
        return result;
    }
    function assertAndGetBroadcastShape(shapeA, shapeB) {
        var result = [];
        var l = Math.max(shapeA.length, shapeB.length);
        for (var i = 0; i < l; i++) {
            var a = shapeA[shapeA.length - i - 1];
            if (a == null) {
                a = 1;
            }
            var b = shapeB[shapeB.length - i - 1];
            if (b == null) {
                b = 1;
            }
            if (a === 1) {
                result.unshift(b);
            }
            else if (b === 1) {
                result.unshift(a);
            }
            else if (a !== b) {
                var errMsg = "Operands could not be broadcast together with shapes " +
                    (shapeA + " and " + shapeB + ".");
                throw Error(errMsg);
            }
            else {
                result.unshift(a);
            }
        }
        return result;
    }

    /**
     * @license
     * Copyright 2017 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.
     * =============================================================================
     */
    function computePool2DInfo(inShape, filterSize, strides, dilations, pad, roundingMode, dataFormat) {
        if (dataFormat === void 0) { dataFormat = 'channelsLast'; }
        var _a = parseTupleParam(filterSize), filterHeight = _a[0], filterWidth = _a[1];
        var filterShape;
        if (dataFormat === 'channelsLast') {
            filterShape = [filterHeight, filterWidth, inShape[3], inShape[3]];
        }
        else if (dataFormat === 'channelsFirst') {
            filterShape = [filterHeight, filterWidth, inShape[1], inShape[1]];
        }
        else {
            throw new Error("Unknown dataFormat " + dataFormat);
        }
        return computeConv2DInfo(inShape, filterShape, strides, dilations, pad, roundingMode, false, dataFormat);
    }
    /**
     * Computes the information for a forward pass of a pooling3D operation.
     */
    function computePool3DInfo(inShape, filterSize, strides, dilations, pad, roundingMode, dataFormat) {
        if (dataFormat === void 0) { dataFormat = 'NDHWC'; }
        var _a = parse3TupleParam(filterSize), filterDepth = _a[0], filterHeight = _a[1], filterWidth = _a[2];
        var filterShape;
        var $dataFormat;
        if (dataFormat === 'NDHWC') {
            $dataFormat = 'channelsLast';
            filterShape =
                [filterDepth, filterHeight, filterWidth, inShape[4], inShape[4]];
        }
        else if (dataFormat === 'NCDHW') {
            $dataFormat = 'channelsFirst';
            filterShape =
                [filterDepth, filterHeight, filterWidth, inShape[1], inShape[1]];
        }
        else {
            throw new Error("Unknown dataFormat " + dataFormat);
        }
        return computeConv3DInfo(inShape, filterShape, strides, dilations, pad, false, $dataFormat, roundingMode);
    }
    /**
     * Computes the information for a forward pass of a convolution/pooling
     * operation.
     */
    function computeConv2DInfo(inShape, filterShape, strides, dilations, pad, roundingMode, depthwise, dataFormat) {
        if (depthwise === void 0) { depthwise = false; }
        if (dataFormat === void 0) { dataFormat = 'channelsLast'; }
        var _a = [-1, -1, -1, -1], batchSize = _a[0], inHeight = _a[1], inWidth = _a[2], inChannels = _a[3];
        if (dataFormat === 'channelsLast') {
            batchSize = inShape[0], inHeight = inShape[1], inWidth = inShape[2], inChannels = inShape[3];
        }
        else if (dataFormat === 'channelsFirst') {
            batchSize = inShape[0], inChannels = inShape[1], inHeight = inShape[2], inWidth = inShape[3];
        }
        else {
            throw new Error("Unknown dataFormat " + dataFormat);
        }
        var filterHeight = filterShape[0], filterWidth = filterShape[1], filterChannels = filterShape[3];
        var _b = parseTupleParam(strides), strideHeight = _b[0], strideWidth = _b[1];
        var _c = parseTupleParam(dilations), dilationHeight = _c[0], dilationWidth = _c[1];
        var effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight);
        var effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth);
        var _d = getPadAndOutInfo(pad, inHeight, inWidth, strideHeight, strideWidth, effectiveFilterHeight, effectiveFilterWidth, roundingMode), padInfo = _d.padInfo, outHeight = _d.outHeight, outWidth = _d.outWidth;
        var outChannels = depthwise ? filterChannels * inChannels : filterChannels;
        var outShape;
        if (dataFormat === 'channelsFirst') {
            outShape = [batchSize, outChannels, outHeight, outWidth];
        }
        else if (dataFormat === 'channelsLast') {
            outShape = [batchSize, outHeight, outWidth, outChannels];
        }
        return {
            batchSize: batchSize,
            dataFormat: dataFormat,
            inHeight: inHeight,
            inWidth: inWidth,
            inChannels: inChannels,
            outHeight: outHeight,
            outWidth: outWidth,
            outChannels: outChannels,
            padInfo: padInfo,
            strideHeight: strideHeight,
            strideWidth: strideWidth,
            filterHeight: filterHeight,
            filterWidth: filterWidth,
            effectiveFilterHeight: effectiveFilterHeight,
            effectiveFilterWidth: effectiveFilterWidth,
            dilationHeight: dilationHeight,
            dilationWidth: dilationWidth,
            inShape: inShape,
            outShape: outShape,
            filterShape: filterShape
        };
    }
    /**
     * Computes the information for a forward pass of a 3D convolution/pooling
     * operation.
     */
    function computeConv3DInfo(inShape, filterShape, strides, dilations, pad, depthwise, dataFormat, roundingMode) {
        if (depthwise === void 0) { depthwise = false; }
        if (dataFormat === void 0) { dataFormat = 'channelsLast'; }
        var _a = [-1, -1, -1, -1, -1], batchSize = _a[0], inDepth = _a[1], inHeight = _a[2], inWidth = _a[3], inChannels = _a[4];
        if (dataFormat === 'channelsLast') {
            batchSize = inShape[0], inDepth = inShape[1], inHeight = inShape[2], inWidth = inShape[3], inChannels = inShape[4];
        }
        else if (dataFormat === 'channelsFirst') {
            batchSize = inShape[0], inChannels = inShape[1], inDepth = inShape[2], inHeight = inShape[3], inWidth = inShape[4];
        }
        else {
            throw new Error("Unknown dataFormat " + dataFormat);
        }
        var filterDepth = filterShape[0], filterHeight = filterShape[1], filterWidth = filterShape[2], filterChannels = filterShape[4];
        var _b = parse3TupleParam(strides), strideDepth = _b[0], strideHeight = _b[1], strideWidth = _b[2];
        var _c = parse3TupleParam(dilations), dilationDepth = _c[0], dilationHeight = _c[1], dilationWidth = _c[2];
        var effectiveFilterDepth = getEffectiveFilterSize(filterDepth, dilationDepth);
        var effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight);
        var effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth);
        var _d = get3DPadAndOutInfo(pad, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth, effectiveFilterDepth, effectiveFilterHeight, effectiveFilterWidth, roundingMode), padInfo = _d.padInfo, outDepth = _d.outDepth, outHeight = _d.outHeight, outWidth = _d.outWidth;
        var outChannels = depthwise ? filterChannels * inChannels : filterChannels;
        var outShape;
        if (dataFormat === 'channelsFirst') {
            outShape = [batchSize, outChannels, outDepth, outHeight, outWidth];
        }
        else if (dataFormat === 'channelsLast') {
            outShape = [batchSize, outDepth, outHeight, outWidth, outChannels];
        }
        return {
            batchSize: batchSize,
            dataFormat: dataFormat,
            inDepth: inDepth,
            inHeight: inHeight,
            inWidth: inWidth,
            inChannels: inChannels,
            outDepth: outDepth,
            outHeight: outHeight,
            outWidth: outWidth,
            outChannels: outChannels,
            padInfo: padInfo,
            strideDepth: strideDepth,
            strideHeight: strideHeight,
            strideWidth: strideWidth,
            filterDepth: filterDepth,
            filterHeight: filterHeight,
            filterWidth: filterWidth,
            effectiveFilterDepth: effectiveFilterDepth,
            effectiveFilterHeight: effectiveFilterHeight,
            effectiveFilterWidth: effectiveFilterWidth,
            dilationDepth: dilationDepth,
            dilationHeight: dilationHeight,
            dilationWidth: dilationWidth,
            inShape: inShape,
            outShape: outShape,
            filterShape: filterShape
        };
    }
    function computeOutputShape2D(inShape, fieldSize, stride, zeroPad, roundingMode) {
        if (zeroPad == null) {
            zeroPad = computeDefaultPad(inShape, fieldSize, stride);
        }
        var inputRows = inShape[0];
        var inputCols = inShape[1];
        var outputRows = conditionalRound((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
        assert(isInt(outputRows), function () { return "The output # of rows (" + outputRows + ") must be an integer. " +
            "Change the stride and/or zero pad parameters"; });
        var outputCols = conditionalRound((inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
        assert(isInt(outputCols), function () { return "The output # of columns (" + outputCols + ") must be an integer. " +
            "Change the stride and/or zero pad parameters"; });
        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 = conditionalRound((inputDepth - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
        assert(isInt(outputDepths), function () { return "The output # of depths (" + outputDepths + ") must be an integer. " +
            "Change the stride and/or zero pad parameters"; });
        var outputRows = conditionalRound((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
        assert(isInt(outputRows), function () { return "The output # of rows (" + outputRows + ") must be an integer. " +
            "Change the stride and/or zero pad parameters"; });
        var outputCols = conditionalRound((inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
        assert(isInt(outputCols), function () { return "The output # of columns (" + outputCols + ") must be an integer. " +
            "Change the stride and/or zero pad parameters"; });
        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) {
        var padInfo;
        var outHeight;
        var outWidth;
        if (typeof pad === 'number') {
            var padType = (pad === 0) ? 'VALID' : 'NUMBER';
            padInfo = { top: pad, bottom: pad, left: pad, right: pad, type: padType };
            var outShape = computeOutputShape2D([inHeight, inWidth], filterHeight, strideHeight, pad, roundingMode);
            outHeight = outShape[0];
            outWidth = outShape[1];
        }
        else if (pad === 'same') {
            outHeight = Math.ceil(inHeight / strideHeight);
            outWidth = Math.ceil(inWidth / strideWidth);
            var padAlongHeight = Math.max(0, (outHeight - 1) * strideHeight + filterHeight - inHeight);
            var padAlongWidth = Math.max(0, (outWidth - 1) * strideWidth + filterWidth - inWidth);
            var top_1 = Math.floor(padAlongHeight / 2);
            var bottom = padAlongHeight - top_1;
            var left = Math.floor(padAlongWidth / 2);
            var right = padAlongWidth - left;
            padInfo = { top: top_1, bottom: bottom, left: left, right: right, type: 'SAME' };
        }
        else if (pad === 'valid') {
            padInfo = { top: 0, bottom: 0, left: 0, right: 0, type: 'VALID' };
            outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);
            outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);
        }
        else {
            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_2 = Math.floor(padAlongHeight / 2);
            var bottom = padAlongHeight - top_2;
            var left = Math.floor(padAlongWidth / 2);
            var right = padAlongWidth - left;
            padInfo = { top: top_2, 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
     */
    function conditionalRound(value, roundingMode) {
        if (!roundingMode) {
            return value;
        }
        switch (roundingMode) {
            case 'round':
                // used for Caffe Conv
                return Math.round(value);
            case 'ceil':
                // used for Caffe Pool
                return Math.ceil(value);
            case 'floor':
                return Math.floor(value);
            default:
                throw new Error("Unknown roundingMode " + roundingMode);
        }
    }
    function tupleValuesAreOne(param) {
        var _a = parseTupleParam(param), dimA = _a[0], dimB = _a[1], dimC = _a[2];
        return dimA === 1 && dimB === 1 && dimC === 1;
    }
    function eitherStridesOrDilationsAreOne(strides, dilations) {
        return tupleValuesAreOne(strides) || tupleValuesAreOne(dilations);
    }
    /**
     * Convert Conv2D dataFormat from 'NHWC'|'NCHW' to
     *    'channelsLast'|'channelsFirst'
     * @param dataFormat in 'NHWC'|'NCHW' mode
     * @return dataFormat in 'channelsLast'|'channelsFirst' mode
     * @throws unknown dataFormat
     */
    function convertConv2DDataFormat(dataFormat) {
        if (dataFormat === 'NHWC') {
            return 'channelsLast';
        }
        else if (dataFormat === 'NCHW') {
            return 'channelsFirst';
        }
        else {
            throw new Error("Unknown dataFormat " + dataFormat);
        }
    }

    /**
     * @license
     * Copyright 2018 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.
     * =============================================================================
     */
    function castTensor(x, dtype, backend) {
        if (dtype === 'complex64') {
            if (x.dtype === 'complex64') {
                return x.clone();
            }
            var zerosTensor = zeros(x.shape);
            var floatX = x.toFloat();
            var result = backend.complex(floatX, zerosTensor);
            zerosTensor.dispose();
            floatX.dispose();
            return result;
        }
        if (!hasEncodingLoss(x.dtype, dtype)) {
            // We don't change the underlying data, since we cast to higher
            // precision.
            return ENGINE.makeTensorFromDataId(x.dataId, x.shape, dtype);
        }
        if (x.dtype === 'complex64') {
            var real = backend.real(x);
            var result = real.cast(dtype);
            real.dispose();
            return result;
        }
        if (dtype === 'int32') {
            return backend.int(x);
        }
        else if (dtype === 'bool') {
            var zero = scalar(0, x.dtype);
            var result = backend.notEqual(x, zero);
            zero.dispose();
            return result;
        }
        else {
            throw new Error("Error in Cast: failed to cast " + x.dtype + " to " + dtype);
        }
    }
    function reshapeTensor(x, shape) {
        return ENGINE.makeTensorFromDataId(x.dataId, shape, x.dtype);
    }
    function linspaceImpl(start, stop, num) {
        var step = (stop - start) / (num - 1);
        var values = makeZerosTypedArray(num, 'float32');
        values[0] = start;
        for (var i = 1; i < values.length; i++) {
            values[i] = values[i - 1] + step;
        }
        return tensor1d(values, 'float32');
    }

    var backend_util = /*#__PURE__*/Object.freeze({
        castTensor: castTensor,
        reshapeTensor: reshapeTensor,
        linspaceImpl: linspaceImpl,
        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,
        computePool2DInfo: computePool2DInfo,
        computePool3DInfo: computePool3DInfo,
        computeConv2DInfo: computeConv2DInfo,
        computeConv3DInfo: computeConv3DInfo,
        computeDefaultPad: computeDefaultPad,
        tupleValuesAreOne: tupleValuesAreOne,
        eitherStridesOrDilationsAreOne: eitherStridesOrDilationsAreOne,
        convertConv2DDataFormat: convertConv2DDataFormat,
        PARALLELIZE_THRESHOLD: PARALLELIZE_THRESHOLD,
        computeOptimalWindowSize: computeOptimalWindowSize
    });

    /**
     * @license
     * Copyright 2018 Google LLC. All Rights Reserved.
     * Licensed under the Apache License, Version 2.0 (the "License");
     * you may not use this file except in compliance with the License.
     * You may obtain a copy of the License at
     *
     * http://www.apache.org/licenses/LICENSE-2.0
     *
     * Unless required by applicable law or agreed to in writing, software
     * distributed under the License is distributed on an "AS IS" BASIS,
     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
     * See the License for the specific language governing permissions and
     * limitations under the License.
     * =============================================================================
     */
    /**
     * Merges real and imaginary Float32Arrays into a single complex Float32Array.
     *
     * The memory layout is interleaved as follows:
     * real: [r0, r1, r2]
     * imag: [i0, i1, i2]
     * complex: [r0, i0, r1, i1, r2, i2]
     *
     * This is the inverse of splitRealAndImagArrays.
     *
     * @param real The real values of the complex tensor values.
     * @param imag The imag values of the complex tensor values.
     * @returns A complex tensor as a Float32Array with merged values.
     */
    function mergeRealAndImagArrays(real, imag) {
        if (real.length !== imag.length) {
            throw new Error("Cannot merge real and imag arrays of different lengths. real:" +
                (real.length + ", imag: " + imag.length + "."));
        }
        var result = new Float32Array(real.length * 2);
        for (var i = 0; i < result.length; i += 2) {
            result[i] = real[i / 2];
            result[i + 1] = imag[i / 2];
        }
        return result;
    }
    /**
     * Splits a complex Float32Array into real and imag parts.
     *
     * The memory layout is interleaved as follows:
     * complex: [r0, i0, r1, i1, r2, i2]
     * real: [r0, r1, r2]
     * imag: [i0, i1, i2]
     *
     * This is the inverse of mergeRealAndImagArrays.
     *
     * @param complex The complex tensor values.
     * @returns An object with real and imag Float32Array components of the complex
     *     tensor.
     */
    function splitRealAndImagArrays(complex) {
        var real = new Float32Array(complex.length / 2);
        var imag = new Float32Array(complex.length / 2);
        for (var i = 0; i < complex.length; i += 2) {
            real[i / 2] = complex[i];
            imag[i / 2] = complex[i + 1];
        }
        return { real: real, imag: imag };
    }
    /**
     * Extracts even indexed complex values in the given array.
     * @param complex The complex tensor values
     */
    function complexWithEvenIndex(complex) {
        var len = Math.ceil(complex.length / 4);
        var real = new Float32Array(len);
        var imag = new Float32Array(len);
        for (var i = 0; i < complex.length; i += 4) {
            real[Math.floor(i / 4)] = complex[i];
            imag[Math.floor(i / 4)] = complex[i + 1];
        }
        return { real: real, imag: imag };
    }
    /**
     * Extracts odd indexed comple values in the given array.
     * @param complex The complex tensor values
     */
    function complexWithOddIndex(complex) {
        var len = Math.floor(complex.length / 4);
        var real = new Float32Array(len);
        var imag = new Float32Array(len);
        for (var i = 2; i < complex.length; i += 4) {
            real[Math.floor(i / 4)] = complex[i];
            imag[Math.floor(i / 4)] = complex[i + 1];
        }
        return { real: real, imag: imag };
    }
    /**
     * Get the map representing a complex value in the given array.
     * @param complex The complex tensor values.
     * @param index An index of the target complex value.
     */
    function getComplexWithIndex(complex, index) {
        var real = complex[index * 2];
        var imag = complex[index * 2 + 1];
        return { real: real, imag: imag };
    }
    /**
     * Insert a given complex value into the TypedArray.
     * @param data The array in which the complex value is inserted.
     * @param c The complex value to be inserted.
     * @param index An index of the target complex value.
     */
    function assignToTypedArray(data, real, imag, index) {
        data[index * 2] = real;
        data[index * 2 + 1] = imag;
    }
    /**
     * Make the list of exponent terms used by FFT.
     */
    function exponents(n, inverse) {
        var real = new Float32Array(n / 2);
        var imag = new Float32Array(n / 2);
        for (var i = 0; i < Math.ceil(n / 2); i++) {
            var x = (inverse ? 2 : -2) * Math.PI * (i / n);
            real[i] = Math.cos(x);
            imag[i] = Math.sin(x);
        }
        return { real: real, imag: imag };
    }
    /**
     * Make the exponent term used by FFT.
     */
    function exponent(k, n, inverse) {
        var x = (inverse ? 2 : -2) * Math.PI * (k / n);
        var real = Math.cos(x);
        var imag = Math.sin(x);
        return { real: real, imag: imag };
    }

    /**
     * @license
     * Copyright 2019 Google LLC. All Rights Reserved.
     * Licensed under the Apache License, Version 2.0 (the "License");
     * you may not use this file except in compliance with the License.
     * You may obtain a copy of the License at
     *
     * http://www.apache.org/licenses/LICENSE-2.0
     *
     * Unless required by applicable law or agreed to in writing, software
     * distributed under the License is distributed on an "AS IS" BASIS,
     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
     * See the License for the specific language governing permissions and
     * limitations under the License.
     * =============================================================================
     */
    /**
     * Inserts a value into a sorted array. This method allows duplicate, meaning it
     * allows inserting duplicate value, in which case, the element will be inserted
     * at the lowest index of the value.
     * @param arr The array to modify.
     * @param element The element to insert.
     * @param comparator Optional. If no comparator is specified, elements are
     * compared using array_util.defaultComparator, which is suitable for Strings
     * and Numbers in ascending arrays. If the array contains multiple instances of
     * the target value, the left-most instance will be returned. To provide a
     * comparator, it should take 2 arguments to compare and return a negative,
     * zero, or a positive number.
     */
    function binaryInsert(arr, element, comparator) {
        var index = binarySearch(arr, element, comparator);
        var insertionPoint = index < 0 ? -(index + 1) : index;
        arr.splice(insertionPoint, 0, element);
    }
    /**
     * Searches the array for the target using binary search, returns the index
     * of the found element, or position to insert if element not found. If no
     * comparator is specified, elements are compared using array_
     * util.defaultComparator, which is suitable for Strings and Numbers in
     * ascending arrays. If the array contains multiple instances of the target
     * value, the left-most instance will be returned.
     * @param arr The array to be searched in.
     * @param target The target to be searched for.
     * @param comparator Should take 2 arguments to compare and return a negative,
     *    zero, or a positive number.
     * @return Lowest index of the target value if found, otherwise the insertion
     *    point where the target should be inserted, in the form of
     *    (-insertionPoint - 1).
     */
    function binarySearch(arr, target, comparator) {
        return binarySearch_(arr, target, comparator || defaultComparator);
    }
    /**
     * Compares its two arguments for order.
     * @param a The first element to be compared.
     * @param b The second element to be compared.
     * @return A negative number, zero, or a positive number as the first
     *     argument is less than, equal to, or greater than the second.
     */
    function defaultComparator(a, b) {
        return a > b ? 1 : a < b ? -1 : 0;
    }
    function binarySearch_(arr, target, comparator) {
        var left = 0;
        var right = arr.length;
        var middle = 0;
        var found = false;
        while (left < right) {
            middle = left + ((right - left) >>> 1);
            var compareResult = comparator(target, arr[middle]);
            if (compareResult > 0) {
                left = middle + 1;
            }
            else {
                right = middle;
                // If compareResult is 0, the value is found. We record it is found,
                // and then keep looking because there may be duplicate.
                found = !compareResult;
            }
        }
        return found ? left : -left - 1;
    }

    /**
     * @license
     * Copyright 2018 Google LLC. All Rights Reserved.
     * Licensed under the Apache License, Version 2.0 (the "License");
     * you may not use this file except in compliance with the License.
     * You may obtain a copy of the License at
     *
     * http://www.apache.org/licenses/LICENSE-2.0
     *
     * Unless required by applicable law or agreed to in writing, software
     * distributed under the License is distributed on an "AS IS" BASIS,
     * WITHOUT 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 nonMaxSuppressionV3(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
        var dummySoftNmsSigma = 0.0;
        return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, dummySoftNmsSigma)
            .selectedIndices;
    }
    function nonMaxSuppressionV5(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {
        // For NonMaxSuppressionV5Op, we always return a second output holding
        // corresponding scores.
        var returnScoresTensor = true;
        var result = nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, returnScoresTensor);
        result.numValidOutputs.dispose();
        return {
            selectedIndices: result.selectedIndices,
            selectedScores: result.selectedScores
        };
    }
    function nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, returnScoresTensor, padToMaxOutputSize) {
        if (returnScoresTensor === void 0) { returnScoresTensor = false; }
        if (padToMaxOutputSize === void 0) { padToMaxOutputSize = 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 = Array.from(scores)
            .map(function (score, boxIndex) { return ({ score: score, boxIndex: boxIndex, suppressBeginIndex: 0 }); })
            .filter(function (c) { return c.score > scoreThreshold; })
            .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 numValidOutputs = selectedIndices.length;
        if (padToMaxOutputSize) {
            selectedIndices.fill(0, numValidOutputs);
            selectedScores.fill(0.0, numValidOutputs);
        }
        return {
            selectedIndices: tensor1d(selectedIndices, 'int32'),
            selectedScores: tensor1d(selectedScores, 'float32'),
            numValidOutputs: scalar(numValidOutputs, 'int32')
        };
    }
    function intersectionOverUnion(boxes, i, j) {
        var iCoord = boxes.subarray(i * 4, i * 4 + 4);
        var jCoord = boxes.subarray(j * 4, j * 4 + 4);
        var yminI = Math.min(iCoord[0], iCoord[2]);
        var xminI = Math.min(iCoord[1], iCoord[3]);
        var ymaxI = Math.max(iCoord[0], iCoord[2]);
        var xmaxI = Math.max(iCoord[1], iCoord[3]);
        var yminJ = Math.min(jCoord[0], jCoord[2]);
        var xminJ = Math.min(jCoord[1], jCoord[3]);
        var ymaxJ = Math.max(jCoord[0], jCoord[2]);
        var xmaxJ = Math.max(jCoord[1], jCoord[3]);
        var areaI = (ymaxI - yminI) * (xmaxI - xminI);
        var areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);
        if (areaI <= 0 || areaJ <= 0) {
            return 0.0;
        }
        var intersectionYmin = Math.max(yminI, yminJ);
        var intersectionXmin = Math.max(xminI, xminJ);
        var intersectionYmax = Math.min(ymaxI, ymaxJ);
        var intersectionXmax = Math.min(xmaxI, xmaxJ);
        var intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0) *
            Math.max(intersectionXmax - intersectionXmin, 0.0);
        return intersectionArea / (areaI + areaJ - intersectionArea);
    }
    // A Gaussian penalty function, this method always returns values in [0, 1].
    // The weight is a function of similarity, the more overlap two boxes are, the
    // smaller the weight is, meaning highly overlapping boxe will be significantly
    // penalized. On the other hand, a non-overlapping box will not be penalized.
    function suppressWeight(iouThreshold, scale, iou) {
        var weight = Math.exp(scale * iou * iou);
        return iou <= iouThreshold ? weight : 0.0;
    }
    function ascendingComparator(c1, c2) {
        // For objects with same scores, we make the object with the larger index go
        // first. In an array that pops from the end, this means that the object with
        // the smaller index will be popped first. This ensures the same output as
        // the TensorFlow python version.
        return (c1.score - c2.score) ||
            ((c1.score === c2.score) && (c2.boxIndex - c1.boxIndex));
    }

    /**
     * @license
     * Copyright 2018 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.
     * =============================================================================
     */
    /** Shared implementation of the split kernel across WebGL and CPU. */
    function split$1(x, sizeSplits, axis) {
        var begin = new Array(x.rank).fill(0);
        var size = x.shape.slice();
        return sizeSplits.map(function (s) {
            size[axis] = s;
            var slice = x.slice(begin, size);
            begin[axis] += s;
            return slice;
        });
    }

    /**
     * @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 tile$1(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 = 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.toTensor();
    }

    /**
     * @license
     * Copyright 2018 Google LLC. All Rights Reserved.
     * Licensed under the Apache License, Version 2.0 (the "License");
     * you may not use this file except in compliance with the License.
     * You may obtain a copy of the License at
     *
     * http://www.apache.org/licenses/LICENSE-2.0
     *
     * Unless required by applicable law or agreed to in writing, software
     * distributed under the License is distributed on an "AS IS" BASIS,
     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
     * See the License for the specific language governing permissions and
     * limitations under the License.
     * =============================================================================
     */
    function topkImpl(x, xShape, xDtype, k, sorted) {
        // Reshape into a 2d tensor [batch, lastDim] and compute topk along lastDim.
        var lastDim = xShape[xShape.length - 1];
        var _a = [x.length / lastDim, lastDim], batch = _a[0], size = _a[1];
        var allTopKVals = getTypedArrayFromDType(xDtype, batch * k);
        var allTopKIndices = getTypedArrayFromDType('int32', batch * k);
        for (var b = 0; b < batch; b++) {
            var offset = b * size;
            var vals = x.subarray(offset, offset + size);
            var valAndInd = [];
            for (var i = 0; i < vals.length; i++) {
                valAndInd.push({ value: vals[i], index: i });
            }
            valAndInd.sort(function (a, b) { return b.value - a.value; });
            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;
            }
        }
        // Reshape back to the original input shape, except that the last
        // dimension is k.
        var outputShape = xShape.slice();
        outputShape[outputShape.length - 1] = k;
        return [
            tensor(allTopKVals, outputShape, xDtype),
            tensor(allTopKIndices, outputShape, '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 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();
    }

    /**
     * @license
     * Copyright 2019 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.
     * =============================================================================
     */
    var AddNProgram = /** @class */ (function () {
        function AddNProgram(outputShape, shapes) {
            this.outputShape = [];
            this.outputShape = outputShape;
            this.variableNames = shapes.map(function (_, i) { return "T" + i; });
            var snippets = [];
            // Get target elements from every input tensor.
            this.variableNames.forEach(function (variable) {
                snippets.push("float v" + variable + " = get" + variable + "AtOutCoords();");
            });
            // Calculate the sum of all elements.
            var operation = this.variableNames
                .map(function (variable) {
                return "v" + variable;
            })
                .join(' + ');
            this.userCode = "\n      void main() {\n        " + snippets.join('\n        ') + "\n\n        float result = " + operation + ";\n        setOutput(result);\n      }\n    ";
        }
        return AddNProgram;
    }());

    /**
     * @license
     * Copyright 2019 Google 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.
     * =============================================================================
     */
    var AddNPackedProgram = /** @class */ (function () {
        function AddNPackedProgram(outputShape, shapes) {
            this.outputShape = [];
            this.packedInputs = true;
            this.packedOutput = true;
            this.outputShape = outputShape;
            this.variableNames = shapes.map(function (_, i) { return "T" + i; });
            var snippets = [];
            // Get target elements from every input tensor.
            this.variableNames.forEach(function (variable) {
                snippets.push("vec4 v" + variable + " = get" + variable + "AtOutCoords();");
            });
            // Calculate the sum of all elements.
            var operation = this.variableNames
                .map(function (variable) {
                return "v" + variable;
            })
                .join(' + ');
            this.userCode = "\n      void main() {\n        " + snippets.join('\n        ') + "\n\n        vec4 result = " + operation + ";\n        setOutput(result);\n      }\n    ";
        }
        return AddNPackedProgram;
    }());

    /**
     * @license
     * Copyright 2017 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.
     * =============================================================================
     */
    var ArgMinMaxProgram = /** @class */ (function () {
        function ArgMinMaxProgram(reduceInfo, op, firstPass) {
            this.variableNames = ['A'];
            var windowSize = reduceInfo.windowSize;
            var batchSize = reduceInfo.batchSize;
            var inSize = reduceInfo.inSize;
            var outSize = Math.ceil(inSize / windowSize);
            if (!firstPass) {
                this.variableNames.push('bestIndicesA');
            }
            this.outputShape = [batchSize, outSize];
            var compOp = (op === 'max') ? '>' : '<';
            var indexSnippet = firstPass ?
                'inOffset + i;' :
                'round(getBestIndicesA(batch, inOffset + i));';
            this.userCode = "\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * " + windowSize + ";\n\n        int bestIndex = inOffset;\n        float bestValue = getA(batch, bestIndex);\n\n        for (int i = 0; i < " + windowSize + "; i++) {\n          int inIdx = " + indexSnippet + ";\n          float candidate = getA(batch, inIdx);\n          if (candidate " + compOp + " bestValue) {\n            bestValue = candidate;\n            bestIndex = inIdx;\n          }\n        }\n        setOutput(float(bestIndex));\n      }\n    ";
        }
        return ArgMinMaxProgram;
    }());

    /**
     * @license
     * Copyright 2018 Google LLC. All Rights Reserved.
     * Licensed under the Apache License, Version 2.0 (the "License");
     * you may not use this file except in compliance with the License.
     * You may obtain a copy of the License at
     *
     * http://www.apache.org/licenses/LICENSE-2.0
     *
     * Unless required by applicable law or agreed to in writing, software
     * distributed under the License is distributed on an "AS IS" BASIS,
     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
     * See the License for the specific language governing permissions and
     * limitations under the License.
     * =============================================================================
     */
    function getVecChannels(name, rank) {
        return ['x', 'y', 'z', 'w', 'u', 'v'].slice(0, rank).map(function (d) { return name + "." + d; });
    }
    function getChannels(name, rank) {
        if (rank === 1) {
            return [name];
        }
        return getVecChannels(name, rank);
    }
    function getSourceCoords(rank, dims) {
        if (rank === 1) {
            return 'rc';
        }
        var coords = '';
        for (var i = 0; i < rank; i++) {
            coords += dims[i];
            if (i < rank - 1) {
                coords += ',';
            }
        }
        return coords;
    }

    /**
     * @license
     * Copyright 2018 Google 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.
     * =============================================================================
     */
    function getGlslDifferences() {
        var version;
        var attribute;
        var varyingVs;
        var varyingFs;
        var texture2D;
        var output;
        var defineOutput;
        var defineSpecialNaN;
        var defineSpecialInf;
        var defineRound;
        if (env().getNumber('WEBGL_VERSION') === 2) {
            version = '#version 300 es';
            attribute = 'in';
            varyingVs = 'out';
            varyingFs = 'in';
            texture2D = 'texture';
            output = 'outputColor';
            defineOutput = 'out vec4 outputColor;';
            // Use custom isnan definition to work across differences between
            // implementations on various platforms. While this should happen in ANGLE
            // we still see differences between android and windows (on chrome) when
            // using isnan directly.
            defineSpecialNaN = "\n      bool isnan_custom(float val) {\n        return (val > 0.0 || val < 0.0) ? false : val != 0.0;\n      }\n\n      bvec4 isnan_custom(vec4 val) {\n        return bvec4(isnan_custom(val.x),\n          isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));\n      }\n\n      #define isnan(value) isnan_custom(value)\n    ";
            // In webgl 2 we do not need to specify a custom isinf so there is no
            // need for a special INFINITY constant.
            defineSpecialInf = "";
            defineRound = "\n      #define round(value) newRound(value)\n      int newRound(float value) {\n        return int(floor(value + 0.5));\n      }\n\n      ivec4 newRound(vec4 value) {\n        return ivec4(floor(value + vec4(0.5)));\n      }\n    ";
        }
        else {
            version = '';
            attribute = 'attribute';
            varyingVs = 'varying';
            varyingFs = 'varying';
            texture2D = 'texture2D';
            output = 'gl_FragColor';
            defineOutput = '';
            // WebGL1 has no built in isnan so we define one here.
            defineSpecialNaN = "\n      #define isnan(value) isnan_custom(value)\n      bool isnan_custom(float val) {\n        return (val > 0. || val < 1. || val == 0.) ? false : true;\n      }\n      bvec4 isnan_custom(vec4 val) {\n        return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));\n      }\n    ";
            defineSpecialInf = "\n      uniform float INFINITY;\n\n      bool isinf(float val) {\n        return abs(val) == INFINITY;\n      }\n      bvec4 isinf(vec4 val) {\n        return equal(abs(val), vec4(INFINITY));\n      }\n    ";
            defineRound = "\n      int round(float value) {\n        return int(floor(value + 0.5));\n      }\n\n      ivec4 round(vec4 value) {\n        return ivec4(floor(value + vec4(0.5)));\n      }\n    ";
        }
        return {
            version: version,
            attribute: attribute,
            varyingVs: varyingVs,
            varyingFs: varyingFs,
            texture2D: texture2D,
            output: output,
            defineOutput: defineOutput,
            defineSpecialNaN: defineSpecialNaN,
            defineSpecialInf: defineSpecialInf,
            defineRound: defineRound
        };
    }

    /**
     * @license
     * Copyright 2018 Google LLC. All Rights Reserved.
     * Licensed under the Apache License, Version 2.0 (the "License");
     * you may not use this file except in compliance with the License.
     * You may obtain a copy of the License at
     *
     * http://www.apache.org/licenses/LICENSE-2.0
     *
     * Unless required by applicable law or agreed to in writing, software
     * distributed under the License is distributed on an "AS IS" BASIS,
     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
     * See the License for the specific language governing permissions and
     * limitations under the License.
     * =============================================================================
     */
    /**
     * Produces GLSL code that derives logical coordinates from a flat
     * index. The code performs integer division with each stride and decrements
     * the index until the index equals the final dimension coordinate.
     */
    function getLogicalCoordinatesFromFlatIndex(coords, shape, index) {
        if (index === void 0) { index = 'index'; }
        var strides = computeStrides(shape);
        return strides
            .map(function (stride, i) {
            var line1 = "int " + coords[i] + " = " + index + " / " + stride;
            var line2 = i === strides.length - 1 ?
                "int " + coords[i + 1] + " = " + index + " - " + coords[i] + " * " + stride :
                "index -= " + coords[i] + " * " + stride;
            return line1 + "; " + line2 + ";";
        })
            .join('');
    }
    /**
     * Produces GLSL that computes the flat index from 3D coordinates.
     */
    function getFlatIndexFrom3D(shape) {
        var strides = computeStrides(shape).map(function (d) { return d.toString(); });
        return "\n  int getFlatIndex(ivec3 coords) {\n    return coords.x * " + strides[0] + " + coords.y * " + strides[1] + " + coords.z;\n  }\n";
    }
    var ENCODE_FLOAT_SNIPPET = "\n  const float FLOAT_MAX = 1.70141184e38;\n  const float FLOAT_MIN = 1.17549435e-38;\n\n  lowp vec4 encode_float(highp float v) {\n    if (isnan(v)) {\n      return vec4(255, 255, 255, 255);\n    }\n\n    highp float av = abs(v);\n\n    if(av < FLOAT_MIN) {\n      return vec4(0.0, 0.0, 0.0, 0.0);\n    } else if(v > FLOAT_MAX) {\n      return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;\n    } else if(v < -FLOAT_MAX) {\n      return vec4(0.0, 0.0,  128.0, 255.0) / 255.0;\n    }\n\n    highp vec4 c = vec4(0,0,0,0);\n\n    highp float e = floor(log2(av));\n    highp float m = exp2(fract(log2(av))) - 1.0;\n\n    c[2] = floor(128.0 * m);\n    m -= c[2] / 128.0;\n    c[1] = floor(32768.0 * m);\n    m -= c[1] / 32768.0;\n    c[0] = floor(8388608.0 * m);\n\n    highp float ebias = e + 127.0;\n    c[3] = floor(ebias / 2.0);\n    ebias -= c[3] * 2.0;\n    c[2] += floor(ebias) * 128.0;\n\n    c[3] += 128.0 * step(0.0, -v);\n\n    return c / 255.0;\n  }\n";

    /**
     * @license
     * Copyright 2017 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.
     * =============================================================================
     */
    function makeShader(inputsInfo, outputShape, userCode, usesPackedTextures) {
        var prefixSnippets = [];
        inputsInfo.forEach(function (x) {
            var size = sizeFromShape(x.shapeInfo.logicalShape);
            // Snippet when we decided to upload the values as uniform.
            if (x.shapeInfo.isUniform) {
                prefixSnippets.push("uniform float " + x.name + (size > 1 ? "[" + size + "]" : '') + ";");
            }
            else {
                prefixSnippets.push("uniform sampler2D " + x.name + ";");
                prefixSnippets.push("uniform int offset" + x.name + ";");
            }
        });
        var inputPrefixSnippet = prefixSnippets.join('\n');
        var inputSamplingSnippet = inputsInfo
            .map(function (x) { return getInputSamplingSnippet(x, outputShape, usesPackedTextures); })
            .join('\n');
        var outTexShape = outputShape.texShape;
        var glsl = getGlslDifferences();
        var floatTextureSampleSnippet = getFloatTextureSampleSnippet(glsl);
        var outputSamplingSnippet;
        var floatTextureSetOutputSnippet;
        var shaderPrefix = getShaderPrefix(glsl);
        if (outputShape.isPacked) {
            outputSamplingSnippet =
                getPackedOutputSamplingSnippet(outputShape.logicalShape, outTexShape);
            floatTextureSetOutputSnippet = getFloatTextureSetRGBASnippet(glsl);
        }
        else {
            outputSamplingSnippet =
                getOutputSamplingSnippet(outputShape.logicalShape, outTexShape);
            floatTextureSetOutputSnippet = getFloatTextureSetRSnippet(glsl);
        }
        if (usesPackedTextures) {
            shaderPrefix += SHADER_PACKED_PREFIX;
        }
        var source = [
            shaderPrefix, floatTextureSampleSnippet, floatTextureSetOutputSnippet,
            inputPrefixSnippet, outputSamplingSnippet, inputSamplingSnippet, userCode
        ].join('\n');
        return source;
    }
    function getSamplerFromInInfo(inInfo) {
        var shape = inInfo.shapeInfo.logicalShape;
        switch (shape.length) {
            case 0:
                return getSamplerScalar(inInfo);
            case 1:
                return getSampler1D(inInfo);
            case 2:
                return getSampler2D(inInfo);
            case 3:
                return getSampler3D(inInfo);
            case 4:
                return getSampler4D(inInfo);
            case 5:
                return getSampler5D(inInfo);
            case 6:
                return getSampler6D(inInfo);
            default:
                throw new Error(shape.length + "-D input sampling" +
                    " is not yet supported");
        }
    }
    function getPackedSamplerFromInInfo(inInfo) {
        var shape = inInfo.shapeInfo.logicalShape;
        switch (shape.length) {
            case 0:
                return getPackedSamplerScalar(inInfo);
            case 1:
                return getPackedSampler1D(inInfo);
            case 2:
                return getPackedSampler2D(inInfo);
            case 3:
                return getPackedSampler3D(inInfo);
            default:
                return getPackedSamplerND(inInfo);
        }
    }
    function getInputSamplingSnippet(inInfo, outShapeInfo, usesPackedTextures) {
        if (usesPackedTextures === void 0) { usesPackedTextures = false; }
        var res = '';
        if (usesPackedTextures) {
            res += getPackedSamplerFromInInfo(inInfo);
        }
        else {
            res += getSamplerFromInInfo(inInfo);
        }
        var inShape = inInfo.shapeInfo.logicalShape;
        var outShape = outShapeInfo.logicalShape;
        if (inShape.length <= outShape.length) {
            if (usesPackedTextures) {
                res += getPackedSamplerAtOutputCoords(inInfo, outShapeInfo);
            }
            else {
                res += getSamplerAtOutputCoords(inInfo, outShapeInfo);
            }
        }
        return res;
    }
    function getPackedOutputSamplingSnippet(outShape, outTexShape) {
        switch (outShape.length) {
            case 0:
                return getOutputScalarCoords();
            case 1:
                return getOutputPacked1DCoords(outShape, outTexShape);
            case 2:
                return getOutputPacked2DCoords(outShape, outTexShape);
            case 3:
                return getOutputPacked3DCoords(outShape, outTexShape);
            default:
                return getOutputPackedNDCoords(outShape, outTexShape);
        }
    }
    function getOutputSamplingSnippet(outShape, outTexShape) {
        switch (outShape.length) {
            case 0:
                return getOutputScalarCoords();
            case 1:
                return getOutput1DCoords(outShape, outTexShape);
            case 2:
                return getOutput2DCoords(outShape, outTexShape);
            case 3:
                return getOutput3DCoords(outShape, outTexShape);
            case 4:
                return getOutput4DCoords(outShape, outTexShape);
            case 5:
                return getOutput5DCoords(outShape, outTexShape);
            case 6:
                return getOutput6DCoords(outShape, outTexShape);
            default:
                throw new Error(outShape.length + "-D output sampling is not yet supported");
        }
    }
    function getFloatTextureSampleSnippet(glsl) {
        return "\n    float sampleTexture(sampler2D textureSampler, vec2 uv) {\n      return " + glsl.texture2D + "(textureSampler, uv).r;\n    }\n  ";
    }
    function getFloatTextureSetRSnippet(glsl) {
        return "\n    void setOutput(float val) {\n      " + glsl.output + " = vec4(val, 0, 0, 0);\n    }\n  ";
    }
    function getFloatTextureSetRGBASnippet(glsl) {
        return "\n    void setOutput(vec4 val) {\n      " + glsl.output + " = val;\n    }\n  ";
    }
    function getShaderPrefix(glsl) {
        var SHADER_PREFIX = glsl.version + "\n    precision highp float;\n    precision highp int;\n    precision highp sampler2D;\n    " + glsl.varyingFs + " vec2 resultUV;\n    " + glsl.defineOutput + "\n    const vec2 halfCR = vec2(0.5, 0.5);\n\n    struct ivec5\n    {\n      int x;\n      int y;\n      int z;\n      int w;\n      int u;\n    };\n\n    struct ivec6\n    {\n      int x;\n      int y;\n      int z;\n      int w;\n      int u;\n      int v;\n    };\n\n    uniform float NAN;\n    " + glsl.defineSpecialNaN + "\n    " + glsl.defineSpecialInf + "\n    " + glsl.defineRound + "\n\n    int imod(int x, int y) {\n      return x - y * (x / y);\n    }\n\n    int idiv(int a, int b, float sign) {\n      int res = a / b;\n      int mod = imod(a, b);\n      if (sign < 0. && mod != 0) {\n        res -= 1;\n      }\n      return res;\n    }\n\n    //Based on the work of Dave Hoskins\n    //https://www.shadertoy.com/view/4djSRW\n    #define HASHSCALE1 443.8975\n    float random(float seed){\n      vec2 p = resultUV * seed;\n      vec3 p3  = fract(vec3(p.xyx) * HASHSCALE1);\n      p3 += dot(p3, p3.yzx + 19.19);\n      return fract((p3.x + p3.y) * p3.z);\n    }\n\n    " + SAMPLE_1D_SNIPPET + "\n    " + SAMPLE_2D_SNIPPET + "\n    " + SAMPLE_3D_SNIPPET + "\n  ";
        return SHADER_PREFIX;
    }
    var SAMPLE_1D_SNIPPET = "\nvec2 uvFromFlat(int texNumR, int texNumC, int index) {\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom1D(int texNumR, int texNumC, int index) {\n  int texelIndex = index / 2;\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n";
    var SAMPLE_2D_SNIPPET = "\nvec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,\n  int texNumC, int row, int col) {\n  int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n";
    var SAMPLE_3D_SNIPPET = "\nvec2 packedUVfrom3D(int texNumR, int texNumC,\n    int texelsInBatch, int texelsInLogicalRow, int b,\n    int row, int col) {\n  int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n";
    var SHADER_PACKED_PREFIX = "\n  float getChannel(vec4 frag, vec2 innerDims) {\n    vec2 modCoord = mod(innerDims, 2.);\n    return modCoord.x == 0. ?\n      (modCoord.y == 0. ? frag.r : frag.g) :\n      (modCoord.y == 0. ? frag.b : frag.a);\n  }\n  float getChannel(vec4 frag, int dim) {\n    float modCoord = mod(float(dim), 2.);\n    return modCoord == 0. ? frag.r : frag.g;\n  }\n";
    function getOutputScalarCoords() {
        return "\n    int getOutputCoords() {\n      return 0;\n    }\n  ";
    }
    function getOutputPacked1DCoords(shape, texShape) {
        var packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
        if (packedTexShape[0] === 1) {
            return "\n      int getOutputCoords() {\n        return 2 * int(resultUV.x * " + packedTexShape[1] + ".0);\n      }\n    ";
        }
        if (packedTexShape[1] === 1) {
            return "\n      int getOutputCoords() {\n        return 2 * int(resultUV.y * " + packedTexShape[0] + ".0);\n      }\n    ";
        }
        return "\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(" + packedTexShape[0] + ", " + packedTexShape[1] + "));\n      return 2 * (resTexRC.x * " + packedTexShape[1] + " + resTexRC.y);\n    }\n  ";
    }
    function getOutput1DCoords(shape, texShape) {
        if (texShape[0] === 1) {
            return "\n      int getOutputCoords() {\n        return int(resultUV.x * " + texShape[1] + ".0);\n      }\n    ";
        }
        if (texShape[1] === 1) {
            return "\n      int getOutputCoords() {\n        return int(resultUV.y * " + texShape[0] + ".0);\n      }\n    ";
        }
        return "\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(" + texShape[0] + ", " + texShape[1] + "));\n      return resTexRC.x * " + texShape[1] + " + resTexRC.y;\n    }\n  ";
    }
    function getOutputPacked3DCoords(shape, texShape) {
        var packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
        var texelsInLogicalRow = Math.ceil(shape[2] / 2);
        var texelsInBatch = texelsInLogicalRow * Math.ceil(shape[1] / 2);
        return "\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(" + packedTexShape[0] + ", " + packedTexShape[1] + "));\n      int index = resTexRC.x * " + packedTexShape[1] + " + resTexRC.y;\n\n      int b = index / " + texelsInBatch + ";\n      index -= b * " + texelsInBatch + ";\n\n      int r = 2 * (index / " + texelsInLogicalRow + ");\n      int c = imod(index, " + texelsInLogicalRow + ") * 2;\n\n      return ivec3(b, r, c);\n    }\n  ";
    }
    function getOutput3DCoords(shape, texShape) {
        var coordsFromIndexSnippet = getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd'], shape);
        return "\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(" + texShape[0] + ", " + texShape[1] + "));\n      int index = resTexRC.x * " + texShape[1] + " + resTexRC.y;\n      " + coordsFromIndexSnippet + "\n      return ivec3(r, c, d);\n    }\n  ";
    }
    function getOutputPackedNDCoords(shape, texShape) {
        var packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
        var texelsInLogicalRow = Math.ceil(shape[shape.length - 1] / 2);
        var texelsInBatch = texelsInLogicalRow * Math.ceil(shape[shape.length - 2] / 2);
        var texelsInBatchN = texelsInBatch;
        var batches = "";
        var coords = 'b, r, c';
        for (var b = 2; b < shape.length - 1; b++) {
            texelsInBatchN *= shape[shape.length - b - 1];
            batches = "\n      int b" + b + " = index / " + texelsInBatchN + ";\n      index -= b" + b + " * " + texelsInBatchN + ";\n    " + batches;
            coords = "b" + b + ", " + coords;
        }
        return "\n    ivec" + shape.length + " getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(" + packedTexShape[0] + ", " + packedTexShape[1] + "));\n      int index = resTexRC.x * " + packedTexShape[1] + " + resTexRC.y;\n\n      " + batches + "\n\n      int b = index / " + texelsInBatch + ";\n      index -= b * " + texelsInBatch + ";\n\n      int r = 2 * (index / " + texelsInLogicalRow + ");\n      int c = imod(index, " + texelsInLogicalRow + ") * 2;\n\n      return ivec" + shape.length + "(" + coords + ");\n    }\n  ";
    }
    function getOutput4DCoords(shape, texShape) {
        var coordsFromIndexSnippet = getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd', 'd2'], shape);
        return "\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(" + texShape[0] + ", " + texShape[1] + "));\n      int index = resTexRC.x * " + texShape[1] + " + resTexRC.y;\n      " + coordsFromIndexSnippet + "\n      return ivec4(r, c, d, d2);\n    }\n  ";
    }
    function getOutput5DCoords(shape, texShape) {
        var coordsFromIndexSnippet = getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd', 'd2', 'd3'], shape);
        return "\n    ivec5 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx * vec2(" + texShape[0] + ",\n                             " + texShape[1] + "));\n\n      int index = resTexRC.x * " + texShape[1] + " + resTexRC.y;\n\n      " + coordsFromIndexSnippet + "\n\n      ivec5 outShape = ivec5(r, c, d, d2, d3);\n      return outShape;\n    }\n  ";
    }
    function getOutput6DCoords(shape, texShape) {
        var coordsFromIndexSnippet = getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd', 'd2', 'd3', 'd4'], shape);
        return "\n    ivec6 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(" + texShape[0] + ", " + texShape[1] + "));\n      int index = resTexRC.x * " + texShape[1] + " + resTexRC.y;\n\n      " + coordsFromIndexSnippet + "\n\n      ivec6 result = ivec6(r, c, d, d2, d3, d4);\n      return result;\n    }\n  ";
    }
    function getOutputPacked2DCoords(shape, texShape) {
        var packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
        if (arraysEqual(shape, texShape)) {
            return "\n      ivec2 getOutputCoords() {\n        return 2 * ivec2(resultUV.yx * vec2(" + packedTexShape[0] + ", " + packedTexShape[1] + "));\n      }\n    ";
        }
        // texels needed to accommodate a logical row
        var texelsInLogicalRow = Math.ceil(shape[1] / 2);
        /**
         * getOutputCoords
         *
         * resTexRC: The rows and columns of the texels. If you move over one
         * texel to the right in the packed texture, you are moving over one column
         * (not two).
         *
         * index: The texel index
         */
        return "\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(" + packedTexShape[0] + ", " + packedTexShape[1] + "));\n\n      int index = resTexRC.x * " + packedTexShape[1] + " + resTexRC.y;\n      int r = 2 * (index / " + texelsInLogicalRow + ");\n      int c = imod(index, " + texelsInLogicalRow + ") * 2;\n\n      return ivec2(r, c);\n    }\n  ";
    }
    function getOutput2DCoords(shape, texShape) {
        if (arraysEqual(shape, texShape)) {
            return "\n      ivec2 getOutputCoords() {\n        return ivec2(resultUV.yx * vec2(" + texShape[0] + ", " + texShape[1] + "));\n      }\n    ";
        }
        if (shape[1] === 1) {
            return "\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(" + texShape[0] + ", " + texShape[1] + "));\n        int index = resTexRC.x * " + texShape[1] + " + resTexRC.y;\n        return ivec2(index, 0);\n      }\n    ";
        }
        if (shape[0] === 1) {
            return "\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(" + texShape[0] + ", " + texShape[1] + "));\n        int index = resTexRC.x * " + texShape[1] + " + resTexRC.y;\n        return ivec2(0, index);\n      }\n    ";
        }
        return "\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(" + texShape[0] + ", " + texShape[1] + "));\n      int index = resTexRC.x * " + texShape[1] + " + resTexRC.y;\n      int r = index / " + shape[1] + ";\n      int c = index - r * " + shape[1] + ";\n      return ivec2(r, c);\n    }\n  ";
    }
    function getFlatOffsetUniformName(texName) {
        return "offset" + texName;
    }
    function getPackedSamplerScalar(inputInfo) {
        var texName = inputInfo.name;
        var funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
        var glsl = getGlslDifferences();
        return "\n    vec4 " + funcName + "() {\n      return " + glsl.texture2D + "(" + texName + ", halfCR);\n    }\n  ";
    }
    function getSamplerScalar(inputInfo) {
        var texName = inputInfo.name;
        var funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
        if (inputInfo.shapeInfo.isUniform) {
            return "float " + funcName + "() {return " + texName + ";}";
        }
        var _a = inputInfo.shapeInfo.texShape, texNumR = _a[0], texNumC = _a[1];
        if (texNumR === 1 && texNumC === 1) {
            return "\n      float " + funcName + "() {\n        return sampleTexture(" + texName + ", halfCR);\n      }\n    ";
        }
        var _b = inputInfo.shapeInfo.texShape, tNumR = _b[0], tNumC = _b[1];
        var offset = getFlatOffsetUniformName(texName);
        return "\n    float " + funcName + "() {\n      vec2 uv = uvFromFlat(" + tNumR + ", " + tNumC + ", " + offset + ");\n      return sampleTexture(" + texName + ", uv);\n    }\n  ";
    }
    function getPackedSampler1D(inputInfo) {
        var texName = inputInfo.name;
        var funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
        var texShape = inputInfo.shapeInfo.texShape;
        var packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
        var glsl = getGlslDifferences();
        return "\n    vec4 " + funcName + "(int index) {\n      vec2 uv = packedUVfrom1D(\n        " + packedTexShape[0] + ", " + packedTexShape[1] + ", index);\n      return " + glsl.texture2D + "(" + texName + ", uv);\n    }\n  ";
    }
    function getSampler1D(inputInfo) {
        var texName = inputInfo.name;
        var funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
        if (inputInfo.shapeInfo.isUniform) {
            // Uniform arrays will be less than 65505 (no risk of float16 overflow).
            return "\n      float " + funcName + "(int index) {\n        " + getUniformSampler(inputInfo) + "\n      }\n    ";
        }
        var texShape = inputInfo.shapeInfo.texShape;
        var tNumR = texShape[0];
        var tNumC = texShape[1];
        if (tNumC === 1 && tNumR === 1) {
            return "\n      float " + funcName + "(int index) {\n        return sampleTexture(" + texName + ", halfCR);\n      }\n    ";
        }
        var offset = getFlatOffsetUniformName(texName);
        if (tNumC === 1) {
            return "\n      float " + funcName + "(int index) {\n        vec2 uv = vec2(0.5, (float(index + " + offset + ") + 0.5) / " + tNumR + ".0);\n        return sampleTexture(" + texName + ", uv);\n      }\n    ";
        }
        if (tNumR === 1) {
            return "\n      float " + funcName + "(int index) {\n        vec2 uv = vec2((float(index + " + offset + ") + 0.5) / " + tNumC + ".0, 0.5);\n        return sampleTexture(" + texName + ", uv);\n      }\n    ";
        }
        return "\n    float " + funcName + "(int index) {\n      vec2 uv = uvFromFlat(" + tNumR + ", " + tNumC + ", index + " + offset + ");\n      return sampleTexture(" + texName + ", uv);\n    }\n  ";
    }
    function getPackedSampler2D(inputInfo) {
        var shape = inputInfo.shapeInfo.logicalShape;
        var texName = inputInfo.name;
        var funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
        var texShape = inputInfo.shapeInfo.texShape;
        var texNumR = texShape[0];
        var texNumC = texShape[1];
        var glsl = getGlslDifferences();
        if (texShape != null && arraysEqual(shape, texShape)) {
            return "\n      vec4 " + funcName + "(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(" + texNumC + ".0, " + texNumR + ".0);\n\n        return " + glsl.texture2D + "(" + texName + ", uv);\n      }\n    ";
        }
        var packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
        var valuesPerRow = Math.ceil(shape[1] / 2);
        return "\n    vec4 " + funcName + "(int row, int col) {\n      vec2 uv = packedUVfrom2D(" + valuesPerRow + ", " + packedTexShape[0] + ", " + packedTexShape[1] + ", row, col);\n      return " + glsl.texture2D + "(" + texName + ", uv);\n    }\n  ";
    }
    function getSampler2D(inputInfo) {
        var shape = inputInfo.shapeInfo.logicalShape;
        var texName = inputInfo.name;
        var funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
        var texShape = inputInfo.shapeInfo.texShape;
        if (texShape != null && arraysEqual(shape, texShape)) {
            var texNumR_1 = texShape[0];
            var texNumC_1 = texShape[1];
            return "\n    float " + funcName + "(int row, int col) {\n      vec2 uv = (vec2(col, row) + halfCR) / vec2(" + texNumC_1 + ".0, " + texNumR_1 + ".0);\n      return sampleTexture(" + texName + ", uv);\n    }\n  ";
        }
        var _a = squeezeShape(shape), newShape = _a.newShape, keptDims = _a.keptDims;
        var squeezedShape = newShape;
        if (squeezedShape.length < shape.length) {
            var newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);
            var params = ['row', 'col'];
            return "\n      " + getSamplerFromInInfo(newInputInfo) + "\n      float " + funcName + "(int row, int col) {\n        return " + funcName + "(" + getSqueezedParams(params, keptDims) + ");\n      }\n    ";
        }
        if (inputInfo.shapeInfo.isUniform) {
            // Uniform arrays will be less than 65505 (no risk of float16 overflow).
            return "\n      float " + funcName + "(int row, int col) {\n        int index = round(dot(vec2(row, col), vec2(" + shape[1] + ", 1)));\n        " + getUniformSampler(inputInfo) + "\n      }\n    ";
        }
        var texNumR = texShape[0];
        var texNumC = texShape[1];
        var offset = getFlatOffsetUniformName(texName);
        if (texNumC === 1) {
            // index is used directly as physical (no risk of float16 overflow).
            return "\n    float " + funcName + "(int row, int col) {\n      float index = dot(vec3(row, col, " + offset + "), vec3(" + shape[1] + ", 1, 1));\n      vec2 uv = vec2(0.5, (index + 0.5) / " + texNumR + ".0);\n      return sampleTexture(" + texName + ", uv);\n    }\n  ";
        }
        if (texNumR === 1) {
            // index is used directly as physical (no risk of float16 overflow).
            return "\n    float " + funcName + "(int row, int col) {\n      float index = dot(vec3(row, col, " + offset + "), vec3(" + shape[1] + ", 1, 1));\n      vec2 uv = vec2((index + 0.5) / " + texNumC + ".0, 0.5);\n      return sampleTexture(" + texName + ", uv);\n    }\n  ";
        }
        return "\n  float " + funcName + "(int row, int col) {\n    // Explicitly use integer operations as dot() only works on floats.\n    int index = row * " + shape[1] + " + col + " + offset + ";\n    vec2 uv = uvFromFlat(" + texNumR + ", " + texNumC + ", index);\n    return sampleTexture(" + texName + ", uv);\n  }\n";
    }
    function getPackedSampler3D(inputInfo) {
        var shape = inputInfo.shapeInfo.logicalShape;
        var texName = inputInfo.name;
        var funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
        var texShape = inputInfo.shapeInfo.texShape;
        var packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
        if (shape[0] === 1) {
            var squeezedShape = shape.slice(1);
            var keptDims = [1, 2];
            var newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);
            var params = ['b', 'row', 'col'];
            return "\n        " + getPackedSamplerFromInInfo(newInputInfo) + "\n        vec4 " + funcName + "(int b, int row, int col) {\n          return " + funcName + "(" + getSqueezedParams(params, keptDims) + ");\n        }\n      ";
        }
        var texNumR = packedTexShape[0];
        var texNumC = packedTexShape[1];
        var valuesPerRow = Math.ceil(shape[2] / 2);
        var texelsInBatch = valuesPerRow * Math.ceil(shape[1] / 2);
        var glsl = getGlslDifferences();
        return "\n    vec4 " + funcName + "(int b, int row, int col) {\n      vec2 uv = packedUVfrom3D(\n        " + texNumR + ", " + texNumC + ", " + texelsInBatch + ", " + valuesPerRow + ", b, row, col);\n      return " + glsl.texture2D + "(" + texName + ", uv);\n    }\n  ";
    }
    function getSampler3D(inputInfo) {
        var shape = inputInfo.shapeInfo.logicalShape;
        var texName = inputInfo.name;
        var funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
        var stride0 = shape[1] * shape[2];
        var stride1 = shape[2];
        var _a = squeezeShape(shape), newShape = _a.newShape, keptDims = _a.keptDims;
        var squeezedShape = newShape;
        if (squeezedShape.length < shape.length) {
            var newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);
            var params = ['row', 'col', 'depth'];
            return "\n        " + getSamplerFromInInfo(newInputInfo) + "\n        float " + funcName + "(int row, int col, int depth) {\n          return " + funcName + "(" + getSqueezedParams(params, keptDims) + ");\n        }\n      ";
        }
        if (inputInfo.shapeInfo.isUniform) {
            // Uniform arrays will be less than 65505 (no risk of float16 overflow).
            return "\n      float " + funcName + "(int row, int col, int depth) {\n        int index = round(dot(vec3(row, col, depth),\n                          vec3(" + stride0 + ", " + stride1 + ", 1)));\n        " + getUniformSampler(inputInfo) + "\n      }\n    ";
        }
        var texShape = inputInfo.shapeInfo.texShape;
        var texNumR = texShape[0];
        var texNumC = texShape[1];
        var flatOffset = inputInfo.shapeInfo.flatOffset;
        if (texNumC === stride0 && flatOffset == null) {
            // texC is used directly as physical (no risk of float16 overflow).
            return "\n        float " + funcName + "(int row, int col, int depth) {\n          float texR = float(row);\n          float texC = dot(vec2(col, depth), vec2(" + stride1 + ", 1));\n          vec2 uv = (vec2(texC, texR) + halfCR) /\n                     vec2(" + texNumC + ".0, " + texNumR + ".0);\n          return sampleTexture(" + texName + ", uv);\n        }\n      ";
        }
        if (texNumC === stride1 && flatOffset == null) {
            // texR is used directly as physical (no risk of float16 overflow).
            return "\n    float " + funcName + "(int row, int col, int depth) {\n      float texR = dot(vec2(row, col), vec2(" + shape[1] + ", 1));\n      float texC = float(depth);\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(" + texNumC + ".0, " + texNumR + ".0);\n      return sampleTexture(" + texName + ", uv);\n    }\n  ";
        }
        var offset = getFlatOffsetUniformName(texName);
        return "\n      float " + funcName + "(int row, int col, int depth) {\n        // Explicitly use integer operations as dot() only works on floats.\n        int index = row * " + stride0 + " + col * " + stride1 + " + depth + " + offset + ";\n        vec2 uv = uvFromFlat(" + texNumR + ", " + texNumC + ", index);\n        return sampleTexture(" + texName + ", uv);\n      }\n  ";
    }
    function getPackedSamplerND(inputInfo) {
        var shape = inputInfo.shapeInfo.logicalShape;
        var rank = shape.length;
        var texName = inputInfo.name;
        var funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
        var texShape = inputInfo.shapeInfo.texShape;
        var packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
        var texNumR = packedTexShape[0];
        var texNumC = packedTexShape[1];
        var valuesPerRow = Math.ceil(shape[rank - 1] / 2);
        var texelsInBatch = valuesPerRow * Math.ceil(shape[rank - 2] / 2);
        var params = "int b, int row, int col";
        var index = "b * " + texelsInBatch + " + (row / 2) * " + valuesPerRow + " + (col / 2)";
        for (var b = 2; b < rank - 1; b++) {
            params = "int b" + b + ", " + params;
            texelsInBatch *= shape[rank - b - 1];
            index = "b" + b + " * " + texelsInBatch + " + " + index;
        }
        var glsl = getGlslDifferences();
        return "\n    vec4 " + funcName + "(" + params + ") {\n      int index = " + index + ";\n      int texR = index / " + texNumC + ";\n      int texC = index - texR * " + texNumC + ";\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(" + texNumC + ", " + texNumR + ");\n      return " + glsl.texture2D + "(" + texName + ", uv);\n    }\n  ";
    }
    function getSampler4D(inputInfo) {
        var shape = inputInfo.shapeInfo.logicalShape;
        var texName = inputInfo.name;
        var funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
        var stride2 = shape[3];
        var stride1 = shape[2] * stride2;
        var stride0 = shape[1] * stride1;
        var _a = squeezeShape(shape), newShape = _a.newShape, keptDims = _a.keptDims;
        if (newShape.length < shape.length) {
            var newInputInfo = squeezeInputInfo(inputInfo, newShape);
            var params = ['row', 'col', 'depth', 'depth2'];
            return "\n      " + getSamplerFromInInfo(newInputInfo) + "\n      float " + funcName + "(int row, int col, int depth, int depth2) {\n        return " + funcName + "(" + getSqueezedParams(params, keptDims) + ");\n      }\n    ";
        }
        if (inputInfo.shapeInfo.isUniform) {
            // Uniform arrays will be less than 65505 (no risk of float16 overflow).
            return "\n      float " + funcName + "(int row, int col, int depth, int depth2) {\n        int index = round(dot(vec4(row, col, depth, depth2),\n                          vec4(" + stride0 + ", " + stride1 + ", " + stride2 + ", 1)));\n        " + getUniformSampler(inputInfo) + "\n      }\n    ";
        }
        var flatOffset = inputInfo.shapeInfo.flatOffset;
        var texShape = inputInfo.shapeInfo.texShape;
        var texNumR = texShape[0];
        var texNumC = texShape[1];
        if (texNumC === stride0 && flatOffset == null) {
            // texC is used directly as physical (no risk of float16 overflow).
            return "\n      float " + funcName + "(int row, int col, int depth, int depth2) {\n        float texR = float(row);\n        float texC =\n            dot(vec3(col, depth, depth2),\n                vec3(" + stride1 + ", " + stride2 + ", 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(" + texNumC + ".0, " + texNumR + ".0);\n        return sampleTexture(" + texName + ", uv);\n      }\n    ";
        }
        if (texNumC === stride2 && flatOffset == null) {
            // texR is used directly as physical (no risk of float16 overflow).
            return "\n      float " + funcName + "(int row, int col, int depth, int depth2) {\n        float texR = dot(vec3(row, col, depth),\n                         vec3(" + shape[1] * shape[2] + ", " + shape[2] + ", 1));\n        float texC = float(depth2);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(" + texNumC + ".0, " + texNumR + ".0);\n        return sampleTexture(" + texName + ", uv);\n      }\n    ";
        }
        var offset = getFlatOffsetUniformName(texName);
        return "\n    float " + funcName + "(int row, int col, int depth, int depth2) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * " + stride0 + " + col * " + stride1 + " +\n          depth * " + stride2 + " + depth2;\n      vec2 uv = uvFromFlat(" + texNumR + ", " + texNumC + ", index + " + offset + ");\n      return sampleTexture(" + texName + ", uv);\n    }\n  ";
    }
    function getSampler5D(inputInfo) {
        var shape = inputInfo.shapeInfo.logicalShape;
        var texName = inputInfo.name;
        var funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
        var stride3 = shape[4];
        var stride2 = shape[3] * stride3;
        var stride1 = shape[2] * stride2;
        var stride0 = shape[1] * stride1;
        var _a = squeezeShape(shape), newShape = _a.newShape, keptDims = _a.keptDims;
        if (newShape.length < shape.length) {
            var newInputInfo = squeezeInputInfo(inputInfo, newShape);
            var params = ['row', 'col', 'depth', 'depth2', 'depth3'];
            return "\n      " + getSamplerFromInInfo(newInputInfo) + "\n      float " + funcName + "(int row, int col, int depth, int depth2, int depth3) {\n        return " + funcName + "(" + getSqueezedParams(params, keptDims) + ");\n      }\n    ";
        }
        if (inputInfo.shapeInfo.isUniform) {
            // Uniform arrays will be less than 65505 (no risk of float16 overflow).
            return "\n      float " + funcName + "(int row, int col, int depth, int depth2, int depth3) {\n        float index = dot(\n          vec4(row, col, depth, depth2),\n          vec4(" + stride0 + ", " + stride1 + ", " + stride2 + ", " + stride3 + ")) +\n          depth3;\n        " + getUniformSampler(inputInfo) + "\n      }\n    ";
        }
        var flatOffset = inputInfo.shapeInfo.flatOffset;
        var texShape = inputInfo.shapeInfo.texShape;
        var texNumR = texShape[0];
        var texNumC = texShape[1];
        if (texNumC === stride0 && flatOffset == null) {
            // texC is used directly as physical (no risk of float16 overflow).
            return "\n      float " + funcName + "(int row, int col, int depth, int depth2, int depth3) {\n        int texR = row;\n        float texC = dot(vec4(col, depth, depth2, depth3),\n                         vec4(" + stride1 + ", " + stride2 + ", " + stride3 + ", 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(" + texNumC + ".0, " + texNumR + ".0);\n        return sampleTexture(" + texName + ", uv);\n      }\n    ";
        }
        if (texNumC === stride3 && flatOffset == null) {
            // texR is used directly as physical (no risk of float16 overflow).
            return "\n      float " + funcName + "(int row, int col, int depth, int depth2, int depth3) {\n        float texR = dot(\n          vec4(row, col, depth, depth2),\n          vec4(" + shape[1] * shape[2] * shape[3] + ",\n               " + shape[2] * shape[3] + ", " + shape[3] + ", 1));\n        int texC = depth3;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(" + texNumC + ".0, " + texNumR + ".0);\n        return sampleTexture(" + texName + ", uv);\n      }\n    ";
        }
        var offset = getFlatOffsetUniformName(texName);
        return "\n    float " + funcName + "(int row, int col, int depth, int depth2, int depth3) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * " + stride0 + " + col * " + stride1 + " + depth * " + stride2 + " +\n          depth2 * " + stride3 + " + depth3 + " + offset + ";\n      vec2 uv = uvFromFlat(" + texNumR + ", " + texNumC + ", index);\n      return sampleTexture(" + texName + ", uv);\n    }\n  ";
    }
    function getSampler6D(inputInfo) {
        var shape = inputInfo.shapeInfo.logicalShape;
        var texName = inputInfo.name;
        var funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
        var _a = squeezeShape(shape), newShape = _a.newShape, keptDims = _a.keptDims;
        if (newShape.length < shape.length) {
            var newInputInfo = squeezeInputInfo(inputInfo, newShape);
            var params = ['row', 'col', 'depth', 'depth2', 'depth3', 'depth4'];
            return "\n      " + getSamplerFromInInfo(newInputInfo) + "\n      float " + funcName + "(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        return " + funcName + "(" + getSqueezedParams(params, keptDims) + ");\n      }\n    ";
        }
        var stride4 = shape[5];
        var stride3 = shape[4] * stride4;
        var stride2 = shape[3] * stride3;
        var stride1 = shape[2] * stride2;
        var stride0 = shape[1] * stride1;
        if (inputInfo.shapeInfo.isUniform) {
            // Uniform arrays will be less than 65505 (no risk of float16 overflow).
            return "\n      float " + funcName + "(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n        int index = round(dot(\n          vec4(row, col, depth, depth2),\n          vec4(" + stride0 + ", " + stride1 + ", " + stride2 + ", " + stride3 + ")) +\n          dot(\n            vec2(depth3, depth4),\n            vec2(" + stride4 + ", 1)));\n        " + getUniformSampler(inputInfo) + "\n      }\n    ";
        }
        var flatOffset = inputInfo.shapeInfo.flatOffset;
        var texShape = inputInfo.shapeInfo.texShape;
        var texNumR = texShape[0];
        var texNumC = texShape[1];
        if (texNumC === stride0 && flatOffset == null) {
            // texC is used directly as physical (no risk of float16 overflow).
            return "\n      float " + funcName + "(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        int texR = row;\n        float texC = dot(vec4(col, depth, depth2, depth3),\n          vec4(" + stride1 + ", " + stride2 + ", " + stride3 + ", " + stride4 + ")) +\n               float(depth4);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(" + texNumC + ".0, " + texNumR + ".0);\n        return sampleTexture(" + texName + ", uv);\n      }\n    ";
        }
        if (texNumC === stride4 && flatOffset == null) {
            // texR is used directly as physical (no risk of float16 overflow).
            return "\n      float " + funcName + "(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        float texR = dot(vec4(row, col, depth, depth2),\n          vec4(" + shape[1] * shape[2] * shape[3] * shape[4] + ",\n               " + shape[2] * shape[3] * shape[4] + ",\n               " + shape[3] * shape[4] + ",\n               " + shape[4] + ")) + float(depth3);\n        int texC = depth4;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(" + texNumC + ".0, " + texNumR + ".0);\n        return sampleTexture(" + texName + ", uv);\n      }\n    ";
        }
        var offset = getFlatOffsetUniformName(texName);
        return "\n    float " + funcName + "(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * " + stride0 + " + col * " + stride1 + " + depth * " + stride2 + " +\n          depth2 * " + stride3 + " + depth3 * " + stride4 + " + depth4 + " + offset + ";\n      vec2 uv = uvFromFlat(" + texNumR + ", " + texNumC + ", index);\n      return sampleTexture(" + texName + ", uv);\n    }\n  ";
    }
    function getUniformSampler(inputInfo) {
        var texName = inputInfo.name;
        var inSize = sizeFromShape(inputInfo.shapeInfo.logicalShape);
        if (inSize < 2) {
            return "return " + texName + ";";
        }
        return "\n    for (int i = 0; i < " + inSize + "; i++) {\n      if (i == index) {\n        return " + texName + "[i];\n      }\n    }\n  ";
    }
    function getPackedSamplerAtOutputCoords(inputInfo, outShapeInfo) {
        var texName = inputInfo.name;
        var texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);
        var funcName = 'get' + texFuncSnippet + 'AtOutCoords';
        var inRank = inputInfo.shapeInfo.logicalShape.length;
        var outRank = outShapeInfo.logicalShape.length;
        var broadcastDims = getBroadcastDims(inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);
        var type = getCoordsDataType(outRank);
        var rankDiff = outRank - inRank;
        var coordsSnippet;
        var fields = ['x', 'y', 'z', 'w', 'u', 'v'];
        if (inRank === 0) {
            coordsSnippet = '';
        }
        else if (outRank < 2 && broadcastDims.length >= 1) {
            coordsSnippet = 'coords = 0;';
        }
        else {
            coordsSnippet =
                broadcastDims.map(function (d) { return "coords." + fields[d + rankDiff] + " = 0;"; })
                    .join('\n');
        }
        var unpackedCoordsSnippet = '';
        if (outRank < 2 && inRank > 0) {
            unpackedCoordsSnippet = 'coords';
        }
        else {
            unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape
                .map(function (s, i) { return "coords." + fields[i + rankDiff]; })
                .join(', ');
        }
        var output = "return outputValue;";
        var inSize = sizeFromShape(inputInfo.shapeInfo.logicalShape);
        var isInputScalar = inSize === 1;
        var outSize = sizeFromShape(outShapeInfo.logicalShape);
        var isOutputScalar = outSize === 1;
        if (inRank === 1 && !isInputScalar && !isOutputScalar) {
            output = "\n      return vec4(outputValue.xy, outputValue.xy);\n    ";
        }
        else if (isInputScalar && !isOutputScalar) {
            if (outRank === 1) {
                output = "\n        return vec4(outputValue.x, outputValue.x, 0., 0.);\n      ";
            }
            else {
                output = "\n        return vec4(outputValue.x);\n      ";
            }
        }
        else if (broadcastDims.length) {
            var rows = inRank - 2;
            var cols = inRank - 1;
            if (broadcastDims.indexOf(rows) > -1 && broadcastDims.indexOf(cols) > -1) {
                output = "return vec4(outputValue.x);";
            }
            else if (broadcastDims.indexOf(rows) > -1) {
                output = "return vec4(outputValue.x, outputValue.y, " +
                    "outputValue.x, outputValue.y);";
            }
            else if (broadcastDims.indexOf(cols) > -1) {
                output = "return vec4(outputValue.xx, outputValue.zz);";
            }
        }
        return "\n    vec4 " + funcName + "() {\n      " + type + " coords = getOutputCoords();\n      " + coordsSnippet + "\n      vec4 outputValue = get" + texFuncSnippet + "(" + unpackedCoordsSnippet + ");\n      " + output + "\n    }\n  ";
    }
    function getSamplerAtOutputCoords(inputInfo, outShapeInfo) {
        var texName = inputInfo.name;
        var texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);
        var funcName = 'get' + texFuncSnippet + 'AtOutCoords';
        var outTexShape = outShapeInfo.texShape;
        var inTexShape = inputInfo.shapeInfo.texShape;
        var inRank = inputInfo.shapeInfo.logicalShape.length;
        var outRank = outShapeInfo.logicalShape.length;
        if (!inputInfo.shapeInfo.isUniform && inRank === outRank &&
            inputInfo.shapeInfo.flatOffset == null &&
            arraysEqual(inTexShape, outTexShape)) {
            return "\n      float " + funcName + "() {\n        return sampleTexture(" + texName + ", resultUV);\n      }\n    ";
        }
        var type = getCoordsDataType(outRank);
        var broadcastDims = getBroadcastDims(inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);
        var rankDiff = outRank - inRank;
        var coordsSnippet;
        var fields = ['x', 'y', 'z', 'w', 'u', 'v'];
        if (inRank === 0) {
            coordsSnippet = '';
        }
        else if (outRank < 2 && broadcastDims.length >= 1) {
            coordsSnippet = 'coords = 0;';
        }
        else {
            coordsSnippet =
                broadcastDims.map(function (d) { return "coords." + fields[d + rankDiff] + " = 0;"; })
                    .join('\n');
        }
        var unpackedCoordsSnippet = '';
        if (outRank < 2 && inRank > 0) {
            unpackedCoordsSnippet = 'coords';
        }
        else {
            unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape
                .map(function (s, i) { return "coords." + fields[i + rankDiff]; })
                .join(', ');
        }
        return "\n    float " + funcName + "() {\n      " + type + " coords = getOutputCoords();\n      " + coordsSnippet + "\n      return get" + texFuncSnippet + "(" + unpackedCoordsSnippet + ");\n    }\n  ";
    }
    function getCoordsDataType(rank) {
        if (rank <= 1) {
            return 'int';
        }
        else if (rank === 2) {
            return 'ivec2';
        }
        else if (rank === 3) {
            return 'ivec3';
        }
        else if (rank === 4) {
            return 'ivec4';
        }
        else if (rank === 5) {
            return 'ivec5';
        }
        else if (rank === 6) {
            return 'ivec6';
        }
        else {
            throw Error("GPU for rank " + rank + " is not yet supported");
        }
    }
    /** Returns a new input info (a copy) that has a squeezed logical shape. */
    function squeezeInputInfo(inInfo, squeezedShape) {
        // Deep copy.
        var newInputInfo = JSON.parse(JSON.stringify(inInfo));
        newInputInfo.shapeInfo.logicalShape = squeezedShape;
        return newInputInfo;
    }
    function getSqueezedParams(params, keptDims) {
        return keptDims.map(function (d) { return params[d]; }).join(', ');
    }

    /**
     * @license
     * Copyright 2019 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.
     * =============================================================================
     */
    var ArgMinMaxPackedProgram = /** @class */ (function () {
        function ArgMinMaxPackedProgram(shape, windowSize, op, firstPass) {
            this.variableNames = ['A'];
            this.packedInputs = true;
            this.packedOutput = true;
            assert(shape.length > 2, function () { return "Packed arg" + (op.charAt(0).toUpperCase() +
                op.slice(1)) + " supports only inputs with rank above 2."; });
            var inSize = shape[shape.length - 1];
            var outSize = Math.ceil(inSize / windowSize);
            this.outputShape = shape.slice(0, -1);
            if (outSize > 1) {
                this.outputShape.push(outSize);
            }
            if (!firstPass) {
                this.variableNames.push('bestIndicesA');
            }
            var outShape = this.outputShape;
            var rank = outShape.length;
            var dtype = getCoordsDataType(rank);
            var coords = getChannels('coords', rank);
            var sourceLocSetup;
            var sourceRank;
            if (outSize === 1) {
                sourceRank = rank + 1;
                var sourceLocDType = getCoordsDataType(sourceRank);
                sourceLocSetup = "\n        " + sourceLocDType + " sourceLocR = " + sourceLocDType + "(" + coords.join() + ", 0);\n        ++" + coords[rank - 1] + ";\n        " + sourceLocDType + " sourceLocG = " + sourceLocDType + "(" + coords.join() + ", 0);\n        ++" + coords[rank - 2] + ";\n        " + sourceLocDType + " sourceLocA = " + sourceLocDType + "(" + coords.join() + ", 0);\n        --" + coords[rank - 1] + ";\n        " + sourceLocDType + " sourceLocB = " + sourceLocDType + "(" + coords.join() + ", 0);\n        --" + coords[rank - 2] + ";";
            }
            else {
                sourceRank = rank;
                sourceLocSetup = "\n        " + dtype + " sourceLocR = coords;\n        ++" + coords[rank - 1] + ";\n        " + dtype + " sourceLocG = coords;\n        ++" + coords[rank - 2] + ";\n        " + dtype + " sourceLocA = coords;\n        --" + coords[rank - 1] + ";\n        " + dtype + " sourceLocB = coords;\n        --" + coords[rank - 2] + ";";
            }
            var channels = ['x', 'y', 'z', 'w', 'u', 'v'].slice(0, sourceRank);
            var inChannel = '.' + channels[sourceRank - 1]; // e.g. ".b" for rank 3.
            var intChannels = channels.map(function (x) { return 'int ' + x; });
            var srcRCoords = getChannels('sourceLocR', sourceRank - 1).concat('inIdx.r');
            var srcGCoords = getChannels('sourceLocG', sourceRank - 1).concat('inIdx.g');
            var srcBCoords = getChannels('sourceLocB', sourceRank - 1).concat('inIdx.b');
            var srcACoords = getChannels('sourceLocA', sourceRank - 1).concat('inIdx.a');
            var compOp = (op === 'max') ? 'greaterThan' : 'lessThan';
            var fetchCandidateIdx = firstPass ? '' : "\n          inIdx = round(vec4(getBestIndicesAChannel(" + srcRCoords.join() + "),\n                             getBestIndicesAChannel(" + srcGCoords.join() + "),\n                             getBestIndicesAChannel(" + srcBCoords.join() + "),\n                             getBestIndicesAChannel(" + srcACoords.join() + ")));";
            var fetchValue = "vec4(\n            getAChannel(" + srcRCoords.join() + "),\n            hasNextCol ? getAChannel(" + srcGCoords.join() + ") : 0.,\n            hasNextRow ? getAChannel(" + srcBCoords.join() + ") : 0.,\n            hasNextRow && hasNextCol ? getAChannel(" + srcACoords.join() + ") : 0.)";
            var getBestIndicesAChannelSnippet = firstPass ? '' : "\n      float getBestIndicesAChannel(" + intChannels.join() + ") {\n        return getChannel(getBestIndicesA(" + channels.join() + "),\n                                          vec2(" + channels.slice(-2).join() + "));\n      }";
            this.userCode = "\n      float getAChannel(" + intChannels.join() + ") {\n        return getChannel(getA(" + channels.join() + "),\n                               vec2(" + channels.slice(-2).join() + "));\n      }\n      " + getBestIndicesAChannelSnippet + "\n      void main() {\n        " + dtype + " coords = getOutputCoords();\n        bool hasNextCol = " + coords[rank - 1] + " < " + (outShape[rank - 1] - 1) + ";\n        bool hasNextRow = " + coords[rank - 2] + " < " + (outShape[rank - 2] - 1) + ";\n        " + sourceLocSetup + "\n        ivec4 srcIdx = ivec4(sourceLocR" + inChannel + ", sourceLocG" + inChannel + ",\n          sourceLocB" + inChannel + ", sourceLocA" + inChannel + ") * " + windowSize + ";\n        ivec4 inIdx = srcIdx;\n        vec4 bestIndex = vec4(inIdx);\n        vec4 bestValue = " + fetchValue + ";\n\n        for (int i = 0; i < " + windowSize + "; i++) {\n          inIdx = srcIdx;\n          " + fetchCandidateIdx + "\n          vec4 candidate = " + fetchValue + ";\n          bvec4 nan = isnan(candidate);\n          bvec4 replace = bvec4(\n            vec4(" + compOp + "(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));\n\n          bestValue = vec4(replace.x  ? candidate.x : bestValue.x,\n                           replace.y  ? candidate.y : bestValue.y,\n                           replace.z  ? candidate.z : bestValue.z,\n                           replace.w  ? candidate.w : bestValue.w);\n          bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));\n          srcIdx++;\n        }\n        setOutput(bestIndex);\n      }\n    ";
        }
        return ArgMinMaxPackedProgram;
    }());

    /**
     * @license
     * Copyright 2017 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.
     * =============================================================================
     */
    var AvgPool2DBackpropProgram = /** @class */ (function () {
        function AvgPool2DBackpropProgram(convInfo) {
            this.variableNames = ['dy'];
            this.outputShape = convInfo.inShape;
            var filterHeight = convInfo.filterHeight;
            var filterWidth = convInfo.filterWidth;
            var strideHeight = convInfo.strideHeight;
            var strideWidth = convInfo.strideWidth;
            var dilationHeight = convInfo.dilationHeight;
            var dilationWidth = convInfo.dilationWidth;
            var effectiveFilterHeight = convInfo.effectiveFilterHeight;
            var effectiveFilterWidth = convInfo.effectiveFilterWidth;
            var padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
            var padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
            var avgMultiplier = 1 / (filterHeight * filterWidth);
            this.userCode = "\n      const ivec2 pads = ivec2(" + padTop + ", " + padLeft + ");\n      const float avgMultiplier = float(" + avgMultiplier + ");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < " + effectiveFilterHeight + ";\n            wR += " + dilationHeight + ") {\n          float dyR = float(dyRCorner + wR) / " + strideHeight + ".0;\n\n          if (dyR < 0.0 || dyR >= " + convInfo.outHeight + ".0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < " + effectiveFilterWidth + ";\n            wC+= " + dilationWidth + ") {\n            float dyC = float(dyCCorner + wC) / " + strideWidth + ".0;\n\n            if (dyC < 0.0 || dyC >= " + convInfo.outWidth + ".0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n\n            dotProd += dyValue * avgMultiplier;\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";
        }
        return AvgPool2DBackpropProgram;
    }());
    var AvgPool3DBackpropProgram = /** @class */ (function () {
        function AvgPool3DBackpropProgram(convInfo) {
            this.variableNames = ['dy'];
            this.outputShape = convInfo.inShape;
            var filterDepth = convInfo.filterDepth;
            var filterHeight = convInfo.filterHeight;
            var filterWidth = convInfo.filterWidth;
            var strideDepth = convInfo.strideDepth;
            var strideHeight = convInfo.strideHeight;
            var strideWidth = convInfo.strideWidth;
            var dilationDepth = convInfo.dilationDepth;
            var dilationHeight = convInfo.dilationHeight;
            var dilationWidth = convInfo.dilationWidth;
            var effectiveFilterDepth = convInfo.effectiveFilterDepth;
            var effectiveFilterHeight = convInfo.effectiveFilterHeight;
            var effectiveFilterWidth = convInfo.effectiveFilterWidth;
            var padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;
            var padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
            var padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
            var avgMultiplier = 1 / (filterDepth * filterHeight * filterWidth);
            this.userCode = "\n      const ivec3 pads = ivec3(" + padFront + ", " + padTop + ", " + padLeft + ");\n      const float avgMultiplier = float(" + avgMultiplier + ");\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyDCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n        // dx(xD, xR, xC, ch).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int wD = 0; wD < " + effectiveFilterDepth + ";\n            wD += " + dilationDepth + ") {\n          float dyD = float(dyDCorner + wD) / " + strideDepth + ".0;\n\n          if (dyD < 0.0 || dyD >= " + convInfo.outDepth + ".0 || fract(dyD) > 0.0) {\n            continue;\n          }\n          int idyD = int(dyD);\n\n          for (int wR = 0; wR < " + effectiveFilterHeight + ";\n              wR += " + dilationHeight + ") {\n            float dyR = float(dyRCorner + wR) / " + strideHeight + ".0;\n\n            if (dyR < 0.0 || dyR >= " + convInfo.outHeight + ".0 ||\n                fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            for (int wC = 0; wC < " + effectiveFilterWidth + ";\n                wC += " + dilationWidth + ") {\n              float dyC = float(dyCCorner + wC) / " + strideWidth + ".0;\n\n              if (dyC < 0.0 || dyC >= " + convInfo.outWidth + ".0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n\n              dotProd += dyValue * avgMultiplier;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";
        }
        return AvgPool3DBackpropProgram;
    }());

    /**
     * @license
     * Copyright 2017 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.
     * =============================================================================
     */
    var BatchNormProgram = /** @class */ (function () {
        function BatchNormProgram(xShape, meanShape, varianceShape, offsetShape, scaleShape, varianceEpsilon) {
            this.outputShape = [];
            this.variableNames = ['x', 'mean', 'variance'];
            assertAndGetBroadcastShape(xShape, meanShape);
            assertAndGetBroadcastShape(xShape, varianceShape);
            var offsetSnippet = '0.0';
            if (offsetShape != null) {
                assertAndGetBroadcastShape(xShape, offsetShape);
                this.variableNames.push('offset');
                offsetSnippet = 'getOffsetAtOutCoords()';
            }
            var scaleSnippet = '1.0';
            if (scaleShape != null) {
                assertAndGetBroadcastShape(xShape, scaleShape);
                this.variableNames.push('scale');
                scaleSnippet = 'getScaleAtOutCoords()';
            }
            this.outputShape = xShape;
            this.userCode = "\n      void main() {\n        float x = getXAtOutCoords();\n        float mean = getMeanAtOutCoords();\n        float variance = getVarianceAtOutCoords();\n        float offset = " + offsetSnippet + ";\n        float scale = " + scaleSnippet + ";\n        float inv = scale * inversesqrt(variance + float(" + varianceEpsilon + "));\n        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n      }\n    ";
        }
        return BatchNormProgram;
    }());

    /**
     * @license
     * Copyright 2018 Google LLC. All Rights Reserved.
     * Licensed under the Apache License, Version 2.0 (the "License");
     * you may not use this file except in compliance with the License.
     * You may obtain a copy of the License at
     *
     * http://www.apache.org/licenses/LICENSE-2.0
     *
     * Unless required by applicable law or agreed to in writing, software
     * distributed under the License is distributed on an "AS IS" BASIS,
     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
     * See the License for the specific language governing permissions and
     * limitations under the License.
     * =============================================================================
     */
    var BatchNormPackedProgram = /** @class */ (function () {
        function BatchNormPackedProgram(xShape, meanShape, varianceShape, offsetShape, scaleShape, varianceEpsilon) {
            this.packedInputs = true;
            this.packedOutput = true;
            this.variableNames = ['x', 'mean', 'variance'];
            assertAndGetBroadcastShape(xShape, meanShape);
            assertAndGetBroadcastShape(xShape, varianceShape);
            var offsetSnippet = 'vec4(0.0)';
            if (offsetShape != null) {
                assertAndGetBroadcastShape(xShape, offsetShape);
                this.variableNames.push('offset');
                offsetSnippet = 'getOffsetAtOutCoords()';
            }
            var scaleSnippet = 'vec4(1.0)';
            if (scaleShape != null) {
                assertAndGetBroadcastShape(xShape, scaleShape);
                this.variableNames.push('scale');
                scaleSnippet = 'getScaleAtOutCoords()';
            }
            this.outputShape = xShape;
            this.userCode = "\n      void main() {\n        vec4 offset = " + offsetSnippet + ";\n        vec4 scale = " + scaleSnippet + ";\n\n        vec4 x = getXAtOutCoords();\n        vec4 mean = getMeanAtOutCoords();\n        vec4 variance = getVarianceAtOutCoords();\n\n        vec4 inv = scale * inversesqrt(variance + vec4(" + varianceEpsilon + "));\n\n        setOutput((x - mean) * inv + offset);\n      }\n    ";
        }
        return BatchNormPackedProgram;
    }());

    /**
     * @license
     * Copyright 2018 Google LLC. All Rights Reserved.
     * Licensed under the Apache License, Version 2.0 (the "License");
     * you may not use this file except in compliance with the License.
     * You may obtain a copy of the License at
     *
     * http://www.apache.org/licenses/LICENSE-2.0
     *
     * Unless required by applicable law or agreed to in writing, software
     * distributed under the License is distributed on an "AS IS" BASIS,
     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
     * See the License for the specific language governing permissions and
     * limitations under the License.
     * =============================================================================
     */
    // (Ar + Ai)(Br + Bi) =
    // ArBr + ArBi + AiBr + AiBi = ArBr - AB + ArBi + AiBr
    // Yr = ArBr - AB
    // Yi = ArBi + AiBr
    var COMPLEX_MULTIPLY = {
        REAL: 'return areal * breal - aimag * bimag;',
        IMAG: 'return areal * bimag + aimag * breal;'
    };
    var BinaryOpComplexProgram = /** @class */ (function () {
        function BinaryOpComplexProgram(op, aShape, bShape) {
            this.variableNames = ['AReal', 'AImag', 'BReal', 'BImag'];
            this.outputShape =
                assertAndGetBroadcastShape(aShape, bShape);
            this.userCode = "\n      float binaryOpComplex(\n          float areal, float aimag, float breal, float bimag) {\n        " + op + "\n      }\n\n      void main() {\n        float areal = getARealAtOutCoords();\n        float aimag = getAImagAtOutCoords();\n        float breal = getBRealAtOutCoords();\n        float bimag = getBImagAtOutCoords();\n        setOutput(binaryOpComplex(areal, aimag, breal, bimag));\n      }\n    ";
        }
        return BinaryOpComplexProgram;
    }());

    /**
     * @license
     * Copyright 2017 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.
     * =============================================================================
     */
    var CHECK_NAN_SNIPPET = "\n  if (isnan(a)) return a;\n  if (isnan(b)) return b;\n";
    var ADD = 'return a + b;';
    var SUB = 'return a - b;';
    var MUL = 'return a * b;';
    // Without the equality check div produces 0.9999 for a = b, which when
    // floored can cause errors.
    var DIV = "\nif (a == b) {\n  return 1.0;\n};\nreturn a / b;";
    // We use native integer division to deal with floating point imprecision. Since
    // we implement floor division and glsl implements truncated division, we
    // correct for this by subtracting 1 from result when the result is negative and
    // there is a remainder.
    var INT_DIV = "\n  float s = sign(a) * sign(b);\n  int ia = round(a);\n  int ib = round(b);\n  if (ib != 0) {\n    // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n    return float(idiv(ia, ib, s));\n  } else {\n    return NAN;\n  }\n";
    var POW = "\nif(a < 0.0 && floor(b) < b){\n  return NAN;\n}\nif (b == 0.0) {\n  return 1.0;\n}\nreturn (round(mod(b, 2.0)) != 1) ?\n    pow(abs(a), b) : sign(a) * pow(abs(a), b);\n";
    var EQUAL = "return float(a == b);";
    var NOT_EQUAL = "return float(a != b);";
    var LESS = "return float(a < b);";
    var LESS_EQUAL = "return float(a <= b);";
    var GREATER = "return float(a > b);";
    var GREATER_EQUAL = "return float(a >= b);";
    var LOGICAL_AND = "return float(a >= 1.0 && b >= 1.0);";
    var LOGICAL_OR = "return float(a >= 1.0 || b >= 1.0);";
    var MAX = CHECK_NAN_SNIPPET + "\n  return max(a, b);\n";
    var MIN = CHECK_NAN_SNIPPET + "\n  return min(a, b);\n";
    var MOD = "if (b == 0.0) return NAN;\n  return mod(a, b);";
    var ATAN2 = CHECK_NAN_SNIPPET + "\n  return atan(a, b);\n";
    var ELU_DER = "return (b >= 1.0) ? a : a * (b + 1.0);";
    var PRELU = "return (a < 0.) ? b * a : a;";
    var BinaryOpProgram = /** @class */ (function () {
        function BinaryOpProgram(op, aShape, bShape) {
            this.variableNames = ['A', 'B'];
            this.outputShape =
                assertAndGetBroadcastShape(aShape, bShape);
            this.userCode = "\n      float binaryOperation(float a, float b) {\n        " + op + "\n      }\n\n      void main() {\n        float a = getAAtOutCoords();\n        float b = getBAtOutCoords();\n        setOutput(binaryOperation(a, b));\n      }\n    ";
        }
        return BinaryOpProgram;
    }());

    /**
     * @license
     * Copyright 2018 Google 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.
     * =============================================================================
     */
    var CHECK_NAN_SNIPPET$1 = "\n  result.r = isNaN.r > 0. ? NAN : result.r;\n  result.g = isNaN.g > 0. ? NAN : result.g;\n  result.b = isNaN.b > 0. ? NAN : result.b;\n  result.a = isNaN.a > 0. ? NAN : result.a;\n";
    // We do the same as in ./binaryop_gpu, with vec4 and ivec4.
    // On Linux, the vectorized implementation produces NaNs when a and b are 0.
    var DIV$1 = "\n  // vec4 one = vec4(equal(a, b));\n  // return one + (vec4(1.0) - one) * a / b;\n  vec4 result = a / b;\n  if(a.x == b.x) {\n    result.x = 1.;\n  }\n  if(a.y == b.y) {\n    result.y = 1.;\n  }\n  if(a.z == b.z) {\n    result.z = 1.;\n  }\n  if(a.w == b.w) {\n    result.w = 1.;\n  }\n\n  return result;\n";
    var INT_DIV$1 = "\n  ivec4 ia = round(a);\n  ivec4 ib = round(b);\n  bvec4 cond = notEqual(ib, ivec4(0));\n  ivec4 result = ivec4(0);\n  vec4 s = sign(a) * sign(b);\n\n  // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n  if (cond[0]) {\n    result[0] = idiv(ia[0], ib[0], s[0]);\n  }\n  if (cond[1]) {\n    result[1] = idiv(ia[1], ib[1], s[1]);\n  }\n  if (cond[2]) {\n    result[2] = idiv(ia[2], ib[2], s[2]);\n  }\n  if (cond[3]) {\n    result[3] = idiv(ia[3], ib[3], s[3]);\n  }\n  return vec4(result);\n";
    var POW$1 = "\n  // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.\n  vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));\n  vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n  vec4 result = multiplier * pow(abs(a), b);\n\n  // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n  bvec4 isExpZero = equal(b, vec4(0.0));\n  result.r = isExpZero.r ? 1.0 : result.r;\n  result.g = isExpZero.g ? 1.0 : result.g;\n  result.b = isExpZero.b ? 1.0 : result.b;\n  result.a = isExpZero.a ? 1.0 : result.a;\n\n  vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));\n  " +
        CHECK_NAN_SNIPPET$1 + "\n  return result;\n";
    var PRELU$1 = "\n  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n";
    var ELU_DER$1 = "\n  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));\n  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));\n";
    var ATAN2$1 = "\n  vec4 result = atan(a, b);\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  " +
        CHECK_NAN_SNIPPET$1 + "\n  return result;\n";
    var EQUAL$1 = "\n  return vec4(equal(a, b));\n";
    var NOT_EQUAL$1 = "\n  return vec4(notEqual(a, b));\n";
    var LESS$1 = "\n  return vec4(lessThan(a, b));\n";
    var LESS_EQUAL$1 = "\n  return vec4(lessThanEqual(a, b));\n";
    var GREATER$1 = "\n  return vec4(greaterThan(a, b));\n";
    var GREATER_EQUAL$1 = "\n  return vec4(greaterThanEqual(a, b));\n";
    var LOGICAL_AND$1 = "\n  return vec4(\n    vec4(greaterThanEqual(a, vec4(1.0))) *\n    vec4(greaterThanEqual(b, vec4(1.0))));\n";
    var LOGICAL_OR$1 = "\n  return min(\n    vec4(greaterThanEqual(a, vec4(1.0))) +\n    vec4(greaterThanEqual(b, vec4(1.0))),\n    vec4(1.0));\n";
    var MAX$1 = "\n  vec4 result = vec4(max(a, b));\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  " +
        CHECK_NAN_SNIPPET$1 + "\n  return result;\n";
    var MIN$1 = "\n  vec4 result = vec4(min(a, b));\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  " +
        CHECK_NAN_SNIPPET$1 + "\n  return result;\n";
    var MOD$1 = "\n  vec4 result = mod(a, b);\n  vec4 isNaN = vec4(equal(b, vec4(0.0)));\n  " +
        CHECK_NAN_SNIPPET$1 + "\n  return result;\n";
    var BinaryOpPackedProgram = /** @class */ (function () {
        function BinaryOpPackedProgram(op, aShape, bShape, checkOutOfBounds) {
            if (checkOutOfBounds === void 0) { checkOutOfBounds = false; }
            this.variableNames = ['A', 'B'];
            this.supportsBroadcasting = true;
            this.packedInputs = true;
            this.packedOutput = true;
            this.outputShape =
                assertAndGetBroadcastShape(aShape, bShape);
            var rank = this.outputShape.length;
            var checkOutOfBoundsString = '';
            if (checkOutOfBounds) {
                if (rank === 0 || sizeFromShape(this.outputShape) === 1) {
                    checkOutOfBoundsString = "\n          result.y = 0.;\n          result.z = 0.;\n          result.w = 0.;\n        ";
                }
                else {
                    var dtype = getCoordsDataType(rank);
                    checkOutOfBoundsString = "\n          " + dtype + " coords = getOutputCoords();\n        ";
                    if (rank === 1) {
                        checkOutOfBoundsString += "\n            result.y = (coords + 1) >= " + this.outputShape[0] + " ? 0. : result.y;\n            result.z = 0.;\n            result.w = 0.;\n          ";
                    }
                    else {
                        var channels = getChannels('coords', rank);
                        checkOutOfBoundsString += "\n            bool nextRowOutOfBounds =\n              (" + channels[rank - 2] + " + 1) >= " + this.outputShape[rank - 2] + ";\n            bool nextColOutOfBounds =\n              (" + channels[rank - 1] + " + 1) >= " + this.outputShape[rank - 1] + ";\n            result.y = nextColOutOfBounds ? 0. : result.y;\n            result.z = nextRowOutOfBounds ? 0. : result.z;\n            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n          ";
                    }
                }
            }
            this.userCode = "\n      vec4 binaryOperation(vec4 a, vec4 b) {\n        " + op + "\n      }\n\n      void main() {\n        vec4 a = getAAtOutCoords();\n        vec4 b = getBAtOutCoords();\n\n        vec4 result = binaryOperation(a, b);\n        " + checkOutOfBoundsString + "\n\n        setOutput(result);\n      }\n    ";
        }
        return BinaryOpPackedProgram;
    }());

    /**
     * @license
     * Copyright 2017 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.
     * =============================================================================
     */
    var ClipProgram = /** @class */ (function () {
        function ClipProgram(aShape) {
            this.variableNames = ['A'];
            this.outputShape = aShape;
            this.userCode = "\n      uniform float minVal;\n      uniform float maxVal;\n\n      void main() {\n        float value = getAAtOutCoords();\n        if (isnan(value)) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, minVal, maxVal));\n      }\n    ";
        }
        ClipProgram.prototype.getCustomSetupFunc = function (min, max) {
            var _this = this;
            return function (gpgpu, webGLProgram) {
                if (_this.minLoc == null) {
                    _this.minLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'minVal');
                    _this.maxLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'maxVal');
                }
                gpgpu.gl.uniform1f(_this.minLoc, min);
                gpgpu.gl.uniform1f(_this.maxLoc, max);
            };
        };
        return ClipProgram;
    }());

    /**
     * @license
     * Copyright 2018 Google LLC. All Rights Reserved.
     * Licensed under the Apache License, Version 2.0 (the "License");
     * you may not use this file except in compliance with the License.
     * You may obtain a copy of the License at
     *
     * http://www.apache.org/licenses/LICENSE-2.0
     *
     * Unless required by applicable law or agreed to in writing, software
     * distributed under the License is distributed on an "AS IS" BASIS,
     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
     * See the License for the specific language governing permissions and
     * limitations under the License.
     * =============================================================================
     */
    var ClipPackedProgram = /** @class */ (function () {
        function ClipPackedProgram(aShape) {
            this.variableNames = ['A'];
            this.packedInputs = true;
            this.packedOutput = true;
            this.outputShape = aShape;
            this.userCode = "\n      uniform float minVal;\n      uniform float maxVal;\n\n      void main() {\n        vec4 value = getAAtOutCoords();\n\n        if (any(isnan(value))) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, vec4(minVal), vec4(maxVal)));\n      }\n    ";
        }
        ClipPackedProgram.prototype.getCustomSetupFunc = function (min, max) {
            var _this = this;
            return function (gpgpu, webGLProgram) {
                if (_this.minLoc == null) {
                    _this.minLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'minVal');
                    _this.maxLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'maxVal');
                }
                gpgpu.gl.uniform1f(_this.minLoc, min);
                gpgpu.gl.uniform1f(_this.maxLoc, max);
            };
        };
        return ClipPackedProgram;
    }());

    /**
     * @license
     * Copyright 2018 Google LLC. All Rights Reserved.
     * Licensed under the Apache License, Version 2.0 (the "License");
     * you may not use this file except in compliance with the License.
     * You may obtain a copy of the License at
     *
     * http://www.apache.org/licenses/LICENSE-2.0
     *
     * Unless required by applicable law or agreed to in writing, software
     * distributed under the License is distributed on an "AS IS" BASIS,
     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
     * See the License for the specific language governing permissions and
     * limitations under the License.
     * =============================================================================
     */
    var ComplexAbsProgram = /** @class */ (function () {
        function ComplexAbsProgram(shape) {
            this.variableNames = ['real', 'imag'];
            this.outputShape = shape;
            this.userCode = "\n      void main() {\n        float re = abs(getRealAtOutCoords());\n        float im = abs(getImagAtOutCoords());\n        float mx = max(re, im);\n\n        // sadly the length function in glsl is not underflow-safe\n        // (at least not on Intel GPUs). So the safe solution is\n        // to ensure underflow-safety in all cases.\n        setOutput(\n          mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n        );\n      }\n    ";
        }
        return ComplexAbsProgram;
    }());

    /**
     * @license
     * Copyright 2017 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.
     * =============================================================================
     */
    var ConcatProgram = /** @class */ (function () {
        // Concats 2d tensors along axis=1. See comments in MathBackendWebGL.concat().
        function ConcatProgram(shapes) {
            this.outputShape = [];
            this.outputShape = computeOutShape(shapes, 1 /* axis */);
            this.variableNames = shapes.map(function (_, i) { return "T" + i; });
            var offsets = new Array(shapes.length - 1);
            offsets[0] = shapes[0][1];
            for (var i = 1; i < offsets.length; i++) {
                offsets[i] = offsets[i - 1] + shapes[i][1];
            }
            var snippets = ["if (yC < " + offsets[0] + ") setOutput(getT0(yR, yC));"];
            for (var i = 1; i < offsets.length; i++) {
                var shift = offsets[i - 1];
                snippets.push("else if (yC < " + offsets[i] + ") " +
                    ("setOutput(getT" + i + "(yR, yC-" + shift + "));"));
            }
            var lastIndex = offsets.length;
            var lastShift = offsets[offsets.length - 1];
            snippets.push("else setOutput(getT" + lastIndex + "(yR, yC-" + lastShift + "));");
            this.userCode = "\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int yR = coords.x;\n        int yC = coords.y;\n\n        " + snippets.join('\n        ') + "\n      }\n    ";
        }
        return ConcatProgram;
    }());

    /**
     * @license
     * Copyright 2019 Google 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.
     * =============================================================================
     */
    var ConcatPackedProgram = /** @class */ (function () {
        function ConcatPackedProgram(shapes, axis) {
            this.packedInputs = true;
            this.packedOutput = true;
            this.outputShape = [];
            this.outputShape = computeOutShape(shapes, axis);
            var shape = this.outputShape;
            var rank = shape.length;
            var dtype = getCoordsDataType(rank);
            var coords = getChannels('coords', rank);
            var channels = ['x', 'y', 'z', 'w', 'u', 'v'].slice(0, rank);
            this.variableNames = shapes.map(function (_, i) { return "T" + i; });
            var offsets = new Array(shapes.length - 1);
            offsets[0] = shapes[0][axis];
            for (var i = 1; i < offsets.length; i++) {
                offsets[i] = offsets[i - 1] + shapes[i][axis];
            }
            var channel = channels[axis];
            var lastChannels = channels.slice(-2);
            var allChannels = channels.join();
            var getValueSnippet = "if (" + channel + " < " + offsets[0] + ") {\n        return getChannel(\n            getT0(" + allChannels + "), vec2(" + lastChannels.join() + "));\n        }";
            for (var i = 1; i < offsets.length; i++) {
                var shift_1 = offsets[i - 1];
                // Note: the >= comparison below may seem unnecessary given the check
                // above but is needed to workaround branch execution issues on some
                // devices. It makes all the conditions exclusive without relying on
                // execution order.
                getValueSnippet += "\n        if (" + channel + " < " + offsets[i] + "  && " + channel + " >= " + offsets[i - 1] + ") {\n          return getChannel(\n            getT" + i + "(" + shiftedChannels(channels, channel, shift_1) + "),\n            vec2(" + shiftedChannels(lastChannels, channel, shift_1) + "));\n        }";
            }
            var lastIndex = offsets.length;
            var shift = offsets[offsets.length - 1];
            getValueSnippet += "\n        return getChannel(\n          getT" + lastIndex + "(" + shiftedChannels(channels, channel, shift) + "),\n          vec2(" + shiftedChannels(lastChannels, channel, shift) + "));";
            this.userCode = "\n      float getValue(" + channels.map(function (x) { return 'int ' + x; }) + ") {\n        " + getValueSnippet + "\n      }\n\n      void main() {\n        " + dtype + " coords = getOutputCoords();\n        vec4 result = vec4(getValue(" + coords + "), 0., 0., 0.);\n\n        " + coords[rank - 1] + " = " + coords[rank - 1] + " + 1;\n        if (" + coords[rank - 1] + " < " + shape[rank - 1] + ") {\n          result.g = getValue(" + coords + ");\n        }\n\n        " + coords[rank - 2] + " = " + coords[rank - 2] + " + 1;\n        if (" + coords[rank - 2] + " < " + shape[rank - 2] + ") {\n          result.a = getValue(" + coords + ");\n        }\n\n        " + coords[rank - 1] + " = " + coords[rank - 1] + " - 1;\n        if (" + coords[rank - 2] + " < " + shape[rank - 2] + " &&\n            " + coords[rank - 1] + " < " + shape[rank - 1] + ") {\n          result.b = getValue(" + coords + ");\n        }\n        setOutput(result);\n      }\n    ";
        }
        return ConcatPackedProgram;
    }());
    /**
     * Return an expression for coordinates into a vector where a given channel
     * will be offset by [shift].
     *
     * @param channels the channels to consider
     * @param channel the channel we want shifted
     * @param shift  the amount to subtract from the channel.
     *
     * @returns a string of the form 'x, y-[shift], z' where any one channel can
     * have the shift applied.
     */
    function shiftedChannels(channels, channel, shift) {
        var channelIdx = channels.indexOf(channel);
        var res = channels.map(function (c, idx) {
            if (idx === channelIdx) {
                return c + " - " + shift;
            }
            else {
                return c;
            }
        });
        return res.join();
    }

    /**
     * @license
     * Copyright 2017 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.
     * =============================================================================
     */
    var Conv2DDerFilterProgram = /** @class */ (function () {
        function Conv2DDerFilterProgram(convInfo) {
            this.variableNames = ['x', 'dy'];
            this.outputShape = convInfo.filterShape;
            var strideHeight = convInfo.strideHeight;
            var strideWidth = convInfo.strideWidth;
            var padTop = convInfo.padInfo.top;
            var padLeft = convInfo.padInfo.left;
            var isChannelsLast = convInfo.dataFormat === 'channelsLast';
            this.userCode = "\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int d2 = coords.w;\n\n        // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int b = 0; b < " + convInfo.batchSize + "; b++) {\n          for (int yR = 0; yR < " + convInfo.outHeight + "; yR++) {\n            int xR = wR + yR * " + strideHeight + " - " + padTop + ";\n\n            if (xR < 0 || xR >= " + convInfo.inHeight + ") {\n              continue;\n            }\n\n            for (int yC = 0; yC < " + convInfo.outWidth + "; yC++) {\n              int xC = wC + yC * " + strideWidth + " - " + padLeft + ";\n\n              if (xC < 0 || xC >= " + convInfo.inWidth + ") {\n                continue;\n              }\n\n              if (" + isChannelsLast + ") {\n                float dyValue = getDy(b, yR, yC, d2);\n                float xValue = getX(b, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              } else {\n                float dyValue = getDy(b, d2, yR, yC);\n                float xValue = getX(b, d1, xR, xC);\n                dotProd += (xValue * dyValue);\n              }\n\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";
        }
        return Conv2DDerFilterProgram;
    }());
    var Conv2DDerInputProgram = /** @class */ (function () {
        function Conv2DDerInputProgram(convInfo) {
            this.variableNames = ['dy', 'W'];
            this.outputShape = convInfo.inShape;
            var filterHeight = convInfo.filterHeight;
            var filterWidth = convInfo.filterWidth;
            var strideHeight = convInfo.strideHeight;
            var strideWidth = convInfo.strideWidth;
            var isChannelsLast = convInfo.dataFormat === 'channelsLast';
            var padTop = filterHeight - 1 - convInfo.padInfo.top;
            var padLeft = filterWidth - 1 - convInfo.padInfo.left;
            var rowDim = isChannelsLast ? 1 : 2;
            var colDim = isChannelsLast ? 2 : 3;
            var channelDim = isChannelsLast ? 3 : 1;
            this.userCode = "\n      const ivec2 pads = ivec2(" + padTop + ", " + padLeft + ");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[" + channelDim + "];\n\n        ivec2 dyCorner = ivec2(coords[" + rowDim + "], coords[" + colDim + "]) - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < " + filterHeight + "; wR++) {\n          float dyR = float(dyRCorner + wR) / " + strideHeight + ".0;\n\n          if (dyR < 0.0 || dyR >= " + convInfo.outHeight + ".0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = " + filterHeight + " - 1 - wR;\n\n          for (int wC = 0; wC < " + filterWidth + "; wC++) {\n            float dyC = float(dyCCorner + wC) / " + strideWidth + ".0;\n\n            if (dyC < 0.0 || dyC >= " + convInfo.outWidth + ".0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = " + filterWidth + " - 1 - wC;\n\n            for (int d2 = 0; d2 < " + convInfo.outChannels + "; d2++) {\n\n              if (" + isChannelsLast + ") {\n                float xValue = getDy(batch, idyR, idyC, d2);\n                float wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              } else {\n                float xValue = getDy(batch, d2, idyR, idyC);\n                float wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";
        }
        return Conv2DDerInputProgram;
    }());
    var Conv3DDerFilterProgram = /** @class */ (function () {
        function Conv3DDerFilterProgram(convInfo) {
            this.variableNames = ['x', 'dy'];
            this.outputShape = convInfo.filterShape;
            var strideDepth = convInfo.strideDepth;
            var strideHeight = convInfo.strideHeight;
            var strideWidth = convInfo.strideWidth;
            var padFront = convInfo.padInfo.front;
            var padTop = convInfo.padInfo.top;
            var padLeft = convInfo.padInfo.left;
            this.userCode = "\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int wF = coords.x;\n        int wR = coords.y;\n        int wC = coords.z;\n        int d1 = coords.w;\n        int d2 = coords.u;\n\n        float dotProd = 0.0;\n\n        for (int b = 0; b < " + convInfo.batchSize + "; b++) {\n          for (int yF = 0; yF < " + convInfo.outDepth + "; yF++) {\n            int xF = wF + yF * " + strideDepth + " - " + padFront + ";\n\n            if (xF < 0 || xF >= " + convInfo.inDepth + ") {\n              continue;\n            }\n\n            for (int yR = 0; yR < " + convInfo.outHeight + "; yR++) {\n              int xR = wR + yR * " + strideHeight + " - " + padTop + ";\n\n              if (xR < 0 || xR >= " + convInfo.inHeight + ") {\n                continue;\n              }\n\n              for (int yC = 0; yC < " + convInfo.outWidth + "; yC++) {\n                int xC = wC + yC * " + strideWidth + " - " + padLeft + ";\n\n                if (xC < 0 || xC >= " + convInfo.inWidth + ") {\n                  continue;\n                }\n\n                float dyValue = getDy(b, yF, yR, yC, d2);\n                float xValue = getX(b, xF, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";
        }
        return Conv3DDerFilterProgram;
    }());
    var Conv3DDerInputProgram = /** @class */ (function () {
        function Conv3DDerInputProgram(convInfo) {
            this.variableNames = ['dy', 'W'];
            this.outputShape = convInfo.inShape;
            var filterDepth = convInfo.filterDepth;
            var filterHeight = convInfo.filterHeight;
            var filterWidth = convInfo.filterWidth;
            var strideDepth = convInfo.strideDepth;
            var strideHeight = convInfo.strideHeight;
            var strideWidth = convInfo.strideWidth;
            var padFront = filterDepth - 1 - convInfo.padInfo.front;
            var padTop = filterHeight - 1 - convInfo.padInfo.top;
            var padLeft = filterWidth - 1 - convInfo.padInfo.left;
            this.userCode = "\n      const ivec3 pads = ivec3(" + padFront + ", " + padTop + ", " + padLeft + ");\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d1 = coords.u;\n\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyFCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        float dotProd = 0.0;\n        for (int wF = 0; wF < " + filterDepth + "; wF++) {\n          float dyF = float(dyFCorner + wF) / " + strideDepth + ".0;\n\n          if (dyF < 0.0 || dyF >= " + convInfo.outDepth + ".0 || fract(dyF) > 0.0) {\n            continue;\n          }\n          int idyF = int(dyF);\n\n          int wFPerm = " + filterDepth + " - 1 - wF;\n\n          for (int wR = 0; wR < " + filterHeight + "; wR++) {\n            float dyR = float(dyRCorner + wR) / " + strideHeight + ".0;\n\n            if (dyR < 0.0 || dyR >= " + convInfo.outHeight + ".0 ||\n              fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            int wRPerm = " + filterHeight + " - 1 - wR;\n\n            for (int wC = 0; wC < " + filterWidth + "; wC++) {\n              float dyC = float(dyCCorner + wC) / " + strideWidth + ".0;\n\n              if (dyC < 0.0 || dyC >= " + convInfo.outWidth + ".0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              int wCPerm = " + filterWidth + " - 1 - wC;\n\n              for (int d2 = 0; d2 < " + convInfo.outChannels + "; d2++) {\n                float xValue = getDy(batch, idyF, idyR, idyC, d2);\n                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";
        }
        return Conv3DDerInputProgram;
    }());

    /**
     * @license
     * Copyright 2018 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.
     * =============================================================================
     */
    var DepthwiseConv2DDerFilterProgram = /** @class */ (function () {
        function DepthwiseConv2DDerFilterProgram(convInfo) {
            this.variableNames = ['x', 'dy'];
            this.outputShape = convInfo.filterShape;
            var strideHeight = convInfo.strideHeight;
            var strideWidth = convInfo.strideWidth;
            var padTop = convInfo.padInfo.top;
            var padLeft = convInfo.padInfo.left;
            var channelMul = convInfo.outChannels / convInfo.inChannels;
            this.userCode = "\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int dm = coords.w;\n        int d2 = d1 * " + channelMul + " + dm;\n\n        float dotProd = 0.0;\n\n        // TO DO: Vec4 over the batch size\n        for (int b = 0; b < " + convInfo.batchSize + "; b++) {\n          for (int yR = 0; yR < " + convInfo.outHeight + "; yR++) {\n            int xR = wR + yR * " + strideHeight + " - " + padTop + ";\n\n            if (xR < 0 || xR >= " + convInfo.inHeight + ") {\n              continue;\n            }\n\n            for (int yC = 0; yC < " + convInfo.outWidth + "; yC++) {\n              int xC = wC + yC * " + strideWidth + " - " + padLeft + ";\n\n              if (xC < 0 || xC >= " + convInfo.inWidth + ") {\n                continue;\n              }\n\n              float dyValue = getDy(b, yR, yC, d2);\n              float xValue = getX(b, xR, xC, d1);\n              dotProd += (xValue * dyValue);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";
        }
        return DepthwiseConv2DDerFilterProgram;
    }());
    var DepthwiseConv2DDerInputProgram = /** @class */ (function () {
        function DepthwiseConv2DDerInputProgram(convInfo) {
            this.variableNames = ['dy', 'W'];
            this.outputShape = convInfo.inShape;
            var filterHeight = convInfo.filterHeight;
            var filterWidth = convInfo.filterWidth;
            var strideHeight = convInfo.strideHeight;
            var strideWidth = convInfo.strideWidth;
            var padTop = filterHeight - 1 - convInfo.padInfo.top;
            var padLeft = filterWidth - 1 - convInfo.padInfo.left;
            var channelMul = convInfo.outChannels / convInfo.inChannels;
            this.userCode = "\n      const ivec2 pads = ivec2(" + padTop + ", " + padLeft + ");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[3];\n        ivec2 dyCorner = coords.yz - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        float dotProd = 0.0;\n\n        for (int wR = 0; wR < " + filterHeight + "; wR++) {\n          float dyR = float(dyRCorner + wR) / " + strideHeight + ".0;\n\n          if (dyR < 0.0 || dyR >= " + convInfo.outHeight + ".0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = " + filterHeight + " - 1 - wR;\n\n          for (int wC = 0; wC < " + filterWidth + "; wC++) {\n            float dyC = float(dyCCorner + wC) / " + strideWidth + ".0;\n\n            if (dyC < 0.0 || dyC >= " + convInfo.outWidth + ".0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = " + filterWidth + " - 1 - wC;\n\n            // TO DO: Vec4 over the channelMul\n            for (int dm = 0; dm < " + channelMul + "; dm++) {\n              int d2 = d1 * " + channelMul + " + dm;\n              float xValue = getDy(batch, idyR, idyC, d2);\n              float wValue = getW(wRPerm, wCPerm, d1, dm);\n              dotProd += xValue * wValue;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";
        }
        return DepthwiseConv2DDerInputProgram;
    }());

    /**
     * @license
     * Copyright 2017 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.
     * =============================================================================
     */
    var Conv2DProgram = /** @class */ (function () {
        function Conv2DProgram(convInfo, addBias, activation, hasPreluActivationWeights) {
            if (addBias === void 0) { addBias = false; }
            if (activation === void 0) { activation = null; }
            if (hasPreluActivationWeights === void 0) { hasPreluActivationWeights = false; }
            this.variableNames = ['x', 'W'];
            this.outputShape = convInfo.outShape;
            var padTop = convInfo.padInfo.top;
            var padLeft = convInfo.padInfo.left;
            var strideHeight = convInfo.strideHeight;
            var strideWidth = convInfo.strideWidth;
            var dilationHeight = convInfo.dilationHeight;
            var dilationWidth = convInfo.dilationWidth;
            var filterHeight = convInfo.filterHeight;
            var filterWidth = convInfo.filterWidth;
            var inputDepthNearestVec4 = Math.floor(convInfo.inChannels / 4) * 4;
            var inputDepthVec4Remainder = convInfo.inChannels % 4;
            var isChannelsLast = convInfo.dataFormat === 'channelsLast';
            var rowDim = isChannelsLast ? 1 : 2;
            var colDim = isChannelsLast ? 2 : 3;
            var channelDim = isChannelsLast ? 3 : 1;
            var activationSnippet = '', applyActivationSnippet = '';
            if (activation) {
                if (hasPreluActivationWeights) {
                    activationSnippet = "float activation(float a) {\n          float b = getPreluActivationWeightsAtOutCoords();\n          " + activation + "\n        }";
                }
                else {
                    activationSnippet = "\n          float activation(float x) {\n            " + activation + "\n          }\n        ";
                }
                applyActivationSnippet = "result = activation(result);";
            }
            var addBiasSnippet = addBias ? 'result += getBiasAtOutCoords();' : '';
            if (addBias) {
                this.variableNames.push('bias');
            }
            if (hasPreluActivationWeights) {
                this.variableNames.push('preluActivationWeights');
            }
            this.userCode = "\n      " + activationSnippet + "\n\n      const ivec2 strides = ivec2(" + strideHeight + ", " + strideWidth + ");\n      const ivec2 pads = ivec2(" + padTop + ", " + padLeft + ");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d2 = coords[" + channelDim + "];\n\n        ivec2 xRCCorner =\n            ivec2(coords[" + rowDim + "], coords[" + colDim + "]) * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < " + filterHeight + "; wR++) {\n          int xR = xRCorner + wR * " + dilationHeight + ";\n\n          if (xR < 0 || xR >= " + convInfo.inHeight + ") {\n            continue;\n          }\n\n          for (int wC = 0; wC < " + filterWidth + "; wC++) {\n            int xC = xCCorner + wC * " + dilationWidth + ";\n\n            if (xC < 0 || xC >= " + convInfo.inWidth + ") {\n              continue;\n            }\n\n            for (int d1 = 0; d1 < " + inputDepthNearestVec4 + "; d1 += 4) {\n              vec4 wValues = vec4(\n                getW(wR, wC, d1, d2),\n                getW(wR, wC, d1 + 1, d2),\n                getW(wR, wC, d1 + 2, d2),\n                getW(wR, wC, d1 + 3, d2)\n              );\n\n              if (" + isChannelsLast + ") {\n                vec4 xValues = vec4(\n                  getX(batch, xR, xC, d1),\n                  getX(batch, xR, xC, d1 + 1),\n                  getX(batch, xR, xC, d1 + 2),\n                  getX(batch, xR, xC, d1 + 3)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec4 xValues = vec4(\n                  getX(batch, d1, xR, xC),\n                  getX(batch, d1 + 1, xR, xC),\n                  getX(batch, d1 + 2, xR, xC),\n                  getX(batch, d1 + 3, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n\n            if (" + (inputDepthVec4Remainder === 1) + ") {\n\n              if (" + isChannelsLast + ") {\n                dotProd +=\n                    getX(batch, xR, xC, " + inputDepthNearestVec4 + ") *\n                    getW(wR, wC, " + inputDepthNearestVec4 + ", d2);\n              } else {\n                dotProd +=\n                    getX(batch, " + inputDepthNearestVec4 + ", xR, xC) *\n                    getW(wR, wC, " + inputDepthNearestVec4 + ", d2);\n              }\n\n            } else if (" + (inputDepthVec4Remainder === 2) + ") {\n              vec2 wValues = vec2(\n                getW(wR, wC, " + inputDepthNearestVec4 + ", d2),\n                getW(wR, wC, " + inputDepthNearestVec4 + " + 1, d2)\n              );\n\n              if (" + isChannelsLast + ") {\n                vec2 xValues = vec2(\n                  getX(batch, xR, xC, " + inputDepthNearestVec4 + "),\n                  getX(batch, xR, xC, " + inputDepthNearestVec4 + " + 1)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec2 xValues = vec2(\n                  getX(batch, " + inputDepthNearestVec4 + ", xR, xC),\n                  getX(batch, " + inputDepthNearestVec4 + " + 1, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            } else if (" + (inputDepthVec4Remainder === 3) + ") {\n              vec3 wValues = vec3(\n                getW(wR, wC, " + inputDepthNearestVec4 + ", d2),\n                getW(wR, wC, " + inputDepthNearestVec4 + " + 1, d2),\n                getW(wR, wC, " + inputDepthNearestVec4 + " + 2, d2)\n              );\n\n              if (" + isChannelsLast + ") {\n                vec3 xValues = vec3(\n                  getX(batch, xR, xC, " + inputDepthNearestVec4 + "),\n                  getX(batch, xR, xC, " + inputDepthNearestVec4 + " + 1),\n                  getX(batch, xR, xC, " + inputDepthNearestVec4 + " + 2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec3 xValues = vec3(\n                  getX(batch, " + inputDepthNearestVec4 + ", xR, xC),\n                  getX(batch, " + inputDepthNearestVec4 + " + 1, xR, xC),\n                  getX(batch, " + inputDepthNearestVec4 + " + 2, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            }\n          }\n        }\n\n        float result = dotProd;\n        " + addBiasSnippet + "\n        " + applyActivationSnippet + "\n        setOutput(result);\n      }\n    ";
        }
        return Conv2DProgram;
    }());
    var Conv3DProgram = /** @class */ (function () {
        function Conv3DProgram(convInfo) {
            this.variableNames = ['x', 'W'];
            this.outputShape = convInfo.outShape;
            var padFront = convInfo.padInfo.front;
            var padTop = convInfo.padInfo.top;
            var padLeft = convInfo.padInfo.left;
            var strideDepth = convInfo.strideDepth;
            var strideHeight = convInfo.strideHeight;
            var strideWidth = convInfo.strideWidth;
            var dilationDepth = convInfo.dilationDepth;
            var dilationHeight = convInfo.dilationHeight;
            var dilationWidth = convInfo.dilationWidth;
            var filterDepth = convInfo.filterDepth;
            var filterHeight = convInfo.filterHeight;
            var filterWidth = convInfo.filterWidth;
            var inputDepthNearestVec4 = Math.floor(convInfo.inChannels / 4) * 4;
            var inputDepthVec4Remainder = convInfo.inChannels % 4;
            this.userCode = "\n      const ivec3 strides = ivec3(" + strideDepth + ", " + strideHeight + ", " + strideWidth + ");\n      const ivec3 pads = ivec3(" + padFront + ", " + padTop + ", " + padLeft + ");\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d2 = coords.u;\n\n        ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xFCorner = xFRCCorner.x;\n        int xRCorner = xFRCCorner.y;\n        int xCCorner = xFRCCorner.z;\n\n        // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n        // y(yF, yR, yC, d2). ? = to be determined. : = across all\n        // values in that axis.\n        float dotProd = 0.0;\n        for (int wF = 0; wF < " + filterDepth + "; wF++) {\n          int xF = xFCorner + wF * " + dilationDepth + ";\n\n          if (xF < 0 || xF >= " + convInfo.inDepth + ") {\n            continue;\n          }\n\n          for (int wR = 0; wR < " + filterHeight + "; wR++) {\n            int xR = xRCorner + wR * " + dilationHeight + ";\n\n            if (xR < 0 || xR >= " + convInfo.inHeight + ") {\n              continue;\n            }\n\n            for (int wC = 0; wC < " + filterWidth + "; wC++) {\n              int xC = xCCorner + wC * " + dilationWidth + ";\n\n              if (xC < 0 || xC >= " + convInfo.inWidth + ") {\n                continue;\n              }\n\n              for (int d1 = 0; d1 < " + inputDepthNearestVec4 + "; d1 += 4) {\n                vec4 xValues = vec4(\n                  getX(batch, xF, xR, xC, d1),\n                  getX(batch, xF, xR, xC, d1 + 1),\n                  getX(batch, xF, xR, xC, d1 + 2),\n                  getX(batch, xF, xR, xC, d1 + 3)\n                );\n                vec4 wValues = vec4(\n                  getW(wF, wR, wC, d1, d2),\n                  getW(wF, wR, wC, d1 + 1, d2),\n                  getW(wF, wR, wC, d1 + 2, d2),\n                  getW(wF, wR, wC, d1 + 3, d2)\n                );\n\n                dotProd += dot(xValues, wValues);\n              }\n\n              if (" + (inputDepthVec4Remainder === 1) + ") {\n                dotProd +=\n                  getX(batch, xF, xR, xC, " + inputDepthNearestVec4 + ") *\n                  getW(wF, wR, wC, " + inputDepthNearestVec4 + ", d2);\n              } else if (" + (inputDepthVec4Remainder === 2) + ") {\n                vec2 xValues = vec2(\n                  getX(batch, xF, xR, xC, " + inputDepthNearestVec4 + "),\n                  getX(batch, xF, xR, xC, " + inputDepthNearestVec4 + " + 1)\n                );\n                vec2 wValues = vec2(\n                  getW(wF, wR, wC, " + inputDepthNearestVec4 + ", d2),\n                  getW(wF, wR, wC, " + inputDepthNearestVec4 + " + 1, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else if (" + (inputDepthVec4Remainder === 3) + ") {\n                vec3 xValues = vec3(\n                  getX(batch, xF, xR, xC, " + inputDepthNearestVec4 + "),\n                  getX(batch, xF, xR, xC, " + inputDepthNearestVec4 + " + 1),\n                  getX(batch, xF, xR, xC, " + inputDepthNearestVec4 + " + 2)\n                );\n                vec3 wValues = vec3(\n                  getW(wF, wR, wC, " + inputDepthNearestVec4 + ", d2),\n                  getW(wF, wR, wC, " + inputDepthNearestVec4 + " + 1, d2),\n                  getW(wF, wR, wC, " + inputDepthNearestVec4 + " + 2, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";
        }
        return Conv3DProgram;
    }());

    /**
     * @license
     * Copyright 2017 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.
     * =============================================================================
     */
    var DepthwiseConv2DProgram = /** @class */ (function () {
        function DepthwiseConv2DProgram(convInfo, addBias, activation, hasPreluActivation) {
            if (addBias === void 0) { addBias = false; }
            if (activation === void 0) { activation = null; }
            if (hasPreluActivation === void 0) { hasPreluActivation = false; }
            this.variableNames = ['x', 'W'];
            this.outputShape = convInfo.outShape;
            var xNumRows = convInfo.inHeight;
            var xNumCols = convInfo.inWidth;
            var padTop = convInfo.padInfo.top;
            var padLeft = convInfo.padInfo.left;
            var strideHeight = convInfo.strideHeight;
            var strideWidth = convInfo.strideWidth;
            var dilationHeight = convInfo.dilationHeight;
            var dilationWidth = convInfo.dilationWidth;
            var filterHeight = convInfo.filterHeight;
            var filterWidth = convInfo.filterWidth;
            var channelMul = convInfo.outChannels / convInfo.inChannels;
            var activationSnippet = '', applyActivationSnippet = '';
            if (activation) {
                if (hasPreluActivation) {
                    activationSnippet = "float activation(float a) {\n          float b = getPreluActivationWeightsAtOutCoords();\n          " + activation + "\n        }";
                }
                else {
                    activationSnippet = "\n          float activation(float x) {\n            " + activation + "\n          }\n        ";
                }
                applyActivationSnippet = "result = activation(result);";
            }
            var addBiasSnippet = addBias ? 'result += getBiasAtOutCoords();' : '';
            if (addBias) {
                this.variableNames.push('bias');
            }
            if (hasPreluActivation) {
                this.variableNames.push('preluActivationWeights');
            }
            this.userCode = "\n      " + activationSnippet + "\n\n      const ivec2 strides = ivec2(" + strideHeight + ", " + strideWidth + ");\n      const ivec2 pads = ivec2(" + padTop + ", " + padLeft + ");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2 / " + channelMul + ";\n        int q = d2 - d1 * " + channelMul + ";\n\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.\n        for (int wR = 0; wR < " + filterHeight + "; wR++) {\n          int xR = xRCorner + wR * " + dilationHeight + ";\n\n          if (xR < 0 || xR >= " + xNumRows + ") {\n            continue;\n          }\n\n          for (int wC = 0; wC < " + filterWidth + "; wC++) {\n            int xC = xCCorner + wC * " + dilationWidth + ";\n\n            if (xC < 0 || xC >= " + xNumCols + ") {\n              continue;\n            }\n\n            float xVal = getX(batch, xR, xC, d1);\n            float wVal = getW(wR, wC, d1, q);\n            dotProd += xVal * wVal;\n          }\n        }\n\n        float result = dotProd;\n        " + addBiasSnippet + "\n        " + applyActivationSnippet + "\n        setOutput(result);\n      }\n    ";
        }
        return DepthwiseConv2DProgram;
    }());

    /**
     * @license
     * Copyright 2018 Google LLC. All Rights Reserved.
     * Licensed under the Apache License, Version 2.0 (the "License");
     * you may not use this file except in compliance with the License.
     * You may obtain a copy of the License at
     *
     * http://www.apache.org/licenses/LICENSE-2.0
     *
     * Unless required by applicable law or agreed to in writing, software
     * distributed under the License is distributed on an "AS IS" BASIS,
     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
     * See the License for the specific language governing permissions and
     * limitations under the License.
     * =============================================================================
     */
    var DepthwiseConvPacked2DProgram = /** @class */ (function () {
        function DepthwiseConvPacked2DProgram(convInfo, addBias, activation, hasPreluActivation) {
            if (addBias === void 0) { addBias = false; }
            if (activation === void 0) { activation = null; }
            if (hasPreluActivation === void 0) { hasPreluActivation = false; }
            this.variableNames = ['x', 'W'];
            this.packedInputs = true;
            this.packedOutput = true;
            this.outputShape = convInfo.outShape;
            var xNumRows = convInfo.inHeight;
            var xNumCols = convInfo.inWidth;
            var padTop = convInfo.padInfo.top;
            var padLeft = convInfo.padInfo.left;
            var strideHeight = convInfo.strideHeight;
            var strideWidth = convInfo.strideWidth;
            var dilationHeight = convInfo.dilationHeight;
            var dilationWidth = convInfo.dilationWidth;
            var filterHeight = convInfo.filterHeight;
            var filterWidth = convInfo.filterWidth;
            var texelsAcross = filterWidth;
            var mainLoop = "int xR; int xC; int xCOffset;";
            for (var r = 0; r < filterHeight; r++) {
                for (var c = 0; c < filterWidth; c++) {
                    mainLoop += "\n          vec4 xTexelR" + r + "C" + c * 2 + " = vec4(0.);\n          vec4 wR" + r + "C" + c + " = vec4(0.);\n          vec4 xR" + r + "C" + c + " = vec4(0.);";
                }
            }
            /**
             * This vectorized implementation works by gathering the values needed for
             * each output channel's dot product into vec4's and then multiplying them
             * all together (this happens in the final double for-loop below). Most of
             * the main loop consists of constructing these vec4's with the minimum
             * number of texture2D calls, which means making use of all four returned
             * values from a texture2D call at once.
             */
            for (var r = 0; r < filterHeight; r++) {
                for (var texelC = 0; texelC < texelsAcross; texelC++) {
                    var c = texelC * 2;
                    mainLoop += "\n          xR = xRCorner + " + r * dilationHeight + ";\n          xC = xCCorner + " + c * dilationWidth + ";\n        ";
                    if (strideWidth === 1) {
                        if (c < filterWidth) {
                            // If padding is odd, the outer texels have to be composed.
                            if (padLeft % 2 === 1) {
                                // TODO: Ensure vec4 previous does not result in redundant sample,
                                // and avoid setting xTexelRC's that exceed the boundary in the
                                // first place rather than resetting them to vec4(0)).
                                // To compute xCOffset:
                                // - If padding is odd, we must add 1 to ensure we ask for an
                                // even-numbered row.
                                // - We subtract 2 to access the previous texel.
                                mainLoop += "\n                xCOffset = xC + 1;\n                if(xR >= 0 && xR < " + xNumRows + " && xCOffset >= 0 && xCOffset < " + xNumCols + ") {\n                  xTexelR" + r + "C" + c + " = getX(batch, xR, xCOffset, d1);\n\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if(xCOffset + 1 >= " + xNumCols + ") {\n                    xTexelR" + r + "C" + c + ".zw = vec2(0.);\n                  }\n                } else {\n                  xTexelR" + r + "C" + c + " = vec4(0.);\n                }\n\n                xCOffset = xC + 1 - 2;\n                if(xR >= 0 && xR < " + xNumRows + " && xCOffset >= 0 && xCOffset < " + xNumCols + ") {\n                  vec4 previous = getX(batch, xR, xCOffset, d1);\n\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if(xCOffset + 1 >= " + xNumCols + ") {\n                    previous.zw = vec2(0.);\n                  }\n\n                  xR" + r + "C" + c + " = vec4(previous.zw, xTexelR" + r + "C" + c + ".xy);\n                } else {\n                  xR" + r + "C" + c + " = vec4(0, 0, xTexelR" + r + "C" + c + ".xy);\n                }\n              ";
                            }
                            else {
                                // Padding is even, so xRC corresponds to a single texel.
                                mainLoop += "\n                if(xR >= 0 && xR < " + xNumRows + " && xC >= 0 && xC < " + xNumCols + ") {\n                  xTexelR" + r + "C" + c + " = getX(batch, xR, xC, d1);\n                } else {\n                  xTexelR" + r + "C" + c + " = vec4(0.);\n                }\n\n                xR" + r + "C" + c + " = xTexelR" + r + "C" + c + ";\n              ";
                            }
                            if (c + 1 < filterWidth) {
                                // If dilation is even, the second entry should match the first
                                // (either both are composed or both are single samples). But if
                                // dilation is odd, then the second entry should be the opposite
                                // of the first (if the first is composed, the second is a single
                                // sample, and vice versa.)
                                var nextTexelOffset = padLeft % 2 === 0 ?
                                    nearestLargerEven(dilationWidth) :
                                    dilationWidth;
                                if ((dilationWidth % 2 === 0 && padLeft % 2 === 1) ||
                                    (dilationWidth % 2 !== 0 && padLeft % 2 !== 1)) {
                                    mainLoop += "\n                  xCOffset = xC + " + padLeft % 2 + " + " + nextTexelOffset + ";\n\n                  if(xR >= 0 && xR < " + xNumRows + " &&\n                    xCOffset >= 0 && xCOffset < " + xNumCols + ") {\n                    xTexelR" + r + "C" + (c + 2) + " = getX(batch, xR, xCOffset, d1);\n                  }\n                ";
                                    // If dilation > 1 then the xRC's will not be able to share any
                                    // values, so each xRC will require two unique calls to getX.
                                    if (dilationWidth > 1) {
                                        mainLoop += "\n                    xCOffset -= 2;\n                    if(xR >= 0 && xR < " + xNumRows + " &&\n                      xCOffset >= 0 && xCOffset < " + xNumCols + ") {\n                      xTexelR" + r + "C" + c + " = getX(batch, xR, xCOffset, d1);\n                    } else {\n                      xTexelR" + r + "C" + c + " = vec4(0.);\n                    }\n                  ";
                                    }
                                    mainLoop += "\n                  xR" + r + "C" + (c + 1) + " = vec4(\n                    xTexelR" + r + "C" + c + ".zw, xTexelR" + r + "C" + (c + 2) + ".xy);\n                ";
                                }
                                else {
                                    mainLoop += "\n                  xCOffset = xC + " + nextTexelOffset + ";\n\n                  if(xR >= 0 && xR < " + xNumRows + " &&\n                    xCOffset >= 0 && xCOffset < " + xNumCols + ") {\n                    xTexelR" + r + "C" + (c + 2) + " = getX(batch, xR, xCOffset, d1);\n                  }\n\n                  xR" + r + "C" + (c + 1) + " = xTexelR" + r + "C" + (c + 2) + ";\n                ";
                                }
                            }
                        }
                    }
                    else { // stride > 1
                        if (c < filterWidth) {
                            mainLoop += "\n              if(xR >= 0 && xR < " + xNumRows + ") {\n            ";
                            // Depending on whether padLeft is even or odd, we want either the
                            // xy or zw channels from X texels for xR${r}C${c}. If padLeft is
                            // even, xR${r}C${c + 1} is simply the zw channels of texels we've
                            // already sampled. But if padLeft is odd, xR${r}C{$c + 1}.zw will
                            // need to come from the xy channels of a new texel, hence the `vec4
                            // final` initialized below.
                            if (padLeft % 2 === 1) {
                                mainLoop += "\n                xCOffset = xC + 1 - " + strideWidth + ";\n                if(xCOffset >= 0 && xCOffset < " + xNumCols + ") {\n                  xTexelR" + r + "C" + c + " = getX(batch, xR, xCOffset, d1);\n                } else {\n                  xTexelR" + r + "C" + c + " = vec4(0.);\n                }\n\n                if(xC + 1 >= 0 && xC + 1 < " + xNumCols + ") {\n                  xTexelR" + r + "C" + (c + 2) + " = getX(batch, xR, xC + 1, d1);\n                } else {\n                  xTexelR" + r + "C" + (c + 2) + " = vec4(0.);\n                }\n\n                xR" + r + "C" + c + " = vec4(\n                  xTexelR" + r + "C" + c + ".zw, xTexelR" + r + "C" + (c + 2) + ".zw);\n              ";
                                if (c + 1 < filterWidth) {
                                    mainLoop += "\n                  vec4 final = vec4(0.);\n                  xCOffset = xC + 1 + " + strideWidth + ";\n                  if(xCOffset >= 0 && xCOffset < " + xNumCols + ") {\n                    final = getX(batch, xR, xCOffset, d1);\n                  }\n                  xR" + r + "C" + (c + 1) + " = vec4(xTexelR" + r + "C" + (c + 2) + ".xy, final.xy);\n                ";
                                }
                            }
                            else {
                                mainLoop += "\n                if(xC >= 0 && xC < " + xNumCols + ") {\n                  xTexelR" + r + "C" + c + " = getX(batch, xR, xC, d1);\n                } else {\n                  xTexelR" + r + "C" + c + " = vec4(0.);\n                }\n\n                xCOffset = xC + " + strideWidth + ";\n                if(xCOffset >= 0 && xCOffset < " + xNumCols + ") {\n                  xTexelR" + r + "C" + (c + 2) + " = getX(batch, xR, xCOffset, d1);\n                } else {\n                  xTexelR" + r + "C" + (c + 2) + " = vec4(0.);\n                }\n\n                xR" + r + "C" + c + " = vec4(\n                  xTexelR" + r + "C" + c + ".xy, xTexelR" + r + "C" + (c + 2) + ".xy);\n              ";
                                if (c + 1 < filterWidth) {
                                    mainLoop += "\n                  xR" + r + "C" + (c + 1) + " = vec4(\n                    xTexelR" + r + "C" + c + ".zw, xTexelR" + r + "C" + (c + 2) + ".zw);\n                ";
                                }
                            }
                            mainLoop += "}";
                        }
                    }
                    if (c < filterWidth) {
                        mainLoop += "\n            vec4 wTexelR" + r + "C" + c + " = getW(" + r + ", " + c + ", d1, q);\n            wR" + r + "C" + c + " = vec4(wTexelR" + r + "C" + c + ".xz, wTexelR" + r + "C" + c + ".xz);\n          ";
                        if (c + 1 < filterWidth) {
                            mainLoop += "\n              vec4 wTexelR" + r + "C" + (c + 1) + " = getW(" + r + ", " + (c + 1) + ", d1, q);\n              wR" + r + "C" + (c + 1) + " =\n                vec4(wTexelR" + r + "C" + (c + 1) + ".xz, wTexelR" + r + "C" + (c + 1) + ".xz);";
                        }
                    }
                }
            }
            for (var r = 0; r < filterHeight; r++) {
                for (var c = 0; c < filterWidth; c++) {
                    mainLoop += "dotProd += xR" + r + "C" + c + " * wR" + r + "C" + c + ";";
                }
            }
            var activationSnippet = '', applyActivationSnippet = '';
            if (activation) {
                if (hasPreluActivation) {
                    activationSnippet = "vec4 activation(vec4 a) {\n          vec4 b = getPreluActivationWeightsAtOutCoords();\n          " + activation + "\n        }";
                }
                else {
                    activationSnippet = "vec4 activation(vec4 x) {\n          " + activation + "\n        }";
                }
                applyActivationSnippet = "result = activation(result);";
            }
            var addBiasSnippet = addBias ? 'result += getBiasAtOutCoords();' : '';
            if (addBias) {
                this.variableNames.push('bias');
            }
            if (hasPreluActivation) {
                this.variableNames.push('preluActivationWeights');
            }
            this.userCode = "\n      " + activationSnippet + "\n\n      const ivec2 strides = ivec2(" + strideHeight + ", " + strideWidth + ");\n      const ivec2 pads = ivec2(" + padTop + ", " + padLeft + ");\n\n      void main() {\n\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2;\n        int q = 0;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        vec4 dotProd = vec4(0.);\n\n        " + mainLoop + "\n\n        vec4 result = dotProd;\n        " + addBiasSnippet + "\n        " + applyActivationSnippet + "\n        setOutput(result);\n      }\n    ";
        }
        return DepthwiseConvPacked2DProgram;
    }());

    /**
     * @license
     * Copyright 2017 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.
     * =============================================================================
     */
    var CropAndResizeProgram = /** @class */ (function () {
        function CropAndResizeProgram(imageShape, boxShape, cropSize, method, extrapolationValue) {
            this.variableNames = ['Image', 'Boxes', 'BoxInd'];
            this.outputShape = [];
            var batch = imageShape[0], imageHeight = imageShape[1], imageWidth = imageShape[2], depth = imageShape[3];
            var numBoxes = boxShape[0];
            var cropHeight = cropSize[0], cropWidth = cropSize[1];
            this.outputShape = [numBoxes, cropHeight, cropWidth, depth];
            var methodId = method === 'bilinear' ? 1 : 0;
            var _a = [imageHeight - 1 + ".0", imageWidth - 1 + ".0"], inputHeightFloat = _a[0], inputWidthFloat = _a[1];
            var _b = cropHeight > 1 ?
                [
                    "" + (imageHeight - 1) / (cropHeight - 1),
                    '(y2-y1) * height_ratio',
                    "y1*" + inputHeightFloat + " + float(y)*(height_scale)",
                ] :
                [
                    '0.0',
                    '0.0',
                    "0.5 * (y1+y2) * " + inputHeightFloat,
                ], heightRatio = _b[0], heightScale = _b[1], inY = _b[2];
            var _c = cropWidth > 1 ?
                [
                    "" + (imageWidth - 1) / (cropWidth - 1),
                    '(x2-x1) * width_ratio',
                    "x1*" + inputWidthFloat + " + float(x)*(width_scale)",
                ] :
                [
                    '0.0',
                    '0.0',
                    "0.5 * (x1+x2) * " + inputWidthFloat,
                ], widthRatio = _c[0], widthScale = _c[1], inX = _c[2];
            // Reference implementation
            // tslint:disable-next-line:max-line-length
            // https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/crop_and_resize_op_gpu.cu.cc
            this.userCode = "\n      const float height_ratio = float(" + heightRatio + ");\n      const float width_ratio = float(" + widthRatio + ");\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int y = coords[1];\n        int x = coords[2];\n        int d = coords[3];\n\n        // get box vals\n        float y1 = getBoxes(b,0);\n        float x1 = getBoxes(b,1);\n        float y2 = getBoxes(b,2);\n        float x2 = getBoxes(b,3);\n\n        // get image in batch index\n        int bInd = round(getBoxInd(b));\n        if(bInd < 0 || bInd >= " + batch + ") {\n          return;\n        }\n\n        float height_scale = " + heightScale + ";\n        float width_scale = " + widthScale + ";\n\n        float in_y = " + inY + ";\n        if( in_y < 0.0 || in_y > " + inputHeightFloat + " ) {\n          setOutput(float(" + extrapolationValue + "));\n          return;\n        }\n        float in_x = " + inX + ";\n        if( in_x < 0.0 || in_x > " + inputWidthFloat + " ) {\n          setOutput(float(" + extrapolationValue + "));\n          return;\n        }\n\n        vec2 sourceFracIndexCR = vec2(in_x,in_y);\n        if(" + methodId + " == 1) {\n          // Compute the four integer indices.\n          ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);\n          ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));\n\n          float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);\n          float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);\n          float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);\n          float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);\n\n          vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n\n          float top = topLeft + (topRight - topLeft) * fracCR.x;\n          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n          float newValue = top + (bottom - top) * fracCR.y;\n          setOutput(newValue);\n        } else {\n          // Compute the coordinators of nearest neighbor point.\n          ivec2 sourceNearestCR = ivec2(floor(\n            sourceFracIndexCR + vec2(0.5,0.5)));\n          float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);\n          setOutput(newValue);\n        }\n      }\n    ";
        }
        return CropAndResizeProgram;
    }());

    /**
     * @license
     * Copyright 2018 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.
     * =============================================================================
     */
    var CumSumProgram = /** @class */ (function () {
        function CumSumProgram(shape, exclusive, reverse) {
            this.variableNames = ['x'];
            this.outputShape = shape;
            var rank = shape.length;
            var finalDim = shape[shape.length - 1];
            var comparator = reverse ? '<' : '>';
            this.userCode = "\n      int getIndex(int i) {\n        " + (reverse ? "return " + finalDim + " -i - 1;" : 'return i;') + "\n      }\n\n      void main() {\n        " + getCoordsDataType(rank) + " coords = getOutputCoords();\n        int end = " + getFinalCoord(rank, 'coords') + ";\n        float val = 0.0;\n        for (int i = " + finalDim + " - 1; i >= 0; i -= 1) {\n          int idx = getIndex(i);\n          if (idx " + comparator + " end) {\n            continue;\n          }\n          if (idx == end && " + exclusive + ") {\n            continue;\n          }\n          " + getFinalCoord(rank, 'coords') + " = idx;\n          val += getX(" + getCoords(rank, 'coords') + ");\n        }\n        setOutput(val);\n      }\n    ";
        }
        return CumSumProgram;
    }());
    function getCoords(rank, name) {
        if (rank === 1) {
            return "" + name;
        }
        else if (rank === 2) {
            return name + ".x, " + name + ".y";
        }
        else if (rank === 3) {
            return name + ".x, " + name + ".y, " + name + ".z";
        }
        else if (rank === 4) {
            return name + ".x, " + name + ".y, " + name + ".z, " + name + ".w";
        }
        else {
            throw Error("Cumulative sum for rank " + rank + " is not yet supported");
        }
    }
    function getFinalCoord(rank, name) {
        if (rank === 1) {
            return "" + name;
        }
        else if (rank === 2) {
            return name + ".y";
        }
        else if (rank === 3) {
            return name + ".z";
        }
        else if (rank === 4) {
            return name + ".w";
        }
        else {
            throw Error("Cumulative sum for rank " + rank + " is not yet supported");
        }
    }

    /**
     * @license
     * Copyright 2019 Google LLC. All Rights Reserved.
     * Licensed under the Apache License, Version 2.0 (the "License");
     * you may not use this file except in compliance with the License.
     * You may obtain a copy of the License at
     *
     * http://www.apache.org/licenses/LICENSE-2.0
     *
     * Unless required by applicable law or agreed to in writing, software
     * distributed under the License is distributed on an "AS IS" BASIS,
     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
     * See the License for the specific language governing permissions and
     * limitations under the License.
     * =============================================================================
     */
    var DecodeMatrixProgram = /** @class */ (function () {
        function DecodeMatrixProgram(outputShape) {
            this.variableNames = ['A'];
            this.packedInputs = false;
            this.packedOutput = true;
            this.outPackingScheme = PackingScheme.DENSE;
            var texShape = getDenseTexShape(outputShape);
            var glsl = getGlslDifferences();
            this.outputShape = outputShape;
            this.userCode = "\n      ivec3 outCoordsFromFlatIndex(int index) {\n        " + getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd'], outputShape) + "\n        return ivec3(r, c, d);\n      }\n\n      void main() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n          vec2(" + texShape[0] + ", " + texShape[1] + "));\n        int index = 4 * (resTexRC.x * " + texShape[1] + " + resTexRC.y);\n\n        vec4 result = vec4(0.);\n\n        for (int i=0; i<4; i++) {\n          int flatIndex = index + i;\n          ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n          result[i] = getA(rc.x, rc.y, rc.z);\n        }\n\n        " + glsl.output + " = result;\n      }\n    ";
        }
        return DecodeMatrixProgram;
    }());

    /**
     * @license
     * Copyright 2019 Google LLC. All Rights Reserved.
     * Licensed under the Apache License, Version 2.0 (the "License");
     * you may not use this file except in compliance with the License.
     * You may obtain a copy of the License at
     *
     * http://www.apache.org/licenses/LICENSE-2.0
     *
     * Unless required by applicable law or agreed to in writing, software
     * distributed under the License is distributed on an "AS IS" BASIS,
     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
     * See the License for the specific language governing permissions and
     * limitations under the License.
     * =============================================================================
     */
    var DecodeMatrixPackedProgram = /** @class */ (function () {
        function DecodeMatrixPackedProgram(outputShape) {
            this.variableNames = ['A'];
            this.packedInputs = true;
            this.packedOutput = true;
            this.outPackingScheme = PackingScheme.DENSE;
            var texShape = getDenseTexShape(outputShape);
            var glsl = getGlslDifferences();
            this.outputShape = outputShape;
            this.userCode = "\n      ivec3 outCoordsFromFlatIndex(int index) {\n        " + getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd'], outputShape) + "\n        return ivec3(r, c, d);\n      }\n\n      void main() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n          vec2(" + texShape[0] + ", " + texShape[1] + "));\n        int index = 4 * (resTexRC.x * " + texShape[1] + " + resTexRC.y);\n\n        vec4 result = vec4(0.);\n\n        for (int i=0; i<4; i++) {\n          int flatIndex = index + i;\n          ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n          result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));\n        }\n\n        " + glsl.output + " = result;\n      }\n    ";
        }
        return DecodeMatrixPackedProgram;
    }());

    /**
     * @license
     * Copyright 2018 Google 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.
     * =============================================================================
     */
    var DepthToSpaceProgram = /** @class */ (function () {
        function DepthToSpaceProgram(outputShape, blockSize, dataFormat) {
            this.variableNames = ['x'];
            this.outputShape = [];
            this.outputShape = outputShape;
            this.blockSize = blockSize;
            this.dataFormat = dataFormat;
            this.userCode = "\n    void main() {\n      ivec4 coords = getOutputCoords();\n      int b = coords[0];\n      int h = " + this.getHeightCoordString() + ";\n      int w = " + this.getWidthCoordString() + ";\n      int d = " + this.getDepthCoordString() + ";\n\n      int in_h = h / " + blockSize + ";\n      int offset_h = imod(h, " + blockSize + ");\n      int in_w = w / " + blockSize + ";\n      int offset_w = imod(w, " + blockSize + ");\n      int offset_d = (offset_h * " + blockSize + " + offset_w) *\n        " + this.getOutputDepthSize() + ";\n      int in_d = d + offset_d;\n\n      float result = " + this.getInputSamplingString() + ";\n      setOutput(result);\n    }\n  ";
        }
        DepthToSpaceProgram.prototype.getHeightCoordString = function () {
            if (this.dataFormat === 'NHWC') {
                return "coords[1]";
            }
            else {
                return "coords[2]";
            }
        };
        DepthToSpaceProgram.prototype.getWidthCoordString = function () {
            if (this.dataFormat === 'NHWC') {
                return "coords[2]";
            }
            else {
                return "coords[3]";
            }
        };
        DepthToSpaceProgram.prototype.getDepthCoordString = function () {
            if (this.dataFormat === 'NHWC') {
                return "coords[3]";
            }
            else {
                return "coords[1]";
            }
        };
        DepthToSpaceProgram.prototype.getOutputDepthSize = function () {
            if (this.dataFormat === 'NHWC') {
                return this.outputShape[3];
            }
            else {
                return this.outputShape[1];
            }
        };
        DepthToSpaceProgram.prototype.getInputSamplingString = function () {
            if (this.dataFormat === 'NHWC') {
                return "getX(b, in_h, in_w, in_d)";
            }
            else {
                return "getX(b, in_d, in_h, in_w)";
            }
        };
        return DepthToSpaceProgram;
    }());

    /**
     * @license
     * Copyright 2019 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.
     * =============================================================================
     */
    var DiagProgram = /** @class */ (function () {
        function DiagProgram(size) {
            this.variableNames = ['X'];
            this.outputShape = [size, size];
            this.userCode = "\n      void main() {\n          ivec2 coords = getOutputCoords();\n          float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;\n          setOutput(val);\n      }\n    ";
        }
        return DiagProgram;
    }());

    /**
     * @license
     * Copyright 2018 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.
     * =============================================================================
     */
    var EncodeFloatProgram = /** @class */ (function () {
        function EncodeFloatProgram(outputShape) {
            this.variableNames = ['A'];
            this.outTexUsage = TextureUsage.DOWNLOAD;
            var glsl = getGlslDifferences();
            this.outputShape = outputShape;
            this.userCode = "\n      " + ENCODE_FLOAT_SNIPPET + "\n\n      void main() {\n        float x = getAAtOutCoords();\n        " + glsl.output + " = encode_float(x);\n      }\n    ";
        }
        return EncodeFloatProgram;
    }());

    /**
     * @license
     * Copyright 2018 Google 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.
     * =============================================================================
     */
    var EncodeFloatPackedProgram = /** @class */ (function () {
        function EncodeFloatPackedProgram(outputShape) {
            this.variableNames = ['A'];
            this.packedInputs = true;
            this.packedOutput = false;
            this.outTexUsage = TextureUsage.DOWNLOAD;
            var glsl = getGlslDifferences();
            this.outputShape = outputShape;
            this.userCode = "\n      " + ENCODE_FLOAT_SNIPPET + "\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n        float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));\n        " + glsl.output + " = encode_float(x);\n      }\n    ";
        }
        return EncodeFloatPackedProgram;
    }());

    /**
     * @license
     * Copyright 2018 Google 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.
     * =============================================================================
     */
    var EncodeMatrixProgram = /** @class */ (function () {
        function EncodeMatrixProgram(outputShape, texShape, inputIsUnsignedByte) {
            if (inputIsUnsignedByte === void 0) { inputIsUnsignedByte = false; }
            this.variableNames = ['A'];
            var glsl = getGlslDifferences();
            var height = texShape[0], width = texShape[1];
            this.outputShape = outputShape;
            var output = "result";
            if (inputIsUnsignedByte) {
                output = "floor(result * 255. + 0.5)";
            }
            this.userCode = "\n      " + getFlatIndexFrom3D(outputShape) + "\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n\n        int flatIndex = getFlatIndex(coords);\n        int offset = imod(flatIndex, 4);\n\n        flatIndex = idiv(flatIndex, 4, 1.);\n        \n        int r = flatIndex / " + width + ";\n        int c = imod(flatIndex, " + width + ");\n        vec2 uv = (vec2(c, r) + halfCR) / vec2(" + width + ".0, " + height + ".0);\n        vec4 values = " + glsl.texture2D + "(A, uv);\n\n        float result;\n\n        if(offset == 0) {\n          result = values[0];\n        } else if(offset == 1) {\n          result = values[1];\n        } else if(offset == 2) {\n          result = values[2];\n        } else {\n          result = values[3];\n        }\n\n        " + glsl.output + " = vec4(" + output + ", 0., 0., 0.);\n      }\n    ";
        }
        return EncodeMatrixProgram;
    }());

    /**
     * @license
     * Copyright 2018 Google 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.
     * =============================================================================
     */
    /*
    This is how the shader encodes a tensor with shape = [2, 3, 5]
    (indices are [batch, row, col]).

    000|001   002|003   004|xxx   020|021   022|023   024|xxx
    -------   -------   -------   -------   -------   -------
    010|011   012|013   014|xxx   xxx|xxx   xxx|xxx   xxx|xxx

    100|101   102|103   104|xxx   120|121   122|123   124|xxx
    -------   -------   -------   -------   -------   -------
    110|111   112|113   114|xxx   xxx|xxx   xxx|xxx   xxx|xxx

    Single texels contain only values from the same batch, and from adjacent rows
    and columns.
     */
    var EncodeMatrixPackedProgram = /** @class */ (function () {
        function EncodeMatrixPackedProgram(outputShape, texShape, inputIsUnsignedByte) {
            if (inputIsUnsignedByte === void 0) { inputIsUnsignedByte = false; }
            this.variableNames = ['A'];
            this.packedInputs = false;
            this.packedOutput = true;
            var glsl = getGlslDifferences();
            var height = texShape[0], width = texShape[1];
            this.outputShape = outputShape;
            var mainLoop = '';
            var output = 'result';
            if (inputIsUnsignedByte) {
                output = 'floor(result * 255. + 0.5)';
            }
            for (var row = 0; row <= 1; row++) {
                for (var col = 0; col <= 1; col++) {
                    var channel = row * 2 + col;
                    mainLoop += "\n          localCoords = coords;\n          if(localCoords[2] + " + col + " < " + outputShape[2] + ") {\n            localCoords[2] += " + col + ";\n            if(localCoords[1] + " + row + " < " + outputShape[1] + ") {\n              localCoords[1] += " + row + ";\n\n              flatIndex = getFlatIndex(localCoords);\n              offset = imod(flatIndex, 4);\n\n              flatIndex = idiv(flatIndex, 4, 1.);\n\n              r = flatIndex / " + width + ";\n              c = imod(flatIndex, " + width + ");\n              uv = (vec2(c, r) + halfCR) / vec2(" + width + ".0, " + height + ".0);\n              values = " + glsl.texture2D + "(A, uv);\n\n              if(offset == 0) {\n                result[" + channel + "] = values[0];\n              } else if(offset == 1) {\n                result[" + channel + "] = values[1];\n              } else if(offset == 2) {\n                result[" + channel + "] = values[2];\n              } else {\n                result[" + channel + "] = values[3];\n              }\n            }\n          }\n        ";
                }
            }
            this.userCode = "\n      " + getFlatIndexFrom3D(outputShape) + "\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n\n        vec4 result = vec4(0.);\n        int flatIndex, r, c, offset;\n        ivec3 localCoords;\n        vec2 uv;\n        vec4 values;\n\n        " + mainLoop + "\n\n        " + glsl.output + " = " + output + ";\n      }\n    ";
        }
        return EncodeMatrixPackedProgram;
    }());

    /**
     * @license
     * Copyright 2018 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.
     * =============================================================================
     */
    var COMPLEX_FFT = {
        REAL: 'return real * expR - imag * expI;',
        IMAG: 'return real * expI + imag * expR;'
    };
    var FFTProgram = /** @class */ (function () {
        function FFTProgram(op, inputShape, inverse) {
            this.variableNames = ['real', 'imag'];
            var innerDim = inputShape[1];
            this.outputShape = inputShape;
            var exponentMultiplierSnippet = inverse ? "2.0 * " + Math.PI : "-2.0 * " + Math.PI;
            var resultDenominator = inverse ? innerDim + ".0" : '1.0';
            this.userCode = "\n      const float exponentMultiplier = " + exponentMultiplierSnippet + ";\n\n      float unaryOpComplex(float real, float expR, float imag, float expI) {\n        " + op + "\n      }\n\n      float mulMatDFT(int batch, int index) {\n        float indexRatio = float(index) / float(" + innerDim + ");\n        float exponentMultiplierTimesIndexRatio =\n            exponentMultiplier * indexRatio;\n\n        float result = 0.0;\n\n        for (int i = 0; i < " + innerDim + "; i++) {\n          // x = (-2|2 * PI / N) * index * i;\n          float x = exponentMultiplierTimesIndexRatio * float(i);\n          float expR = cos(x);\n          float expI = sin(x);\n          float real = getReal(batch, i);\n          float imag = getImag(batch, i);\n\n          result +=\n              unaryOpComplex(real, expR, imag, expI) / " + resultDenominator + ";\n        }\n\n        return result;\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        setOutput(mulMatDFT(coords[0], coords[1]));\n      }\n    ";
        }
        return FFTProgram;
    }());

    /**
     * @license
     * Copyright 2019 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.
     * =============================================================================
     */
    var FillProgram = /** @class */ (function () {
        function FillProgram(shape, value) {
            this.outputShape = [];
            this.variableNames = ['x'];
            this.outputShape = shape;
            this.userCode = "\n      uniform float value;\n      void main() {\n        // Input can be obtained from uniform value.\n        setOutput(value);\n      }\n    ";
        }
        FillProgram.prototype.getCustomSetupFunc = function (value) {
            var _this = this;
            return function (gpgpu, webGLProgram) {
                if (_this.valueLoc == null) {
                    _this.valueLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'value');
                }
                gpgpu.gl.uniform1f(_this.valueLoc, value);
            };
        };
        return FillProgram;
    }());

    /**
     * @license
     * Copyright 2017 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.
     * =============================================================================
     */
    var GatherProgram = /** @class */ (function () {
        function GatherProgram(aShape, indicesLength, axis) {
            this.variableNames = ['A', 'indices'];
            var outputShape = aShape.slice();
            outputShape[axis] = indicesLength;
            this.outputShape = outputShape;
            this.rank = outputShape.length;
            var dtype = getCoordsDataType(this.rank);
            var sourceCoords = getSourceCoords$1(aShape, axis);
            this.userCode = "\n      void main() {\n        " + dtype + " resRC = getOutputCoords();\n        setOutput(getA(" + sourceCoords + "));\n      }\n    ";
        }
        return GatherProgram;
    }());
    function getSourceCoords$1(aShape, axis) {
        var rank = aShape.length;
        if (rank > 4) {
            throw Error("Gather for rank " + rank + " is not yet supported");
        }
        if (rank === 1) {
            return "int(getIndices(resRC))";
        }
        var currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w'];
        var sourceCoords = [];
        for (var i = 0; i < aShape.length; i++) {
            if (i === axis) {
                sourceCoords.push("int(getIndices(" + currentCoords[i] + "))");
            }
            else {
                sourceCoords.push("" + currentCoords[i]);
            }
        }
        return sourceCoords.join();
    }

    var GatherNDProgram = /** @class */ (function () {
        function GatherNDProgram(sliceDim, strides, shape) {
            this.sliceDim = sliceDim;
            this.strides = strides;
            this.variableNames = ['x', 'indices'];
            this.outputShape = shape;
            var stridesType = getCoordsDataType(strides.length);
            var dtype = getCoordsDataType(shape.length);
            var strideString = this.sliceDim > 1 ? 'strides[j]' : 'strides';
            this.userCode = "\n        " + stridesType + " strides = " + stridesType + "(" + this.strides + ");\n         void main() {\n          " + dtype + " coords = getOutputCoords();\n          int flattenIndex = 0;\n          for (int j = 0; j < " + this.sliceDim + "; j++) {\n            int index = round(getIndices(coords[0], j));\n            flattenIndex += index * " + strideString + ";\n          }\n          setOutput(getX(flattenIndex, coords[1]));\n        }\n      ";
        }
        return GatherNDProgram;
    }());

    /**
     * @license
     * Copyright 2017 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.
     * =============================================================================
     */
    function createVertexShader$1(gl, debug) {
        var glsl = getGlslDifferences();
        var vertexShaderSource = glsl.version + "\n    precision highp float;\n    " + glsl.attribute + " vec3 clipSpacePos;\n    " + glsl.attribute + " vec2 uv;\n    " + glsl.varyingVs + " vec2 resultUV;\n\n    void main() {\n      gl_Position = vec4(clipSpacePos, 1);\n      resultUV = uv;\n    }";
        return createVertexShader(gl, debug, vertexShaderSource);
    }
    function createVertexBuffer(gl, debug) {
        // [x y z u v] * [upper-left, lower-left, upper-right, lower-right]
        var vertexArray = new Float32Array([-1, 1, 0, 0, 1, -1, -1, 0, 0, 0, 1, 1, 0, 1, 1, 1, -1, 0, 1, 0]);
        return createStaticVertexBuffer(gl, debug, vertexArray);
    }
    function createIndexBuffer(gl, debug) {
        // OpenGL (and WebGL) have "CCW == front" winding
        var triangleVertexIndices = new Uint16Array([0, 1, 2, 2, 1, 3]);
        return createStaticIndexBuffer(gl, debug, triangleVertexIndices);
    }
    function createAndConfigureTexture(gl, debug, width, height, internalFormat, textureFormat, textureType) {
        validateTextureSize(width, height);
        var texture = createTexture(gl, debug);
        var tex2d = gl.TEXTURE_2D;
        callAndCheck(gl, debug, function () { return gl.bindTexture(tex2d, texture); });
        callAndCheck(gl, debug, function () { return gl.texParameteri(tex2d, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE); });
        callAndCheck(gl, debug, function () { return gl.texParameteri(tex2d, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE); });
        callAndCheck(gl, debug, function () { return gl.texParameteri(tex2d, gl.TEXTURE_MIN_FILTER, gl.NEAREST); });
        callAndCheck(gl, debug, function () { return gl.texParameteri(tex2d, gl.TEXTURE_MAG_FILTER, gl.NEAREST); });
        callAndCheck(gl, debug, function () { return gl.texImage2D(tex2d, 0, internalFormat, width, height, 0, textureFormat, textureType, null); });
        callAndCheck(gl, debug, function () { return gl.bindTexture(gl.TEXTURE_2D, null); });
        return texture;
    }
    function createFloat32MatrixTexture(gl, debug, rows, columns, textureConfig) {
        var _a = getUnpackedMatrixTextureShapeWidthHeight(rows, columns), width = _a[0], height = _a[1];
        return createAndConfigureTexture(gl, debug, width, height, textureConfig.internalFormatFloat, textureConfig.textureFormatFloat, gl.FLOAT);
    }
    function createFloat16MatrixTexture(gl, debug, rows, columns, textureConfig) {
        var _a = getUnpackedMatrixTextureShapeWidthHeight(rows, columns), width = _a[0], height = _a[1];
        return createAndConfigureTexture(gl, debug, width, height, textureConfig.internalFormatHalfFloat, textureConfig.textureFormatFloat, textureConfig.textureTypeHalfFloat);
    }
    function createUnsignedBytesMatrixTexture(gl, debug, rows, columns, textureConfig) {
        var _a = getUnpackedMatrixTextureShapeWidthHeight(rows, columns), width = _a[0], height = _a[1];
        return createAndConfigureTexture(gl, debug, width, height, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE);
    }
    function createPackedMatrixTexture(gl, debug, rows, columns, textureConfig) {
        var _a = getPackedMatrixTextureShapeWidthHeight(rows, columns), width = _a[0], height = _a[1];
        return createAndConfigureTexture(gl, debug, width, height, textureConfig.internalFormatPackedFloat, gl.RGBA, gl.FLOAT);
    }
    function createFloat16PackedMatrixTexture(gl, debug, rows, columns, textureConfig) {
        var _a = getPackedMatrixTextureShapeWidthHeight(rows, columns), width = _a[0], height = _a[1];
        return createAndConfigureTexture(gl, debug, width, height, textureConfig.internalFormatPackedHalfFloat, gl.RGBA, textureConfig.textureTypeHalfFloat);
    }
    function bindVertexProgramAttributeStreams(gl, debug, program, vertexBuffer) {
        var posOffset = 0; // x is the first buffer element
        var uvOffset = 3 * 4; // uv comes after [x y z]
        var stride = (3 * 4) + (2 * 4); // xyz + uv, each entry is 4-byte float.
        callAndCheck(gl, debug, function () { return gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer); });
        var success = bindVertexBufferToProgramAttribute(gl, debug, program, 'clipSpacePos', vertexBuffer, 3, stride, posOffset);
        return success &&
            bindVertexBufferToProgramAttribute(gl, debug, program, 'uv', vertexBuffer, 2, stride, uvOffset);
    }
    function uploadDenseMatrixToTexture(gl, debug, texture, width, height, data, textureConfig) {
        callAndCheck(gl, debug, function () { return gl.bindTexture(gl.TEXTURE_2D, texture); });
        var dataForUpload, texelDataType, internalFormat;
        if (data instanceof Uint8Array) {
            dataForUpload = new Uint8Array(width * height * 4);
            texelDataType = gl.UNSIGNED_BYTE;
            internalFormat = gl.RGBA;
        }
        else {
            dataForUpload = new Float32Array(width * height * 4);
            texelDataType = gl.FLOAT;
            internalFormat = textureConfig.internalFormatPackedFloat;
        }
        dataForUpload.set(data);
        callAndCheck(gl, debug, function () { return gl.texImage2D(gl.TEXTURE_2D, 0, internalFormat, width, height, 0, gl.RGBA, texelDataType, dataForUpload); });
        callAndCheck(gl, debug, function () { return gl.bindTexture(gl.TEXTURE_2D, null); });
    }
    function uploadPixelDataToTexture(gl, debug, texture, pixels) {
        callAndCheck(gl, debug, function () { return gl.bindTexture(gl.TEXTURE_2D, texture); });
        if (pixels.data instanceof Uint8Array) {
            callAndCheck(gl, debug, function () { return gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, pixels.width, pixels.height, 0, gl.RGBA, gl.UNSIGNED_BYTE, pixels.data); });
        }
        else {
            callAndCheck(gl, debug, function () { return gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, pixels); });
        }
        callAndCheck(gl, debug, function () { return gl.bindTexture(gl.TEXTURE_2D, null); });
    }
    function createBufferFromOutputTexture(gl2, debug, rows, columns, textureConfig) {
        // Create and bind the buffer.
        var buffer = gl2.createBuffer();
        callAndCheck(gl2, debug, function () { return gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer); });
        // Initialize the buffer to the size of the texture in bytes.
        var bytesPerFloat = 4;
        var valuesPerTexel = 4;
        var bufferSizeBytes = bytesPerFloat * valuesPerTexel * rows * columns;
        callAndCheck(gl2, debug, function () { return gl2.bufferData(gl2.PIXEL_PACK_BUFFER, bufferSizeBytes, gl2.STREAM_READ); });
        // Enqueue a command on the GPU command queue to copy of texture into the
        // buffer.
        callAndCheck(gl2, debug, function () { return gl2.readPixels(0, 0, columns, rows, gl2.RGBA, gl2.FLOAT, 0); });
        callAndCheck(gl2, debug, function () { return gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null); });
        return buffer;
    }
    function downloadFloat32MatrixFromBuffer(gl, buffer, size) {
        var gl2 = gl;
        var downloadTarget = new Float32Array(size);
        gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer);
        gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER, 0, downloadTarget);
        gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null);
        return downloadTarget;
    }
    function downloadByteEncodedFloatMatrixFromOutputTexture(gl, debug, rows, columns, textureConfig) {
        var _a = getUnpackedMatrixTextureShapeWidthHeight(rows, columns), w = _a[0], h = _a[1];
        var numChannels = 4;
        var downloadTarget = new Uint8Array(getUnpackedArraySizeFromMatrixSize(rows * columns, numChannels));
        callAndCheck(gl, debug, function () { return gl.readPixels(0, 0, w, h, textureConfig.downloadTextureFormat, gl.UNSIGNED_BYTE, downloadTarget); });
        // By wrapping the buffer in a Float32Array, we use native browser IEEE 754
        // decoding of the 4 bytes that back each 32 bit float.
        return new Float32Array(downloadTarget.buffer);
    }
    function downloadPackedMatrixFromBuffer(gl, buffer, batch, rows, cols, physicalRows, physicalCols, textureConfig) {
        var gl2 = gl;
        var downloadTarget = new Float32Array(getPackedRGBAArraySizeFromMatrixShape(physicalRows, physicalCols));
        gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer);
        gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER, 0, downloadTarget);
        gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null);
        return downloadTarget;
    }
    function downloadMatrixFromPackedOutputTexture(gl, debug, physicalRows, physicalCols) {
        var packedRGBA = new Float32Array(physicalRows * physicalCols * 4);
        callAndCheck(gl, debug, function () { return gl.readPixels(0, 0, physicalCols, physicalRows, gl.RGBA, gl.FLOAT, packedRGBA); });
        return packedRGBA;
    }

    var gpgpu_util = /*#__PURE__*/Object.freeze({
        createVertexShader: createVertexShader$1,
        createVertexBuffer: createVertexBuffer,
        createIndexBuffer: createIndexBuffer,
        createFloat32MatrixTexture: createFloat32MatrixTexture,
        createFloat16MatrixTexture: createFloat16MatrixTexture,
        createUnsignedBytesMatrixTexture: createUnsignedBytesMatrixTexture,
        createPackedMatrixTexture: createPackedMatrixTexture,
        createFloat16PackedMatrixTexture: createFloat16PackedMatrixTexture,
        bindVertexProgramAttributeStreams: bindVertexProgramAttributeStreams,
        uploadDenseMatrixToTexture: uploadDenseMatrixToTexture,
        uploadPixelDataToTexture: uploadPixelDataToTexture,
        createBufferFromOutputTexture: createBufferFromOutputTexture,
        downloadFloat32MatrixFromBuffer: downloadFloat32MatrixFromBuffer,
        downloadByteEncodedFloatMatrixFromOutputTexture: downloadByteEncodedFloatMatrixFromOutputTexture,
        downloadPackedMatrixFromBuffer: downloadPackedMatrixFromBuffer,
        downloadMatrixFromPackedOutputTexture: downloadMatrixFromPackedOutputTexture
    });

    /**
     * @license
     * Copyright 2017 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.
     * =============================================================================
     */
    var GPGPUContext = /** @class */ (function () {
        function GPGPUContext(gl) {
            this.outputTexture = null;
            this.program = null;
            this.disposed = false;
            this.vertexAttrsAreBound = false;
            this.itemsToPoll = [];
            var glVersion = env().getNumber('WEBGL_VERSION');
            if (gl != null) {
                this.gl = gl;
                setWebGLContext(glVersion, gl);
            }
            else {
                this.gl = getWebGLContext(glVersion);
            }
            // WebGL 2.0 enables texture floats without an extension.
            var COLOR_BUFFER_FLOAT = 'WEBGL_color_buffer_float';
            var COLOR_BUFFER_HALF_FLOAT = 'EXT_color_buffer_half_float';
            if (env().getNumber('WEBGL_VERSION') === 1) {
                var TEXTURE_FLOAT = 'OES_texture_float';
                var TEXTURE_HALF_FLOAT = 'OES_texture_half_float';
                this.textureFloatExtension =
                    getExtensionOrThrow(this.gl, this.debug, TEXTURE_FLOAT);
                if (hasExtension(this.gl, TEXTURE_HALF_FLOAT)) {
                    this.textureHalfFloatExtension = getExtensionOrThrow(this.gl, this.debug, TEXTURE_HALF_FLOAT);
                }
                else if (env().get('WEBGL_FORCE_F16_TEXTURES')) {
                    throw new Error('GL context does not support half float textures, yet the ' +
                        'environment flag WEBGL_FORCE_F16_TEXTURES is set to true.');
                }
                this.colorBufferFloatExtension = this.gl.getExtension(COLOR_BUFFER_FLOAT);
                if (hasExtension(this.gl, COLOR_BUFFER_HALF_FLOAT)) {
                    this.colorBufferHalfFloatExtension = getExtensionOrThrow(this.gl, this.debug, COLOR_BUFFER_HALF_FLOAT);
                }
                else if (env().get('WEBGL_FORCE_F16_TEXTURES')) {
                    throw new Error('GL context does not support color renderable half floats, yet ' +
                        'the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.');
                }
            }
            else {
                COLOR_BUFFER_FLOAT = 'EXT_color_buffer_float';
                if (hasExtension(this.gl, COLOR_BUFFER_FLOAT)) {
                    this.colorBufferFloatExtension =
                        this.gl.getExtension(COLOR_BUFFER_FLOAT);
                }
                else if (hasExtension(this.gl, COLOR_BUFFER_HALF_FLOAT)) {
                    this.colorBufferHalfFloatExtension =
                        this.gl.getExtension(COLOR_BUFFER_HALF_FLOAT);
                }
                else {
                    throw new Error('GL context does not support color renderable floats');
                }
            }
            this.vertexBuffer = createVertexBuffer(this.gl, this.debug);
            this.indexBuffer = createIndexBuffer(this.gl, this.debug);
            this.framebuffer = createFramebuffer(this.gl, this.debug);
            this.textureConfig =
                getTextureConfig(this.gl, this.textureHalfFloatExtension);
        }
        Object.defineProperty(GPGPUContext.prototype, "debug", {
            get: function () {
                return env().getBool('DEBUG');
            },
            enumerable: true,
            configurable: true
        });
        GPGPUContext.prototype.dispose = function () {
            var _this = this;
            if (this.disposed) {
                return;
            }
            if (this.program != null) {
                console.warn('Disposing a GPGPUContext that still has a bound WebGLProgram.' +
                    ' This is probably a resource leak, delete the program with ' +
                    'GPGPUContext.deleteProgram before disposing.');
            }
            if (this.outputTexture != null) {
                console.warn('Disposing a GPGPUContext that still has a bound output matrix ' +
                    'texture.  This is probably a resource leak, delete the output ' +
                    'matrix texture with GPGPUContext.deleteMatrixTexture before ' +
                    'disposing.');
            }
            var gl = this.gl;
            callAndCheck(gl, this.debug, function () { return gl.finish(); });
            callAndCheck(gl, this.debug, function () { return gl.bindFramebuffer(gl.FRAMEBUFFER, null); });
            callAndCheck(gl, this.debug, function () { return gl.deleteFramebuffer(_this.framebuffer); });
            callAndCheck(gl, this.debug, function () { return gl.bindBuffer(gl.ARRAY_BUFFER, null); });
            callAndCheck(gl, this.debug, function () { return gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, null); });
            callAndCheck(gl, this.debug, function () { return gl.deleteBuffer(_this.indexBuffer); });
            this.disposed = true;
        };
        GPGPUContext.prototype.createFloat32MatrixTexture = function (rows, columns) {
            this.throwIfDisposed();
            return createFloat32MatrixTexture(this.gl, this.debug, rows, columns, this.textureConfig);
        };
        GPGPUContext.prototype.createFloat16MatrixTexture = function (rows, columns) {
            this.throwIfDisposed();
            return createFloat16MatrixTexture(this.gl, this.debug, rows, columns, this.textureConfig);
        };
        GPGPUContext.prototype.createUnsignedBytesMatrixTexture = function (rows, columns) {
            this.throwIfDisposed();
            return createUnsignedBytesMatrixTexture(this.gl, this.debug, rows, columns, this.textureConfig);
        };
        GPGPUContext.prototype.uploadPixelDataToTexture = function (texture, pixels) {
            this.throwIfDisposed();
            uploadPixelDataToTexture(this.gl, this.debug, texture, pixels);
        };
        GPGPUContext.prototype.uploadDenseMatrixToTexture = function (texture, width, height, data) {
            this.throwIfDisposed();
            uploadDenseMatrixToTexture(this.gl, this.debug, texture, width, height, data, this.textureConfig);
        };
        GPGPUContext.prototype.createFloat16PackedMatrixTexture = function (rows, columns) {
            this.throwIfDisposed();
            return createFloat16PackedMatrixTexture(this.gl, this.debug, rows, columns, this.textureConfig);
        };
        GPGPUContext.prototype.createPackedMatrixTexture = function (rows, columns) {
            this.throwIfDisposed();
            return createPackedMatrixTexture(this.gl, this.debug, rows, columns, this.textureConfig);
        };
        GPGPUContext.prototype.deleteMatrixTexture = function (texture) {
            var _this = this;
            this.throwIfDisposed();
            if (this.outputTexture === texture) {
                unbindColorTextureFromFramebuffer(this.gl, this.debug, this.framebuffer);
                this.outputTexture = null;
            }
            callAndCheck(this.gl, this.debug, function () { return _this.gl.deleteTexture(texture); });
        };
        GPGPUContext.prototype.downloadByteEncodedFloatMatrixFromOutputTexture = function (texture, rows, columns) {
            var _this = this;
            return this.downloadMatrixDriver(texture, function () { return downloadByteEncodedFloatMatrixFromOutputTexture(_this.gl, _this.debug, rows, columns, _this.textureConfig); });
        };
        GPGPUContext.prototype.downloadPackedMatrixFromBuffer = function (buffer, batch, rows, columns, physicalRows, physicalCols) {
            return downloadPackedMatrixFromBuffer(this.gl, buffer, batch, rows, columns, physicalRows, physicalCols, this.textureConfig);
        };
        GPGPUContext.prototype.downloadFloat32MatrixFromBuffer = function (buffer, size) {
            return downloadFloat32MatrixFromBuffer(this.gl, buffer, size);
        };
        GPGPUContext.prototype.createBufferFromTexture = function (texture, rows, columns) {
            this.bindTextureToFrameBuffer(texture);
            var result = createBufferFromOutputTexture(this.gl, this.debug, rows, columns, this.textureConfig);
            this.unbindTextureToFrameBuffer();
            return result;
        };
        GPGPUContext.prototype.createAndWaitForFence = function () {
            var fenceContext = this.createFence(this.gl);
            return this.pollFence(fenceContext);
        };
        GPGPUContext.prototype.createFence = function (gl) {
            var _this = this;
            var query;
            var isFencePassed;
            if (env().getBool('WEBGL_FENCE_API_ENABLED')) {
                var gl2_1 = gl;
                var sync_1 = gl2_1.fenceSync(gl2_1.SYNC_GPU_COMMANDS_COMPLETE, 0);
                gl.flush();
                isFencePassed = function () {
                    var status = gl2_1.clientWaitSync(sync_1, 0, 0);
                    return status === gl2_1.ALREADY_SIGNALED ||
                        status === gl2_1.CONDITION_SATISFIED;
                };
                query = sync_1;
            }
            else if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') > 0) {
                query = this.beginQuery();
                this.endQuery();
                isFencePassed = function () { return _this.isQueryAvailable(query, env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION')); };
            }
            else {
                // If we have no way to fence, return true immediately. This will fire in
                // WebGL 1.0 when there is no disjoint query timer. In this case, because
                // the fence passes immediately, we'll immediately ask for a download of
                // the texture, which will cause the UI thread to hang.
                isFencePassed = function () { return true; };
            }
            return { query: query, isFencePassed: isFencePassed };
        };
        GPGPUContext.prototype.downloadMatrixFromPackedTexture = function (texture, physicalRows, physicalCols) {
            var _this = this;
            return this.downloadMatrixDriver(texture, function () { return downloadMatrixFromPackedOutputTexture(_this.gl, _this.debug, physicalRows, physicalCols); });
        };
        GPGPUContext.prototype.createProgram = function (fragmentShaderSource) {
            this.throwIfDisposed();
            var gl = this.gl;
            var fragmentShader = createFragmentShader(gl, this.debug, fragmentShaderSource);
            var vertexShader = createVertexShader$1(gl, this.debug);
            var program = createProgram(gl, this.debug);
            callAndCheck(gl, this.debug, function () { return gl.attachShader(program, vertexShader); });
            callAndCheck(gl, this.debug, function () { return gl.attachShader(program, fragmentShader); });
            linkProgram(gl, this.debug, program);
            if (this.debug) {
                validateProgram(gl, this.debug, program);
            }
            if (!this.vertexAttrsAreBound) {
                this.setProgram(program);
                this.vertexAttrsAreBound = bindVertexProgramAttributeStreams(gl, this.debug, this.program, this.vertexBuffer);
            }
            return program;
        };
        GPGPUContext.prototype.deleteProgram = function (program) {
            var _this = this;
            this.throwIfDisposed();
            if (program === this.program) {
                this.program = null;
            }
            if (program != null) {
                callAndCheck(this.gl, this.debug, function () { return _this.gl.deleteProgram(program); });
            }
        };
        GPGPUContext.prototype.setProgram = function (program) {
            var _this = this;
            this.throwIfDisposed();
            this.program = program;
            if ((this.program != null) && this.debug) {
                validateProgram(this.gl, this.debug, this.program);
            }
            callAndCheck(this.gl, this.debug, function () { return _this.gl.useProgram(program); });
        };
        GPGPUContext.prototype.getUniformLocation = function (program, uniformName, shouldThrow) {
            if (shouldThrow === void 0) { shouldThrow = true; }
            this.throwIfDisposed();
            if (shouldThrow) {
                return getProgramUniformLocationOrThrow(this.gl, this.debug, program, uniformName);
            }
            else {
                return getProgramUniformLocation(this.gl, program, uniformName);
            }
        };
        GPGPUContext.prototype.getAttributeLocation = function (program, attribute) {
            var _this = this;
            this.throwIfDisposed();
            return callAndCheck(this.gl, this.debug, function () { return _this.gl.getAttribLocation(program, attribute); });
        };
        GPGPUContext.prototype.getUniformLocationNoThrow = function (program, uniformName) {
            this.throwIfDisposed();
            return this.gl.getUniformLocation(program, uniformName);
        };
        GPGPUContext.prototype.setInputMatrixTexture = function (inputMatrixTexture, uniformLocation, textureUnit) {
            this.throwIfDisposed();
            this.throwIfNoProgram();
            bindTextureToProgramUniformSampler(this.gl, this.debug, this.program, inputMatrixTexture, uniformLocation, textureUnit);
        };
        GPGPUContext.prototype.setOutputMatrixTexture = function (outputMatrixTexture, rows, columns) {
            this.setOutputMatrixTextureDriver(outputMatrixTexture, columns, rows);
        };
        GPGPUContext.prototype.setOutputPackedMatrixTexture = function (outputPackedMatrixTexture, rows, columns) {
            this.throwIfDisposed();
            var _a = getPackedMatrixTextureShapeWidthHeight(rows, columns), width = _a[0], height = _a[1];
            this.setOutputMatrixTextureDriver(outputPackedMatrixTexture, width, height);
        };
        GPGPUContext.prototype.setOutputMatrixWriteRegion = function (startRow, numRows, startColumn, numColumns) {
            this.setOutputMatrixWriteRegionDriver(startColumn, startRow, numColumns, numRows);
        };
        GPGPUContext.prototype.setOutputPackedMatrixWriteRegion = function (startRow, numRows, startColumn, numColumns) {
            throw new Error('setOutputPackedMatrixWriteRegion not implemented.');
        };
        GPGPUContext.prototype.debugValidate = function () {
            if (this.program != null) {
                validateProgram(this.gl, this.debug, this.program);
            }
            validateFramebuffer(this.gl);
        };
        GPGPUContext.prototype.executeProgram = function () {
            this.throwIfDisposed();
            this.throwIfNoProgram();
            var gl = this.gl;
            if (this.debug) {
                this.debugValidate();
            }
            callAndCheck(gl, this.debug, function () { return gl.drawElements(gl.TRIANGLES, 6, gl.UNSIGNED_SHORT, 0); });
        };
        GPGPUContext.prototype.blockUntilAllProgramsCompleted = function () {
            var _this = this;
            this.throwIfDisposed();
            callAndCheck(this.gl, this.debug, function () { return _this.gl.finish(); });
        };
        GPGPUContext.prototype.getQueryTimerExtension = function () {
            if (this.disjointQueryTimerExtension == null) {
                this.disjointQueryTimerExtension =
                    getExtensionOrThrow(this.gl, this.debug, env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') === 2 ?
                        'EXT_disjoint_timer_query_webgl2' :
                        'EXT_disjoint_timer_query');
            }
            return this.disjointQueryTimerExtension;
        };
        GPGPUContext.prototype.getQueryTimerExtensionWebGL2 = function () {
            return this.getQueryTimerExtension();
        };
        GPGPUContext.prototype.getQueryTimerExtensionWebGL1 = function () {
            return this.getQueryTimerExtension();
        };
        GPGPUContext.prototype.beginQuery = function () {
            if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') === 2) {
                var gl2 = this.gl;
                var ext_1 = this.getQueryTimerExtensionWebGL2();
                var query_1 = gl2.createQuery();
                gl2.beginQuery(ext_1.TIME_ELAPSED_EXT, query_1);
                return query_1;
            }
            var ext = this.getQueryTimerExtensionWebGL1();
            var query = ext.createQueryEXT();
            ext.beginQueryEXT(ext.TIME_ELAPSED_EXT, query);
            return query;
        };
        GPGPUContext.prototype.endQuery = function () {
            if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') === 2) {
                var gl2 = this.gl;
                var ext_2 = this.getQueryTimerExtensionWebGL2();
                gl2.endQuery(ext_2.TIME_ELAPSED_EXT);
                return;
            }
            var ext = this.getQueryTimerExtensionWebGL1();
            ext.endQueryEXT(ext.TIME_ELAPSED_EXT);
        };
        GPGPUContext.prototype.waitForQueryAndGetTime = function (query) {
            return __awaiter(this, void 0, void 0, function () {
                var _this = this;
                return __generator(this, function (_a) {
                    switch (_a.label) {
                        case 0: return [4 /*yield*/, repeatedTry(function () { return _this.disposed || // while testing contexts are created / disposed
                                // in rapid succession, so without this check we
                                // may poll for the query timer indefinitely
                                _this.isQueryAvailable(query, env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION')); })];
                        case 1:
                            _a.sent();
                            return [2 /*return*/, this.getQueryTime(query, env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION'))];
                    }
                });
            });
        };
        GPGPUContext.prototype.getQueryTime = function (query, queryTimerVersion) {
            if (queryTimerVersion === 0) {
                return null;
            }
            if (queryTimerVersion === 2) {
                var gl2 = this.gl;
                var timeElapsedNanos = gl2.getQueryParameter(query, gl2.QUERY_RESULT);
                // Return milliseconds.
                return timeElapsedNanos / 1000000;
            }
            else {
                var ext = this.getQueryTimerExtensionWebGL1();
                var timeElapsedNanos = ext.getQueryObjectEXT(query, ext.QUERY_RESULT_EXT);
                // Return milliseconds.
                return timeElapsedNanos / 1000000;
            }
        };
        GPGPUContext.prototype.isQueryAvailable = function (query, queryTimerVersion) {
            if (queryTimerVersion === 0) {
                return true;
            }
            if (queryTimerVersion === 2) {
                var gl2 = this.gl;
                var ext = this.getQueryTimerExtensionWebGL2();
                var available = gl2.getQueryParameter(query, gl2.QUERY_RESULT_AVAILABLE);
                if (this.disjoint == null) {
                    this.disjoint = this.gl.getParameter(ext.GPU_DISJOINT_EXT);
                }
                return available && !this.disjoint;
            }
            else {
                var ext = this.getQueryTimerExtensionWebGL1();
                var available = ext.getQueryObjectEXT(query, ext.QUERY_RESULT_AVAILABLE_EXT);
                if (this.disjoint == null) {
                    this.disjoint = this.gl.getParameter(ext.GPU_DISJOINT_EXT);
                }
                return available && !this.disjoint;
            }
        };
        GPGPUContext.prototype.pollFence = function (fenceContext) {
            var _this = this;
            return new Promise(function (resolve) {
                _this.addItemToPoll(function () { return fenceContext.isFencePassed(); }, function () { return resolve(); });
            });
        };
        GPGPUContext.prototype.pollItems = function () {
            // Find the last query that has finished.
            var index = linearSearchLastTrue(this.itemsToPoll.map(function (x) { return x.isDoneFn; }));
            for (var i = 0; i <= index; ++i) {
                var resolveFn = this.itemsToPoll[i].resolveFn;
                resolveFn();
            }
            this.itemsToPoll = this.itemsToPoll.slice(index + 1);
        };
        GPGPUContext.prototype.addItemToPoll = function (isDoneFn, resolveFn) {
            var _this = this;
            this.itemsToPoll.push({ isDoneFn: isDoneFn, resolveFn: resolveFn });
            if (this.itemsToPoll.length > 1) {
                // We already have a running loop that polls.
                return;
            }
            // Start a new loop that polls.
            repeatedTry(function () {
                _this.pollItems();
                // End the loop if no more items to poll.
                return _this.itemsToPoll.length === 0;
            });
        };
        GPGPUContext.prototype.bindTextureToFrameBuffer = function (texture) {
            this.throwIfDisposed();
            bindColorTextureToFramebuffer(this.gl, this.debug, texture, this.framebuffer);
            if (this.debug) {
                validateFramebuffer(this.gl);
            }
        };
        GPGPUContext.prototype.unbindTextureToFrameBuffer = function () {
            if (this.outputTexture != null) {
                bindColorTextureToFramebuffer(this.gl, this.debug, this.outputTexture, this.framebuffer);
                if (this.debug) {
                    validateFramebuffer(this.gl);
                }
            }
            else {
                unbindColorTextureFromFramebuffer(this.gl, this.debug, this.framebuffer);
            }
        };
        GPGPUContext.prototype.downloadMatrixDriver = function (texture, downloadAndDecode) {
            this.bindTextureToFrameBuffer(texture);
            var result = downloadAndDecode();
            this.unbindTextureToFrameBuffer();
            return result;
        };
        GPGPUContext.prototype.setOutputMatrixTextureDriver = function (outputMatrixTextureMaybePacked, width, height) {
            this.throwIfDisposed();
            var gl = this.gl;
            bindColorTextureToFramebuffer(gl, this.debug, outputMatrixTextureMaybePacked, this.framebuffer);
            if (this.debug) {
                validateFramebuffer(gl);
            }
            this.outputTexture = outputMatrixTextureMaybePacked;
            callAndCheck(gl, this.debug, function () { return gl.viewport(0, 0, width, height); });
            callAndCheck(gl, this.debug, function () { return gl.scissor(0, 0, width, height); });
        };
        GPGPUContext.prototype.setOutputMatrixWriteRegionDriver = function (x, y, width, height) {
            var _this = this;
            this.throwIfDisposed();
            callAndCheck(this.gl, this.debug, function () { return _this.gl.scissor(x, y, width, height); });
        };
        GPGPUContext.prototype.throwIfDisposed = function () {
            if (this.disposed) {
                throw new Error('Attempted to use disposed GPGPUContext.');
            }
        };
        GPGPUContext.prototype.throwIfNoProgram = function () {
            if (this.program == null) {
                throw new Error('No GPU program is currently set.');
            }
        };
        return GPGPUContext;
    }());
    /**
     * Finds the index of the last true element using linear search.
     * Note: We can't do binary search because Chrome expects us to explicitly
     * test all fences before download:
     * https://github.com/tensorflow/tfjs/issues/1145
     */
    function linearSearchLastTrue(arr) {
        var i = 0;
        for (; i < arr.length; ++i) {
            var isDone = arr[i]();
            if (!isDone) {
                break;
            }
        }
        return i - 1;
    }

    /**
     * @license
     * Copyright 2017 Google 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.
     * =============================================================================
     */
    function compileProgram(gpgpu, program, inputs, output) {
        var userCode = program.userCode;
        var inputInfos = inputs.map(function (input, i) {
            var shapeInfo = {
                logicalShape: input.shape,
                texShape: input.isUniform ? null : input.texData.texShape,
                isUniform: input.isUniform,
                isPacked: input.isUniform ? false : input.texData.isPacked,
                flatOffset: null
            };
            if (input.texData != null && input.texData.slice != null &&
                input.texData.slice.flatOffset > 0) {
                shapeInfo.flatOffset = input.texData.slice.flatOffset;
            }
            return { name: program.variableNames[i], shapeInfo: shapeInfo };
        });
        var inShapeInfos = inputInfos.map(function (x) { return x.shapeInfo; });
        var outShapeInfo = {
            logicalShape: output.shape,
            texShape: output.texData.texShape,
            isUniform: false,
            isPacked: output.texData.isPacked,
            flatOffset: null
        };
        var source = makeShader(inputInfos, outShapeInfo, userCode, program.packedInputs);
        var webGLProgram = gpgpu.createProgram(source);
        // Add special uniforms (NAN, INFINITY)
        var infLoc = null;
        var nanLoc = gpgpu.getUniformLocation(webGLProgram, 'NAN', false);
        if (env().getNumber('WEBGL_VERSION') === 1) {
            infLoc = gpgpu.getUniformLocation(webGLProgram, 'INFINITY', false);
        }
        // Add user-defined uniforms
        var uniformLocations = {};
        for (var i = 0; i < program.variableNames.length; i++) {
            var varName = program.variableNames[i];
            var shouldThrow = false;
            uniformLocations[varName] =
                gpgpu.getUniformLocation(webGLProgram, varName, shouldThrow);
            uniformLocations["offset" + varName] =
                gpgpu.getUniformLocation(webGLProgram, "offset" + varName, shouldThrow);
        }
        return {
            program: program,
            source: source,
            webGLProgram: webGLProgram,
            uniformLocations: uniformLocations,
            inShapeInfos: inShapeInfos,
            outShapeInfo: outShapeInfo,
            infLoc: infLoc,
            nanLoc: nanLoc,
        };
    }
    function validateBinaryAndProgram(shapeInfos, inputs) {
        if (shapeInfos.length !== inputs.length) {
            throw Error("Binary was compiled with " + shapeInfos.length + " inputs, but " +
                ("was executed with " + inputs.length + " inputs"));
        }
        shapeInfos.forEach(function (s, i) {
            var shapeA = s.logicalShape;
            var input = inputs[i];
            var shapeB = input.shape;
            if (!arraysEqual(shapeA, shapeB)) {
                throw Error("Binary was compiled with different shapes than " +
                    ("the current args. Shapes " + shapeA + " and " + shapeB + " must match"));
            }
            // The input is uploaded as uniform.
            if (s.isUniform && input.isUniform) {
                return;
            }
            var texShapeA = s.texShape;
            var texShapeB = input.isUniform ? null : input.texData.texShape;
            if (!arraysEqual(texShapeA, texShapeB)) {
                throw Error("Binary was compiled with different texture shapes than the" +
                    (" current args. Shape " + texShapeA + " and " + texShapeB + " must match"));
            }
        });
    }
    function runProgram(gpgpu, binary, inputs, output, customSetup) {
        validateBinaryAndProgram(binary.inShapeInfos, inputs);
        validateBinaryAndProgram([binary.outShapeInfo], [output]);
        var outTex = output.texData.texture;
        var outTexShape = output.texData.texShape;
        if (output.texData.isPacked) {
            gpgpu.setOutputPackedMatrixTexture(outTex, outTexShape[0], outTexShape[1]);
        }
        else {
            gpgpu.setOutputMatrixTexture(outTex, outTexShape[0], outTexShape[1]);
        }
        gpgpu.setProgram(binary.webGLProgram);
        // Set special uniforms (NAN, INFINITY)
        if (env().getNumber('WEBGL_VERSION') === 1) {
            if (binary.infLoc !== null) {
                gpgpu.gl.uniform1f(binary.infLoc, Infinity);
            }
        }
        if (binary.nanLoc !== null) {
            gpgpu.gl.uniform1f(binary.nanLoc, NaN);
        }
        // Set user-defined inputs
        inputs.forEach(function (input, i) {
            var varName = binary.program.variableNames[i];
            var varLoc = binary.uniformLocations[varName];
            var varOffsetLoc = binary.uniformLocations["offset" + varName];
            if (varLoc == null) {
                // The compiler inferred that this variable is not used in this shader.
                return;
            }
            if (input.isUniform) {
                // Upload the values of the tensor as uniform.
                if (sizeFromShape(input.shape) < 2) {
                    gpgpu.gl.uniform1f(varLoc, input.uniformValues[0]);
                }
                else {
                    var vals = input.uniformValues;
                    if (!(vals instanceof Float32Array)) {
                        vals = new Float32Array(vals);
                    }
                    gpgpu.gl.uniform1fv(varLoc, vals);
                }
                return;
            }
            // If the input was sliced, upload the flat offset index.
            if (input.texData.slice != null && varOffsetLoc != null) {
                gpgpu.gl.uniform1i(varOffsetLoc, input.texData.slice.flatOffset);
            }
            gpgpu.setInputMatrixTexture(input.texData.texture, varLoc, i);
        });
        if (customSetup != null) {
            customSetup(gpgpu, binary.webGLProgram);
        }
        gpgpu.executeProgram();
    }
    function makeShaderKey(program, inputs, output) {
        var keyInputs = '';
        inputs.concat(output).forEach(function (x) {
            var hasOffset = x.texData != null && x.texData.slice != null &&
                x.texData.slice.flatOffset > 0;
            var texShape = x.isUniform ? 'uniform' : x.texData.texShape;
            keyInputs += x.shape + "_" + texShape + "_" + hasOffset;
        });
        var keyUserCode = program.userCode;
        var key = program.constructor.name;
        // Fast string concat. See https://jsperf.com/string-concatenation/14.
        key += '_' + keyInputs + '_' + keyUserCode;
        return key;
    }

    /**
     * @license
     * Copyright 2019 Google LLC. All Rights Reserved.
     * Licensed under the Apache License, Version 2.0 (the "License");
     * you may not use this file except in compliance with the License.
     * You may obtain a copy of the License at
     *
     * http://www.apache.org/licenses/LICENSE-2.0
     *
     * Unless required by applicable law or agreed to in writing, software
     * distributed under the License is distributed on an "AS IS" BASIS,
     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
     * See the License for the specific language governing permissions and
     * limitations under the License.
     * =============================================================================
     */
    var Im2ColPackedProgram = /** @class */ (function () {
        function Im2ColPackedProgram(outputShape, inputShape, convInfo) {
            this.variableNames = ['A'];
            this.packedInputs = true;
            this.packedOutput = true;
            this.outputShape = outputShape;
            var filterWidth = convInfo.filterWidth, inChannels = convInfo.inChannels, strideWidth = convInfo.strideWidth, strideHeight = convInfo.strideHeight, padInfo = convInfo.padInfo, outWidth = convInfo.outWidth, dilationWidth = convInfo.dilationWidth, dilationHeight = convInfo.dilationHeight, dataFormat = convInfo.dataFormat;
            var left = padInfo.left, top = padInfo.top;
            var itemsPerBlockRow = inChannels * filterWidth;
            var glsl = getGlslDifferences();
            var isChannelsLast = dataFormat === 'channelsLast';
            var rowDim = isChannelsLast ? 0 : 1;
            var colDim = isChannelsLast ? 1 : 2;
            var unrolled = "";
            for (var row = 0; row <= 1; row++) {
                for (var col = 0; col <= 1; col++) {
                    unrolled += "\n          blockIndex = rc.y + " + col + ";\n          pos = rc.x + " + row + ";\n\n          if(blockIndex < " + outputShape[1] + " && pos < " + outputShape[0] + ") {\n            offsetY = int(blockIndex / (" + outWidth + ")) * " + strideHeight + " - " + top + ";\n            d0 = offsetY + " + dilationHeight + " * (pos / " + itemsPerBlockRow + ");\n\n            if(d0 < " + inputShape[rowDim] + " && d0 >= 0) {\n\n              offsetX = int(mod(float(blockIndex), " + outWidth + ".) * " + strideWidth + ". - " + left + ".);\n              d1 = offsetX + " + dilationWidth + " * (int(mod(float(pos), " + itemsPerBlockRow + ".) / " + inChannels + ".));\n\n              if(d1 < " + inputShape[colDim] + " && d1 >= 0) {\n\n                ch = int(mod(float(pos), " + inChannels + ".));\n\n                if (" + isChannelsLast + ") {\n                  innerDims = vec2(d1, ch);\n                  result[" + (row * 2 + col) + "] = getChannel(\n                    getA(d0, int(innerDims.x),\n                    int(innerDims.y)), innerDims);\n                } else {\n                  innerDims = vec2(d0, d1);\n                  result[" + (row * 2 + col) + "] = getChannel(\n                    getA(ch, int(innerDims.x),\n                    int(innerDims.y)), innerDims);\n                }\n              }\n            }\n          }\n        ";
                }
            }
            this.userCode = "\n      void main() {\n        ivec2 rc = getOutputCoords();\n\n        vec4 result = vec4(0);\n\n        int blockIndex, pos, offsetY, d0, offsetX, d1, ch;\n        vec2 innerDims;\n\n        " + unrolled + "\n\n        " + glsl.output + " = result;\n      }\n    ";
        }
        return Im2ColPackedProgram;
    }());

    /**
     * @license
     * Copyright 2017 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.
     * =============================================================================
     */
    var LRNProgram = /** @class */ (function () {
        function LRNProgram(xShape, radius, bias, alpha, beta) {
            this.variableNames = ['x'];
            this.outputShape = [];
            var rad = radius;
            var maxD = xShape[3] - 1;
            this.outputShape = xShape;
            // optimize pow(bias + alpha * sum, -beta)
            // src: https://github.com/tensorflow/tensorflow/..
            // blob/26033a1644a9c4a5fbe3170ab2e864b6a4ccd4ca/..
            // tensorflow/core/kernels/mkl_lrn_op.cc#L320
            var powOperator;
            var basis = "float(" + bias + ") + float(" + alpha + ") * sum";
            if (beta === 0.5) {
                powOperator = "inversesqrt(" + basis + ")";
            }
            else if (beta === 1.0) {
                powOperator = "1.0/(" + basis + ")";
            }
            else {
                powOperator = "exp(log(" + basis + ") * float(-" + beta + "));";
            }
            this.userCode = "\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n        int d = coords[3];\n        float x = getX(b, r, c, d);\n        float sum = 0.0;\n        for (int j = -" + rad + "; j <= " + rad + "; j++) {\n          int idx = d + j;\n          if (idx >= 0 && idx <=  " + maxD + ") {\n            float z = getX(b, r, c, idx);\n            sum += z * z;\n          }\n        }\n        float val = x * " + powOperator + ";\n        setOutput(val);\n      }\n    ";
        }
        return LRNProgram;
    }());

    /**
     * @license
     * Copyright 2018 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.
     * =============================================================================
     */
    var LRNGradProgram = /** @class */ (function () {
        function LRNGradProgram(inputShape, depthRadius, bias, alpha, beta) {
            this.variableNames = ['inputImage', 'outputImage', 'dy'];
            this.outputShape = [];
            this.outputShape = inputShape;
            this.depth = inputShape[3];
            this.depthRadius = depthRadius;
            this.bias = bias;
            this.alpha = alpha;
            this.beta = beta;
            this.userCode = "\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n\n        float result = 0.0;\n        for (int d = 0; d < " + this.depth + "; ++d) {\n          int depthBegin = int(max(0.0, float(d - " + depthRadius + ")));\n          int depthEnd = int(min(float(" + this.depth + "),\n              float(d + " + depthRadius + " + 1)));\n\n          const int MIN_DEPTH_BEGIN = 0;\n          const int MAX_DEPTH_END = " + this.depth + ";\n\n          float norm = 0.0;\n          for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd) {\n              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n            }\n            else {\n              break;\n            }\n          }\n\n          norm = float(" + alpha + ") * norm + float(" + bias + ");\n\n          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd){\n              float dyi = -2.0 * float(" + alpha + ")\n                * float(" + beta + ")\n                * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)\n                / norm;\n              if (k == d) {\n                dyi += pow(norm, -1.0 * " + beta + ");\n              }\n              if (k == coords[3]) {\n                dyi *= getDy(b, r, c, d);\n                result += dyi;\n              }\n            }\n            else {\n              break;\n            }\n          }\n      }\n      setOutput(result);\n      }\n    ";
        }
        return LRNGradProgram;
    }());

    /**
     * @license
     * Copyright 2019 Google LLC All Rights Reserved.
     * Licensed under the Apache License, Version 2.0 (the "License");
     * you may not use this file except in compliance with the License.
     * You may obtain a copy of the License at
     *
     * http://www.apache.org/licenses/LICENSE-2.0
     *
     * Unless required by applicable law or agreed to in writing, software
     * distributed under the License is distributed on an "AS IS" BASIS,
     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
     * See the License for the specific language governing permissions and
     * limitations under the License.
     * =============================================================================
     */
    var LRNPackedProgram = /** @class */ (function () {
        function LRNPackedProgram(xShape, radius, bias, alpha, beta) {
            this.variableNames = ['x'];
            this.outputShape = [];
            this.packedInputs = true;
            this.packedOutput = true;
            var rad = radius;
            var maxD = xShape[3] - 1;
            this.outputShape = xShape;
            // optimize pow(bias + alpha * sum, -beta)
            // src: https://github.com/tensorflow/tensorflow/..
            // blob/26033a1644a9c4a5fbe3170ab2e864b6a4ccd4ca/..
            // tensorflow/core/kernels/mkl_lrn_op.cc#L320
            var powOperator;
            var basis = "float(" + bias + ") + float(" + alpha + ") * sum";
            if (beta === 0.5) {
                powOperator = "inversesqrt(" + basis + ")";
            }
            else if (beta === 1.0) {
                powOperator = "1.0/(" + basis + ")";
            }
            else {
                powOperator = "exp(log(" + basis + ") * float(-" + beta + "));";
            }
            this.userCode = "\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords.x;\n        int r = coords.y;\n        int c = coords.z;\n        int d = coords.w;\n\n        bool hasNextCol = d < " + this.outputShape[3] + ";\n        bool hasNextRow = c < " + this.outputShape[2] + ";\n\n        vec4 sum = vec4(0.);\n        vec4 xFragAtOutputCoords = getX(b, r, c, d);\n\n        vec4 xAtOutputCoords = vec4(\n          getChannel(xFragAtOutputCoords, vec2(c, d)),\n          hasNextCol ?\n            getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,\n          hasNextRow ?\n            getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0\n        );\n\n        int firstChannel = d - " + rad + ";\n        vec2 cache = vec2(0.);\n        if(firstChannel >= 0){\n          vec4 firstChannelFrag = getX(b, r, c, firstChannel);\n          cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));\n            if(hasNextRow){\n              cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));\n            }\n        }\n\n        ivec2 depth = ivec2(d, d + 1);\n        for (int j = - " + rad + "; j <= " + rad + "; j++) {\n          ivec2 idx = depth + j;\n          bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));\n          bvec2 belowUpperBound = lessThanEqual(idx, ivec2(" + maxD + "));\n\n          bool depthInRange = aboveLowerBound.x && belowUpperBound.x;\n          bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;\n\n          if(depthInRange || depthPlusOneInRange){\n            vec4 z = vec4(0.);\n            vec4 xFragAtCurrentDepth;\n            z.xz = cache.xy;\n            if(depthPlusOneInRange && hasNextCol){\n              xFragAtCurrentDepth = idx.y != d ?\n                getX(b, r, c, idx.y) : xFragAtOutputCoords;\n              z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));\n              if(hasNextRow){\n                z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));\n              }\n            }\n            cache.xy = z.yw;\n            sum += z * z;\n          }\n        }\n        vec4 result = xAtOutputCoords * " + powOperator + ";\n        setOutput(result);\n      }\n    ";
        }
        return LRNPackedProgram;
    }());

    /**
     * @license
     * Copyright 2017 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.
     * =============================================================================
     */
    var MaxPool2DBackpropProgram = /** @class */ (function () {
        function MaxPool2DBackpropProgram(convInfo) {
            this.variableNames = ['dy', 'maxPos'];
            this.outputShape = convInfo.inShape;
            var strideHeight = convInfo.strideHeight;
            var strideWidth = convInfo.strideWidth;
            var dilationHeight = convInfo.dilationHeight;
            var effectiveFilterHeight = convInfo.effectiveFilterHeight;
            var effectiveFilterWidth = convInfo.effectiveFilterWidth;
            var padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
            var padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
            var lastIndex = effectiveFilterHeight * effectiveFilterWidth - 1;
            this.userCode = "\n      const ivec2 pads = ivec2(" + padTop + ", " + padLeft + ");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < " + effectiveFilterHeight + ";\n          wR += " + dilationHeight + ") {\n          float dyR = float(dyRCorner + wR) / " + strideHeight + ".0;\n\n          if (dyR < 0.0 || dyR >= " + convInfo.outHeight + ".0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < " + effectiveFilterWidth + "; wC++) {\n            float dyC = float(dyCCorner + wC) / " + strideWidth + ".0;\n\n            if (dyC < 0.0 || dyC >= " + convInfo.outWidth + ".0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n            int maxPosValue = " + lastIndex + " - int(getMaxPos(b, idyR, idyC, d));\n\n            // Get the current value, check it against the value from the\n            // position matrix.\n            int curPosValue = wR * " + effectiveFilterWidth + " + wC;\n            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n            dotProd += dyValue * mask;\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";
        }
        return MaxPool2DBackpropProgram;
    }());
    var MaxPool3DBackpropProgram = /** @class */ (function () {
        function MaxPool3DBackpropProgram(convInfo) {
            this.variableNames = ['dy', 'maxPos'];
            this.outputShape = convInfo.inShape;
            var strideDepth = convInfo.strideDepth;
            var strideHeight = convInfo.strideHeight;
            var strideWidth = convInfo.strideWidth;
            var dilationDepth = convInfo.dilationDepth;
            var dilationHeight = convInfo.dilationHeight;
            var dilationWidth = convInfo.dilationWidth;
            var effectiveFilterDepth = convInfo.effectiveFilterDepth;
            var effectiveFilterHeight = convInfo.effectiveFilterHeight;
            var effectiveFilterWidth = convInfo.effectiveFilterWidth;
            var padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;
            var padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
            var padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
            var lastIndex = effectiveFilterDepth * effectiveFilterHeight * effectiveFilterWidth - 1;
            this.userCode = "\n      const ivec3 pads = ivec3(" + padFront + ", " + padTop + ", " + padLeft + ");\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyDCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n        // dx(xD, xR, xC, ch).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int wD = 0; wD < " + effectiveFilterDepth + ";\n           wD += " + dilationDepth + ") {\n          float dyD = float(dyDCorner + wD) / " + strideDepth + ".0;\n\n          if (dyD < 0.0 || dyD >= " + convInfo.outDepth + ".0 || fract(dyD) > 0.0) {\n            continue;\n          }\n          int idyD = int(dyD);\n\n          for (int wR = 0; wR < " + effectiveFilterHeight + ";\n              wR += " + dilationHeight + ") {\n            float dyR = float(dyRCorner + wR) / " + strideHeight + ".0;\n\n            if (dyR < 0.0 || dyR >= " + convInfo.outHeight + ".0 ||\n                fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            for (int wC = 0; wC < " + effectiveFilterWidth + ";\n                wC += " + dilationWidth + ") {\n              float dyC = float(dyCCorner + wC) / " + strideWidth + ".0;\n\n              if (dyC < 0.0 || dyC >= " + convInfo.outWidth + ".0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n              int maxPosValue = " + lastIndex + " -\n                  int(getMaxPos(batch, idyD, idyR, idyC, ch));\n\n              // Get the current value, check it against the value from the\n              // position matrix.\n              int curPosValue =\n                  wD * " + effectiveFilterHeight + " * " + effectiveFilterWidth + " +\n                  wR * " + effectiveFilterWidth + " + wC;\n              float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n              dotProd += dyValue * mask;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";
        }
        return MaxPool3DBackpropProgram;
    }());

    /**
     * @license
     * Copyright 2018 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.
     * =============================================================================
     */
    var MatMulPackedProgram = /** @class */ (function () {
        function MatMulPackedProgram(aShape, outputShape, transposeA, transposeB, addBias, activation, hasPreluActivation) {
            if (transposeA === void 0) { transposeA = false; }
            if (transposeB === void 0) { transposeB = false; }
            if (addBias === void 0) { addBias = false; }
            if (activation === void 0) { activation = null; }
            if (hasPreluActivation === void 0) { hasPreluActivation = false; }
            this.variableNames = ['matrixA', 'matrixB'];
            this.packedInputs = true;
            this.packedOutput = true;
            this.outputShape = outputShape;
            var sharedDim = transposeA ? aShape[1] : aShape[2];
            var sharedDimensionPacked = Math.ceil(sharedDim / 2);
            var aSample = transposeA ? 'i * 2, rc.y' : 'rc.y, i * 2';
            var bSample = transposeB ? 'rc.z, i * 2' : 'i * 2, rc.z';
            var aSwizzle = transposeA ? ['a.xxyy', 'a.zzww'] : ['a.xxzz', 'a.yyww'];
            var bSwizzle = transposeB ? ['b.xzxz', 'b.ywyw'] : ['b.xyxy', 'b.zwzw'];
            var activationSnippet = '', applyActivationSnippet = '';
            if (activation) {
                if (hasPreluActivation) {
                    activationSnippet = "vec4 activation(vec4 a) {\n          vec4 b = getPreluActivationWeightsAtOutCoords();\n          " + activation + "\n        }";
                }
                else {
                    activationSnippet = "vec4 activation(vec4 x) {\n          " + activation + "\n        }";
                }
                applyActivationSnippet = "result = activation(result);";
            }
            var addBiasSnippet = addBias ? 'result += getBiasAtOutCoords();' : '';
            if (addBias) {
                this.variableNames.push('bias');
            }
            if (hasPreluActivation) {
                this.variableNames.push('preluActivationWeights');
            }
            this.userCode = "\n      " + activationSnippet + "\n\n      const float sharedDimension = " + sharedDimensionPacked + ".0;\n\n      vec4 dot2x2ARowBCol(ivec3 rc) {\n        vec4 result = vec4(0);\n        for (int i = 0; i < " + sharedDimensionPacked + "; i++) {\n          vec4 a = getMatrixA(rc.x, " + aSample + ");\n          vec4 b = getMatrixB(rc.x, " + bSample + ");\n\n          // These swizzled products need to be separately added.\n          // See: https://github.com/tensorflow/tfjs/issues/1735\n          result += (" + aSwizzle[0] + " * " + bSwizzle[0] + ");\n          result += (" + aSwizzle[1] + " * " + bSwizzle[1] + ");\n        }\n        return result;\n      }\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n        vec4 result = dot2x2ARowBCol(rc);\n\n        " + addBiasSnippet + "\n\n        " + applyActivationSnippet + "\n\n        setOutput(result);\n      }\n    ";
        }
        return MatMulPackedProgram;
    }());

    /**
     * @license
     * Copyright 2017 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.
     * =============================================================================
     */
    var MultinomialProgram = /** @class */ (function () {
        function MultinomialProgram(batchSize, numOutcomes, numSamples) {
            this.variableNames = ['probs'];
            this.outputShape = [batchSize, numSamples];
            this.userCode = "\n      uniform float seed;\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n\n        float r = random(seed);\n        float cdf = 0.0;\n\n        for (int i = 0; i < " + (numOutcomes - 1) + "; i++) {\n          cdf += getProbs(batch, i);\n\n          if (r < cdf) {\n            setOutput(float(i));\n            return;\n          }\n        }\n\n        // If no other event happened, last event happened.\n        setOutput(float(" + (numOutcomes - 1) + "));\n      }\n    ";
        }
        MultinomialProgram.prototype.getCustomSetupFunc = function (seed) {
            var _this = this;
            return function (gpgpu, webGLProgram) {
                if (_this.seedLoc == null) {
                    _this.seedLoc = gpgpu.getUniformLocation(webGLProgram, 'seed');
                }
                gpgpu.gl.uniform1f(_this.seedLoc, seed);
            };
        };
        return MultinomialProgram;
    }());

    /**
     * @license
     * Copyright 2017 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.
     * =============================================================================
     */
    var OneHotProgram = /** @class */ (function () {
        function OneHotProgram(numIndices, depth, onValue, offValue) {
            this.variableNames = ['indices'];
            this.outputShape = [numIndices, depth];
            this.userCode = "\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int index = round(getIndices(coords.x));\n        setOutput(mix(float(" + offValue + "), float(" + onValue + "),\n                      float(index == coords.y)));\n      }\n    ";
        }
        return OneHotProgram;
    }());

    /**
     * @license
     * Copyright 2018 Google LLC. All Rights Reserved.
     * Licensed under the Apache License, Version 2.0 (the "License");
     * you may not use this file except in compliance with the License.
     * You may obtain a copy of the License at
     *
     * http://www.apache.org/licenses/LICENSE-2.0
     *
     * Unless required by applicable law or agreed to in writing, software
     * distributed under the License is distributed on an "AS IS" BASIS,
     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
     * See the License for the specific language governing permissions and
     * limitations under the License.
     * =============================================================================
     */
    var PackProgram = /** @class */ (function () {
        function PackProgram(outputShape) {
            this.variableNames = ['A'];
            this.packedInputs = false;
            this.packedOutput = true;
            // Only input / output 3D tensors.
            this.outputShape = outputShape;
            var rank = outputShape.length;
            if (rank === 0) {
                this.userCode = "\n        void main() {\n          setOutput(vec4(getA(), 0., 0., 0.));\n        }\n      ";
            }
            else {
                var channels = getChannels('rc', rank);
                var dtype = getCoordsDataType(rank);
                var outOfBoundsCondition = getOutOfBoundsCondition(rank, outputShape, channels);
                var setup = getSetup(rank, outputShape[outputShape.length - 1], outputShape[outputShape.length - 2], channels);
                var output = getOutput(outputShape, channels);
                this.userCode = "\n        void main() {\n          " + dtype + " rc = getOutputCoords();\n\n          if(" + outOfBoundsCondition + ") {\n            setOutput(vec4(0));\n          } else {\n            " + setup + "\n\n            setOutput(vec4(" + output + "));\n          }\n        }\n      ";
            }
        }
        return PackProgram;
    }());
    function getSourceCoordsArr(rank, dims) {
        var coords = [];
        for (var row = 0; row <= 1; row++) {
            for (var col = 0; col <= 1; col++) {
                var coord = (row === 0 ? 'r' : 'rp1') + ", " + (col === 0 ? 'c' : 'cp1');
                for (var d = 2; d < rank; d++) {
                    coord = dims[dims.length - 1 - d] + "," + coord;
                }
                coords.push(coord);
            }
        }
        return coords;
    }
    function getOutOfBoundsCondition(rank, shape, dims) {
        if (rank === 1) {
            return "rc > " + shape[0];
        }
        var cond = '';
        for (var i = rank - 2; i < rank; i++) {
            cond += dims[i] + " >= " + shape[i];
            if (i < rank - 1) {
                cond += '||';
            }
        }
        return cond;
    }
    function getSetup(rank, cols, rows, dims) {
        if (rank === 1) {
            return '';
        }
        var innerDims = dims.slice(-2);
        return "\n    int r = " + innerDims[0] + ";\n    int c = " + innerDims[1] + ";\n    int rp1 = r + 1;\n    int cp1 = c + 1;\n\n    bool cEdge = cp1 >= " + cols + ";\n    bool rEdge = rp1 >= " + rows + ";\n  ";
    }
    function getOutput(shape, dims) {
        var rank = shape.length;
        var sourceCoords = getSourceCoordsArr(rank, dims);
        if (rank === 1) {
            return "getA(rc),\n            rc + 1 >= " + shape[0] + " ? 0. : getA(rc + 1),\n            0, 0";
        }
        return "getA(" + sourceCoords[0] + "),\n          cEdge ? 0. : getA(" + sourceCoords[1] + "),\n          rEdge ? 0. : getA(" + sourceCoords[2] + "),\n          rEdge || cEdge ? 0. : getA(" + sourceCoords[3] + ")";
    }

    /**
     * @license
     * Copyright 2017 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.
     * =============================================================================
     */
    var PadProgram = /** @class */ (function () {
        function PadProgram(xShape, paddings, constantValue) {
            this.variableNames = ['x'];
            this.outputShape = paddings.map(function (p, i) { return p[0] /* beforePad */ + xShape[i] + p[1]; } /* afterPad */);
            var rank = xShape.length;
            var type = getCoordsDataType(rank);
            var start = paddings.map(function (p) { return p[0]; }).join(',');
            var end = paddings.map(function (p, i) { return p[0] + xShape[i]; }).join(',');
            var unpackedCoords = ['coords[0]', 'coords[1]', 'coords[2]', 'coords[3]'].slice(0, rank);
            if (rank === 1) {
                this.userCode = "\n        int start = " + start + ";\n        int end = " + end + ";\n\n        void main() {\n          int outC = getOutputCoords();\n          if (outC < start || outC >= end) {\n            setOutput(float(" + constantValue + "));\n          } else {\n            setOutput(getX(outC - start));\n          }\n        }\n      ";
                return;
            }
            this.userCode = "\n      " + type + " start = " + type + "(" + start + ");\n      " + type + " end = " + type + "(" + end + ");\n\n      void main() {\n        " + type + " outC = getOutputCoords();\n        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {\n          setOutput(float(" + constantValue + "));\n        } else {\n          " + type + " coords = outC - start;\n          setOutput(getX(" + unpackedCoords + "));\n        }\n      }\n    ";
        }
        return PadProgram;
    }());

    /**
     * @license
     * Copyright 2019 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.
     * =============================================================================
     */
    var PadPackedProgram = /** @class */ (function () {
        function PadPackedProgram(xShape, paddings, constantValue) {
            this.variableNames = ['x'];
            this.packedInputs = true;
            this.packedOutput = true;
            this.outputShape = paddings.map(function (p, i) { return p[0] /* beforePad */ + xShape[i] + p[1]; } /* afterPad */);
            var rank = xShape.length;
            var dtype = getCoordsDataType(rank);
            var start = paddings.map(function (p) { return p[0]; }).join(',');
            var end = paddings.map(function (p, i) { return p[0] + xShape[i]; }).join(',');
            var coords = getChannels('rc', rank);
            var source = getChannels('source', rank);
            var cLimit = coords[rank - 1] + " < " + this.outputShape[rank - 1];
            var innerDims = rank === 1 ? 'source' : "vec2(" + source.slice(-2).join() + ")";
            var componentSetup = [
                dtype + " rc = outputLoc;", coords[rank - 1] + " += 1;\n       if(" + cLimit + ") {\n      ",
                rank === 1 ? '' : "}\n       rc = outputLoc;\n       " + coords[rank - 2] + " += 1;\n       if(" + coords[rank - 2] + " < " + this.outputShape[rank - 2] + ") {",
                rank === 1 ? '' : "  " + coords[rank - 1] + " += 1;\n         if(" + cLimit + ") {"
            ];
            var paddingArea = rank === 1 ?
                'rc < start || rc >= end' :
                'any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))';
            var mainLoop = '';
            for (var i = 0, j = rank === 1 ? 2 : 4; i < j; i++) {
                mainLoop += "\n        " + componentSetup[i] + "\n        if (" + paddingArea + ") {\n          result[" + i + "] = float(" + constantValue + ");\n        } else {\n          " + dtype + " source = rc - start;\n          result[" + i + "] = getChannel(getX(" + source.join() + "), " + innerDims + ");\n        }\n      ";
            }
            mainLoop += (rank === 1 ? "} " : "}}");
            this.userCode = "\n      const " + dtype + " start = " + dtype + "(" + start + ");\n      const " + dtype + " end = " + dtype + "(" + end + ");\n\n      void main() {\n        " + dtype + " outputLoc = getOutputCoords();\n        vec4 result = vec4(0.);\n        " + mainLoop + "\n        setOutput(result);\n      }\n    ";
        }
        return PadPackedProgram;
    }());

    /**
     * @license
     * Copyright 2017 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.
     * =============================================================================
     */
    var Pool2DProgram = /** @class */ (function () {
        function Pool2DProgram(convInfo, poolType, computePositions) {
            this.variableNames = ['x'];
            if (poolType === 'avg' && computePositions) {
                throw new Error('Cannot compute positions for average pool.');
            }
            var filterWidth = convInfo.filterWidth;
            var strideHeight = convInfo.strideHeight;
            var strideWidth = convInfo.strideWidth;
            var dilationHeight = convInfo.dilationHeight;
            var dilationWidth = convInfo.dilationWidth;
            var effectiveFilterHeight = convInfo.effectiveFilterHeight;
            var effectiveFilterWidth = convInfo.effectiveFilterWidth;
            var padTop = convInfo.padInfo.top;
            var padLeft = convInfo.padInfo.left;
            this.outputShape = convInfo.outShape;
            var isAvgPool = poolType === 'avg';
            var initializationValue = '0.0';
            if (!isAvgPool) {
                // WebGL on Firefox Linux can't compile 1/0 so we do 1/eps.
                initializationValue = '-1.0 / 1e-20';
            }
            if (computePositions) {
                var compareOp_1 = '>=';
                this.userCode = "\n        const ivec2 strides = ivec2(" + strideHeight + ", " + strideWidth + ");\n        const ivec2 pads = ivec2(" + padTop + ", " + padLeft + ");\n\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int batch = coords[0];\n          int d = coords[3];\n\n          ivec2 xRCCorner = coords.yz * strides - pads;\n          int xRCorner = xRCCorner.x;\n          int xCCorner = xRCCorner.y;\n\n          // max/min x(?, ?, d) to get y(yR, yC, d).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n          float avgValue = 0.0;\n\n          for (int wR = 0; wR < " + effectiveFilterHeight + ";\n              wR += " + dilationHeight + ") {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= " + convInfo.inHeight + ") {\n              continue;\n            }\n\n            for (int wC = 0; wC < " + effectiveFilterWidth + ";\n                wC += " + dilationWidth + ") {\n              int xC = xCCorner + wC;\n\n              if (xC < 0 || xC >= " + convInfo.inWidth + ") {\n                continue;\n              }\n\n              float value = getX(batch, xR, xC, d);\n\n              // If a min / max value has already been found, use it. If not,\n              // use the current value.\n              float currMinMaxValue = mix(\n                  value, minMaxValue, minMaxValueFound);\n              if (value " + compareOp_1 + " currMinMaxValue) {\n                minMaxValue = value;\n                minMaxValueFound = 1.0;\n                minMaxPosition = wR * " + effectiveFilterWidth + " + wC;\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      ";
                return;
            }
            var compareOp = 'max';
            var returnValue = poolType + "(" + poolType + "(" + poolType + "(" +
                'minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])';
            if (poolType === 'avg') {
                returnValue = "avgValue / count";
            }
            var filterWidthNearestVec4 = Math.floor(filterWidth / 4) * 4;
            var filterWidthVec4Remainder = filterWidth % 4;
            var updateSnippet = "\n      if (" + isAvgPool + ") {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = " + compareOp + "(values, minMaxValue);\n      }\n    ";
            this.userCode = "\n      const ivec2 strides = ivec2(" + strideHeight + ", " + strideWidth + ");\n      const ivec2 pads = ivec2(" + padTop + ", " + padLeft + ");\n      const float initializationValue = " + initializationValue + ";\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xR, int xC, int d) {\n        if (xC < 0 || xC >= " + convInfo.inWidth + ") {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xR, xC, d);\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d = coords[3];\n\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // max/min x(?, ?, d) to get y(yR, yC, d).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(" + initializationValue + ");\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wR = 0; wR < " + effectiveFilterHeight + ";\n            wR += " + dilationHeight + ") {\n          int xR = xRCorner + wR;\n\n          if (xR < 0 || xR >= " + convInfo.inHeight + ") {\n            continue;\n          }\n\n          for (int wC = 0; wC < " + filterWidthNearestVec4 + "; wC += 4) {\n            int xC = xCCorner + wC * " + dilationWidth + ";\n\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + " + dilationWidth + ", d),\n              getValue(batch, xR, xC + 2 * " + dilationWidth + ", d),\n              getValue(batch, xR, xC + 3 * " + dilationWidth + ", d)\n            );\n\n            " + updateSnippet + "\n          }\n\n          int xC = xCCorner + " + filterWidthNearestVec4 + ";\n          if (" + (filterWidthVec4Remainder === 1) + ") {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              initializationValue,\n              initializationValue,\n              initializationValue\n            );\n\n            " + updateSnippet + "\n          } else if (" + (filterWidthVec4Remainder === 2) + ") {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + " + dilationWidth + ", d),\n              initializationValue,\n              initializationValue\n            );\n\n            " + updateSnippet + "\n          } else if (" + (filterWidthVec4Remainder === 3) + ") {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + " + dilationWidth + ", d),\n              getValue(batch, xR, xC + 2 * " + dilationWidth + ", d),\n              initializationValue\n            );\n\n            " + updateSnippet + "\n          }\n        }\n        setOutput(" + returnValue + ");\n      }\n    ";
        }
        return Pool2DProgram;
    }());
    var Pool3DProgram = /** @class */ (function () {
        function Pool3DProgram(convInfo, poolType, computePositions) {
            this.variableNames = ['x'];
            if (poolType === 'avg' && computePositions) {
                throw new Error('Cannot compute positions for average pool.');
            }
            var filterWidth = convInfo.filterWidth;
            var strideDepth = convInfo.strideDepth;
            var strideHeight = convInfo.strideHeight;
            var strideWidth = convInfo.strideWidth;
            var dilationDepth = convInfo.dilationDepth;
            var dilationHeight = convInfo.dilationHeight;
            var dilationWidth = convInfo.dilationWidth;
            var effectiveFilterDepth = convInfo.effectiveFilterDepth;
            var effectiveFilterHeight = convInfo.effectiveFilterHeight;
            var effectiveFilterWidth = convInfo.effectiveFilterWidth;
            var padFront = convInfo.padInfo.front;
            var padTop = convInfo.padInfo.top;
            var padLeft = convInfo.padInfo.left;
            this.outputShape = convInfo.outShape;
            var isAvgPool = poolType === 'avg';
            var initializationValue = '0.0';
            if (!isAvgPool) {
                // WebGL on Firefox Linux can't compile 1/0 so we do 1/eps.
                initializationValue = '-1.0 / 1e-20';
            }
            if (computePositions) {
                var compareOp_2 = '>=';
                this.userCode = "\n        const ivec3 strides =\n            ivec3(" + strideDepth + ", " + strideHeight + ", " + strideWidth + ");\n        const ivec3 pads = ivec3(" + padFront + ", " + padTop + ", " + padLeft + ");\n\n        void main() {\n          ivec5 coords = getOutputCoords();\n          int batch = coords.x;\n          int ch = coords.u;\n\n          ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n          int xDCorner = xCorner.x;\n          int xRCorner = xCorner.y;\n          int xCCorner = xCorner.z;\n\n          // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n\n          for (int wD = 0; wD < " + effectiveFilterDepth + ";\n              wD += " + dilationDepth + ") {\n            int xD = xDCorner + wD;\n\n            if (xD < 0 || xD >= " + convInfo.inDepth + ") {\n              continue;\n            }\n\n            for (int wR = 0; wR < " + effectiveFilterHeight + ";\n                wR += " + dilationHeight + ") {\n              int xR = xRCorner + wR;\n\n              if (xR < 0 || xR >= " + convInfo.inHeight + ") {\n                continue;\n              }\n\n              for (int wC = 0; wC < " + effectiveFilterWidth + ";\n                  wC += " + dilationWidth + ") {\n                int xC = xCCorner + wC;\n\n                if (xC < 0 || xC >= " + convInfo.inWidth + ") {\n                  continue;\n                }\n\n                float value = getX(batch, xD, xR, xC, ch);\n\n                // If a min / max value has already been found, use it. If not,\n                // use the current value.\n                float currMinMaxValue = mix(\n                    value, minMaxValue, minMaxValueFound);\n                if (value " + compareOp_2 + " currMinMaxValue) {\n                  minMaxValue = value;\n                  minMaxValueFound = 1.0;\n                  minMaxPosition =\n                      wD * " + effectiveFilterHeight + " * " + effectiveFilterWidth + " +\n                      wR * " + effectiveFilterWidth + " + wC;;\n                }\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      ";
                return;
            }
            var compareOp = 'max';
            var returnValue = poolType + "(" + poolType + "(" + poolType + "(" +
                'minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])';
            if (poolType === 'avg') {
                returnValue = "avgValue / count";
            }
            var filterWidthNearestVec4 = Math.floor(filterWidth / 4) * 4;
            var filterWidthVec4Remainder = filterWidth % 4;
            var updateSnippet = "\n      if (" + isAvgPool + ") {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = " + compareOp + "(values, minMaxValue);\n      }\n    ";
            this.userCode = "\n      const ivec3 strides =\n        ivec3(" + strideDepth + ", " + strideHeight + ", " + strideWidth + ");\n      const ivec3 pads = ivec3(" + padFront + ", " + padTop + ", " + padLeft + ");\n      const float initializationValue = " + initializationValue + ";\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xD, int xR, int xC, int ch) {\n        if (xC < 0 || xC >= " + convInfo.inWidth + ") {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xD, xR, xC, ch);\n      }\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xDCorner = xCorner.x;\n        int xRCorner = xCorner.y;\n        int xCCorner = xCorner.z;\n\n        // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(" + initializationValue + ");\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wD = 0; wD < " + effectiveFilterDepth + ";\n            wD += " + dilationDepth + ") {\n          int xD = xDCorner + wD;\n\n          if (xD < 0 || xD >= " + convInfo.inDepth + ") {\n            continue;\n          }\n\n          for (int wR = 0; wR < " + effectiveFilterHeight + ";\n            wR += " + dilationHeight + ") {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= " + convInfo.inHeight + ") {\n              continue;\n            }\n\n            for (int wC = 0; wC < " + filterWidthNearestVec4 + "; wC += 4) {\n              int xC = xCCorner + wC * " + dilationWidth + ";\n\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + " + dilationWidth + ", ch),\n                getValue(batch, xD, xR, xC + 2 * " + dilationWidth + ", ch),\n                getValue(batch, xD, xR, xC + 3 * " + dilationWidth + ", ch)\n              );\n\n              " + updateSnippet + "\n            }\n\n            int xC = xCCorner + " + filterWidthNearestVec4 + ";\n            if (" + (filterWidthVec4Remainder === 1) + ") {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                initializationValue,\n                initializationValue,\n                initializationValue\n              );\n\n              " + updateSnippet + "\n            } else if (" + (filterWidthVec4Remainder === 2) + ") {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + " + dilationWidth + ", ch),\n                initializationValue,\n                initializationValue\n              );\n\n              " + updateSnippet + "\n            } else if (" + (filterWidthVec4Remainder === 3) + ") {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + " + dilationWidth + ", ch),\n                getValue(batch, xD, xR, xC + 2 * " + dilationWidth + ", ch),\n                initializationValue\n              );\n\n              " + updateSnippet + "\n            }\n          }\n          setOutput(" + returnValue + ");\n        }\n      }\n    ";
        }
        return Pool3DProgram;
    }());

    /**
     * @license
     * Copyright 2017 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.
     * =============================================================================
     */
    var ReduceProgram = /** @class */ (function () {
        function ReduceProgram(reduceInfo, reduceType) {
            this.variableNames = ['x'];
            var windowSize = reduceInfo.windowSize;
            var batchSize = reduceInfo.batchSize;
            var inSize = reduceInfo.inSize;
            var outSize = Math.ceil(inSize / windowSize);
            this.outputShape = [batchSize, outSize];
            var initializationValue = '0.0';
            var compareOp = "";
            if (reduceType === 'prod') {
                initializationValue = '1.0';
            }
            else if (reduceType === 'min') {
                // WebGL on Firefox Linux can't compile 1/0 so we do 1/eps.
                initializationValue = '1.0 / 1e-20';
                compareOp = "min";
            }
            else if (reduceType === 'max') {
                // WebGL on Firefox Linux can't compile 1/0 so we do 1/eps.
                initializationValue = '-1.0 / 1e-20';
                compareOp = "max";
            }
            var returnValue = reduceType + "(" + reduceType + "(" + reduceType + "(" +
                'minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])';
            if (reduceType === 'sum') {
                returnValue = "sumValue";
            }
            else if (reduceType === 'prod') {
                returnValue = "prodValue";
            }
            else if (reduceType === 'all') {
                returnValue = "allValue";
            }
            else if (reduceType === 'any') {
                returnValue = "anyValue";
            }
            var windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;
            var windowSizeVec4Remainder = windowSize % 4;
            var updateSnippet = "\n      if (" + (reduceType === 'sum') + ") {\n        sumValue += dot(values, ones);\n      } else if (" + (reduceType === 'prod') + ") {\n        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n        prodValue *= tmp[0] * tmp[1];\n      } else {\n        minMaxValue = " + compareOp + "(values, minMaxValue);\n      }\n    ";
            var vecType = "vec4";
            if (reduceType === 'all') {
                initializationValue = '1.0';
                updateSnippet = "\n        bool reducedAllValue = all(values);\n        float floatedReducedAllValue = float(reducedAllValue);\n        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n      ";
                vecType = "bvec4";
            }
            else if (reduceType === 'any') {
                initializationValue = '0.0';
                updateSnippet = "\n        bool reducedAnyValue = any(values);\n        float floatedReducedAnyValue = float(reducedAnyValue);\n        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n      ";
                vecType = "bvec4";
            }
            var checkOutOfBounds = '';
            if (inSize % windowSize > 0) {
                checkOutOfBounds = "\n        if (inIdx < 0 || inIdx >= " + inSize + ") {\n          return initializationValue;\n        }\n      ";
            }
            this.userCode = "\n      const float initializationValue = " + initializationValue + ";\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        " + checkOutOfBounds + "\n        return getX(batch, inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * " + windowSize + ";\n\n        vec4 minMaxValue = vec4(" + initializationValue + ");\n        float prodValue = 1.0;\n        float sumValue = 0.0;\n        float allValue = 1.0;\n        float anyValue = 0.0;\n\n        for (int i = 0; i < " + windowSizeNearestVec4 + "; i += 4) {\n          int inIdx = inOffset + i;\n          " + vecType + " values = " + vecType + "(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          " + updateSnippet + "\n        }\n\n        int inIdx = inOffset + " + windowSizeNearestVec4 + ";\n        if (" + (windowSizeVec4Remainder === 1) + ") {\n          " + vecType + " values = " + vecType + "(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          " + updateSnippet + "\n        } else if (" + (windowSizeVec4Remainder === 2) + ") {\n          " + vecType + " values = " + vecType + "(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          " + updateSnippet + "\n        } else if (" + (windowSizeVec4Remainder === 3) + ") {\n          " + vecType + " values = " + vecType + "(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          " + updateSnippet + "\n        }\n        setOutput(" + returnValue + ");\n      }\n    ";
        }
        return ReduceProgram;
    }());

    /**
     * @license
     * Copyright 2018 Google LLC. All Rights Reserved.
     * Licensed under the Apache License, Version 2.0 (the "License");
     * you may not use this file except in compliance with the License.
     * You may obtain a copy of the License at
     *
     * http://www.apache.org/licenses/LICENSE-2.0
     *
     * Unless required by applicable law or agreed to in writing, software
     * distributed under the License is distributed on an "AS IS" BASIS,
     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
     * See the License for the specific language governing permissions and
     * limitations under the License.
     * =============================================================================
     */
    var ReshapePackedProgram = /** @class */ (function () {
        function ReshapePackedProgram(outputShape, inputShape) {
            this.variableNames = ['A'];
            this.packedInputs = true;
            this.packedOutput = true;
            this.outputShape = outputShape;
            var mainLoop = "";
            for (var i = 0; i < 4; i++) {
                var thisRC = "thisRC = rc;";
                if (i % 2 === 1) {
                    thisRC += "thisRC.z += 1;";
                }
                if (i > 1) {
                    thisRC += "thisRC.y += 1;";
                }
                mainLoop += "\n        " + thisRC + "\n        " + (i > 0 ? "if(thisRC.y < rows && thisRC.z < cols){" : '') + "\n          int flatIndex = getFlatIndex(thisRC);\n\n          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n          vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n          result[" + i + "] =\n            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n        " + (i > 0 ? '}' : '') + "\n      ";
            }
            this.userCode = "\n      " + getReshapedInputCoords(inputShape) + "\n      " + getFlatIndexFrom3D(outputShape) + "\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n\n        vec4 result = vec4(0.);\n\n        ivec3 thisRC;\n        int rows = " + outputShape[1] + ";\n        int cols = " + outputShape[2] + ";\n\n        " + mainLoop + "\n\n        setOutput(result);\n      }\n    ";
        }
        return ReshapePackedProgram;
    }());
    function getReshapedInputCoords(shape) {
        var coordsFromIndexSnippet = getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd'], shape);
        return "\n    ivec3 inputCoordsFromReshapedOutCoords(int index) {\n      " + coordsFromIndexSnippet + "\n      return ivec3(r, c, d);\n    }\n  ";
    }

    /**
     * @license
     * Copyright 2018 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.
     * =============================================================================
     */
    var ResizeBilinearBackpropProgram = /** @class */ (function () {
        function ResizeBilinearBackpropProgram(dy, x, alignCorners) {
            this.variableNames = ['dy'];
            this.outputShape = [];
            this.outputShape = x.shape;
            var _a = x.shape, xHeight = _a[1], xWidth = _a[2];
            var _b = dy.shape, yHeight = _b[1], yWidth = _b[2];
            // In the backwards pass, we want to find the pixels that were generated for
            // each pixel in the input image the forward pass and add the corresponding
            // coefficient from dy to the gradient (with some interpolation).
            var effectiveXSize = [
                (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,
                (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth
            ];
            var effectiveYSize = [
                (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,
                (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth
            ];
            var heightScale = effectiveXSize[0] / effectiveYSize[0];
            var widthScale = effectiveXSize[1] / effectiveYSize[1];
            var invHeightScale = 1 / heightScale;
            var invWidthScale = 1 / widthScale;
            // This defines the size of the window of values around a particular
            // index in dy that we want to search for contributions to dx.
            var winHeight = (Math.ceil(invHeightScale) * 2) + 2;
            var winWidth = (Math.ceil(invWidthScale) * 2) + 2;
            this.userCode = "\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(" + heightScale + ");\n        const float widthScale = float(" + widthScale + ");\n\n        const float invHeightScale = float(" + invHeightScale + ");\n        const float invWidthScale = float(" + invWidthScale + ");\n\n        const int winHeight = int(" + winHeight + ");\n        const int winWidth = int(" + winWidth + ");\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(startRLerp - float(winHeight / 2));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(startCLerp - float(winWidth / 2));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= " + yHeight + ") {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= " + yWidth + ") {\n              continue;\n            }\n\n            float dxR = float(dyR) * heightScale;\n            int topDxRIndex = int(floor(dxR));\n            int bottomDxRIndex = int(min(ceil(dxR), " + (xHeight - 1) + ".0));\n            float dxRLerp = dxR - float(topDxRIndex);\n            float inverseDxRLerp = 1.0 - dxRLerp;\n\n            float dxC = float(dyC) * widthScale;\n            int leftDxCIndex = int(floor(dxC));\n            int rightDxCIndex = int(min(ceil(dxC), " + (xWidth - 1) + ".0));\n            float dxCLerp = dxC - float(leftDxCIndex);\n            float inverseDxCLerp = 1.0 - dxCLerp;\n\n            if (r == topDxRIndex && c == leftDxCIndex) {\n              // topLeft\n              accumulator +=\n                getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n            }\n\n            if (r == topDxRIndex && c == rightDxCIndex) {\n              // topRight\n              accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == leftDxCIndex) {\n              // bottomLeft\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == rightDxCIndex) {\n              // bottomRight\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    ";
        }
        return ResizeBilinearBackpropProgram;
    }());

    /**
     * @license
     * Copyright 2017 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.
     * =============================================================================
     */
    var ResizeBilinearProgram = /** @class */ (function () {
        function ResizeBilinearProgram(inputShape, newHeight, newWidth, alignCorners) {
            this.variableNames = ['A'];
            this.outputShape = [];
            var batch = inputShape[0], oldHeight = inputShape[1], oldWidth = inputShape[2], depth = inputShape[3];
            this.outputShape = [batch, newHeight, newWidth, depth];
            var effectiveInSize = [
                (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,
                (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth
            ];
            var effectiveOutSize = [
                (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,
                (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth
            ];
            this.userCode = "\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          " + effectiveInSize[0] / effectiveOutSize[0] + ",\n          " + effectiveInSize[1] / effectiveOutSize[1] + ");\n      const vec2 inputShapeRC = vec2(" + oldHeight + ".0, " + oldWidth + ".0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\n\n        // Compute the four integer indices.\n        ivec2 sourceFloorRC = ivec2(sourceFracIndexRC);\n        ivec2 sourceCeilRC = ivec2(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n        float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n        float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n        float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n        vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n        float top = topLeft + (topRight - topLeft) * fracRC.y;\n        float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n        float newValue = top + (bottom - top) * fracRC.x;\n\n        setOutput(newValue);\n      }\n    ";
        }
        return ResizeBilinearProgram;
    }());

    /**
     * @license
     * Copyright 2019 Google 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.
     * =============================================================================
     */
    var ResizeBilinearPackedProgram = /** @class */ (function () {
        function ResizeBilinearPackedProgram(inputShape, newHeight, newWidth, alignCorners) {
            this.variableNames = ['A'];
            this.packedInputs = true;
            this.packedOutput = true;
            this.outputShape = [];
            var batch = inputShape[0], oldHeight = inputShape[1], oldWidth = inputShape[2], depth = inputShape[3];
            this.outputShape = [batch, newHeight, newWidth, depth];
            var effectiveInSize = [
                (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,
                (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth
            ];
            var effectiveOutSize = [
                (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,
                (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth
            ];
            this.userCode = "\n      const vec3 effectiveInputOverOutputRatioRC = vec3(\n          " + effectiveInSize[0] / effectiveOutSize[0] + ",\n          " + effectiveInSize[1] / effectiveOutSize[1] + ",\n          " + effectiveInSize[1] / effectiveOutSize[1] + ");\n      const vec3 inputShapeRC = vec3(" + oldHeight + ".0, " + oldWidth + ".0,\n                                     " + oldWidth + ".0);\n\n      float getAValue(int b, int r, int c, int d) {\n        return getChannel(getA(b, r, c, d), vec2(c, d));\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        // Calculate values for next column in yRC.z.\n        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n        // Fractional source index.\n        vec3 sourceFracIndexRC = vec3(yRC) * effectiveInputOverOutputRatioRC;\n\n        // Compute the four integer indices.\n        ivec3 sourceFloorRC = ivec3(sourceFracIndexRC);\n        ivec3 sourceCeilRC = ivec3(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        // Should we calculate next column and row elements in 2x2 packed cell.\n        bool hasNextCol = d < " + (depth - 1) + ";\n        bool hasNextRow = coords.z < " + (newWidth - 1) + ";\n\n        // In parallel, construct four corners for all four components in\n        // packed 2x2 cell.\n        vec4 topLeft = vec4(\n          getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),\n          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n        vec4 bottomLeft = vec4(\n          getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),\n          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n        vec4 topRight = vec4(\n          getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),\n          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n        vec4 bottomRight = vec4(\n          getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),\n          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n        vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);\n\n        vec4 top = mix(topLeft, topRight, fracRC.yyzz);\n        vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);\n        vec4 newValue = mix(top, bottom, fracRC.x);\n\n        setOutput(newValue);\n      }\n    ";
        }
        return ResizeBilinearPackedProgram;
    }());

    /**
     * @license
     * Copyright 2018 Google LLC All Rights Reserved.
     * Licensed under the Apache License, Version 2.0 (the "License");
     * you may not use this file except in compliance with the License.
     * You may obtain a copy of the License at
     *
     * http://www.apache.org/licenses/LICENSE-2.0
     *
     * Unless required by applicable law or agreed to in writing, software
     * distributed under the License is distributed on an "AS IS" BASIS,
     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
     * See the License for the specific language governing permissions and
     * limitations under the License.
     * =============================================================================
     */
    var ResizeNearestNeigborBackpropProgram = /** @class */ (function () {
        function ResizeNearestNeigborBackpropProgram(dy, x, alignCorners) {
            this.variableNames = ['dy'];
            this.outputShape = [];
            this.outputShape = x.shape;
            var _a = x.shape, xHeight = _a[1], xWidth = _a[2];
            var _b = dy.shape, yHeight = _b[1], yWidth = _b[2];
            // In the backwards pass, we want to find the pixels that were generated for
            // each pixel in the input image the forward pass and add the corresponding
            // coefficient from dy to the gradient (with some interpolation).
            var effectiveXSize = [
                (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,
                (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth
            ];
            var effectiveYSize = [
                (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,
                (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth
            ];
            var heightScale = effectiveXSize[0] / effectiveYSize[0];
            var widthScale = effectiveXSize[1] / effectiveYSize[1];
            var invHeightScale = 1 / heightScale;
            var invWidthScale = 1 / widthScale;
            // This defines the size of the window of values around a particular
            // index in dy that we want to search for contributions to dx.
            var winHeight = (Math.ceil(invHeightScale) * 2) + 2;
            var winWidth = (Math.ceil(invWidthScale) * 2) + 2;
            this.userCode = "\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(" + heightScale + ");\n        const float widthScale = float(" + widthScale + ");\n\n        const float invHeightScale = float(" + invHeightScale + ");\n        const float invWidthScale = float(" + invWidthScale + ");\n\n        const int winHeight = int(" + winHeight + ");\n        const int winWidth = int(" + winWidth + ");\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= " + yHeight + ") {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= " + yWidth + ") {\n              continue;\n            }\n\n            float sourceFracRow =\n              float(" + effectiveXSize[0] + ") *\n                (float(dyR) / float(" + effectiveYSize[0] + "));\n\n            float sourceFracCol =\n                float(" + effectiveXSize[1] + ") *\n                  (float(dyC) / float(" + effectiveYSize[1] + "));\n\n            int sourceNearestRow = int(min(\n                float(int(" + xHeight + ") - 1),\n                " + alignCorners + " ? float(round(sourceFracRow)) :\n                                  float(floor(sourceFracRow))));\n\n            int sourceNearestCol = int(min(\n                float(int(" + xWidth + ") - 1),\n                " + alignCorners + " ? float(round(sourceFracCol)) :\n                                  float(floor(sourceFracCol))));\n\n            if (r == sourceNearestRow && c == sourceNearestCol) {\n              accumulator += getDy(b, dyR, dyC, d);\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    ";
        }
        return ResizeNearestNeigborBackpropProgram;
    }());

    /**
     * @license
     * Copyright 2018 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.
     * =============================================================================
     */
    var ResizeNearestNeighborProgram = /** @class */ (function () {
        function ResizeNearestNeighborProgram(inputShape, newHeight, newWidth, alignCorners) {
            this.variableNames = ['A'];
            this.outputShape = [];
            var batch = inputShape[0], oldHeight = inputShape[1], oldWidth = inputShape[2], depth = inputShape[3];
            this.outputShape = [batch, newHeight, newWidth, depth];
            var effectiveInSize = [
                (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,
                (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth
            ];
            var effectiveOutSize = [
                (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,
                (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth
            ];
            // When align corners is false, we rounds the value with floor.
            var roundBase = alignCorners ? '0.5' : '0.0';
            this.userCode = "\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          " + effectiveInSize[0] / effectiveOutSize[0] + ",\n          " + effectiveInSize[1] / effectiveOutSize[1] + ");\n      const vec2 inputShapeRC = vec2(" + oldHeight + ".0, " + oldWidth + ".0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\n\n        // Compute the coordinators of nearest neighbor point.\n        ivec2 sourceNearestRC = ivec2(\n          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + " + roundBase + ")));\n\n        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n        setOutput(newValue);\n      }\n    ";
        }
        return ResizeNearestNeighborProgram;
    }());

    /**
     * @license
     * Copyright 2017 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.
     * =============================================================================
     */
    var ReverseProgram = /** @class */ (function () {
        function ReverseProgram(xShape, axis) {
            this.variableNames = ['x'];
            var rank = xShape.length;
            if (rank > 4) {
                throw new Error("WebGL backend: Reverse of rank-" + rank + " tensor is not yet supported");
            }
            this.outputShape = xShape;
            if (rank === 1) {
                this.userCode = "\n        void main() {\n          int coord = getOutputCoords();\n          setOutput(getX(" + xShape[0] + " - coord - 1));\n        }\n      ";
                return;
            }
            var getInCoord = function (i) {
                if (axis.indexOf(i) !== -1 && xShape[i] !== 1) {
                    return xShape[i] + " - coords[" + i + "] - 1";
                }
                return "coords[" + i + "]";
            };
            var inCoords = xShape.map(function (_, i) { return getInCoord(i); }).join(',');
            var type = getCoordsDataType(rank);
            this.userCode = "\n      void main() {\n        " + type + " coords = getOutputCoords();\n        setOutput(getX(" + inCoords + "));\n      }\n    ";
        }
        return ReverseProgram;
    }());

    /**
     * @license
     * Copyright 2019 Google LLC All Rights Reserved.
     * Licensed under the Apache License, Version 2.0 (the "License");
     * you may not use this file except in compliance with the License.
     * You may obtain a copy of the License at
     *
     * http://www.apache.org/licenses/LICENSE-2.0
     *
     * Unless required by applicable law or agreed to in writing, software
     * distributed under the License is distributed on an "AS IS" BASIS,
     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
     * See the License for the specific language governing permissions and
     * limitations under the License.
     * =============================================================================
     */
    var ReversePackedProgram = /** @class */ (function () {
        function ReversePackedProgram(xShape, axis) {
            this.variableNames = ['x'];
            this.packedInputs = true;
            this.packedOutput = true;
            var rank = xShape.length;
            if (rank > 4) {
                throw new Error("WebGL backend: Reverse of rank-" + rank + " tensor is not yet supported");
            }
            this.outputShape = xShape;
            var channels = getChannels('rc', rank);
            var nextColumn = channels[rank - 1] + " + 1 < " + this.outputShape[rank - 1];
            var nextRow = channels[rank - 2] + " + 1 < " + this.outputShape[rank - 2];
            var type = getCoordsDataType(rank);
            if (rank === 1) {
                this.userCode = "\n        void main(){\n          int rc = getOutputCoords();\n          vec4 result = vec4(0.);\n          result.r = getChannel(getX(" + xShape[0] + " - rc - 1),\n            " + xShape[0] + " - rc - 1);\n          if(" + nextColumn + "){\n              result.g = getChannel(getX(" + xShape[0] + " - (rc  + 1) - 1),\n                " + xShape[0] + " - (rc  + 1) - 1);\n          }\n          setOutput(result);\n        }\n      ";
            }
            else {
                this.userCode = "\n        void main() {\n          " + type + " rc = getOutputCoords();\n          vec4 result = vec4(0.);\n          result.r = " + getR(channels.slice()) + ";\n          if(" + nextColumn + "){\n            result.g = " + getG(channels.slice()) + ";\n          }\n          if(" + nextRow + ") {\n            result.b = " + getB(channels.slice()) + ";\n            if(" + nextColumn + ") {\n              result.a = " + getA(channels.slice()) + ";\n            }\n          }\n          setOutput(result);\n        }\n    ";
            }
            function getR(channels) {
                return getChannel(channels);
            }
            function getG(channels) {
                channels[rank - 1] = '(' + channels[rank - 1] + " + 1)";
                return getChannel(channels);
            }
            function getB(channels) {
                channels[rank - 2] = '(' + channels[rank - 2] + " + 1)";
                return getChannel(channels);
            }
            function getA(channels) {
                channels[rank - 1] = '(' + channels[rank - 1] + " + 1)";
                channels[rank - 2] = '(' + channels[rank - 2] + " + 1)";
                return getChannel(channels);
            }
            function getChannel(channels) {
                var inCoordsArray = xShape.map(function (_, i) { return getInCoord(i, channels); });
                var inCoords = inCoordsArray.join(',');
                var innerDims = inCoordsArray.slice(-2).join(',');
                return "getChannel(getX(" + inCoords + "), vec2(" + innerDims + "))";
            }
            function getInCoord(i, channels1) {
                if (axis.indexOf(i) !== -1 && xShape[i] !== 1) {
                    return xShape[i] + " - " + channels1[i] + " - 1";
                }
                else {
                    return "" + channels1[i];
                }
            }
        }
        return ReversePackedProgram;
    }());

    /**
     * @license
     * Copyright 2018 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.
     * =============================================================================
     */
    var ScatterProgram = /** @class */ (function () {
        function ScatterProgram(updateSize, sliceDim, indicesRank, updatesRank, strides, shape, summingDupeIndex) {
            if (summingDupeIndex === void 0) { summingDupeIndex = true; }
            this.variableNames = ['updates', 'indices', 'defaultValue'];
            this.outputShape = shape;
            var stridesType = getCoordsDataType(strides.length);
            var dtype = getCoordsDataType(shape.length);
            var indicesString = '';
            if (indicesRank === 1) {
                indicesString = 'i';
            }
            else if (indicesRank === 2) {
                indicesString = 'i, j';
            }
            var indicesSnippet = "getIndices(" + indicesString + ")";
            var updatesString = '';
            if (updatesRank === 1) {
                updatesString = 'i';
            }
            else if (updatesRank === 2) {
                updatesString = 'i, coords[1]';
            }
            var updatesSnippet = "getUpdates(" + updatesString + ")";
            var strideString = sliceDim > 1 ? 'strides[j]' : 'strides';
            this.userCode = "\n        " + stridesType + " strides = " + stridesType + "(" + strides + ");\n\n        void main() {\n          " + dtype + " coords = getOutputCoords();\n          float sum = 0.0;\n          bool found = false;\n          for (int i = 0; i < " + updateSize + "; i++) {\n            int flattenedIndex = 0;\n            for (int j = 0; j < " + sliceDim + "; j++) {\n              int index = round(" + indicesSnippet + ");\n              flattenedIndex += index * " + strideString + ";\n            }\n            if (flattenedIndex == coords[0]) {\n              sum += " + updatesSnippet + ";\n              found = true;\n            }\n          }\n          setOutput(mix(getDefaultValue(), sum, float(found)));\n        }\n      ";
        }
        return ScatterProgram;
    }());

    /**
     * @license
     * Copyright 2018 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.
     * =============================================================================
     */
    var SegmentOpProgram = /** @class */ (function () {
        function SegmentOpProgram(segOpInfo, segOpType) {
            this.variableNames = ['x', 'segmentIds'];
            var windowSize = segOpInfo.windowSize;
            var batchSize = segOpInfo.batchSize;
            var inSize = segOpInfo.inSize;
            var numSegments = segOpInfo.numSegments;
            var outSize = numSegments * Math.ceil(inSize / windowSize);
            this.outputShape = [batchSize, outSize];
            var initializationValue = '0.0';
            var returnValue = "sumValue";
            var windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;
            var windowSizeVec4Remainder = windowSize % 4;
            var updateSnippet = "\n        sumValue += dot(values, segFilter);\n    ";
            var checkValueOutOfBounds = '';
            if (inSize % windowSize > 0) {
                checkValueOutOfBounds = "\n        if (inIdx < 0 || inIdx >= " + inSize + ") {\n          return initializationValue;\n        }\n      ";
            }
            var checkSegmentIdOutOfBounds = '';
            if (inSize % windowSize > 0) {
                checkSegmentIdOutOfBounds = "\n        if (inIdx < 0 || inIdx >= " + inSize + ") {\n          return -1.0;\n        }\n      ";
            }
            this.userCode = "\n      const float initializationValue = " + initializationValue + ";\n\n      float getValue(int batch, int inIdx) {\n        " + checkValueOutOfBounds + "\n        return getX(batch, inIdx);\n      }\n\n      float getSegmentIdAtIndex(int inIdx) {\n        " + checkSegmentIdOutOfBounds + "\n        return getSegmentIds(inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = int(floor(float(outIdx) / float(\n          " + numSegments + ")) * float(" + windowSize + "));\n        int currentSeg = int(mod(float(outIdx), float(" + numSegments + ")));\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < " + windowSizeNearestVec4 + "; i += 4) {\n          int inIdx = inOffset + i;\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\n          );\n\n          " + updateSnippet + "\n        }\n\n        int inIdx = inOffset + " + windowSizeNearestVec4 + ";\n        if (" + (windowSizeVec4Remainder === 1) + ") {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            0,\n            0,\n            0\n          );\n\n          " + updateSnippet + "\n        } else if (" + (windowSizeVec4Remainder === 2) + ") {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n              0,\n              0\n          );\n\n          " + updateSnippet + "\n        } else if (" + (windowSizeVec4Remainder === 3) + ") {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            0\n          );\n\n          " + updateSnippet + "\n        }\n        setOutput(" + returnValue + ");\n      }\n    ";
        }
        return SegmentOpProgram;
    }());

    /**
     * @license
     * Copyright 2017 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.
     * =============================================================================
     */
    var SelectProgram = /** @class */ (function () {
        function SelectProgram(cRank, shape, rank) {
            this.variableNames = ['c', 'a', 'b'];
            this.outputShape = shape;
            var cCoords;
            var abCoords;
            if (rank > 4) {
                throw Error("Where for rank " + rank + " is not yet supported");
            }
            if (rank === 1) {
                abCoords = "resRC";
                cCoords = "resRC";
            }
            else {
                var currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w'];
                var cCoordVars = [];
                var abCoordVars = [];
                for (var i = 0; i < shape.length; i++) {
                    abCoordVars.push("" + currentCoords[i]);
                    if (i < cRank) {
                        cCoordVars.push("" + currentCoords[i]);
                    }
                }
                cCoords = cCoordVars.join();
                abCoords = abCoordVars.join();
            }
            var dtype = getCoordsDataType(rank);
            this.userCode = "\n      void main() {\n        " + dtype + " resRC = getOutputCoords();\n        float cVal = getC(" + cCoords + ");\n        if (cVal >= 1.0) {\n          setOutput(getA(" + abCoords + "));\n        } else {\n          setOutput(getB(" + abCoords + "));\n        }\n      }\n    ";
        }
        return SelectProgram;
    }());

    /**
     * @license
     * Copyright 2017 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.
     * =============================================================================
     */
    var SliceProgram = /** @class */ (function () {
        function SliceProgram(destSize) {
            this.variableNames = ['source'];
            this.outputShape = destSize;
            this.rank = destSize.length;
            var dtype = getCoordsDataType(this.rank);
            var uniformPart = "uniform int start[" + this.rank + "];";
            var sourceCoords = getCoords$1(this.rank);
            var body;
            var coordSum = destSize.map(function (_, i) {
                return "sourceLoc." + coords[i] + " = start[" + i + "] + coords." + coords[i] + ";";
            });
            body = "\n        " + dtype + " sourceLoc;\n        " + dtype + " coords = getOutputCoords();\n        " + coordSum.join('\n') + "\n      ";
            this.userCode = "\n      " + uniformPart + "\n      void main() {\n        " + body + "\n        setOutput(getSource(" + sourceCoords + "));\n      }\n    ";
        }
        SliceProgram.prototype.getCustomSetupFunc = function (start) {
            var _this = this;
            if (start.length !== this.rank) {
                throw Error("The rank (" + this.rank + ") of the program must match the " +
                    ("length of start (" + start.length + ")"));
            }
            return function (gpgpu, webGLProgram) {
                if (_this.startLoc == null) {
                    _this.startLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'start');
                    if (_this.startLoc == null) {
                        // This means the compiler has optimized and realized it doesn't need
                        // the uniform.
                        return;
                    }
                }
                gpgpu.gl.uniform1iv(_this.startLoc, start);
            };
        };
        return SliceProgram;
    }());
    var coords = ['x', 'y', 'z', 'w', 'u', 'v'];
    function getCoords$1(rank) {
        if (rank === 1) {
            return 'sourceLoc';
        }
        else if (rank <= 6) {
            return coords.slice(0, rank).map(function (x) { return 'sourceLoc.' + x; }).join(',');
        }
        else {
            throw Error("Slicing for rank " + rank + " is not yet supported");
        }
    }

    /**
     * @license
     * Copyright 2019 Google 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.
     * =============================================================================
     */
    var SlicePackedProgram = /** @class */ (function () {
        function SlicePackedProgram(destSize) {
            this.variableNames = ['source'];
            this.packedInputs = true;
            this.packedOutput = true;
            this.outputShape = destSize;
            this.rank = destSize.length;
            var dtype = getCoordsDataType(this.rank);
            var coords = getChannels('coords', this.rank);
            var sourceLoc = getChannels('sourceLoc', this.rank);
            var innerDims = this.rank === 1 ? 'sourceLoc' : "vec2(" + sourceLoc.slice(-2).join() + ")";
            var getChannel = "getChannel(getSource(" + sourceLoc.join() + "), " + innerDims + ")";
            var upperRow = "\n      result.x = " + getChannel + ";\n      if (++" + coords[this.rank - 1] + " < " + destSize[this.rank - 1] + ") {\n        ++" + sourceLoc[this.rank - 1] + ";\n        result.y = " + getChannel + ";\n        --" + sourceLoc[this.rank - 1] + ";\n      }\n    ";
            var lowerRow = this.rank === 1 ? '' : "\n      --" + coords[this.rank - 1] + ";\n      if (++" + coords[this.rank - 2] + " < " + destSize[this.rank - 2] + ") {\n        ++" + sourceLoc[this.rank - 2] + ";\n        result.z = " + getChannel + ";\n        if (++" + coords[this.rank - 1] + " < " + destSize[this.rank - 1] + ") {\n          ++" + sourceLoc[this.rank - 1] + ";\n          result.w = " + getChannel + ";\n        }\n      }\n    ";
            var sourceLocSetup = this.rank <= 4 ?
                "sourceLoc = coords +\n            " + dtype + "(" + destSize.map(function (_, i) { return "start[" + i + "]"; }).join() + ");" :
                destSize.map(function (_, i) { return sourceLoc[i] + " = " + coords[i] + " + start[" + i + "];"; })
                    .join('\n');
            this.userCode = "\n      uniform int start[" + this.rank + "];\n      void main() {\n        " + dtype + " coords = getOutputCoords();\n        " + dtype + " sourceLoc;\n        " + sourceLocSetup + "\n        vec4 result = vec4(0.);\n        " + upperRow + "\n        " + lowerRow + "\n        setOutput(result);\n      }\n    ";
        }
        SlicePackedProgram.prototype.getCustomSetupFunc = function (start) {
            var _this = this;
            if (start.length !== this.rank) {
                throw Error("The rank (" + this.rank + ") of the program must match the " +
                    ("length of start (" + start.length + ")"));
            }
            return function (gpgpu, webGLProgram) {
                if (_this.startLoc == null) {
                    _this.startLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'start');
                    if (_this.startLoc == null) {
                        // This means the compiler has optimized and realized it doesn't need
                        // the uniform.
                        return;
                    }
                }
                gpgpu.gl.uniform1iv(_this.startLoc, start);
            };
        };
        return SlicePackedProgram;
    }());

    /**
     * @license
     * Copyright 2017 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.
     * =============================================================================
     */
    var StridedSliceProgram = /** @class */ (function () {
        function StridedSliceProgram(begin, strides, size) {
            this.variableNames = ['x'];
            this.outputShape = size;
            var rank = size.length;
            var inputDtype = getCoordsDataType(size.length);
            var dtype = getCoordsDataType(size.length);
            var newCoords = '';
            if (rank === 1) {
                newCoords = 'coords * strides + begin';
            }
            else {
                var outputAxis_1 = 0;
                newCoords =
                    size.map(function (_, i) {
                        outputAxis_1++;
                        return size.length === 1 ?
                            "coords * strides[" + i + "] + begin[" + i + "]" :
                            "coords[" + (outputAxis_1 - 1) + "] * strides[" + i + "] + begin[" + i + "]";
                    })
                        .join(',');
            }
            this.userCode = "\n      " + inputDtype + " begin = " + inputDtype + "(" + begin + ");\n      " + inputDtype + " strides = " + inputDtype + "(" + strides + ");\n\n      void main() {\n        " + dtype + " coords = getOutputCoords();\n        setOutput(getX(" + newCoords + "));\n      }\n    ";
        }
        return StridedSliceProgram;
    }());

    /**
     * @license
     * Copyright 2017 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.
     * =============================================================================
     */
    var TextureManager = /** @class */ (function () {
        function TextureManager(gpgpu) {
            this.gpgpu = gpgpu;
            this.numUsedTextures = 0;
            this.numFreeTextures = 0;
            this.freeTextures = {};
            this.logEnabled = false;
            this.usedTextures = {};
        }
        TextureManager.prototype.acquireTexture = function (shapeRC, usage, isPacked) {
            var physicalTexType = getPhysicalFromLogicalTextureType(usage, isPacked);
            var shapeKey = getKeyFromTextureShape(shapeRC, physicalTexType, isPacked);
            if (!(shapeKey in this.freeTextures)) {
                this.freeTextures[shapeKey] = [];
            }
            if (!(shapeKey in this.usedTextures)) {
                this.usedTextures[shapeKey] = [];
            }
            if (this.freeTextures[shapeKey].length > 0) {
                this.numFreeTextures--;
                this.numUsedTextures++;
                this.log();
                var newTexture_1 = this.freeTextures[shapeKey].shift();
                this.usedTextures[shapeKey].push(newTexture_1);
                return newTexture_1;
            }
            this.numUsedTextures++;
            this.log();
            var newTexture;
            if (physicalTexType === PhysicalTextureType.PACKED_2X2_FLOAT32) {
                newTexture = this.gpgpu.createPackedMatrixTexture(shapeRC[0], shapeRC[1]);
            }
            else if (physicalTexType === PhysicalTextureType.PACKED_2X2_FLOAT16) {
                newTexture =
                    this.gpgpu.createFloat16PackedMatrixTexture(shapeRC[0], shapeRC[1]);
            }
            else if (physicalTexType === PhysicalTextureType.UNPACKED_FLOAT32) {
                newTexture =
                    this.gpgpu.createFloat32MatrixTexture(shapeRC[0], shapeRC[1]);
            }
            else if (physicalTexType === PhysicalTextureType.UNPACKED_FLOAT16) {
                newTexture =
                    this.gpgpu.createFloat16MatrixTexture(shapeRC[0], shapeRC[1]);
            }
            else if (physicalTexType === PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE) {
                newTexture =
                    this.gpgpu.createUnsignedBytesMatrixTexture(shapeRC[0], shapeRC[1]);
            }
            this.usedTextures[shapeKey].push(newTexture);
            return newTexture;
        };
        TextureManager.prototype.releaseTexture = function (texture, shape, logicalTexType, isPacked) {
            if (this.freeTextures == null) {
                // Already disposed.
                return;
            }
            var physicalTexType = getPhysicalFromLogicalTextureType(logicalTexType, isPacked);
            var shapeKey = getKeyFromTextureShape(shape, physicalTexType, isPacked);
            if (!(shapeKey in this.freeTextures)) {
                this.freeTextures[shapeKey] = [];
            }
            this.freeTextures[shapeKey].push(texture);
            this.numFreeTextures++;
            this.numUsedTextures--;
            var texList = this.usedTextures[shapeKey];
            var texIndex = texList.indexOf(texture);
            if (texIndex < 0) {
                throw new Error('Cannot release a texture that was never provided by this ' +
                    'texture manager');
            }
            texList.splice(texIndex, 1);
            this.log();
        };
        TextureManager.prototype.log = function () {
            if (!this.logEnabled) {
                return;
            }
            var total = this.numFreeTextures + this.numUsedTextures;
            console.log('Free/Used', this.numFreeTextures + " / " + this.numUsedTextures, "(" + total + ")");
        };
        TextureManager.prototype.getNumUsedTextures = function () {
            return this.numUsedTextures;
        };
        TextureManager.prototype.getNumFreeTextures = function () {
            return this.numFreeTextures;
        };
        TextureManager.prototype.dispose = function () {
            var _this = this;
            if (this.freeTextures == null) {
                // Already disposed.
                return;
            }
            for (var texShape in this.freeTextures) {
                this.freeTextures[texShape].forEach(function (tex) {
                    _this.gpgpu.deleteMatrixTexture(tex);
                });
            }
            for (var texShape in this.usedTextures) {
                this.usedTextures[texShape].forEach(function (tex) {
                    _this.gpgpu.deleteMatrixTexture(tex);
                });
            }
            this.freeTextures = null;
            this.usedTextures = null;
            this.numUsedTextures = 0;
            this.numFreeTextures = 0;
        };
        return TextureManager;
    }());
    function getPhysicalTextureForRendering(isPacked) {
        if (env().getBool('WEBGL_RENDER_FLOAT32_ENABLED')) {
            if (isPacked) {
                return PhysicalTextureType.PACKED_2X2_FLOAT32;
            }
            return PhysicalTextureType.UNPACKED_FLOAT32;
        }
        if (isPacked) {
            return PhysicalTextureType.PACKED_2X2_FLOAT16;
        }
        return PhysicalTextureType.UNPACKED_FLOAT16;
    }
    function getPhysicalFromLogicalTextureType(logicalTexType, isPacked) {
        if (logicalTexType === TextureUsage.UPLOAD) {
            return PhysicalTextureType.PACKED_2X2_FLOAT32;
        }
        else if (logicalTexType === TextureUsage.RENDER || logicalTexType == null) {
            return getPhysicalTextureForRendering(isPacked);
        }
        else if (logicalTexType === TextureUsage.DOWNLOAD ||
            logicalTexType === TextureUsage.PIXELS) {
            return PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE;
        }
        throw new Error("Unknown logical texture type " + logicalTexType);
    }
    function getKeyFromTextureShape(shapeRowsCol, physicalTexType, isPacked) {
        return shapeRowsCol[0] + "_" + shapeRowsCol[1] + "_" + physicalTexType + "_" + isPacked;
    }

    /**
     * @license
     * Copyright 2017 Google 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.
     * =============================================================================
     */
    var TileProgram = /** @class */ (function () {
        function TileProgram(aShape, reps) {
            this.variableNames = ['A'];
            var outputShape = new Array(aShape.length);
            for (var i = 0; i < outputShape.length; i++) {
                outputShape[i] = aShape[i] * reps[i];
            }
            this.outputShape = outputShape;
            this.rank = outputShape.length;
            var dtype = getCoordsDataType(this.rank);
            var sourceCoords = getSourceCoords$2(aShape);
            this.userCode = "\n      void main() {\n        " + dtype + " resRC = getOutputCoords();\n        setOutput(getA(" + sourceCoords + "));\n      }\n    ";
        }
        return TileProgram;
    }());
    function getSourceCoords$2(aShape) {
        var rank = aShape.length;
        if (rank > 5) {
            throw Error("Tile for rank " + rank + " is not yet supported");
        }
        if (rank === 1) {
            return "imod(resRC, " + aShape[0] + ")";
        }
        var currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w', 'resRC.u'];
        var sourceCoords = [];
        for (var i = 0; i < aShape.length; i++) {
            sourceCoords.push("imod(" + currentCoords[i] + ", " + aShape[i] + ")");
        }
        return sourceCoords.join();
    }

    /**
     * @license
     * Copyright 2017 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.
     * =============================================================================
     */
    var TransposeProgram = /** @class */ (function () {
        function TransposeProgram(aShape, newDim) {
            this.variableNames = ['A'];
            var outputShape = new Array(aShape.length);
            for (var i = 0; i < outputShape.length; i++) {
                outputShape[i] = aShape[newDim[i]];
            }
            this.outputShape = outputShape;
            this.rank = outputShape.length;
            var dtype = getCoordsDataType(this.rank);
            var switched = getSwitchedCoords(newDim);
            this.userCode = "\n    void main() {\n      " + dtype + " resRC = getOutputCoords();\n      setOutput(getA(" + switched + "));\n    }\n    ";
        }
        return TransposeProgram;
    }());
    function getSwitchedCoords(newDim) {
        var rank = newDim.length;
        if (rank > 6) {
            throw Error("Transpose for rank " + rank + " is not yet supported");
        }
        var originalOrder = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w', 'resRC.u', 'resRC.v'];
        var switchedCoords = new Array(rank);
        for (var i = 0; i < newDim.length; i++) {
            switchedCoords[newDim[i]] = originalOrder[i];
        }
        return switchedCoords.join();
    }

    /**
     * @license
     * Copyright 2019 Google 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.
     * =============================================================================
     */
    var TransposePackedProgram = /** @class */ (function () {
        function TransposePackedProgram(aShape, newDim) {
            this.variableNames = ['A'];
            this.packedInputs = true;
            this.packedOutput = true;
            var outputShape = new Array(aShape.length);
            for (var i = 0; i < outputShape.length; i++) {
                outputShape[i] = aShape[newDim[i]];
            }
            this.outputShape = outputShape;
            this.rank = outputShape.length;
            if (this.rank > 6) {
                throw Error("Packed transpose for rank " + this.rank + " is not yet supported.");
            }
            var dtype = getCoordsDataType(this.rank);
            var outputOrder = getVecChannels('rc', this.rank);
            var switchedOrder = new Array(this.rank);
            for (var i = 0; i < newDim.length; i++) {
                switchedOrder[newDim[i]] = outputOrder[i];
            }
            var innerDims = "vec2(" + switchedOrder.slice(-2).join() + ")";
            var nextColumn = "++" + outputOrder[this.rank - 1] + " < " + outputShape[this.rank - 1];
            var getc = "getChannel(getA(" + switchedOrder.join() + "), " + innerDims + ")";
            this.userCode = "\n    void main() {\n      " + dtype + " rc = getOutputCoords();\n      vec4 result = vec4(0.);\n      result[0] = " + getc + ";\n      if(" + nextColumn + ") {\n        result[1] = " + getc + ";\n      }\n      --" + outputOrder[this.rank - 1] + ";\n      if(++" + outputOrder[this.rank - 2] + " < " + outputShape[this.rank - 2] + ") {\n        result[2] = " + getc + ";\n        if(" + nextColumn + ") {\n          result[3] = " + getc + ";\n        }\n      }\n      setOutput(result);\n    }\n    ";
        }
        return TransposePackedProgram;
    }());

    /**
     * @license
     * Copyright 2018 Google LLC. All Rights Reserved.
     * Licensed under the Apache License, Version 2.0 (the "License");
     * you may not use this file except in compliance with the License.
     * You may obtain a copy of the License at
     *
     * http://www.apache.org/licenses/LICENSE-2.0
     *
     * Unless required by applicable law or agreed to in writing, software
     * distributed under the License is distributed on an "AS IS" BASIS,
     * WITHOUT WARRANTIES 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.
     * =============================================================================
     */
    var SELU_SCALEALPHA = 1.7580993408473768599402175208123;
    var SELU_SCALE = 1.0507009873554804934193349852946;

    /**
     * @license
     * Copyright 2017 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.
     * =============================================================================
     */
    var UnaryOpProgram = /** @class */ (function () {
        function UnaryOpProgram(aShape, opSnippet) {
            this.variableNames = ['A'];
            this.outputShape = aShape;
            this.userCode = "\n      float unaryOperation(float x) {\n        " + opSnippet + "\n      }\n\n      void main() {\n        float x = getAAtOutCoords();\n        float y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    ";
        }
        return UnaryOpProgram;
    }());
    var CHECK_NAN_SNIPPET$2 = "if (isnan(x)) return x;";
    var LINEAR = "return x;";
    var ABS = "return abs(x);";
    var RELU = CHECK_NAN_SNIPPET$2 + "\n  return (x < 0.0) ? 0.0 : x;\n";
    var RELU6 = CHECK_NAN_SNIPPET$2 + "\n  return (x < 0.0) ? 0.0 : min(6.0, x);\n";
    var ELU = "return (x >= 0.0) ? x : (exp(x) - 1.0);";
    var SELU = "\n  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n  // see: https://arxiv.org/abs/1706.02515\n  float scaleAlpha = " + SELU_SCALEALPHA + ";\n  float scale = " + SELU_SCALE + ";\n  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\n";
    function STEP(alpha) {
        if (alpha === void 0) { alpha = 0.0; }
        return CHECK_NAN_SNIPPET$2 + ("\n    return x > 0.0 ? 1.0 : float(" + alpha + ");\n  ");
    }
    var NEG = "return -x;";
    var CEIL = "return ceil(x);";
    var FLOOR = "return floor(x);";
    var SIGN = "\n  if (isnan(x)) { return 0.0; }\n  return sign(x);\n";
    var IS_NAN = "return float(isnan(x));";
    var IS_INF = "return float(isinf(x));";
    var IS_FINITE = "return float(!isnan(x) && !isinf(x));";
    var ROUND = "\n  // OpenGL ES does not support round function.\n  // The algorithm is based on banker's rounding.\n  float base = floor(x);\n  if ((x - base) < 0.5) {\n    return floor(x);\n  } else if ((x - base) > 0.5) {\n    return ceil(x);\n  } else {\n    if (mod(base, 2.0) == 0.0) {\n      return base;\n    } else {\n      return base + 1.0;\n    }\n  }\n";
    var EXP = "return exp(x);";
    var EXPM1 = "return exp(x) - 1.0;";
    var LOG = "if (x < 0.0) return NAN;\n  return log(x);";
    var LOG1P = "return log(1.0 + x);";
    var SQRT = "return sqrt(x);";
    var RSQRT = "return inversesqrt(x);";
    var SIGMOID = "return 1.0 / (1.0 + exp(-1.0 * x));";
    /**
     * 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
     *
     * too_large = (x > -threshold) is value above which exp(x) may overflow
     * but softplus(x) == x is within machine epsilon
     *
     * too_small = (x < threshold) is value below which exp(x) may underflow,
     * but softplus(x) == exp(x) is within machine epsilon.
     */
    var SOFTPLUS = "\n  float epsilon = 1.1920928955078125e-7;\n  float threshold = log(epsilon) + 2.0;\n\n  bool too_large = x > -threshold;\n  bool too_small = x < threshold;\n\n  float result;\n  float exp_x = exp(x);\n\n  if (too_large){\n    result = x;\n  }\n  else if (too_small){\n    result = exp_x;\n  }\n  else{\n    result = log(exp_x + 1.0);\n  }\n  return result;\n";
    var SIN = CHECK_NAN_SNIPPET$2 + "\n  return sin(x);\n";
    var COS = CHECK_NAN_SNIPPET$2 + "\n  return cos(x);\n";
    var TAN = "return tan(x);";
    var ASIN = CHECK_NAN_SNIPPET$2 + "\n  if (abs(x) > 1.) {\n    return NAN;\n  }\n  return asin(x);\n";
    var ACOS = CHECK_NAN_SNIPPET$2 + "\n  if (abs(x) > 1.) {\n    return NAN;\n  }\n  return acos(x);\n";
    var ATAN = CHECK_NAN_SNIPPET$2 + "\n  return atan(x);\n";
    var SINH = "\n  float e2x = exp(x);\n  return (e2x - 1.0 / e2x) / 2.0;\n";
    var COSH = "\n  float e2x = exp(-x);\n  return (e2x + 1.0 / e2x) / 2.0;\n";
    var TANH = "\n  float e2x = exp(-2.0 * abs(x));\n  return sign(x) * (1.0 - e2x) / (1.0 + e2x);\n";
    var ASINH = CHECK_NAN_SNIPPET$2 + "return log(x + sqrt(x * x + 1.0));";
    var ACOSH = CHECK_NAN_SNIPPET$2 + "\n  if (x < 1.0) return NAN;\n  return log(x + sqrt(x * x - 1.0));";
    var ATANH = CHECK_NAN_SNIPPET$2 + "\n  if ((x < -1.0) || (x > 1.0)) return NAN;\n  return (log(1.0 + x) - log(1.0 - x)) / 2.0;";
    var ERF = "\n  // Error function is calculated approximately with elementary function.\n  // See \"Handbook of Mathematical Functions with Formulas,\n  // Graphs, and Mathematical Tables\", Abramowitz and Stegun.\n  float p = " + ERF_P + ";\n  float a1 = " + ERF_A1 + ";\n  float a2 = " + ERF_A2 + ";\n  float a3 = " + ERF_A3 + ";\n  float a4 = " + ERF_A4 + ";\n  float a5 = " + ERF_A5 + ";\n\n  float sign = sign(x);\n  x = abs(x);\n  float t = 1.0 / (1.0 + p * x);\n  return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));\n";
    var SQUARE = "return x * x;";
    var RECIPROCAL = "return 1.0 / x;";
    var LOGICAL_NOT = "return float(!(x >= 1.0));";
    var TO_INT = "return float(int(x));";
    var CLONE = 'return x;';

    /**
     * @license
     * Copyright 2018 Google LLC. All Rights Reserved.
     * Licensed under the Apache License, Version 2.0 (the "License");
     * you may not use this file except in compliance with the License.
     * You may obtain a copy of the License at
     *
     * http://www.apache.org/licenses/LICENSE-2.0
     *
     * Unless required by applicable law or agreed to in writing, software
     * distributed under the License is distributed on an "AS IS" BASIS,
     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
     * See the License for the specific language governing permissions and
     * limitations under the License.
     * =============================================================================
     */
    var LINEAR$1 = "return x;";
    var LOG$1 = "\n  vec4 result = log(x);\n  vec4 isNaN = vec4(lessThan(x, vec4(0.0)));\n  result.r = isNaN.r == 1.0 ? NAN : result.r;\n  result.g = isNaN.g == 1.0 ? NAN : result.g;\n  result.b = isNaN.b == 1.0 ? NAN : result.b;\n  result.a = isNaN.a == 1.0 ? NAN : result.a;\n\n  return result;\n";
    var RELU$1 = "\n  vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n";
    var RELU6$1 = "\n  vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n";
    var ELU$1 = "\n  vec4 result;\n\n  result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n  result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n  result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n  result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n  return result;\n";
    var UnaryOpPackedProgram = /** @class */ (function () {
        function UnaryOpPackedProgram(aShape, opSnippet) {
            this.variableNames = ['A'];
            this.packedInputs = true;
            this.packedOutput = true;
            this.outputShape = aShape;
            this.userCode = "\n      vec4 unaryOperation(vec4 x) {\n        " + opSnippet + "\n      }\n\n      void main() {\n        vec4 x = getAAtOutCoords();\n        vec4 y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    ";
        }
        return UnaryOpPackedProgram;
    }());

    /**
     * @license
     * Copyright 2018 Google LLC. All Rights Reserved.
     * Licensed under the Apache License, Version 2.0 (the "License");
     * you may not use this file except in compliance with the License.
     * You may obtain a copy of the License at
     *
     * http://www.apache.org/licenses/LICENSE-2.0
     *
     * Unless required by applicable law or agreed to in writing, software
     * distributed under the License is distributed on an "AS IS" BASIS,
     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
     * See the License for the specific language governing permissions and
     * limitations under the License.
     * =============================================================================
     */
    var UnpackProgram = /** @class */ (function () {
        function UnpackProgram(outputShape) {
            this.variableNames = ['A'];
            this.packedInputs = true;
            this.packedOutput = false;
            this.outputShape = outputShape;
            var rank = outputShape.length;
            var channels = getChannels('rc', rank);
            var dtype = getCoordsDataType(rank);
            var sourceCoords = getSourceCoords(rank, channels);
            var innerDims = channels.slice(-2);
            var coords = rank <= 1 ? 'rc' : "vec2(" + innerDims.join(',') + ")";
            this.userCode = "\n      void main() {\n        " + dtype + " rc = getOutputCoords();\n        vec4 packedInput = getA(" + sourceCoords + ");\n\n        setOutput(getChannel(packedInput, " + coords + "));\n      }\n    ";
        }
        return UnpackProgram;
    }());

    /**
     * @license
     * Copyright 2017 Google 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.
     * =============================================================================
     */
    var binaryCaches = {};
    function getBinaryCache(webGLVersion) {
        if (webGLVersion in binaryCaches) {
            return binaryCaches[webGLVersion];
        }
        binaryCaches[webGLVersion] = {};
        return binaryCaches[webGLVersion];
    }
    function mapActivationToShaderProgram(activation, packed) {
        if (packed === void 0) { packed = false; }
        if (activation === 'linear') {
            if (packed) {
                return LINEAR$1;
            }
            return LINEAR;
        }
        else if (activation === 'relu') {
            if (packed) {
                return RELU$1;
            }
            return RELU;
        }
        else if (activation === 'elu') {
            if (packed) {
                return ELU$1;
            }
            return ELU;
        }
        else if (activation === 'relu6') {
            if (packed) {
                return RELU6$1;
            }
            return RELU6;
        }
        else if (activation === 'prelu') {
            if (packed) {
                return PRELU$1;
            }
            return PRELU;
        }
        throw new Error("Activation " + activation + " has not been implemented for the WebGL backend.");
    }
    // Empirically determined constant used to determine size threshold for handing
    // off execution to the CPU.
    var CPU_HANDOFF_SIZE_THRESHOLD = 128;
    // Empirically determined constant used to decide the number of MB on GPU
    // before we warn about high memory use. The MB are this constant * screen area
    // * dpi / 1024 / 1024.
    var BEFORE_PAGING_CONSTANT = 600;
    function numMBBeforeWarning() {
        if (env().global.screen == null) {
            return 1024; // 1 GB.
        }
        return (env().global.screen.height * env().global.screen.width *
            window.devicePixelRatio) *
            BEFORE_PAGING_CONSTANT / 1024 / 1024;
    }
    // Empirically determined minimal shared dimension in matmul before we forward
    // to a.mul(b).sum() in order to take advantage of GPU parallelism. See
    // https://github.com/tensorflow/tfjs-core/pull/1379 for benchmarks.
    var MATMUL_SHARED_DIM_THRESHOLD = 1000;
    var MathBackendWebGL = /** @class */ (function (_super) {
        __extends(MathBackendWebGL, _super);
        function MathBackendWebGL(gpgpu) {
            var _this = _super.call(this) || this;
            // Maps data ids that have a pending read operation, to list of subscribers.
            _this.pendingRead = new WeakMap();
            // List of data ids that are scheduled for disposal, but are waiting on a
            // pending read operation.
            _this.pendingDisposal = new WeakSet();
            // Used to count the number of 'shallow' sliced tensors that point to the
            // same data id.
            _this.dataRefCount = new WeakMap();
            _this.numBytesInGPU = 0;
            // Accumulated time spent (including blocking) in uploading data to webgl.
            _this.uploadWaitMs = 0;
            // Accumulated time spent (including blocking in downloading data from webgl.
            _this.downloadWaitMs = 0;
            _this.warnedAboutMemory = false;
            _this.pendingDeletes = 0;
            _this.disposed = false;
            if (!env().getBool('HAS_WEBGL')) {
                throw new Error('WebGL is not supported on this device');
            }
            if (gpgpu == null) {
                var gl = getWebGLContext(env().getNumber('WEBGL_VERSION'));
                _this.binaryCache = getBinaryCache(env().getNumber('WEBGL_VERSION'));
                _this.gpgpu = new GPGPUContext(gl);
                _this.canvas = gl.canvas;
                _this.gpgpuCreatedLocally = true;
            }
            else {
                _this.gpgpu = gpgpu;
                _this.binaryCache = {};
                _this.gpgpuCreatedLocally = false;
                _this.canvas = gpgpu.gl.canvas;
            }
            _this.textureManager = new TextureManager(_this.gpgpu);
            _this.numMBBeforeWarning = numMBBeforeWarning();
            _this.texData = new DataStorage(_this, ENGINE);
            return _this;
        }
        MathBackendWebGL.prototype.numDataIds = function () {
            return this.texData.numDataIds() +
                (this.cpuBackend ? this.cpuBackend.numDataIds() : 0) -
                this.pendingDeletes;
        };
        MathBackendWebGL.prototype.write = function (values, shape, dtype) {
            if (env().getBool('DEBUG')) {
                this.checkNumericalProblems(values);
            }
            if (dtype === 'complex64' && values != null) {
                throw new Error("Cannot write to a complex64 dtype. " +
                    "Please use tf.complex(real, imag).");
            }
            var dataId = {};
            this.texData.set(dataId, { shape: shape, dtype: dtype, values: values, usage: TextureUsage.UPLOAD });
            return dataId;
        };
        MathBackendWebGL.prototype.move = function (dataId, values, shape, dtype) {
            if (env().getBool('DEBUG')) {
                this.checkNumericalProblems(values);
            }
            if (dtype === 'complex64') {
                throw new Error("Cannot write to a complex64 dtype. " +
                    "Please use tf.complex(real, imag).");
            }
            this.texData.set(dataId, { shape: shape, dtype: dtype, values: values, usage: TextureUsage.UPLOAD });
        };
        MathBackendWebGL.prototype.readSync = function (dataId) {
            var texData = this.texData.get(dataId);
            var values = texData.values, dtype = texData.dtype, complexTensors = texData.complexTensors, slice = texData.slice, shape = texData.shape, isPacked = texData.isPacked;
            if (slice != null) {
                var program = void 0;
                if (isPacked) {
                    program = new UnaryOpPackedProgram(shape, CLONE);
                }
                else {
                    program = new UnaryOpProgram(shape, CLONE);
                }
                var res = this.runWebGLProgram(program, [{ dataId: dataId, shape: shape, dtype: dtype }], dtype);
                var data = this.readSync(res.dataId);
                this.disposeData(res.dataId);
                return data;
            }
            if (values != null) {
                return this.convertAndCacheOnCPU(dataId);
            }
            if (dtype === 'string') {
                return values;
            }
            var shouldTimeProgram = this.activeTimers != null;
            var start;
            if (shouldTimeProgram) {
                start = now();
            }
            var result;
            if (dtype === 'complex64') {
                var realValues = complexTensors.real.dataSync();
                var imagValues = complexTensors.imag.dataSync();
                result = mergeRealAndImagArrays(realValues, imagValues);
            }
            else {
                result = this.getValuesFromTexture(dataId);
            }
            if (shouldTimeProgram) {
                this.downloadWaitMs += now() - start;
            }
            return this.convertAndCacheOnCPU(dataId, result);
        };
        MathBackendWebGL.prototype.read = function (dataId) {
            return __awaiter(this, void 0, void 0, function () {
                var subscribers_1, texData, values, shape, slice, dtype, complexTensors, isPacked, program, res, data, buffer, tmpDownloadTarget, tmpData, vals, ps, realValues, imagValues, size, dTypeVals, subscribers;
                var _a;
                return __generator(this, function (_b) {
                    switch (_b.label) {
                        case 0:
                            if (this.pendingRead.has(dataId)) {
                                subscribers_1 = this.pendingRead.get(dataId);
                                return [2 /*return*/, new Promise(function (resolve) { return subscribers_1.push(resolve); })];
                            }
                            texData = this.texData.get(dataId);
                            values = texData.values, shape = texData.shape, slice = texData.slice, dtype = texData.dtype, complexTensors = texData.complexTensors, isPacked = texData.isPacked;
                            if (slice != null) {
                                program = void 0;
                                if (isPacked) {
                                    program = new UnaryOpPackedProgram(shape, CLONE);
                                }
                                else {
                                    program = new UnaryOpProgram(shape, CLONE);
                                }
                                res = this.runWebGLProgram(program, [{ dataId: dataId, shape: shape, dtype: dtype }], dtype);
                                data = this.read(res.dataId);
                                this.disposeData(res.dataId);
                                return [2 /*return*/, data];
                            }
                            if (values != null) {
                                return [2 /*return*/, this.convertAndCacheOnCPU(dataId)];
                            }
                            if (!env().getBool('WEBGL_DOWNLOAD_FLOAT_ENABLED') &&
                                env().getNumber('WEBGL_VERSION') === 2) {
                                throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and " +
                                    "WEBGL_VERSION=2 not yet supported.");
                            }
                            buffer = null;
                            if (dtype !== 'complex64' && env().get('WEBGL_BUFFER_SUPPORTED')) {
                                // Possibly copy the texture into a buffer before inserting a fence.
                                tmpDownloadTarget = this.decode(dataId);
                                tmpData = this.texData.get(tmpDownloadTarget.dataId);
                                buffer = (_a = this.gpgpu).createBufferFromTexture.apply(_a, [tmpData.texture].concat(getDenseTexShape(shape)));
                            }
                            this.pendingRead.set(dataId, []);
                            if (!(dtype !== 'complex64')) return [3 /*break*/, 2];
                            // Create a fence and wait for it to resolve.
                            return [4 /*yield*/, this.gpgpu.createAndWaitForFence()];
                        case 1:
                            // Create a fence and wait for it to resolve.
                            _b.sent();
                            _b.label = 2;
                        case 2:
                            if (!(dtype === 'complex64')) return [3 /*break*/, 4];
                            return [4 /*yield*/, Promise.all([complexTensors.real.data(), complexTensors.imag.data()])];
                        case 3:
                            ps = _b.sent();
                            realValues = ps[0];
                            imagValues = ps[1];
                            vals = mergeRealAndImagArrays(realValues, imagValues);
                            return [3 /*break*/, 5];
                        case 4:
                            if (buffer == null) {
                                vals = this.getValuesFromTexture(dataId);
                            }
                            else {
                                size = sizeFromShape(shape);
                                vals = this.gpgpu.downloadFloat32MatrixFromBuffer(buffer, size);
                            }
                            _b.label = 5;
                        case 5:
                            if (tmpDownloadTarget != null) {
                                this.disposeData(tmpDownloadTarget.dataId);
                            }
                            dTypeVals = this.convertAndCacheOnCPU(dataId, vals);
                            subscribers = this.pendingRead.get(dataId);
                            this.pendingRead.delete(dataId);
                            // Notify all pending reads.
                            subscribers.forEach(function (resolve) { return resolve(dTypeVals); });
                            if (this.pendingDisposal.has(dataId)) {
                                this.pendingDisposal.delete(dataId);
                                this.disposeData(dataId);
                                this.pendingDeletes--;
                            }
                            return [2 /*return*/, dTypeVals];
                    }
                });
            });
        };
        MathBackendWebGL.prototype.checkNumericalProblems = function (values) {
            if (values == null) {
                return;
            }
            for (var i = 0; i < values.length; i++) {
                var num = values[i];
                if (!canBeRepresented(num)) {
                    if (env().getBool('WEBGL_RENDER_FLOAT32_CAPABLE')) {
                        throw Error("The value " + num + " cannot be represented with your " +
                            "current settings. Consider enabling float32 rendering: " +
                            "'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'");
                    }
                    throw Error("The value " + num + " cannot be represented on this device.");
                }
            }
        };
        MathBackendWebGL.prototype.getValuesFromTexture = function (dataId) {
            var _a;
            var _b = this.texData.get(dataId), shape = _b.shape, dtype = _b.dtype, isPacked = _b.isPacked;
            var size = sizeFromShape(shape);
            if (env().getBool('WEBGL_DOWNLOAD_FLOAT_ENABLED')) {
                var tmpTarget = this.decode(dataId);
                var tmpData_1 = this.texData.get(tmpTarget.dataId);
                var vals_1 = (_a = this.gpgpu).downloadMatrixFromPackedTexture.apply(_a, [tmpData_1.texture].concat(getDenseTexShape(shape))).subarray(0, size);
                this.disposeData(tmpTarget.dataId);
                return vals_1;
            }
            var shouldUsePackedProgram = env().getBool('WEBGL_PACK') && isPacked === true;
            var outputShape = shouldUsePackedProgram ? getShapeAs3D(shape) : shape;
            var program = shouldUsePackedProgram ?
                new EncodeFloatPackedProgram(outputShape) :
                new EncodeFloatProgram(outputShape);
            var output = this.runWebGLProgram(program, [{ shape: outputShape, dtype: dtype, dataId: dataId }], 'float32');
            var tmpData = this.texData.get(output.dataId);
            var vals = this.gpgpu
                .downloadByteEncodedFloatMatrixFromOutputTexture(tmpData.texture, tmpData.texShape[0], tmpData.texShape[1])
                .subarray(0, size);
            this.disposeData(output.dataId);
            return vals;
        };
        MathBackendWebGL.prototype.time = function (f) {
            return __awaiter(this, void 0, void 0, function () {
                var oldActiveTimers, newActiveTimers, outerMostTime, flattenedActiveTimerQueries, flattenedActiveTimerNames, res, kernelMs_1;
                return __generator(this, function (_a) {
                    switch (_a.label) {
                        case 0:
                            oldActiveTimers = this.activeTimers;
                            newActiveTimers = [];
                            outerMostTime = false;
                            if (this.programTimersStack == null) {
                                this.programTimersStack = newActiveTimers;
                                outerMostTime = true;
                            }
                            else {
                                this.activeTimers.push(newActiveTimers);
                            }
                            this.activeTimers = newActiveTimers;
                            f();
                            flattenedActiveTimerQueries = flatten(this.activeTimers.map(function (d) { return d.query; }))
                                .filter(function (d) { return d != null; });
                            flattenedActiveTimerNames = flatten(this.activeTimers.map(function (d) { return d.name; }))
                                .filter(function (d) { return d != null; });
                            this.activeTimers = oldActiveTimers;
                            if (outerMostTime) {
                                this.programTimersStack = null;
                            }
                            res = {
                                uploadWaitMs: this.uploadWaitMs,
                                downloadWaitMs: this.downloadWaitMs,
                                kernelMs: null,
                                wallMs: null // will be filled by the engine
                            };
                            if (!(env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') > 0)) return [3 /*break*/, 2];
                            return [4 /*yield*/, Promise.all(flattenedActiveTimerQueries)];
                        case 1:
                            kernelMs_1 = _a.sent();
                            res['kernelMs'] = sum(kernelMs_1);
                            res['getExtraProfileInfo'] = function () {
                                return kernelMs_1.map(function (d, i) { return ({ name: flattenedActiveTimerNames[i], ms: d }); })
                                    .map(function (d) { return d.name + ": " + d.ms; })
                                    .join(', ');
                            };
                            return [3 /*break*/, 3];
                        case 2:
                            res['kernelMs'] = {
                                error: 'WebGL query timers are not supported in this environment.'
                            };
                            _a.label = 3;
                        case 3:
                            this.uploadWaitMs = 0;
                            this.downloadWaitMs = 0;
                            return [2 /*return*/, res];
                    }
                });
            });
        };
        MathBackendWebGL.prototype.memory = function () {
            return { unreliable: false, numBytesInGPU: this.numBytesInGPU };
        };
        MathBackendWebGL.prototype.startTimer = function () {
            if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') > 0) {
                return this.gpgpu.beginQuery();
            }
            return { startMs: now(), endMs: null };
        };
        MathBackendWebGL.prototype.endTimer = function (query) {
            if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') > 0) {
                this.gpgpu.endQuery();
                return query;
            }
            query.endMs = now();
            return query;
        };
        MathBackendWebGL.prototype.getQueryTime = function (query) {
            return __awaiter(this, void 0, void 0, function () {
                var timerQuery;
                return __generator(this, function (_a) {
                    if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') > 0) {
                        return [2 /*return*/, this.gpgpu.waitForQueryAndGetTime(query)];
                    }
                    timerQuery = query;
                    return [2 /*return*/, timerQuery.endMs - timerQuery.startMs];
                });
            });
        };
        MathBackendWebGL.prototype.disposeData = function (dataId) {
            if (this.pendingDisposal.has(dataId)) {
                return;
            }
            if (this.pendingRead.has(dataId)) {
                this.pendingDisposal.add(dataId);
                this.pendingDeletes++;
                return;
            }
            // No-op if already disposed.
            if (!this.texData.has(dataId)) {
                return;
            }
            this.releaseGPUData(dataId);
            var complexTensors = this.texData.get(dataId).complexTensors;
            if (complexTensors != null) {
                complexTensors.real.dispose();
                complexTensors.imag.dispose();
            }
            this.texData.delete(dataId);
        };
        MathBackendWebGL.prototype.releaseGPUData = function (dataId) {
            var _a = this.texData.get(dataId), texture = _a.texture, dtype = _a.dtype, texShape = _a.texShape, usage = _a.usage, isPacked = _a.isPacked, slice = _a.slice;
            var key = slice && slice.origDataId || dataId;
            var refCount = this.dataRefCount.get(key);
            if (refCount > 1) {
                this.dataRefCount.set(key, refCount - 1);
            }
            else {
                this.dataRefCount.delete(key);
                if (texture != null) {
                    this.numBytesInGPU -= this.computeBytes(texShape, dtype);
                    this.textureManager.releaseTexture(texture, texShape, usage, isPacked);
                }
            }
            var texData = this.texData.get(dataId);
            texData.texture = null;
            texData.texShape = null;
            texData.isPacked = false;
            texData.slice = null;
        };
        MathBackendWebGL.prototype.getTexture = function (dataId) {
            this.uploadToGPU(dataId);
            return this.texData.get(dataId).texture;
        };
        /**
         * Returns internal information for the specific data bucket. Used in unit
         * tests.
         */
        MathBackendWebGL.prototype.getDataInfo = function (dataId) {
            return this.texData.get(dataId);
        };
        MathBackendWebGL.prototype.getCPUBackend = function () {
            if (!env().getBool('WEBGL_CPU_FORWARD')) {
                return null;
            }
            if (this.cpuBackend == null) {
                this.cpuBackend = ENGINE.findBackend('cpu');
            }
            return this.cpuBackend;
        };
        /*
        Tests whether all the inputs to an op are small and on the CPU. This heuristic
        determines when it would be faster to execute a kernel on the CPU. WebGL
        kernels opt into running this check and forwarding when appropriate.
        TODO(https://github.com/tensorflow/tfjs/issues/872): Develop a more
        sustainable strategy for optimizing backend execution of ops.
         */
        MathBackendWebGL.prototype.shouldExecuteOnCPU = function (inputs, sizeThreshold) {
            var _this = this;
            if (sizeThreshold === void 0) { sizeThreshold = CPU_HANDOFF_SIZE_THRESHOLD; }
            return this.getCPUBackend() != null &&
                inputs.every(function (input) { return _this.texData.get(input.dataId).texture == null &&
                    input.size < sizeThreshold; });
        };
        MathBackendWebGL.prototype.getGPGPUContext = function () {
            return this.gpgpu;
        };
        MathBackendWebGL.prototype.complex = function (real, imag) {
            var result = this.makeOutput(real.shape, 'complex64');
            var resultData = this.texData.get(result.dataId);
            // The backend owns the reference to the underlying real and imaginary
            // clones. These will explicitly get disposed when the complex tensor is
            // disposed.
            resultData.complexTensors = {
                real: ENGINE.keep(real.clone()),
                imag: ENGINE.keep(imag.clone())
            };
            return result;
        };
        MathBackendWebGL.prototype.real = function (input) {
            var resultData = this.texData.get(input.dataId);
            return resultData.complexTensors.real.clone();
        };
        MathBackendWebGL.prototype.imag = function (input) {
            var resultData = this.texData.get(input.dataId);
            return resultData.complexTensors.imag.clone();
        };
        MathBackendWebGL.prototype.slice = function (x, begin, size) {
            if (this.shouldExecuteOnCPU([x])) {
                return this.cpuBackend.slice(x, begin, size);
            }
            // Short-circuit computation if the slice is zero-sized.
            if (sizeFromShape(size) === 0) {
                return tensor([], size, x.dtype);
            }
            var isPacked = this.texData.get(x.dataId).isPacked;
            var isContinous = isSliceContinous(x.shape, begin, size);
            if (isPacked || !isContinous) {
                var program = env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') ?
                    new SlicePackedProgram(size) :
                    new SliceProgram(size);
                var customSetup = program.getCustomSetupFunc(begin);
                return this.compileAndRun(program, [x], null, customSetup);
            }
            this.uploadToGPU(x.dataId);
            return this.shallowSlice(x, begin, size);
        };
        MathBackendWebGL.prototype.shallowSlice = function (x, begin, size) {
            var xTexData = this.texData.get(x.dataId);
            var t = this.makeOutput(size, x.dtype);
            var newTexData = this.texData.get(t.dataId);
            // Copy texture data from the original tensor.
            Object.assign(newTexData, xTexData);
            newTexData.shape = size;
            newTexData.dtype = x.dtype;
            var flatOffset = computeFlatOffset(begin, x.strides);
            if (xTexData.slice) {
                // We are slicing an already sliced tensor, so we have to accumulate
                // the offset.
                flatOffset += xTexData.slice.flatOffset;
            }
            newTexData.slice = {
                flatOffset: flatOffset,
                // Point to the original dataId, which is used to do ref counting.
                origDataId: xTexData.slice && xTexData.slice.origDataId || x.dataId
            };
            // Increase the ref count for that data bucket.
            var refCount = this.dataRefCount.get(newTexData.slice.origDataId) || 1;
            this.dataRefCount.set(newTexData.slice.origDataId, refCount + 1);
            return t;
        };
        MathBackendWebGL.prototype.stridedSlice = function (x, begin, end, strides) {
            if (this.shouldExecuteOnCPU([x])) {
                return this.cpuBackend.stridedSlice(x, begin, end, strides);
            }
            var outShape = computeOutShape$2(begin, end, strides);
            if (outShape.some(function (axis) { return axis === 0; })) {
                return tensor([], outShape);
            }
            var program = new StridedSliceProgram(begin, strides, outShape);
            return this.compileAndRun(program, [x]);
        };
        MathBackendWebGL.prototype.reverse = function (x, axis) {
            var program = env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') ?
                new ReversePackedProgram(x.shape, axis) :
                new ReverseProgram(x.shape, axis);
            return this.compileAndRun(program, [x]);
        };
        MathBackendWebGL.prototype.concat = function (tensors, axis) {
            if (tensors[0].dtype === 'complex64') {
                var reals = tensors.map(function (t) { return real(t); });
                var imags = tensors.map(function (t) { return imag(t); });
                return complex(this.concat(reals, axis), this.concat(imags, axis));
            }
            if (this.shouldExecuteOnCPU(tensors)) {
                return this.cpuBackend.concat(tensors, axis);
            }
            if (tensors.length === 1) {
                return tensors[0];
            }
            if (tensors.length > env().getNumber('WEBGL_MAX_TEXTURES_IN_SHADER')) {
                var midIndex = Math.floor(tensors.length / 2);
                var leftSide = this.concat(tensors.slice(0, midIndex), axis);
                var rightSide = this.concat(tensors.slice(midIndex), axis);
                return this.concat([leftSide, rightSide], axis);
            }
            if (env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') && tensors[0].rank > 1) {
                var program_1 = new ConcatPackedProgram(tensors.map(function (t) { return t.shape; }), axis);
                return this.compileAndRun(program_1, tensors);
            }
            // Any concat of n-dimensional tensors across any axis can be reduced to
            // a concatenation of two-dimensional tensors across the axis 1 by first
            // partitioning the axes of the original tensors into those less than the
            // axis to be concatenated and the rest. Then reshape the tensors
            // into a two-dimensional tensor by collapsing these two sets of axes and
            // concatenate the resulting matrices across the axis 1, finally reshaping
            // the result to have the proper shape.
            var outShape = computeOutShape(tensors.map(function (t) { return t.shape; }), axis);
            var tensors2D = tensors.map(function (t) { return t.as2D(-1, sizeFromShape(t.shape.slice(axis))); });
            var program = new ConcatProgram(tensors2D.map(function (t) { return t.shape; }));
            var res = this.compileAndRun(program, tensors2D);
            return res.reshape(outShape);
        };
        MathBackendWebGL.prototype.neg = function (x) {
            if (this.shouldExecuteOnCPU([x])) {
                return this.cpuBackend.neg(x);
            }
            if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {
                return this.packedUnaryOp(x, NEG, x.dtype);
            }
            var program = new UnaryOpProgram(x.shape, NEG);
            return this.compileAndRun(program, [x]);
        };
        MathBackendWebGL.prototype.batchMatMul = function (a, b, transposeA, transposeB) {
            var outerShapeA = transposeA ? a.shape[2] : a.shape[1];
            var outerShapeB = transposeB ? b.shape[1] : b.shape[2];
            var sharedDim = transposeA ? a.shape[1] : a.shape[2];
            var _a = a.shape, batch = _a[0];
            // Since the matrices are vectors, it is faster to call mul().sum()
            // because sum() is O(sqrt(N)) due to divide-and-conquer.
            if ((outerShapeA === 1 || outerShapeB === 1) &&
                sharedDim > MATMUL_SHARED_DIM_THRESHOLD) {
                if (transposeA) {
                    a = a.transpose([0, 2, 1]);
                }
                if (transposeB) {
                    b = b.transpose([0, 2, 1]);
                }
                var a3D = outerShapeB === 1 ? a : a.as3D(batch, sharedDim, 1);
                var axis = outerShapeB === 1 ? 2 : 1;
                var b3D = outerShapeB === 1 ? b.as3D(batch, 1, sharedDim) : b;
                return this.multiply(a3D, b3D).sum(axis, true /* keepDims */);
            }
            var dtype = upcastType(a.dtype, b.dtype);
            var program = new MatMulPackedProgram(a.shape, [batch, outerShapeA, outerShapeB], transposeA, transposeB);
            return this.compileAndRun(program, [a, b], dtype);
        };
        MathBackendWebGL.prototype.fusedBatchMatMul = function (_a) {
            var a = _a.a, b = _a.b, transposeA = _a.transposeA, transposeB = _a.transposeB, bias = _a.bias, activation = _a.activation, preluActivationWeights = _a.preluActivationWeights;
            var outerShapeA = transposeA ? a.shape[2] : a.shape[1];
            var outerShapeB = transposeB ? b.shape[1] : b.shape[2];
            var _b = a.shape, batch = _b[0];
            var dtype = upcastType(a.dtype, b.dtype);
            var hasBias = bias != null;
            var hasPreluActivationWeights = preluActivationWeights != null;
            var fusedActivation = activation ? mapActivationToShaderProgram(activation, true) : null;
            var program = new MatMulPackedProgram(a.shape, [batch, outerShapeA, outerShapeB], transposeA, transposeB, hasBias, fusedActivation, hasPreluActivationWeights);
            var inputs = [a, b];
            if (bias) {
                inputs.push(bias);
            }
            if (preluActivationWeights) {
                inputs.push(preluActivationWeights);
            }
            return this.compileAndRun(program, inputs, dtype);
        };
        MathBackendWebGL.prototype.multiply = function (a, b) {
            if (a.dtype === 'complex64') {
                var aData = this.texData.get(a.dataId);
                var bData = this.texData.get(b.dataId);
                var realProgram = new BinaryOpComplexProgram(COMPLEX_MULTIPLY.REAL, a.shape, b.shape);
                var imagProgram = new BinaryOpComplexProgram(COMPLEX_MULTIPLY.IMAG, a.shape, b.shape);
                var inputs = [
                    this.makeComplexComponentTensorInfo(a, aData.complexTensors.real),
                    this.makeComplexComponentTensorInfo(a, aData.complexTensors.imag),
                    this.makeComplexComponentTensorInfo(b, bData.complexTensors.real),
                    this.makeComplexComponentTensorInfo(b, bData.complexTensors.imag)
                ];
                var real_1 = this.compileAndRun(realProgram, inputs);
                var imag_1 = this.compileAndRun(imagProgram, inputs);
                var complex_1 = this.complex(real_1, imag_1);
                real_1.dispose();
                imag_1.dispose();
                return complex_1;
            }
            if (this.shouldExecuteOnCPU([a, b])) {
                return this.cpuBackend.multiply(a, b);
            }
            if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {
                return this.packedBinaryOp(a, b, MUL, a.dtype);
            }
            var program = new BinaryOpProgram(MUL, a.shape, b.shape);
            return this.compileAndRun(program, [a, b], a.dtype);
        };
        MathBackendWebGL.prototype.batchNormalization = function (x, mean, variance, varianceEpsilon, scale, offset) {
            var inputs = [x, mean, variance];
            var offsetShape = null;
            if (offset != null) {
                offsetShape = offset.shape;
                inputs.push(offset);
            }
            var scaleShape = null;
            if (scale != null) {
                scaleShape = scale.shape;
                inputs.push(scale);
            }
            if (env().getBool('WEBGL_PACK_NORMALIZATION')) {
                var batchNormPackedProgram = new BatchNormPackedProgram(x.shape, mean.shape, variance.shape, offsetShape, scaleShape, varianceEpsilon);
                return this.compileAndRun(batchNormPackedProgram, inputs);
            }
            var batchNormProgram = new BatchNormProgram(x.shape, mean.shape, variance.shape, offsetShape, scaleShape, varianceEpsilon);
            return this.compileAndRun(batchNormProgram, inputs);
        };
        MathBackendWebGL.prototype.localResponseNormalization4D = function (x, radius, bias, alpha, beta) {
            var program = env().getBool('WEBGL_PACK_NORMALIZATION') ?
                new LRNPackedProgram(x.shape, radius, bias, alpha, beta) :
                new LRNProgram(x.shape, radius, bias, alpha, beta);
            return this.compileAndRun(program, [x]);
        };
        MathBackendWebGL.prototype.LRNGrad = function (dy, inputImage, outputImage, depthRadius, bias, alpha, beta) {
            var program = new LRNGradProgram(inputImage.shape, depthRadius, bias, alpha, beta);
            return this.compileAndRun(program, [inputImage, outputImage, dy]);
        };
        MathBackendWebGL.prototype.tile = function (x, reps) {
            if (x.dtype === 'string') {
                var data = this.readSync(x.dataId);
                var decodedData = data.map(function (d) { return decodeString(d); });
                var buf = buffer(x.shape, x.dtype, decodedData);
                return tile$1(buf, reps);
            }
            var program = new TileProgram(x.shape, reps);
            return this.compileAndRun(program, [x]);
        };
        MathBackendWebGL.prototype.pad = function (x, paddings, constantValue) {
            var program = env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') ?
                new PadPackedProgram(x.shape, paddings, constantValue) :
                new PadProgram(x.shape, paddings, constantValue);
            return this.compileAndRun(program, [x]);
        };
        MathBackendWebGL.prototype.transpose = function (x, perm) {
            if (this.shouldExecuteOnCPU([x])) {
                return this.cpuBackend.transpose(x, perm);
            }
            var program = env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') ?
                new TransposePackedProgram(x.shape, perm) :
                new TransposeProgram(x.shape, perm);
            return this.compileAndRun(program, [x]);
        };
        MathBackendWebGL.prototype.gather = function (x, indices, axis) {
            if (this.shouldExecuteOnCPU([x, indices])) {
                return this.cpuBackend.gather(x, indices, axis);
            }
            var program = new GatherProgram(x.shape, indices.size, axis);
            return this.compileAndRun(program, [x, indices]);
        };
        MathBackendWebGL.prototype.batchToSpaceND = function (x, blockShape, crops) {
            assert(x.rank <= 4, function () { return 'batchToSpaceND for rank > 4 with a WebGL backend not ' +
                'implemented yet'; });
            var prod = blockShape.reduce(function (a, b) { return a * b; });
            var reshaped = getReshaped(x.shape, blockShape, prod);
            var permuted = getPermuted(reshaped.length, blockShape.length);
            var reshapedPermuted = getReshapedPermuted(x.shape, blockShape, prod);
            var sliceBeginCoords = getSliceBeginCoords(crops, blockShape.length);
            var sliceSize = getSliceSize(reshapedPermuted, crops, blockShape.length);
            return x.reshape(reshaped)
                .transpose(permuted)
                .reshape(reshapedPermuted)
                .slice(sliceBeginCoords, sliceSize);
        };
        MathBackendWebGL.prototype.spaceToBatchND = function (x, blockShape, paddings) {
            assert(x.rank <= 4, function () { return 'spaceToBatchND for rank > 4 with a WebGL backend not ' +
                'implemented yet'; });
            var prod = blockShape.reduce(function (a, b) { return a * b; });
            var completePaddings = [[0, 0]];
            completePaddings.push.apply(completePaddings, paddings);
            for (var i = 1 + blockShape.length; i < x.shape.length; ++i) {
                completePaddings.push([0, 0]);
            }
            var paddedX = x.pad(completePaddings);
            var reshapedPaddedShape = getReshaped(paddedX.shape, blockShape, prod, false);
            var permutedReshapedPaddedPermutation = getPermuted(reshapedPaddedShape.length, blockShape.length, false);
            var flattenShape = getReshapedPermuted(paddedX.shape, blockShape, prod, false);
            return paddedX.reshape(reshapedPaddedShape)
                .transpose(permutedReshapedPaddedPermutation)
                .reshape(flattenShape);
        };
        MathBackendWebGL.prototype.reduce = function (x, reduceType, dtype) {
            var batchSize = x.shape[0];
            var inSize = x.shape[1];
            var windowSize = computeOptimalWindowSize(inSize);
            var reduceInfo = { windowSize: windowSize, inSize: inSize, batchSize: batchSize };
            var program = new ReduceProgram(reduceInfo, reduceType);
            var output = this.compileAndRun(program, [x], dtype);
            // No need to run another GPGPU program.
            if (output.shape[1] === 1) {
                return output;
            }
            return this.reduce(output, reduceType, dtype);
        };
        MathBackendWebGL.prototype.argReduce = function (x, reduceType, bestIndicesA) {
            if (bestIndicesA === void 0) { bestIndicesA = null; }
            var batchSize = x.shape[0];
            var inSize = x.shape[1];
            if (bestIndicesA != null) {
                batchSize = bestIndicesA.shape[0];
                inSize = bestIndicesA.shape[1];
            }
            var windowSize = computeOptimalWindowSize(inSize);
            var reduceInfo = { windowSize: windowSize, inSize: inSize, batchSize: batchSize };
            var program = new ArgMinMaxProgram(reduceInfo, reduceType, bestIndicesA == null);
            var inputs = [x];
            if (bestIndicesA != null) {
                inputs.push(bestIndicesA);
            }
            var output = this.compileAndRun(program, inputs, 'int32');
            // No need to run another GPGPU program.
            if (output.shape[1] === 1) {
                return output;
            }
            return this.argReduce(x, reduceType, output);
        };
        MathBackendWebGL.prototype.argReducePacked = function (x, reduceType, bestIndicesA) {
            if (bestIndicesA === void 0) { bestIndicesA = null; }
            var inShape = bestIndicesA != null ? bestIndicesA.shape : x.shape;
            var inSize = inShape[inShape.length - 1];
            var windowSize = computeOptimalWindowSize(inSize);
            var program = new ArgMinMaxPackedProgram(inShape, windowSize, reduceType, bestIndicesA == null);
            var inputs = bestIndicesA == null ? [x] : [x, bestIndicesA];
            var output = this.compileAndRun(program, inputs, 'int32');
            if (output.rank === x.rank) {
                return this.argReducePacked(x, reduceType, output);
            }
            return output;
        };
        MathBackendWebGL.prototype.sum = function (x, axes) {
            assertAxesAreInnerMostDims('sum', axes, x.rank);
            var _a = computeOutAndReduceShapes(x.shape, axes), outShape = _a[0], reduceShape = _a[1];
            var inSize = sizeFromShape(reduceShape);
            var a2D = x.as2D(-1, inSize);
            var outputDType = sumOutType(x.dtype);
            return this.reduce(a2D, 'sum', outputDType).reshape(outShape);
        };
        MathBackendWebGL.prototype.prod = function (x, axes) {
            if (this.shouldExecuteOnCPU([x])) {
                return this.cpuBackend.prod(x, axes);
            }
            var _a = computeOutAndReduceShapes(x.shape, axes), outShape = _a[0], reduceShape = _a[1];
            var inSize = sizeFromShape(reduceShape);
            var a2D = x.as2D(-1, inSize);
            var outputDType = sumOutType(x.dtype);
            return this.reduce(a2D, 'prod', outputDType).reshape(outShape);
        };
        MathBackendWebGL.prototype.unsortedSegmentSum = function (x, segmentIds, numSegments) {
            var axis = 0;
            var permutation = getAxesPermutation([axis], x.rank);
            var permutedX = x;
            if (permutation != null) {
                permutedX = x.transpose(permutation);
                axis = getInnerMostAxes(1, x.rank)[0];
            }
            var outShape = computeOutShape$1(permutedX.shape, axis, numSegments);
            var inSize = sizeFromShape([permutedX.shape[axis]]);
            var a2D = permutedX.as2D(-1, inSize);
            var outputDType = sumOutType(x.dtype);
            var result = this.segOpCompute(a2D, 'unsortedSegmentSum', segmentIds, outputDType, numSegments)
                .reshape(outShape);
            if (permutation != null) {
                result = result.transpose(getUndoAxesPermutation(permutation));
            }
            return result;
        };
        MathBackendWebGL.prototype.segOpCompute = function (x, segOpType, segmentIds, dtype, numSegments) {
            var batchSize = x.shape[0];
            var inSize = x.shape[1];
            var windowSize = segOpComputeOptimalWindowSize(inSize, numSegments);
            var segOpInfo = { windowSize: windowSize, inSize: inSize, batchSize: batchSize, numSegments: numSegments };
            var program = new SegmentOpProgram(segOpInfo, segOpType);
            var output = this.compileAndRun(program, [x, segmentIds], dtype);
            // No need to run another GPGPU program.
            if (output.shape[1] === numSegments) {
                return output;
            }
            segmentIds = range(0, numSegments).tile([inSize / windowSize]);
            return this.segOpCompute(output, segOpType, segmentIds, dtype, numSegments);
        };
        MathBackendWebGL.prototype.argMinMaxReduce = function (x, axis, reduceType) {
            var axes = [axis];
            assertAxesAreInnerMostDims('arg' + reduceType.charAt(0).toUpperCase() + reduceType.slice(1), axes, x.rank);
            if (!env().getBool('WEBGL_PACK_REDUCE') || x.rank <= 2) {
                var _a = computeOutAndReduceShapes(x.shape, axes), outShape = _a[0], reduceShape = _a[1];
                var inSize = sizeFromShape(reduceShape);
                var a2D = x.as2D(-1, inSize);
                return this.argReduce(a2D, reduceType).reshape(outShape);
            }
            return this.argReducePacked(x, reduceType);
        };
        MathBackendWebGL.prototype.argMin = function (x, axis) {
            return this.argMinMaxReduce(x, axis, 'min');
        };
        MathBackendWebGL.prototype.argMax = function (x, axis) {
            return this.argMinMaxReduce(x, axis, 'max');
        };
        MathBackendWebGL.prototype.cumsum = function (x, axis, exclusive, reverse) {
            if (axis !== x.rank - 1) {
                throw new Error("WebGL cumsum shader expects an inner-most axis=" + (x.rank - 1) + " " +
                    ("but got axis=" + axis));
            }
            var program = new CumSumProgram(x.shape, exclusive, reverse);
            return this.compileAndRun(program, [x]);
        };
        MathBackendWebGL.prototype.equal = function (a, b) {
            if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {
                return this.packedBinaryOp(a, b, EQUAL$1, 'bool');
            }
            var program = new BinaryOpProgram(EQUAL, a.shape, b.shape);
            return this.compileAndRun(program, [a, b], 'bool');
        };
        MathBackendWebGL.prototype.notEqual = function (a, b) {
            if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {
                return this.packedBinaryOp(a, b, NOT_EQUAL$1, 'bool');
            }
            var program = new BinaryOpProgram(NOT_EQUAL, a.shape, b.shape);
            return this.compileAndRun(program, [a, b], 'bool');
        };
        MathBackendWebGL.prototype.less = function (a, b) {
            if (this.shouldExecuteOnCPU([a, b])) {
                return this.cpuBackend.less(a, b);
            }
            if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {
                return this.packedBinaryOp(a, b, LESS$1, 'bool');
            }
            var program = new BinaryOpProgram(LESS, a.shape, b.shape);
            return this.compileAndRun(program, [a, b], 'bool');
        };
        MathBackendWebGL.prototype.lessEqual = function (a, b) {
            if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {
                return this.packedBinaryOp(a, b, LESS_EQUAL$1, 'bool');
            }
            var program = new BinaryOpProgram(LESS_EQUAL, a.shape, b.shape);
            return this.compileAndRun(program, [a, b], 'bool');
        };
        MathBackendWebGL.prototype.greater = function (a, b) {
            if (this.shouldExecuteOnCPU([a, b])) {
                return this.cpuBackend.greater(a, b);
            }
            if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {
                return this.packedBinaryOp(a, b, GREATER$1, 'bool');
            }
            var program = new BinaryOpProgram(GREATER, a.shape, b.shape);
            return this.compileAndRun(program, [a, b], 'bool');
        };
        MathBackendWebGL.prototype.greaterEqual = function (a, b) {
            if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {
                return this.packedBinaryOp(a, b, GREATER_EQUAL$1, 'bool');
            }
            var program = new BinaryOpProgram(GREATER_EQUAL, a.shape, b.shape);
            return this.compileAndRun(program, [a, b], 'bool');
        };
        MathBackendWebGL.prototype.logicalNot = function (x) {
            var program = new UnaryOpProgram(x.shape, LOGICAL_NOT);
            return this.compileAndRun(program, [x]);
        };
        MathBackendWebGL.prototype.logicalAnd = function (a, b) {
            if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {
                return this.packedBinaryOp(a, b, LOGICAL_AND$1, 'bool');
            }
            var program = new BinaryOpProgram(LOGICAL_AND, a.shape, b.shape);
            return this.compileAndRun(program, [a, b], 'bool');
        };
        MathBackendWebGL.prototype.logicalOr = function (a, b) {
            if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {
                return this.packedBinaryOp(a, b, LOGICAL_OR$1, 'bool');
            }
            var program = new BinaryOpProgram(LOGICAL_OR, a.shape, b.shape);
            return this.compileAndRun(program, [a, b], 'bool');
        };
        MathBackendWebGL.prototype.select = function (condition, a, b) {
            var program = new SelectProgram(condition.rank, a.shape, a.rank);
            return this.compileAndRun(program, [condition, a, b], upcastType(a.dtype, b.dtype));
        };
        MathBackendWebGL.prototype.where = function (condition) {
            warn('tf.where() in webgl locks the UI thread. ' +
                'Call tf.whereAsync() instead');
            var condVals = condition.dataSync();
            return whereImpl(condition.shape, condVals);
        };
        MathBackendWebGL.prototype.topk = function (x, k, sorted) {
            var xVals = x.dataSync();
            return topkImpl(xVals, x.shape, x.dtype, k, sorted);
        };
        MathBackendWebGL.prototype.min = function (x, axes) {
            assertAxesAreInnerMostDims('min', axes, x.rank);
            var _a = computeOutAndReduceShapes(x.shape, axes), outShape = _a[0], reduceShape = _a[1];
            var inSize = sizeFromShape(reduceShape);
            var a2D = x.as2D(-1, inSize);
            return this.reduce(a2D, 'min', a2D.dtype).reshape(outShape);
        };
        MathBackendWebGL.prototype.minimum = function (a, b) {
            if (this.shouldExecuteOnCPU([a, b])) {
                return this.cpuBackend.minimum(a, b);
            }
            var program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?
                new BinaryOpPackedProgram(MIN$1, a.shape, b.shape) :
                new BinaryOpProgram(MIN, a.shape, b.shape);
            return this.compileAndRun(program, [a, b]);
        };
        MathBackendWebGL.prototype.mod = function (a, b) {
            var program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?
                new BinaryOpPackedProgram(MOD$1, a.shape, b.shape) :
                new BinaryOpProgram(MOD, a.shape, b.shape);
            return this.compileAndRun(program, [a, b]);
        };
        MathBackendWebGL.prototype.max = function (x, axes) {
            if (this.shouldExecuteOnCPU([x])) {
                return this.cpuBackend.max(x, axes);
            }
            assertAxesAreInnerMostDims('max', axes, x.rank);
            var _a = computeOutAndReduceShapes(x.shape, axes), outShape = _a[0], reduceShape = _a[1];
            var inSize = sizeFromShape(reduceShape);
            var a2D = x.as2D(-1, inSize);
            return this.reduce(a2D, 'max', a2D.dtype).reshape(outShape);
        };
        MathBackendWebGL.prototype.maximum = function (a, b) {
            if (this.shouldExecuteOnCPU([a, b])) {
                return this.cpuBackend.maximum(a, b);
            }
            var program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?
                new BinaryOpPackedProgram(MAX$1, a.shape, b.shape) :
                new BinaryOpProgram(MAX, a.shape, b.shape);
            return this.compileAndRun(program, [a, b]);
        };
        MathBackendWebGL.prototype.all = function (x, axes) {
            assertAxesAreInnerMostDims('all', axes, x.rank);
            var _a = computeOutAndReduceShapes(x.shape, axes), outShape = _a[0], reduceShape = _a[1];
            var inSize = sizeFromShape(reduceShape);
            var a2D = x.as2D(-1, inSize);
            return this.reduce(a2D, 'all', a2D.dtype).reshape(outShape);
        };
        MathBackendWebGL.prototype.any = function (x, axes) {
            assertAxesAreInnerMostDims('any', axes, x.rank);
            var _a = computeOutAndReduceShapes(x.shape, axes), outShape = _a[0], reduceShape = _a[1];
            var inSize = sizeFromShape(reduceShape);
            var a2D = x.as2D(-1, inSize);
            return this.reduce(a2D, 'any', a2D.dtype).reshape(outShape);
        };
        MathBackendWebGL.prototype.realDivide = function (a, b) {
            var op = DIV;
            var outputDtype = 'float32';
            if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {
                var checkOutOfBounds = true;
                return this.packedBinaryOp(a, b, DIV$1, outputDtype, checkOutOfBounds);
            }
            var program = new BinaryOpProgram(op, a.shape, b.shape);
            return this.compileAndRun(program, [a, b], outputDtype);
        };
        MathBackendWebGL.prototype.floorDiv = function (a, b) {
            var op = INT_DIV;
            var outputDtype = 'int32';
            if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {
                return this.packedBinaryOp(a, b, INT_DIV$1, outputDtype);
            }
            var program = new BinaryOpProgram(op, a.shape, b.shape);
            return this.compileAndRun(program, [a, b], outputDtype);
        };
        MathBackendWebGL.prototype.add = function (a, b) {
            if (a.dtype === 'complex64' && b.dtype === 'complex64') {
                return this.complexSeparableBinaryOp(a, b, ADD);
            }
            if (this.shouldExecuteOnCPU([a, b])) {
                return this.cpuBackend.add(a, b);
            }
            var dtype = upcastType(a.dtype, b.dtype);
            if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {
                return this.packedBinaryOp(a, b, ADD, dtype);
            }
            var program = new BinaryOpProgram(ADD, a.shape, b.shape);
            return this.compileAndRun(program, [a, b], dtype);
        };
        MathBackendWebGL.prototype.packedUnaryOp = function (x, op, dtype) {
            var program = new UnaryOpPackedProgram(x.shape, op);
            return this.compileAndRun(program, [x], dtype);
        };
        MathBackendWebGL.prototype.packedBinaryOp = function (a, b, op, dtype, checkOutOfBounds) {
            if (checkOutOfBounds === void 0) { checkOutOfBounds = false; }
            var program = new BinaryOpPackedProgram(op, a.shape, b.shape, checkOutOfBounds);
            return this.compileAndRun(program, [a, b], dtype);
        };
        /**
         * Computes a complex binary operation that can be decomposed into a simple
         * binary operation on both the real and imagary parts.
         */
        MathBackendWebGL.prototype.complexSeparableBinaryOp = function (a, b, op) {
            var _this = this;
            var aData = this.texData.get(a.dataId);
            var bData = this.texData.get(b.dataId);
            var _a = [
                [aData.complexTensors.real, bData.complexTensors.real],
                [aData.complexTensors.imag, bData.complexTensors.imag]
            ].map(function (complexParts) {
                var aPart = complexParts[0], bPart = complexParts[1];
                var aHandle = _this.makeComplexComponentTensorInfo(a, aPart);
                var bHandle = _this.makeComplexComponentTensorInfo(b, bPart);
                var program = new BinaryOpProgram(op, a.shape, b.shape);
                return _this.compileAndRun(program, [aHandle, bHandle], upcastType(aPart.dtype, bPart.dtype));
            }), real = _a[0], imag = _a[1];
            var complex = this.complex(real, imag);
            real.dispose();
            imag.dispose();
            return complex;
        };
        // Returns a TensorInfo with the complex shape and the dataId of the
        // underlying part. We need to do this because a reshaped complex tensor is
        // not reflected in its parts.
        MathBackendWebGL.prototype.makeComplexComponentTensorInfo = function (complexTensor, complexPart) {
            return {
                dataId: complexPart.dataId,
                dtype: complexPart.dtype,
                shape: complexTensor.shape
            };
        };
        MathBackendWebGL.prototype.addN = function (tensors) {
            if (tensors.length === 1) {
                return tensors[0];
            }
            // Limit the number of uploaded textures for optimization.
            if (tensors.length > env().get('WEBGL_MAX_TEXTURES_IN_SHADER')) {
                var midIndex = Math.floor(tensors.length / 2);
                var leftSide = this.addN(tensors.slice(0, midIndex));
                var rightSide = this.addN(tensors.slice(midIndex));
                return this.addN([leftSide, rightSide]);
            }
            var dtype = tensors.map(function (t) { return t.dtype; }).reduce(function (d1, d2) { return upcastType(d1, d2); });
            var shapes = tensors.map(function (t) { return t.shape; });
            // We can make sure shapes are identical in op level.
            var usePackedOp = env().getBool('WEBGL_PACK');
            var program = usePackedOp ?
                new AddNPackedProgram(tensors[0].shape, shapes) :
                new AddNProgram(tensors[0].shape, shapes);
            return this.compileAndRun(program, tensors, dtype);
        };
        MathBackendWebGL.prototype.subtract = function (a, b) {
            if (a.dtype === 'complex64' && b.dtype === 'complex64') {
                return this.complexSeparableBinaryOp(a, b, SUB);
            }
            if (this.shouldExecuteOnCPU([a, b])) {
                return this.cpuBackend.subtract(a, b);
            }
            var dtype = upcastType(a.dtype, b.dtype);
            if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {
                return this.packedBinaryOp(a, b, SUB, a.dtype);
            }
            var program = new BinaryOpProgram(SUB, a.shape, b.shape);
            return this.compileAndRun(program, [a, b], dtype);
        };
        MathBackendWebGL.prototype.pow = function (a, b) {
            var usePackedOp = env().getBool('WEBGL_PACK_BINARY_OPERATIONS');
            var program = usePackedOp ?
                new BinaryOpPackedProgram(POW$1, a.shape, b.shape) :
                new BinaryOpProgram(POW, a.shape, b.shape);
            var dtype = upcastType(a.dtype, b.dtype);
            return this.compileAndRun(program, [a, b], dtype);
        };
        MathBackendWebGL.prototype.ceil = function (x) {
            if (this.shouldExecuteOnCPU([x])) {
                return this.cpuBackend.ceil(x);
            }
            if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {
                return this.packedUnaryOp(x, CEIL, x.dtype);
            }
            var program = new UnaryOpProgram(x.shape, CEIL);
            return this.compileAndRun(program, [x]);
        };
        MathBackendWebGL.prototype.floor = function (x) {
            if (this.shouldExecuteOnCPU([x])) {
                return this.cpuBackend.floor(x);
            }
            if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {
                return this.packedUnaryOp(x, FLOOR, x.dtype);
            }
            var program = new UnaryOpProgram(x.shape, FLOOR);
            return this.compileAndRun(program, [x]);
        };
        MathBackendWebGL.prototype.sign = function (x) {
            var program = new UnaryOpProgram(x.shape, SIGN);
            return this.compileAndRun(program, [x]);
        };
        MathBackendWebGL.prototype.isNaN = function (x) {
            var program = new UnaryOpProgram(x.shape, IS_NAN);
            return this.compileAndRun(program, [x], 'bool');
        };
        MathBackendWebGL.prototype.isInf = function (x) {
            var program = new UnaryOpProgram(x.shape, IS_INF);
            return this.compileAndRun(program, [x], 'bool');
        };
        MathBackendWebGL.prototype.isFinite = function (x) {
            var program = new UnaryOpProgram(x.shape, IS_FINITE);
            return this.compileAndRun(program, [x], 'bool');
        };
        MathBackendWebGL.prototype.round = function (x) {
            var program = new UnaryOpProgram(x.shape, ROUND);
            return this.compileAndRun(program, [x]);
        };
        MathBackendWebGL.prototype.exp = function (x) {
            if (this.shouldExecuteOnCPU([x])) {
                return this.cpuBackend.exp(x);
            }
            if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {
                return this.packedUnaryOp(x, EXP, x.dtype);
            }
            var program = new UnaryOpProgram(x.shape, EXP);
            return this.compileAndRun(program, [x]);
        };
        MathBackendWebGL.prototype.expm1 = function (x) {
            if (this.shouldExecuteOnCPU([x])) {
                return this.cpuBackend.expm1(x);
            }
            if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {
                return this.packedUnaryOp(x, EXPM1, x.dtype);
            }
            var program = new UnaryOpProgram(x.shape, EXPM1);
            return this.compileAndRun(program, [x]);
        };
        MathBackendWebGL.prototype.softmax = function (logits, dim) {
            var axes = parseAxisParam([dim], logits.shape);
            var maxLogit = this.max(logits, axes);
            var expandedShape = expandShapeToKeepDim(maxLogit.shape, axes);
            var a = this.subtract(logits, maxLogit.reshape(expandedShape));
            var b = this.exp(a);
            var sumExp = this.sum(b, axes).reshape(expandedShape);
            return this.realDivide(b, sumExp);
        };
        MathBackendWebGL.prototype.log = function (x) {
            if (this.shouldExecuteOnCPU([x])) {
                return this.cpuBackend.log(x);
            }
            if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {
                return this.packedUnaryOp(x, LOG$1, x.dtype);
            }
            var program = new UnaryOpProgram(x.shape, LOG);
            return this.compileAndRun(program, [x]);
        };
        MathBackendWebGL.prototype.log1p = function (x) {
            var program = new UnaryOpProgram(x.shape, LOG1P);
            return this.compileAndRun(program, [x]);
        };
        MathBackendWebGL.prototype.sqrt = function (x) {
            var program = new UnaryOpProgram(x.shape, SQRT);
            return this.compileAndRun(program, [x]);
        };
        MathBackendWebGL.prototype.rsqrt = function (x) {
            if (this.shouldExecuteOnCPU([x])) {
                return this.cpuBackend.rsqrt(x);
            }
            var program = new UnaryOpProgram(x.shape, RSQRT);
            return this.compileAndRun(program, [x]);
        };
        MathBackendWebGL.prototype.reciprocal = function (x) {
            var program = new UnaryOpProgram(x.shape, RECIPROCAL);
            return this.compileAndRun(program, [x]);
        };
        MathBackendWebGL.prototype.relu = function (x) {
            var program;
            if (env().getBool('WEBGL_PACK')) {
                program = new UnaryOpPackedProgram(x.shape, RELU$1);
            }
            else {
                program = new UnaryOpProgram(x.shape, RELU);
            }
            return this.compileAndRun(program, [x]);
        };
        MathBackendWebGL.prototype.relu6 = function (x) {
            var program;
            if (env().getBool('WEBGL_PACK')) {
                program = new UnaryOpPackedProgram(x.shape, RELU6$1);
            }
            else {
                program = new UnaryOpProgram(x.shape, RELU6);
            }
            return this.compileAndRun(program, [x]);
        };
        MathBackendWebGL.prototype.prelu = function (x, alpha) {
            var program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?
                new BinaryOpPackedProgram(PRELU$1, x.shape, alpha.shape) :
                new BinaryOpProgram(PRELU, x.shape, alpha.shape);
            return this.compileAndRun(program, [x, alpha]);
        };
        MathBackendWebGL.prototype.elu = function (x) {
            if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {
                return this.packedUnaryOp(x, ELU$1, x.dtype);
            }
            var program = new UnaryOpProgram(x.shape, ELU);
            return this.compileAndRun(program, [x]);
        };
        MathBackendWebGL.prototype.eluDer = function (dy, y) {
            var program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?
                new BinaryOpPackedProgram(ELU_DER$1, dy.shape, y.shape) :
                new BinaryOpProgram(ELU_DER, dy.shape, y.shape);
            return this.compileAndRun(program, [dy, y]);
        };
        MathBackendWebGL.prototype.selu = function (x) {
            var program = new UnaryOpProgram(x.shape, SELU);
            return this.compileAndRun(program, [x]);
        };
        MathBackendWebGL.prototype.int = function (x) {
            var program = new UnaryOpProgram(x.shape, TO_INT);
            return this.compileAndRun(program, [x], 'int32');
        };
        MathBackendWebGL.prototype.clip = function (x, min, max) {
            var program;
            if (env().getBool('WEBGL_PACK_CLIP')) {
                program = new ClipPackedProgram(x.shape);
            }
            else {
                program = new ClipProgram(x.shape);
            }
            var customSetup = program.getCustomSetupFunc(min, max);
            return this.compileAndRun(program, [x], null, customSetup);
        };
        MathBackendWebGL.prototype.abs = function (x) {
            if (this.shouldExecuteOnCPU([x])) {
                return this.cpuBackend.abs(x);
            }
            if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {
                return this.packedUnaryOp(x, ABS, x.dtype);
            }
            var program = new UnaryOpProgram(x.shape, ABS);
            return this.compileAndRun(program, [x]);
        };
        MathBackendWebGL.prototype.complexAbs = function (x) {
            var xData = this.texData.get(x.dataId);
            var program = new ComplexAbsProgram(x.shape);
            var inputs = [
                this.makeComplexComponentTensorInfo(x, xData.complexTensors.real),
                this.makeComplexComponentTensorInfo(x, xData.complexTensors.imag),
            ];
            return this.compileAndRun(program, inputs);
        };
        MathBackendWebGL.prototype.sigmoid = function (x) {
            var program = new UnaryOpProgram(x.shape, SIGMOID);
            return this.compileAndRun(program, [x]);
        };
        MathBackendWebGL.prototype.softplus = function (x) {
            var program = new UnaryOpProgram(x.shape, SOFTPLUS);
            return this.compileAndRun(program, [x]);
        };
        MathBackendWebGL.prototype.sin = function (x) {
            var program = new UnaryOpProgram(x.shape, SIN);
            return this.compileAndRun(program, [x]);
        };
        MathBackendWebGL.prototype.cos = function (x) {
            var program = new UnaryOpProgram(x.shape, COS);
            return this.compileAndRun(program, [x]);
        };
        MathBackendWebGL.prototype.tan = function (x) {
            var program = new UnaryOpProgram(x.shape, TAN);
            return this.compileAndRun(program, [x]);
        };
        MathBackendWebGL.prototype.asin = function (x) {
            var program = new UnaryOpProgram(x.shape, ASIN);
            return this.compileAndRun(program, [x]);
        };
        MathBackendWebGL.prototype.acos = function (x) {
            var program = new UnaryOpProgram(x.shape, ACOS);
            return this.compileAndRun(program, [x]);
        };
        MathBackendWebGL.prototype.atan = function (x) {
            var program = new UnaryOpProgram(x.shape, ATAN);
            return this.compileAndRun(program, [x]);
        };
        MathBackendWebGL.prototype.atan2 = function (a, b) {
            var program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?
                new BinaryOpPackedProgram(ATAN2$1, a.shape, b.shape) :
                new BinaryOpProgram(ATAN2, a.shape, b.shape);
            return this.compileAndRun(program, [a, b]);
        };
        MathBackendWebGL.prototype.sinh = function (x) {
            var program = new UnaryOpProgram(x.shape, SINH);
            return this.compileAndRun(program, [x]);
        };
        MathBackendWebGL.prototype.cosh = function (x) {
            var program = new UnaryOpProgram(x.shape, COSH);
            return this.compileAndRun(program, [x]);
        };
        MathBackendWebGL.prototype.tanh = function (x) {
            var program = new UnaryOpProgram(x.shape, TANH);
            return this.compileAndRun(program, [x]);
        };
        MathBackendWebGL.prototype.asinh = function (x) {
            var program = new UnaryOpProgram(x.shape, ASINH);
            return this.compileAndRun(program, [x]);
        };
        MathBackendWebGL.prototype.acosh = function (x) {
            var program = new UnaryOpProgram(x.shape, ACOSH);
            return this.compileAndRun(program, [x]);
        };
        MathBackendWebGL.prototype.atanh = function (x) {
            var program = new UnaryOpProgram(x.shape, ATANH);
            return this.compileAndRun(program, [x]);
        };
        MathBackendWebGL.prototype.erf = function (x) {
            var program = new UnaryOpProgram(x.shape, ERF);
            return this.compileAndRun(program, [x]);
        };
        MathBackendWebGL.prototype.step = function (x, alpha) {
            var program = new UnaryOpProgram(x.shape, STEP(alpha));
            return this.compileAndRun(program, [x]);
        };
        MathBackendWebGL.prototype.conv2dByMatMul = function (x, filter, convInfo, bias, activation, preluActivationWeights) {
            // Reshapes conv2D input to 2D tensors, uses matMul and then reshape the
            // result from 2D to 4D.
            var xShape = x.shape;
            var xTexData = this.texData.get(x.dataId);
            var sharedMatMulDim = convInfo.inChannels;
            var outerShapeX = xShape[0] * xShape[1] * xShape[2];
            var outerShapeFilter = convInfo.outChannels;
            var isChannelsLast = convInfo.dataFormat === 'channelsLast';
            var transposeA = false;
            var transposeB = false;
            // TODO: Once reduction ops are packed, batchMatMul will always be packed
            // and we can remove this condition.
            var batchMatMulWillBeUnpacked = (outerShapeX === 1 || outerShapeFilter === 1) &&
                sharedMatMulDim > MATMUL_SHARED_DIM_THRESHOLD;
            var reshapeWillBeExpensive = xShape[2] % 2 !== 0 && !!xTexData.isPacked;
            if (batchMatMulWillBeUnpacked || !env().getBool('WEBGL_LAZILY_UNPACK') ||
                !env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ||
                !reshapeWillBeExpensive) {
                var targetShape_1 = isChannelsLast ? xShape[0] * xShape[1] * xShape[2] :
                    xShape[0] * xShape[2] * xShape[3];
                var xReshaped_1 = this.reshape(x, [1, targetShape_1, convInfo.inChannels]);
                var filterReshaped_1 = this.reshape(filter, [1, convInfo.inChannels, convInfo.outChannels]);
                return this.reshape(this.fusedBatchMatMul({
                    a: xReshaped_1,
                    b: filterReshaped_1,
                    transposeA: transposeA,
                    transposeB: transposeB,
                    bias: bias,
                    activation: activation,
                    preluActivationWeights: preluActivationWeights
                }), convInfo.outShape);
            }
            // Following optimization is specific to packed |x| with odd row count
            // (For example, in channelLast mode, 'row count' refers to x.shape[2]):
            // we avoid expensive packed 2x2 reshape by padding row count to next,
            // even number. When x.shape[2] is odd, the result of packed batchMatMul is
            // the same (has the same texture layout and and values in the texture) as
            // it is for even x.shape[2] + 1. We make the odd-rows tensor to look like
            // even-rows tensor before the operation and, after the batchMatMul,
            // fix the even-rows result to have odd number of rows.
            var targetShape = isChannelsLast ?
                xShape[0] * xShape[1] * (xShape[2] + 1) :
                xShape[0] * xShape[2] * (xShape[3] + 1);
            var xReshaped = {
                dataId: x.dataId,
                shape: [1, targetShape, convInfo.inChannels],
                dtype: x.dtype
            };
            // xTexData.shape gets referenced from GPGPUBinary.inShapeInfos.
            // Decrementing row count, after batchMatMul->...->compileProgram leads to
            // invalid row count within the reference in GPGPUBinary.inShapeInfos.
            // Alternative fix would be to provide a copy to GPGPUBinary.inShapeInfos
            // in compileProgram method, but that would affect compilation of all
            // programs - instead, provide a copy here, with even row count, before
            // calling batchMatMul->...->compileProgram and after that, the original
            // xTexData.shape is restored.
            var originalXTexDataShape = xTexData.shape;
            xTexData.shape = xTexData.shape.slice();
            xTexData.shape[xTexData.shape.length - 2]++;
            assert(isReshapeFree(xTexData.shape, xReshaped.shape), function () { return "packed reshape " + xTexData.shape + " to " + xReshaped.shape + " isn't free"; });
            var filterReshaped = this.reshape(filter, [1, convInfo.inChannels, convInfo.outChannels]);
            var pointwiseConv = this.fusedBatchMatMul({
                a: xReshaped,
                b: filterReshaped,
                transposeA: transposeA,
                transposeB: transposeB,
                bias: bias,
                activation: activation,
                preluActivationWeights: preluActivationWeights
            });
            var pointwiseConvTexData = this.texData.get(pointwiseConv.dataId);
            assert(pointwiseConvTexData.isPacked, function () { return 'batchMatMul result is expected to be packed'; });
            // Restore the input shape to original.
            xTexData.shape = originalXTexDataShape;
            // Set the output shape - there is no need for expensive reshape as data
            // layout is already correct.
            pointwiseConvTexData.shape = convInfo.outShape;
            return ENGINE.makeTensorFromDataId(pointwiseConv.dataId, convInfo.outShape, pointwiseConv.dtype);
        };
        MathBackendWebGL.prototype.conv2dWithIm2Row = function (x, filter, convInfo, bias, activation, preluActivationWeights) {
            // Rearranges conv2d input so each block to be convolved over forms the
            // column of a new matrix with shape [filterWidth * filterHeight *
            // inChannels, outHeight * outWidth]. The filter is also rearranged so each
            // output channel forms a row of a new matrix with shape [outChannels,
            // filterWidth * filterHeight * inChannels]. The convolution is then
            // computed by multiplying these matrices and reshaping the result.
            var filterWidth = convInfo.filterWidth, filterHeight = convInfo.filterHeight, inChannels = convInfo.inChannels, outWidth = convInfo.outWidth, outHeight = convInfo.outHeight, dataFormat = convInfo.dataFormat;
            var isChannelsLast = dataFormat === 'channelsLast';
            var sharedDim = filterWidth * filterHeight * inChannels;
            var numCols = outHeight * outWidth;
            var x2ColShape = [sharedDim, numCols];
            var transposeA = true;
            var transposeB = false;
            var xSqueezed = x.squeeze([0]);
            var w2Row = filter.reshape([1, sharedDim, -1]);
            var im2ColProgram = new Im2ColPackedProgram(x2ColShape, xSqueezed.shape, convInfo);
            var im2Col = this.compileAndRun(im2ColProgram, [xSqueezed]).reshape([
                1, x2ColShape[0], x2ColShape[1]
            ]);
            var hasBias = bias != null;
            var hasPreluActivationWeights = preluActivationWeights != null;
            var fusedActivation = activation ? mapActivationToShaderProgram(activation, true) : null;
            var matmulProgram = new MatMulPackedProgram(im2Col.shape, [1, numCols, convInfo.outChannels], transposeA, transposeB, hasBias, fusedActivation, hasPreluActivationWeights);
            var inputs = [im2Col, w2Row];
            if (bias) {
                inputs.push(bias);
            }
            if (hasPreluActivationWeights) {
                inputs.push(preluActivationWeights);
            }
            var product = this.compileAndRun(matmulProgram, inputs);
            if (isChannelsLast) {
                return product.reshape([1, outHeight, outWidth, convInfo.outChannels]);
            }
            else {
                return product.reshape([1, convInfo.outChannels, outHeight, outWidth]);
            }
        };
        MathBackendWebGL.prototype.fusedConv2d = function (_a) {
            var input = _a.input, filter = _a.filter, convInfo = _a.convInfo, bias = _a.bias, activation = _a.activation, preluActivationWeights = _a.preluActivationWeights;
            if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1 &&
                convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 &&
                convInfo.strideHeight === 1 && convInfo.strideWidth === 1 &&
                (convInfo.padInfo.type === 'SAME' ||
                    convInfo.padInfo.type === 'VALID')) {
                return this.conv2dByMatMul(input, filter, convInfo, bias, activation, preluActivationWeights);
            }
            if (env().getBool('WEBGL_CONV_IM2COL') && input.shape[0] === 1) {
                return this.conv2dWithIm2Row(input, filter, convInfo, bias, activation, preluActivationWeights);
            }
            var hasBias = bias != null;
            var hasPreluActivationWeights = preluActivationWeights != null;
            var fusedActivation = activation ? mapActivationToShaderProgram(activation, false) : null;
            var program = new Conv2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights);
            var inputs = [input, filter];
            if (bias) {
                inputs.push(bias);
            }
            if (preluActivationWeights) {
                inputs.push(preluActivationWeights);
            }
            return this.compileAndRun(program, inputs);
        };
        MathBackendWebGL.prototype.conv2d = function (x, filter, convInfo) {
            if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1 &&
                convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 &&
                convInfo.strideHeight === 1 && convInfo.strideWidth === 1 &&
                (convInfo.padInfo.type === 'SAME' ||
                    convInfo.padInfo.type === 'VALID')) {
                return this.conv2dByMatMul(x, filter, convInfo);
            }
            if (env().getBool('WEBGL_CONV_IM2COL') && x.shape[0] === 1) {
                return this.conv2dWithIm2Row(x, filter, convInfo);
            }
            var program = new Conv2DProgram(convInfo);
            return this.compileAndRun(program, [x, filter]);
        };
        MathBackendWebGL.prototype.conv2dDerInput = function (dy, filter, convInfo) {
            var program = new Conv2DDerInputProgram(convInfo);
            return this.compileAndRun(program, [dy, filter]);
        };
        MathBackendWebGL.prototype.conv2dDerFilter = function (x, dy, convInfo) {
            var program = new Conv2DDerFilterProgram(convInfo);
            return this.compileAndRun(program, [x, dy]);
        };
        MathBackendWebGL.prototype.fusedDepthwiseConv2D = function (_a) {
            var input = _a.input, filter = _a.filter, convInfo = _a.convInfo, bias = _a.bias, activation = _a.activation, preluActivationWeights = _a.preluActivationWeights;
            var shouldPackDepthwiseConv = env().getBool('WEBGL_PACK_DEPTHWISECONV') &&
                convInfo.strideWidth <= 2 &&
                convInfo.outChannels / convInfo.inChannels === 1;
            var fusedActivation = activation ?
                mapActivationToShaderProgram(activation, shouldPackDepthwiseConv) :
                null;
            var inputs = [input, filter];
            var hasBias = bias != null;
            var hasPreluActivationWeights = preluActivationWeights != null;
            if (hasBias) {
                inputs.push(bias);
            }
            if (hasPreluActivationWeights) {
                inputs.push(preluActivationWeights);
            }
            var program;
            if (shouldPackDepthwiseConv) {
                program = new DepthwiseConvPacked2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights);
                return this.compileAndRun(program, inputs);
            }
            program = new DepthwiseConv2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights);
            return this.compileAndRun(program, inputs);
        };
        MathBackendWebGL.prototype.depthwiseConv2D = function (x, filter, convInfo) {
            var program;
            if (env().getBool('WEBGL_PACK_DEPTHWISECONV') &&
                convInfo.strideWidth <= 2 &&
                convInfo.outChannels / convInfo.inChannels === 1) {
                program = new DepthwiseConvPacked2DProgram(convInfo);
                return this.compileAndRun(program, [x, filter]);
            }
            program = new DepthwiseConv2DProgram(convInfo);
            return this.compileAndRun(program, [x, filter]);
        };
        MathBackendWebGL.prototype.depthwiseConv2DDerInput = function (dy, filter, convInfo) {
            var program = new DepthwiseConv2DDerInputProgram(convInfo);
            return this.compileAndRun(program, [dy, filter]);
        };
        MathBackendWebGL.prototype.depthwiseConv2DDerFilter = function (x, dy, convInfo) {
            var program = new DepthwiseConv2DDerFilterProgram(convInfo);
            return this.compileAndRun(program, [x, dy]);
        };
        MathBackendWebGL.prototype.conv3d = function (x, filter, convInfo) {
            var program = new Conv3DProgram(convInfo);
            return this.compileAndRun(program, [x, filter]);
        };
        MathBackendWebGL.prototype.conv3dDerInput = function (dy, filter, convInfo) {
            var program = new Conv3DDerInputProgram(convInfo);
            return this.compileAndRun(program, [dy, filter]);
        };
        MathBackendWebGL.prototype.conv3dDerFilter = function (x, dy, convInfo) {
            var program = new Conv3DDerFilterProgram(convInfo);
            return this.compileAndRun(program, [x, dy]);
        };
        MathBackendWebGL.prototype.maxPool = function (x, convInfo) {
            var program = new Pool2DProgram(convInfo, 'max', false);
            return this.compileAndRun(program, [x]);
        };
        MathBackendWebGL.prototype.avgPool = function (x, convInfo) {
            var program = new Pool2DProgram(convInfo, 'avg', false);
            return this.compileAndRun(program, [x], 'float32');
        };
        MathBackendWebGL.prototype.maxPoolBackprop = function (dy, x, y, convInfo) {
            var getPositions = true;
            var maxPoolPositionsProgram = new Pool2DProgram(convInfo, 'max', getPositions);
            var maxPoolPositions = this.compileAndRun(maxPoolPositionsProgram, [x]);
            var maxPoolBackPropProgram = new MaxPool2DBackpropProgram(convInfo);
            var result = this.compileAndRun(maxPoolBackPropProgram, [dy, maxPoolPositions], x.dtype);
            maxPoolPositions.dispose();
            return result;
        };
        MathBackendWebGL.prototype.avgPoolBackprop = function (dy, x, convInfo) {
            var avgPoolBackpropProgram = new AvgPool2DBackpropProgram(convInfo);
            return this.compileAndRun(avgPoolBackpropProgram, [dy], x.dtype);
        };
        MathBackendWebGL.prototype.cast = function (x, dtype) {
            return castTensor(x, dtype, this);
        };
        MathBackendWebGL.prototype.unstack = function (x, axis) {
            var num = x.shape[axis];
            var outShape = new Array(x.rank - 1);
            var outIndex = 0;
            for (var i = 0; i < x.rank; i++) {
                if (i !== axis) {
                    outShape[outIndex++] = x.shape[i];
                }
            }
            var begin = new Array(x.rank).fill(0);
            var size = x.shape.slice();
            size[axis] = 1;
            var res = new Array(num);
            for (var i = 0; i < res.length; i++) {
                begin[axis] = i;
                res[i] = this.slice(x, begin, size).reshape(outShape);
            }
            return res;
        };
        MathBackendWebGL.prototype.avgPool3d = function (x, convInfo) {
            var program = new Pool3DProgram(convInfo, 'avg', false);
            return this.compileAndRun(program, [x], 'float32');
        };
        MathBackendWebGL.prototype.avgPool3dBackprop = function (dy, x, convInfo) {
            var avgPool3dBackpropProgram = new AvgPool3DBackpropProgram(convInfo);
            return this.compileAndRun(avgPool3dBackpropProgram, [dy], x.dtype);
        };
        MathBackendWebGL.prototype.maxPool3d = function (x, convInfo) {
            var program = new Pool3DProgram(convInfo, 'max', false);
            return this.compileAndRun(program, [x], 'float32');
        };
        MathBackendWebGL.prototype.maxPool3dBackprop = function (dy, x, y, convInfo) {
            var getPositions = true;
            var maxPool3dPositionsProgram = new Pool3DProgram(convInfo, 'max', getPositions);
            var maxPool3dPositions = this.compileAndRun(maxPool3dPositionsProgram, [x]);
            var maxPool3dBackPropProgram = new MaxPool3DBackpropProgram(convInfo);
            var result = this.compileAndRun(maxPool3dBackPropProgram, [dy, maxPool3dPositions], x.dtype);
            maxPool3dPositions.dispose();
            return result;
        };
        MathBackendWebGL.prototype.reshape = function (x, shape) {
            var texData = this.texData.get(x.dataId);
            if (texData.isPacked && !isReshapeFree(x.shape, shape) &&
                !(texData.texture !== null &&
                    isReshapeFree(texData.shape, shape))) {
                var info = this.packedReshape(x, shape);
                return ENGINE.makeTensorFromDataId(info.dataId, info.shape, info.dtype);
            }
            return reshapeTensor(x, shape);
        };
        MathBackendWebGL.prototype.resizeBilinear = function (x, newHeight, newWidth, alignCorners) {
            var program = env().getBool('WEBGL_PACK_IMAGE_OPERATIONS') ?
                new ResizeBilinearPackedProgram(x.shape, newHeight, newWidth, alignCorners) :
                new ResizeBilinearProgram(x.shape, newHeight, newWidth, alignCorners);
            return this.compileAndRun(program, [x], 'float32');
        };
        MathBackendWebGL.prototype.resizeBilinearBackprop = function (dy, x, alignCorners) {
            var program = new ResizeBilinearBackpropProgram(dy, x, alignCorners);
            return this.compileAndRun(program, [dy]);
        };
        MathBackendWebGL.prototype.resizeNearestNeighbor = function (x, newHeight, newWidth, alignCorners) {
            var program = new ResizeNearestNeighborProgram(x.shape, newHeight, newWidth, alignCorners);
            return this.compileAndRun(program, [x]);
        };
        MathBackendWebGL.prototype.resizeNearestNeighborBackprop = function (dy, x, alignCorners) {
            var program = new ResizeNearestNeigborBackpropProgram(dy, x, alignCorners);
            return this.compileAndRun(program, [dy]);
        };
        MathBackendWebGL.prototype.multinomial = function (logits, normalized, numSamples, seed) {
            var probs = normalized ? logits : softmax(logits);
            var batchSize = probs.shape[0];
            var numOutcomes = probs.shape[1];
            var program = new MultinomialProgram(batchSize, numOutcomes, numSamples);
            var customSetup = program.getCustomSetupFunc(seed);
            return this.compileAndRun(program, [probs], 'int32', customSetup);
        };
        MathBackendWebGL.prototype.oneHot = function (indices, depth, onValue, offValue) {
            var program = new OneHotProgram(indices.size, depth, onValue, offValue);
            return this.compileAndRun(program, [indices]);
        };
        MathBackendWebGL.prototype.diag = function (x) {
            var program = new DiagProgram(x.size);
            return this.compileAndRun(program, [x]);
        };
        MathBackendWebGL.prototype.nonMaxSuppression = function (boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
            warn('tf.nonMaxSuppression() in webgl locks the UI thread. ' +
                'Call tf.nonMaxSuppressionAsync() instead');
            var boxesVals = boxes.dataSync();
            var scoresVals = scores.dataSync();
            return nonMaxSuppressionV3(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);
        };
        MathBackendWebGL.prototype.cropAndResize = function (image, boxes, boxIndex, cropSize, method, extrapolationValue) {
            var program = new CropAndResizeProgram(image.shape, boxes.shape, cropSize, method, extrapolationValue);
            return this.compileAndRun(program, [image, boxes, boxIndex], 'float32');
        };
        MathBackendWebGL.prototype.depthToSpace = function (x, blockSize, dataFormat) {
            assert(blockSize > 1, function () {
                return "blockSize should be > 1 for depthToSpace, but was: " + blockSize;
            });
            var batchSize = x.shape[0];
            var inputHeight = (dataFormat === 'NHWC') ? x.shape[1] : x.shape[2];
            var inputWidth = (dataFormat === 'NHWC') ? x.shape[2] : x.shape[3];
            var inputDepth = (dataFormat === 'NHWC') ? x.shape[3] : x.shape[1];
            var outputHeight = inputHeight * blockSize;
            var outputWidth = inputWidth * blockSize;
            var outputDepth = inputDepth / (blockSize * blockSize);
            var outputShape = (dataFormat === 'NHWC') ?
                [batchSize, outputHeight, outputWidth, outputDepth] :
                [batchSize, outputDepth, outputHeight, outputWidth];
            var program = new DepthToSpaceProgram(outputShape, blockSize, dataFormat);
            return this.compileAndRun(program, [x]);
        };
        MathBackendWebGL.prototype.split = function (x, sizeSplits, axis) {
            return split$1(x, sizeSplits, axis);
        };
        MathBackendWebGL.prototype.scatterND = function (indices, updates, shape) {
            var _a = calculateShapes(updates, indices, shape), sliceRank = _a.sliceRank, numUpdates = _a.numUpdates, sliceSize = _a.sliceSize, strides = _a.strides, outputSize = _a.outputSize;
            var flattenShape = [outputSize / sliceSize, sliceSize];
            var flattenIndices = indices.reshape([numUpdates, sliceRank]);
            var flattenX = updates.reshape([numUpdates, sliceSize]);
            if (outputSize === 0) {
                return reshapeTensor(tensor([]), shape);
            }
            var defaultValue = scalar(0);
            var program = new ScatterProgram(numUpdates, sliceRank, flattenIndices.rank, flattenX.rank, strides, flattenShape);
            var res = this.compileAndRun(program, [flattenX, flattenIndices, defaultValue]);
            return res.reshape(shape);
        };
        MathBackendWebGL.prototype.sparseToDense = function (sparseIndices, sparseValues, outputShape, defaultValue) {
            var _a = calculateShapes(sparseValues, sparseIndices, outputShape), sliceRank = _a.sliceRank, numUpdates = _a.numUpdates, strides = _a.strides, outputSize = _a.outputSize;
            var sumDupeIndices = false;
            var program = new ScatterProgram(numUpdates, sliceRank, sparseIndices.rank, sparseValues.rank, strides, [outputSize, 1], sumDupeIndices);
            var res = this.compileAndRun(program, [sparseValues, sparseIndices, defaultValue]);
            return res.reshape(outputShape);
        };
        MathBackendWebGL.prototype.fft = function (x) {
            var inverse = false;
            return this.fftImpl(x, inverse);
        };
        MathBackendWebGL.prototype.ifft = function (x) {
            var inverse = true;
            return this.fftImpl(x, inverse);
        };
        MathBackendWebGL.prototype.fftImpl = function (x, inverse) {
            var xData = this.texData.get(x.dataId);
            var realProgram = new FFTProgram(COMPLEX_FFT.REAL, x.shape, inverse);
            var imagProgram = new FFTProgram(COMPLEX_FFT.IMAG, x.shape, inverse);
            var inputs = [
                this.makeComplexComponentTensorInfo(x, xData.complexTensors.real),
                this.makeComplexComponentTensorInfo(x, xData.complexTensors.imag),
            ];
            var real = this.compileAndRun(realProgram, inputs);
            var imag = this.compileAndRun(imagProgram, inputs);
            var complex = this.complex(real, imag).as2D(x.shape[0], x.shape[1]);
            real.dispose();
            imag.dispose();
            return complex;
        };
        MathBackendWebGL.prototype.gatherND = function (x, indices) {
            var indicesShape = indices.shape;
            var sliceRank = indicesShape[indicesShape.length - 1];
            var _a = prepareAndValidate(x, indices), resultShape = _a[0], numSlices = _a[1], sliceSize = _a[2], strides = _a[3];
            var flattenIndices = indices.reshape([numSlices, sliceRank]);
            var flattenX = x.reshape([x.size / sliceSize, sliceSize]);
            var program = new GatherNDProgram(sliceRank, strides, [numSlices, sliceSize]);
            var res = this.compileAndRun(program, [flattenX, flattenIndices]);
            return res.reshape(resultShape);
        };
        MathBackendWebGL.prototype.fill = function (shape, value, dtype) {
            dtype = dtype || inferDtype(value);
            if (dtype === 'string') {
                // String type should be handled in CPU memory.
                var values = getArrayFromDType(dtype, sizeFromShape(shape));
                values.fill(value);
                return ENGINE.makeTensor(values, shape, dtype, this);
            }
            else {
                var program = new FillProgram(shape, value);
                var customSetup = program.getCustomSetupFunc(value);
                return this.compileAndRun(program, [], dtype, customSetup);
            }
        };
        MathBackendWebGL.prototype.onesLike = function (x) {
            if (x.dtype === 'string') {
                throw new Error('onesLike is not supported under string dtype');
            }
            else {
                // TODO(cais, smilkov): Add WebGL shader for onesLike:
                //   https://github.com/tensorflow/tfjs/issues/1293
                return this.fill(x.shape, 1, x.dtype);
            }
        };
        MathBackendWebGL.prototype.zerosLike = function (x) {
            return this.fill(x.shape, x.dtype === 'string' ? '' : 0, x.dtype);
        };
        MathBackendWebGL.prototype.linspace = function (start, stop, num) {
            // TODO: Use CPU implementation due to the precision problem in Safari.
            return linspaceImpl(start, stop, num);
        };
        MathBackendWebGL.prototype.makeTensorInfo = function (shape, dtype) {
            var dataId = this.write(null /* values */, shape, dtype);
            this.texData.get(dataId).usage = null;
            return { dataId: dataId, shape: shape, dtype: dtype };
        };
        MathBackendWebGL.prototype.makeOutput = function (shape, dtype) {
            var dataId = this.makeTensorInfo(shape, dtype).dataId;
            return ENGINE.makeTensorFromDataId(dataId, shape, dtype, this);
        };
        MathBackendWebGL.prototype.unpackTensor = function (input) {
            var program = new UnpackProgram(input.shape);
            return this.runWebGLProgram(program, [input], input.dtype);
        };
        MathBackendWebGL.prototype.packTensor = function (input) {
            var program = new PackProgram(input.shape);
            var preventEagerUnpackingOutput = true;
            return this.runWebGLProgram(program, [input], input.dtype, null /* customSetup */, preventEagerUnpackingOutput);
        };
        MathBackendWebGL.prototype.packedReshape = function (input, afterShape) {
            var input3DShape = [
                getBatchDim(input.shape)
            ].concat(getRowsCols(input.shape));
            var input3D = {
                dtype: input.dtype,
                shape: input3DShape,
                dataId: input.dataId
            };
            var afterShapeAs3D = [
                getBatchDim(afterShape)
            ].concat(getRowsCols(afterShape));
            var program = new ReshapePackedProgram(afterShapeAs3D, input3DShape);
            var preventEagerUnpackingOfOutput = true;
            var output = this.runWebGLProgram(program, [input3D], input.dtype, null /* customSetup */, preventEagerUnpackingOfOutput);
            return { dataId: output.dataId, shape: afterShape, dtype: output.dtype };
        };
        MathBackendWebGL.prototype.decode = function (dataId) {
            var texData = this.texData.get(dataId);
            var isPacked = texData.isPacked, shape = texData.shape, dtype = texData.dtype;
            var shapeAs3D = getShapeAs3D(shape);
            var program;
            if (isPacked) {
                program = new DecodeMatrixPackedProgram(shapeAs3D);
            }
            else {
                program = new DecodeMatrixProgram(shapeAs3D);
            }
            var preventEagerUnpackingOfOutput = true;
            var out = this.runWebGLProgram(program, [{ shape: shapeAs3D, dtype: dtype, dataId: dataId }], dtype, null /* customSetup */, preventEagerUnpackingOfOutput);
            return { dtype: dtype, shape: shape, dataId: out.dataId };
        };
        MathBackendWebGL.prototype.runWebGLProgram = function (program, inputs, outputDtype, customSetup, preventEagerUnpackingOfOutput) {
            var _this = this;
            if (preventEagerUnpackingOfOutput === void 0) { preventEagerUnpackingOfOutput = false; }
            var output = this.makeTensorInfo(program.outputShape, outputDtype);
            var outData = this.texData.get(output.dataId);
            if (program.packedOutput) {
                outData.isPacked = true;
            }
            if (program.outPackingScheme === PackingScheme.DENSE) {
                var texelShape = getDenseTexShape(program.outputShape);
                // For a densely packed output, we explicitly set texShape
                // so it doesn't get assigned later according to our typical packing
                // scheme wherein a single texel can only contain values from adjacent
                // rows/cols.
                outData.texShape = texelShape.map(function (d) { return d * 2; });
            }
            if (program.outTexUsage != null) {
                outData.usage = program.outTexUsage;
            }
            if (sizeFromShape(output.shape) === 0) {
                // Short-circuit the computation since the result is empty (has 0 in its
                // shape).
                outData.values = getTypedArrayFromDType(output.dtype, 0);
                return output;
            }
            var dataToDispose = [];
            var inputsData = inputs.map(function (input) {
                if (input.dtype === 'complex64') {
                    throw new Error("GPGPUProgram does not support complex64 input. For complex64 " +
                        "dtypes, please separate the program into real and imaginary " +
                        "parts.");
                }
                var texData = _this.texData.get(input.dataId);
                if (texData.texture == null) {
                    if (!program.packedInputs &&
                        sizeFromShape(input.shape) <=
                            env().getNumber('WEBGL_SIZE_UPLOAD_UNIFORM')) {
                        // Upload small tensors that live on the CPU as uniforms, not as
                        // textures. Do this only when the environment supports 32bit floats
                        // due to problems when comparing 16bit floats with 32bit floats.
                        // TODO(https://github.com/tensorflow/tfjs/issues/821): Make it
                        // possible for packed shaders to sample from uniforms.
                        return {
                            shape: input.shape,
                            texData: null,
                            isUniform: true,
                            uniformValues: texData.values
                        };
                    }
                    // This ensures that if a packed program's inputs have not yet been
                    // uploaded to the GPU, they get uploaded as packed right off the bat.
                    if (program.packedInputs) {
                        texData.isPacked = true;
                        texData.shape = input.shape;
                    }
                }
                else if (!!texData.isPacked !== !!program.packedInputs) {
                    input = texData.isPacked ? _this.unpackTensor(input) :
                        _this.packTensor(input);
                    dataToDispose.push(input);
                    texData = _this.texData.get(input.dataId);
                }
                else if (texData.isPacked &&
                    !isReshapeFree(texData.shape, input.shape)) {
                    // This is a special case where a texture exists for a tensor
                    // but the shapes are incompatible (due to packing constraints) because
                    // the tensor did not have a chance to go through the packed reshape
                    // shader. This only happens when we reshape the *same* tensor to form
                    // *distinct* inputs to an op, e.g. dotting a vector with itself. This
                    // case will disappear once packed uploading is the default.
                    var savedInput = input;
                    var targetShape = input.shape;
                    input.shape = texData.shape;
                    input = _this.packedReshape(input, targetShape);
                    dataToDispose.push(input);
                    texData = _this.texData.get(input.dataId);
                    savedInput.shape = targetShape;
                }
                _this.uploadToGPU(input.dataId);
                return { shape: input.shape, texData: texData, isUniform: false };
            });
            this.uploadToGPU(output.dataId);
            var outputData = { shape: output.shape, texData: outData, isUniform: false };
            var key = makeShaderKey(program, inputsData, outputData);
            var binary = this.getAndSaveBinary(key, function () {
                return compileProgram(_this.gpgpu, program, inputsData, outputData);
            });
            var shouldTimeProgram = this.activeTimers != null;
            var query;
            if (shouldTimeProgram) {
                query = this.startTimer();
            }
            runProgram(this.gpgpu, binary, inputsData, outputData, customSetup);
            dataToDispose.forEach(function (info) { return _this.disposeData(info.dataId); });
            if (shouldTimeProgram) {
                query = this.endTimer(query);
                this.activeTimers.push({ name: program.constructor.name, query: this.getQueryTime(query) });
            }
            if (!env().getBool('WEBGL_LAZILY_UNPACK') && outData.isPacked &&
                preventEagerUnpackingOfOutput === false) {
                var unpacked = this.unpackTensor(output);
                this.disposeData(output.dataId);
                return unpacked;
            }
            return output;
        };
        MathBackendWebGL.prototype.compileAndRun = function (program, inputs, outputDtype, customSetup, preventEagerUnpackingOfOutput) {
            if (preventEagerUnpackingOfOutput === void 0) { preventEagerUnpackingOfOutput = false; }
            outputDtype = outputDtype || inputs[0].dtype;
            var outInfo = this.runWebGLProgram(program, inputs, outputDtype, customSetup, preventEagerUnpackingOfOutput);
            return ENGINE.makeTensorFromDataId(outInfo.dataId, outInfo.shape, outInfo.dtype);
        };
        MathBackendWebGL.prototype.getAndSaveBinary = function (key, getBinary) {
            if (!(key in this.binaryCache)) {
                this.binaryCache[key] = getBinary();
            }
            return this.binaryCache[key];
        };
        MathBackendWebGL.prototype.getTextureManager = function () {
            return this.textureManager;
        };
        MathBackendWebGL.prototype.dispose = function () {
            var _this = this;
            if (this.disposed) {
                return;
            }
            // Avoid disposing the compiled webgl programs during unit testing because
            // it slows down test execution.
            if (!env().getBool('IS_TEST')) {
                var allKeys = Object.keys(this.binaryCache);
                allKeys.forEach(function (key) {
                    _this.gpgpu.deleteProgram(_this.binaryCache[key].webGLProgram);
                    delete _this.binaryCache[key];
                });
            }
            this.textureManager.dispose();
            if (this.canvas != null &&
                (typeof (HTMLCanvasElement) !== 'undefined' &&
                    this.canvas instanceof HTMLCanvasElement)) {
                this.canvas.remove();
            }
            else {
                this.canvas = null;
            }
            if (this.gpgpuCreatedLocally) {
                this.gpgpu.program = null;
                this.gpgpu.dispose();
            }
            this.disposed = true;
        };
        MathBackendWebGL.prototype.floatPrecision = function () {
            var _this = this;
            if (this.floatPrecisionValue == null) {
                this.floatPrecisionValue = tidy(function () {
                    if (!env().get('WEBGL_RENDER_FLOAT32_ENABLED')) {
                        // Momentarily switching DEBUG flag to false so we don't throw an
                        // error trying to upload a small value.
                        var debugFlag = env().getBool('DEBUG');
                        env().set('DEBUG', false);
                        var underflowCheckValue = _this.abs(scalar(1e-8)).dataSync()[0];
                        env().set('DEBUG', debugFlag);
                        if (underflowCheckValue > 0) {
                            return 32;
                        }
                    }
                    return 16;
                });
            }
            return this.floatPrecisionValue;
        };
        /** Returns the smallest representable number.  */
        MathBackendWebGL.prototype.epsilon = function () {
            return this.floatPrecision() === 32 ? EPSILON_FLOAT32 : EPSILON_FLOAT16;
        };
        MathBackendWebGL.prototype.uploadToGPU = function (dataId) {
            var _a;
            var texData = this.texData.get(dataId);
            var shape = texData.shape, dtype = texData.dtype, values = texData.values, texture = texData.texture, usage = texData.usage, isPacked = texData.isPacked;
            if (texture != null) {
                // Array is already on GPU. No-op.
                return;
            }
            var shouldTimeProgram = this.activeTimers != null;
            var start;
            if (shouldTimeProgram) {
                start = now();
            }
            var texShape = texData.texShape;
            if (texShape == null) {
                texShape = getTextureShapeFromLogicalShape(shape, isPacked);
                texData.texShape = texShape;
            }
            if (values != null) {
                var shapeAs3D = getShapeAs3D(shape);
                var program = void 0;
                var width = texShape[1], height = texShape[0];
                var isByteArray = values instanceof Uint8Array;
                if (isPacked) {
                    _a = getPackedMatrixTextureShapeWidthHeight(texShape[0], texShape[1]), width = _a[0], height = _a[1];
                    program = new EncodeMatrixPackedProgram(shapeAs3D, [height, width], isByteArray);
                }
                else {
                    program =
                        new EncodeMatrixProgram(shapeAs3D, [height, width], isByteArray);
                }
                var tempDenseInputHandle = this.makeTensorInfo([height, width], dtype);
                if (isByteArray) {
                    this.texData.get(tempDenseInputHandle.dataId).usage =
                        TextureUsage.PIXELS;
                }
                else {
                    this.texData.get(tempDenseInputHandle.dataId).usage =
                        TextureUsage.UPLOAD;
                }
                this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(tempDenseInputHandle.dataId), width, height, values);
                // We want the output to remain packed regardless of the value of
                // WEBGL_PACK.
                var preventEagerUnpacking = true;
                var encodedOutputTarget = this.runWebGLProgram(program, [tempDenseInputHandle], dtype, null, preventEagerUnpacking);
                // Have the original texture assume the identity of the encoded output.
                var outputTexData = this.texData.get(encodedOutputTarget.dataId);
                texData.texture = outputTexData.texture;
                texData.texShape = outputTexData.texShape;
                texData.isPacked = outputTexData.isPacked;
                texData.usage = outputTexData.usage;
                this.disposeData(tempDenseInputHandle.dataId);
                this.texData.delete(encodedOutputTarget.dataId);
                // Once uploaded, don't store the values on cpu.
                texData.values = null;
                if (shouldTimeProgram) {
                    this.uploadWaitMs += now() - start;
                }
            }
            else {
                var newTexture = this.acquireTexture(texShape, usage, dtype, isPacked);
                texData.texture = newTexture;
            }
        };
        MathBackendWebGL.prototype.convertAndCacheOnCPU = function (dataId, float32Values) {
            var texData = this.texData.get(dataId);
            var dtype = texData.dtype;
            this.releaseGPUData(dataId);
            if (float32Values != null) {
                texData.values = float32ToTypedArray(float32Values, dtype);
            }
            return texData.values;
        };
        MathBackendWebGL.prototype.acquireTexture = function (texShape, texType, dtype, isPacked) {
            this.numBytesInGPU += this.computeBytes(texShape, dtype);
            if (!this.warnedAboutMemory &&
                this.numBytesInGPU > this.numMBBeforeWarning * 1024 * 1024) {
                var mb = (this.numBytesInGPU / 1024 / 1024).toFixed(2);
                this.warnedAboutMemory = true;
                console.warn("High memory usage in GPU: " + mb + " MB, " +
                    "most likely due to a memory leak");
            }
            return this.textureManager.acquireTexture(texShape, texType, isPacked);
        };
        MathBackendWebGL.prototype.computeBytes = function (shape, dtype) {
            return shape[0] * shape[1] * bytesPerElement(dtype);
        };
        return MathBackendWebGL;
    }(KernelBackend));
    if (isBrowser()) {
        ENGINE.registerBackend('webgl', function () { return new MathBackendWebGL(); }, 2 /* priority */);
    }
    function float32ToTypedArray(a, dtype) {
        if (dtype === 'float32' || dtype === 'complex64') {
            return a;
        }
        else if (dtype === 'int32' || dtype === 'bool') {
            var result = (dtype === 'int32') ? new Int32Array(a.length) :
                new Uint8Array(a.length);
            for (var i = 0; i < result.length; ++i) {
                result[i] = Math.round(a[i]);
            }
            return result;
        }
        else {
            throw new Error("Unknown dtype " + dtype);
        }
    }

    /**
     * @license
     * Copyright 2019 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 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 $x = convertToTensor(x, 'x', 'square');
        var attrs = {};
        var inputsToSave = [$x];
        var outputsToSave = [];
        return ENGINE.runKernelFunc(function (backend, save) {
            save([$x]);
            return backend.square($x);
        }, { x: $x }, null /* grad */, 'Square', attrs, inputsToSave, outputsToSave);
    }
    var square = op({ square_: square_ });

    var SquaredDifference = 'SquaredDifference';
    var Square = 'Square';
    var NonMaxSuppressionV5 = 'NonMaxSuppressionV5';
    /**
     * TensorFlow.js-only kernels
     */
    var FromPixels = 'FromPixels';

    /**
     * @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 (a - b) * (a - b) element-wise.
     * Supports broadcasting.
     *
     * We also expose `tf.squaredDifferenceStrict` 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.squaredDifference(b).print();  // or tf.squaredDifference(a, b)
     * ```
     *
     * ```js
     * // Broadcast squared difference  a with b.
     * const a = tf.tensor1d([2, 4, 6, 8]);
     * const b = tf.scalar(5);
     *
     * a.squaredDifference(b).print();  // or tf.squaredDifference(a, b)
     * ```
     *
     * @param a The first tensor.
     * @param b The second tensor. Must have the same type as `a`.
     */
    /** @doc {heading: 'Operations', subheading: 'Arithmetic'} */
    function squaredDifference_(a, b) {
        var _a;
        var $a = convertToTensor(a, 'a', 'squaredDifference');
        var $b = convertToTensor(b, 'b', 'squaredDifference');
        _a = makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
        assertAndGetBroadcastShape($a.shape, $b.shape);
        var der = function (dy, saved) {
            var $a = saved[0], $b = saved[1];
            var two = scalar(2);
            var derA = function () { return dy.mul($a.sub($b).mul(two)); };
            var derB = function () { return dy.mul($b.sub($a).mul(two)); };
            return { a: derA, b: derB };
        };
        var forward = function (backend, save) {
            var res = backend.squaredDifference($a, $b);
            save([$a, $b]);
            return res;
        };
        var inputs = { a: $a, b: $b };
        var attrs = {};
        var inputsToSave = [$a, $b];
        var outputToSave = [];
        return ENGINE.runKernelFunc(forward, inputs, der, SquaredDifference, attrs, inputsToSave, outputToSave);
    }
    var squaredDifference = op({ squaredDifference_: squaredDifference_ });

    /**
     * @license
     * Copyright 2018 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 `-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 $x = convertToTensor(x, 'x', 'neg');
        var grad = function (dy) {
            return { x: function () { return dy.neg(); } };
        };
        var attrs = {};
        var inputsToSave = [$x];
        return ENGINE.runKernelFunc(function (backend) { return backend.neg($x); }, { x: $x }, grad, 'Neg', attrs, inputsToSave);
    }
    /**
     * 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');
        // TODO(manrajgrover): Return null for gradients when backprop supports it.
        var grad = function (dy) {
            return { $x: function () { return zerosLike(dy); } };
        };
        return ENGINE.runKernelFunc(function (backend) { return backend.ceil($x); }, { $x: $x }, grad);
    }
    /**
     * 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');
        // TODO(nsthorat): Let gradients be null for cases where we want to stop
        // backpropgation.
        var grad = function (dy) {
            return { $x: function () { return zerosLike(dy); } };
        };
        return ENGINE.runKernelFunc(function (backend) { return backend.floor($x); }, { $x: $x }, grad);
    }
    /**
     * 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 grad = function (dy) {
            return { $x: function () { return zerosLike(dy); } };
        };
        return ENGINE.runKernelFunc(function (backend) { return backend.sign($x); }, { $x: $x }, grad);
    }
    /**
     * 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');
        // TODO(nsthorat): Let gradients be null for cases where we want to stop
        // backpropgation.
        var grad = function (dy) {
            return { $x: function () { return zerosLike(dy); } };
        };
        return ENGINE.runKernelFunc(function (backend) { return backend.isNaN($x); }, { $x: $x }, grad);
    }
    /**
     * 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');
        // TODO(nsthorat): Let gradients be null for cases where we want to stop
        // backpropgation.
        var grad = function (dy) {
            return { $x: function () { return zerosLike(dy); } };
        };
        return ENGINE.runKernelFunc(function (backend) { return backend.isInf($x); }, { $x: $x }, grad);
    }
    /**
     * 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');
        // TODO(nsthorat): Let gradients be null for cases where we want to stop
        // backpropgation.
        var grad = function (dy) {
            return { $x: function () { return zerosLike(dy); } };
        };
        return ENGINE.runKernelFunc(function (backend) { return backend.isFinite($x); }, { $x: $x }, grad);
    }
    /**
     * 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');
        // TODO(nsthorat): Let gradients be null for cases where we want to stop
        // backpropgation.
        var grad = function (dy) {
            return { $x: function () { return zerosLike(dy); } };
        };
        return ENGINE.runKernelFunc(function (backend) { return backend.round($x); }, { $x: $x }, grad);
    }
    /**
     * 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 $x = convertToTensor(x, 'x', 'exp');
        var bck = function (dy, saved) {
            return { x: function () { return dy.mulStrict(saved[0]); } };
        };
        var attrs = {};
        var inputsToSave = [];
        var outputsToSave = [true];
        return ENGINE.runKernelFunc(function (backend, save) {
            var y = backend.exp($x);
            save([y]);
            return y;
        }, { x: $x }, bck, 'Exp', attrs, inputsToSave, outputsToSave);
    }
    /**
     * 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 grad = function (dy, saved) {
            var $x = saved[0];
            return { $x: function () { return dy.mul($x.exp()); } };
        };
        return ENGINE.runKernelFunc(function (backend, save) {
            var res = backend.expm1($x);
            save([$x]);
            return res;
        }, { $x: $x }, grad);
    }
    /**
     * Computes natural logarithm of the input `tf.Tensor` element-wise: `ln(x)`
     *
     * ```js
     * const x = tf.tensor1d([1, 2, Math.E]);
     *
     * x.log().print();  // or tf.log(x)
     * ```
     * @param x The input tensor.
     */
    /** @doc {heading: 'Operations', subheading: 'Basic math'} */
    function log_(x) {
        var $x = convertToTensor(x, 'x', 'log');
        var grad = function (dy, saved) {
            var $x = saved[0];
            return { x: function () { return dy.div($x.toFloat()); } };
        };
        var attrs = {};
        var inputsToSave = [$x];
        return ENGINE.runKernelFunc(function (backend, save) {
            var res = backend.log($x);
            save([$x]);
            return res;
        }, { x: $x }, grad, 'Log', attrs, inputsToSave);
    }
    /**
     * 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 grad = function (dy, saved) {
            var $x = saved[0];
            return { $x: function () { return dy.div($x.add(1)); } };
        };
        return ENGINE.runKernelFunc(function (backend, save) {
            var res = backend.log1p($x);
            save([$x]);
            return res;
        }, { $x: $x }, grad);
    }
    /**
     * Computes square root of the input `tf.Tensor` element-wise: `y = sqrt(x)`
     *
     * ```js
     * const x = tf.tensor1d([1, 2, 4, -1]);
     *
     * x.sqrt().print();  // or tf.sqrt(x)
     * ```
     * @param x The input tensor.
     */
    /** @doc {heading: 'Operations', subheading: 'Basic math'} */
    function sqrt_(x) {
        var $x = convertToTensor(x, 'x', 'sqrt');
        var grad = function (dy, saved) {
            var $x = saved[0];
            return { $x: function () { return dy.div($x.toFloat().sqrt().mul(2)); } };
        };
        return ENGINE.runKernelFunc(function (backend, save) {
            var res = backend.sqrt($x);
            save([$x]);
            return res;
        }, { $x: $x }, grad);
    }
    /**
     * Computes reciprocal of square root of the input `tf.Tensor` element-wise:
     * `y = 1 / sqrt(x)`
     *
     * ```js
     * const x = tf.tensor1d([1, 2, 4, -1]);
     *
     * x.rsqrt().print();  // or tf.rsqrt(x)
     * ```
     * @param x The input tensor.
     */
    /** @doc {heading: 'Operations', subheading: 'Basic math'} */
    function rsqrt_(x) {
        var $x = convertToTensor(x, 'x', 'rsqrt');
        var grad = function (dy, saved) {
            var $x = saved[0];
            return { x: function () { return dy.div($x.pow(1.5).mul(2)).neg(); } };
        };
        var inputsToSave = [$x];
        return ENGINE.runKernelFunc(function (backend, save) {
            var res = backend.rsqrt($x);
            save([$x]);
            return res;
        }, { x: $x }, grad, 'Rsqrt', {} /* attrs */, inputsToSave);
    }
    /**
     * 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 grad = function (dy, saved) {
            var $x = saved[0];
            return { $x: function () { return dy.div($x.square().neg()); } };
        };
        return ENGINE.runKernelFunc(function (backend, save) {
            var res = backend.reciprocal($x);
            save([$x]);
            return res;
        }, { $x: $x }, grad);
    }
    /**
     * 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') {
            return ENGINE.runKernelFunc(function (backend) { return backend.complexAbs($x); }, { $x: $x });
        }
        var grad = function (dy, saved) {
            var $x = saved[0];
            return { x: function () { return dy.mul($x.toFloat().step(-1)); } };
        };
        return ENGINE.runKernelFunc(function (backend, save) {
            var res = backend.abs($x);
            save([$x]);
            return res;
        }, { x: $x }, grad, 'Abs');
    }
    /**
     * 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 grad = function (dy, saved) {
            var $x = saved[0];
            return {
                x: function () { return dy.where($x.greaterEqual(clipValueMin)
                    .logicalAnd($x.lessEqual(clipValueMax)), zerosLike(dy)); },
            };
        };
        var inputsToSave = [$x];
        var attr = { min: clipValueMin, max: clipValueMax };
        return ENGINE.runKernelFunc(function (backend, save) {
            var res = backend.clip($x, clipValueMin, clipValueMax);
            save([$x]);
            return res;
        }, { x: $x }, grad, 'ClipByValue', attr, inputsToSave);
    }
    /**
     * Computes sigmoid element-wise, `1 / (1 + exp(-x))`
     *
     * ```js
     * const x = tf.tensor1d([0, -1, 2, -3]);
     *
     * x.sigmoid().print();  // or tf.sigmoid(x)
     * ```
     * @param x The input tensor.
     */
    /** @doc {heading: 'Operations', subheading: 'Basic math'} */
    function sigmoid_(x) {
        var $x = convertToTensor(x, 'x', 'sigmoid');
        var grad = function (dy, saved) {
            var y = saved[0];
            return { x: function () { return dy.mul(y.mul(scalar(1).sub(y))); } };
        };
        return ENGINE.runKernelFunc(function (backend, save) {
            var y = backend.sigmoid($x);
            save([y]);
            return y;
        }, { x: $x }, grad, 'Sigmoid');
    }
    /**
     * 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');
        var grad = function (dy, saved) {
            var $x = saved[0];
            return { $x: function () { return dy.mul($x.neg().sigmoid()); } };
        };
        return ENGINE.runKernelFunc(function (backend, save) {
            var res = backend.softplus($x.neg()).neg();
            save([$x]);
            return res;
        }, { $x: $x }, grad);
    }
    /**
     * 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 grad = function (dy, saved) {
            var $x = saved[0];
            return { $x: function () { return dy.mul($x.sigmoid()); } };
        };
        return ENGINE.runKernelFunc(function (backend, save) {
            var res = backend.softplus($x);
            save([$x]);
            return res;
        }, { $x: $x }, grad);
    }
    /**
     * Computes sin of the input Tensor element-wise: `sin(x)`
     *
     * ```js
     * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);
     *
     * x.sin().print();  // or tf.sin(x)
     * ```
     * @param x The input tensor.
     */
    /** @doc {heading: 'Operations', subheading: 'Basic math'} */
    function sin_(x) {
        var $x = convertToTensor(x, 'x', 'sin');
        var grad = function (dy, saved) {
            var $x = saved[0];
            return { x: function () { return $x.toFloat().cos().mul(dy); } };
        };
        var inputsToSave = [$x];
        return ENGINE.runKernelFunc(function (backend, save) {
            var res = backend.sin($x);
            save([$x]);
            return res;
        }, { x: $x }, grad, 'Sin', {} /* attrs */, inputsToSave);
    }
    /**
     * Computes cos of the input `tf.Tensor` element-wise: `cos(x)`
     *
     * ```js
     * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);
     *
     * x.cos().print();  // or tf.cos(x)
     * ```
     * @param x The input tensor.
     */
    /** @doc {heading: 'Operations', subheading: 'Basic math'} */
    function cos_(x) {
        var $x = convertToTensor(x, 'x', 'cos');
        var grad = function (dy, saved) {
            var $x = saved[0];
            return { x: function () { return $x.toFloat().sin().neg().mul(dy); } };
        };
        var inputsToSave = [$x];
        return ENGINE.runKernelFunc(function (backend, save) {
            var res = backend.cos($x);
            save([$x]);
            return res;
        }, { x: $x }, grad, 'Cos', {} /* attrs */, inputsToSave);
    }
    /**
     * Computes tan of the input `tf.Tensor` element-wise, `tan(x)`
     *
     * ```js
     * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);
     *
     * x.tan().print();  // or tf.tan(x)
     * ```
     * @param x The input tensor.
     */
    /** @doc {heading: 'Operations', subheading: 'Basic math'} */
    function tan_(x) {
        var $x = convertToTensor(x, 'x', 'tan');
        var grad = function (dy, saved) {
            var $x = saved[0];
            return { $x: function () { return dy.div($x.cos().square()); } };
        };
        return ENGINE.runKernelFunc(function (backend, save) {
            var res = backend.tan($x);
            save([$x]);
            return res;
        }, { $x: $x }, grad);
    }
    /**
     * 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 grad = function (dy, saved) {
            var $x = saved[0];
            return {
                $x: function () { return dy.divStrict(scalar(1).sub($x.toFloat().square()).sqrt()); }
            };
        };
        return ENGINE.runKernelFunc(function (backend, save) {
            var res = backend.asin($x);
            save([$x]);
            return res;
        }, { $x: $x }, grad);
    }
    /**
     * 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 grad = function (dy, saved) {
            var $x = saved[0];
            return {
                $x: function () {
                    return dy.divStrict(scalar(1).sub($x.toFloat().square()).sqrt()).neg();
                }
            };
        };
        return ENGINE.runKernelFunc(function (backend, save) {
            var res = backend.acos($x);
            save([$x]);
            return res;
        }, { $x: $x }, grad);
    }
    /**
     * 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 grad = function (dy, saved) {
            var $x = saved[0];
            return { $x: function () { return dy.div($x.toFloat().square().add(1)); } };
        };
        return ENGINE.runKernelFunc(function (backend, save) {
            var res = backend.atan($x);
            save([$x]);
            return res;
        }, { $x: $x }, grad);
    }
    /**
     * 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 grad = function (dy, saved) {
            var $x = saved[0];
            return { $x: function () { return $x.toFloat().cosh().mulStrict(dy); } };
        };
        return ENGINE.runKernelFunc(function (backend, save) {
            var res = backend.sinh($x);
            save([$x]);
            return res;
        }, { $x: $x }, grad);
    }
    /**
     * Computes hyperbolic cos of the input `tf.Tensor` element-wise: `cosh(x)`
     *
     * ```js
     * const x = tf.tensor1d([0, 1, -1, .7]);
     *
     * x.cosh().print();  // or tf.cosh(x)
     * ```
     * @param x The input tensor.
     */
    /** @doc {heading: 'Operations', subheading: 'Basic math'} */
    function cosh_(x) {
        var $x = convertToTensor(x, 'x', 'cosh');
        var grad = function (dy, saved) {
            var $x = saved[0];
            return { $x: function () { return $x.toFloat().sinh().mulStrict(dy); } };
        };
        return ENGINE.runKernelFunc(function (backend, save) {
            var res = backend.cosh($x);
            save([$x]);
            return res;
        }, { $x: $x }, grad);
    }
    /**
     * Computes hyperbolic tangent of the input `tf.Tensor` element-wise: `tanh(x)`
     *
     * ```js
     * const x = tf.tensor1d([0, 1, -1, 70]);
     *
     * x.tanh().print();  // or tf.tanh(x)
     * ```
     * @param x The input tensor.
     */
    /** @doc {heading: 'Operations', subheading: 'Basic math'} */
    function tanh_(x) {
        var $x = convertToTensor(x, 'x', 'tanh');
        var grad = function (dy, saved) {
            var y = saved[0];
            return { x: function () { return scalar(1).sub(y.square()).mulStrict(dy); } };
        };
        var outputsToSave = [true];
        return ENGINE.runKernelFunc(function (backend, save) {
            var y = backend.tanh($x);
            save([y]);
            return y;
        }, { x: $x }, grad, 'Tanh', {} /* attrs */, null /* inputsToSave */, outputsToSave);
    }
    /**
     * 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 grad = function (dy, saved) {
            var $x = saved[0];
            return {
                $x: function () { return dy.divStrict(scalar(1).add($x.toFloat().square()).sqrt()); }
            };
        };
        return ENGINE.runKernelFunc(function (backend, save) {
            var res = backend.asinh($x);
            save([$x]);
            return res;
        }, { $x: $x }, grad);
    }
    /**
     * 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 grad = function (dy, saved) {
            var $x = saved[0];
            return { $x: function () { return dy.divStrict($x.toFloat().square().sub(1).sqrt()); } };
        };
        return ENGINE.runKernelFunc(function (backend, save) {
            var res = backend.acosh($x);
            save([$x]);
            return res;
        }, { $x: $x }, grad);
    }
    /**
     * 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 grad = function (dy, saved) {
            var $x = saved[0];
            return { $x: function () { return dy.div(scalar(1).sub($x.toFloat().square())); } };
        };
        return ENGINE.runKernelFunc(function (backend, save) {
            var res = backend.atanh($x);
            save([$x]);
            return res;
        }, { $x: $x }, grad);
    }
    /**
     * 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 = $x.toFloat();
        }
        var grad = function (dy, saved) {
            var $x = saved[0];
            return {
                $x: function () { return dy.mul($x.square().neg().exp().mul(2 / Math.sqrt(Math.PI))); }
            };
        };
        return ENGINE.runKernelFunc(function (backend, save) {
            var res = backend.erf($x);
            save([$x]);
            return res;
        }, { $x: $x }, grad);
    }
    /**
     * 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');
        // TODO(manrajgrover): Return null for gradients when backprop supports
        // it.
        var grad = function (dy) {
            return { $x: function () { return zerosLike(dy); } };
        };
        return ENGINE.runKernelFunc(function (backend) { return backend.step($x, alpha); }, { $x: $x }, grad);
    }
    var abs = op({ abs_: abs_ });
    var acos = op({ acos_: acos_ });
    var acosh = op({ acosh_: acosh_ });
    var asin = op({ asin_: asin_ });
    var asinh = op({ asinh_: asinh_ });
    var atan = op({ atan_: atan_ });
    var atanh = op({ atanh_: atanh_ });
    var ceil = op({ ceil_: ceil_ });
    var clipByValue = op({ clipByValue_: clipByValue_ });
    var cos = op({ cos_: cos_ });
    var cosh = op({ cosh_: cosh_ });
    var erf = op({ erf_: erf_ });
    var exp = op({ exp_: exp_ });
    var expm1 = op({ expm1_: expm1_ });
    var floor = op({ floor_: floor_ });
    var log = op({ log_: log_ });
    var log1p = op({ log1p_: log1p_ });
    var logSigmoid = op({ logSigmoid_: logSigmoid_ });
    var neg = op({ neg_: neg_ });
    var reciprocal = op({ reciprocal_: reciprocal_ });
    var round = op({ round_: round_ });
    var rsqrt = op({ rsqrt_: rsqrt_ });
    var sigmoid = op({ sigmoid_: sigmoid_ });
    var sign = op({ sign_: sign_ });
    var isNaN$1 = op({ isNaN_: isNaN_ });
    var isInf = op({ isInf_: isInf_ });
    var isFinite$1 = op({ isFinite_: isFinite_ });
    var sin = op({ sin_: sin_ });
    var sinh = op({ sinh_: sinh_ });
    var softplus = op({ softplus_: softplus_ });
    var sqrt = op({ sqrt_: sqrt_ });
    var step = op({ step_: step_ });
    var tan = op({ tan_: tan_ });
    var tanh$1 = op({ tanh_: tanh_ });

    /**
     * @license
     * Copyright 2018 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.
     * =============================================================================
     */
    /**
     * 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 batchNorm3D: x must be rank 3 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);
    }
    /**
     * 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);
    }
    /**
     * 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);
    }
    /**
     * @deprecated Please use `tf.batchNorm` instead and note the positional
     *     argument change of scale, offset, and varianceEpsilon.
     */
    function batchNormalization_(x, mean, variance, varianceEpsilon, scale, offset) {
        if (varianceEpsilon === void 0) { varianceEpsilon = .001; }
        warnDeprecation();
        return batchNorm_(x, mean, variance, offset, scale, varianceEpsilon);
    }
    /**
     * 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 === $scale.rank, function () { return 'Batch normalization gradient requires mean and scale to have ' +
            'equal ranks.'; });
        var x4D;
        if ($x.rank === 0 || $x.rank === 1) {
            x4D = $x.as4D(1, 1, 1, $x.size);
        }
        else if ($x.rank === 2) {
            x4D = $x.as4D(1, 1, $x.shape[0], $x.shape[1]);
        }
        else if ($x.rank === 3) {
            x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);
        }
        else {
            x4D = $x;
        }
        var der = function (dy, saved) {
            var _a = saved, $x = _a[0], $mean = _a[1], $variance = _a[2], $scale = _a[3];
            var scaleValue = $scale == null ? scalar(1) : $scale;
            var reductionAxes = getReductionAxes($mean.shape, x4D.shape);
            var tileShape = [];
            if ($mean.rank === 1) {
                for (var i = 0; i < x4D.shape.length - 1; ++i) {
                    tileShape.push(x4D.shape[i]);
                }
                tileShape.push(1);
            }
            var xMinusMean = $x.sub($mean);
            var dyTimesScaleValue = dy.mul(scaleValue);
            var oneOverSqrtVariance = rsqrt($variance.add(scalar(varianceEpsilon)));
            var minusHalfRCube = oneOverSqrtVariance.mul(oneOverSqrtVariance)
                .mul(oneOverSqrtVariance)
                .mul(scalar(-0.5));
            var derX = function () {
                if ($mean.rank === 1) {
                    return dy
                        .mul(tile(oneOverSqrtVariance.as4D(1, 1, 1, $mean.shape[0]), tileShape))
                        .mul(scaleValue)
                        .reshape($x.shape);
                }
                else {
                    return dy.mul(oneOverSqrtVariance).mul(scaleValue).reshape($x.shape);
                }
            };
            var derMean = function () {
                var meanDer = oneOverSqrtVariance.mul(scalar(-1)).mul(dyTimesScaleValue);
                if ($mean.rank === 1) {
                    meanDer = meanDer.sum(reductionAxes);
                }
                return meanDer.reshape($mean.shape);
            };
            var derVariance = function () {
                var varianceDer = minusHalfRCube.mul(xMinusMean).mul(dyTimesScaleValue);
                if ($mean.rank === 1) {
                    varianceDer = varianceDer.sum(reductionAxes);
                }
                return varianceDer.reshape($mean.shape);
            };
            var derScale = function () {
                var xMinusMean2TimesRsqrt = xMinusMean.mul(oneOverSqrtVariance);
                var scaleDer = dy.mul(xMinusMean2TimesRsqrt);
                if ($mean.rank === 1) {
                    scaleDer = scaleDer.sum(reductionAxes);
                }
                return scaleDer.reshape($mean.shape);
            };
            var derOffset = function () {
                var offsetDer = dy;
                if ($mean.rank === 1) {
                    offsetDer = offsetDer.sum(reductionAxes);
                }
                return offsetDer.reshape($mean.shape);
            };
            return {
                x: derX,
                mean: derMean,
                variance: derVariance,
                scale: derScale,
                offset: derOffset
            };
        };
        var inputsToSave = [$x, $mean, $variance, $scale];
        var res = ENGINE.runKernelFunc(function (backend, save) {
            var res = backend.batchNormalization(x4D, batchnormReshape4D($mean), batchnormReshape4D($variance), varianceEpsilon, batchnormReshape4D($scale), batchnormReshape4D($offset));
            save([$x, $mean, $variance, $scale]);
            return res;
        }, { x: $x, mean: $mean, variance: $variance, scale: $scale, offset: $offset }, der, 'BatchNormalization', { varianceEpsilon: varianceEpsilon }, inputsToSave);
        return res.reshape($x.shape);
    }
    function batchnormReshape4D(x) {
        if (x == null) {
            return null;
        }
        if (x.rank === 0) {
            return x.as1D();
        }
        else if (x.rank === 1) {
            return x;
        }
        else if (x.rank === 2) {
            return x.as4D(1, 1, x.shape[0], x.shape[1]);
        }
        else if (x.rank === 3) {
            return x.as4D(1, x.shape[0], x.shape[1], x.shape[2]);
        }
        return x;
    }
    /**
     * @deprecated Please use `tf.batchNorm2d` instead and note the positional
     *     argument change of scale, offset, and varianceEpsilon.
     */
    function batchNormalization2d_(x, mean, variance, varianceEpsilon, scale, offset) {
        if (varianceEpsilon === void 0) { varianceEpsilon = .001; }
        warnDeprecation();
        return batchNorm2d_(x, mean, variance, offset, scale, varianceEpsilon);
    }
    /**
     * @deprecated Please use `tf.batchNorm3d` instead and note the positional
     *     argument change of scale, offset, and varianceEpsilon.
     */
    function batchNormalization3d_(x, mean, variance, varianceEpsilon, scale, offset) {
        if (varianceEpsilon === void 0) { varianceEpsilon = .001; }
        warnDeprecation();
        return batchNorm3d_(x, mean, variance, offset, scale, varianceEpsilon);
    }
    /**
     * @deprecated Please use `tf.batchNorm4d` instead and note the positional
     *     argument change of scale, offset, and varianceEpsilon.
     */
    function batchNormalization4d_(x, mean, variance, varianceEpsilon, scale, offset) {
        if (varianceEpsilon === void 0) { varianceEpsilon = .001; }
        warnDeprecation();
        return batchNorm4d_(x, mean, variance, offset, scale, varianceEpsilon);
    }
    function warnDeprecation() {
        deprecationWarn('tf.batchNormalization() is going away. ' +
            'Use tf.batchNorm() instead, and note the positional argument change ' +
            'of scale, offset, and varianceEpsilon');
    }
    var batchNormalization2d = op({ batchNormalization2d_: batchNormalization2d_ });
    var batchNormalization3d = op({ batchNormalization3d_: batchNormalization3d_ });
    var batchNormalization4d = op({ batchNormalization4d_: batchNormalization4d_ });
    var batchNormalization = op({ batchNormalization_: batchNormalization_ });
    var batchNorm = op({ batchNorm_: batchNorm_ });
    var batchNorm2d = op({ batchNorm2d_: batchNorm2d_ });
    var batchNorm3d = op({ batchNorm3d_: batchNorm3d_ });
    var batchNorm4d = op({ batchNorm4d_: batchNorm4d_ });

    /**
     * @license
     * Copyright 2018 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 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 $x = convertToTensor(x, 'x', 'logicalNot', 'bool');
        return ENGINE.runKernelFunc(function (backend) { return backend.logicalNot($x); }, { $x: $x });
    }
    /**
     * 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, $b.shape);
        return ENGINE.runKernelFunc(function (backend) { return backend.logicalAnd($a, $b); }, { a: $a, b: $b }, null /* grad */, 'LogicalAnd');
    }
    /**
     * 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, $b.shape);
        return ENGINE.runKernelFunc(function (backend) { return backend.logicalOr($a, $b); }, { $a: $a, $b: $b });
    }
    /**
     * 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 logicalOr(a, b).logicalAnd(logicalAnd(a, b).logicalNot());
    }
    /**
     * 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 shape and type as `a`.
     */
    /** @doc {heading: 'Operations', subheading: 'Logical'} */
    function where_(condition, a, b) {
        var $a = convertToTensor(a, 'a', 'where');
        var $b = convertToTensor(b, 'b', 'where');
        var $condition = convertToTensor(condition, 'condition', 'where', 'bool');
        assertShapesMatch($a.shape, $b.shape, 'Error in where: ');
        if ($condition.rank === 1) {
            // If condition rank is 1, then the first dimension must match the size of
            // condition.
            assert($condition.shape[0] === $a.shape[0], function () { return 'The first dimension of `a` must match the size of `condition`.'; });
        }
        else {
            // A must have the same shape as condition.
            assertShapesMatch($condition.shape, $b.shape, 'Error in where: ');
        }
        // TODO(julianoks): Return null for condition gradient
        // when backprop supports it.
        var grad = function (dy, saved) {
            var $condition = saved[0];
            return {
                $condition: function () { return zerosLike($condition).toFloat(); },
                $a: function () { return dy.mul($condition.cast(dy.dtype)); },
                $b: function () { return dy.mul($condition.logicalNot().cast(dy.dtype)); }
            };
        };
        return ENGINE.runKernelFunc(function (backend, save) {
            var res = backend.select($condition, $a, $b);
            save([$condition]);
            return res;
        }, { $condition: $condition, $a: $a, $b: $b }, grad);
    }
    /**
     * 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 logicalAnd = op({ logicalAnd_: logicalAnd_ });
    var logicalNot = op({ logicalNot_: logicalNot_ });
    var logicalOr = op({ logicalOr_: logicalOr_ });
    var logicalXor = op({ logicalXor_: logicalXor_ });
    var where = op({ where_: where_ });
    var whereAsync = whereAsync_;

    /**
     * @license
     * Copyright 2018 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.
     * =============================================================================
     */
    /**
     * Adds two `tf.Tensor`s element-wise, A + B. Supports broadcasting.
     *
     * We also expose `tf.addStrict` 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([10, 20, 30, 40]);
     *
     * a.add(b).print();  // or tf.add(a, b)
     * ```
     *
     * ```js
     * // Broadcast add a with b.
     * const a = tf.scalar(5);
     * const b = tf.tensor1d([10, 20, 30, 40]);
     *
     * a.add(b).print();  // or tf.add(a, b)
     * ```
     * @param a The first `tf.Tensor` to add.
     * @param b The second `tf.Tensor` to add. Must have the same type as `a`.
     */
    /** @doc {heading: 'Operations', subheading: 'Arithmetic'} */
    function add_(a, b) {
        var _a;
        var $a = convertToTensor(a, 'a', 'add');
        var $b = convertToTensor(b, 'b', 'add');
        _a = makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
        var outShape = assertAndGetBroadcastShape($a.shape, $b.shape);
        var der = function (dy) {
            var derA = function () {
                var res = dy;
                var reduceAxes = getReductionAxes($a.shape, outShape);
                if (reduceAxes.length > 0) {
                    res = res.sum(reduceAxes);
                }
                return res.reshape($a.shape);
            };
            var derB = function () {
                var res = dy;
                var reduceAxes = getReductionAxes($b.shape, outShape);
                if (reduceAxes.length > 0) {
                    res = res.sum(reduceAxes);
                }
                return res.reshape($b.shape);
            };
            return { a: derA, b: derB };
        };
        return ENGINE.runKernelFunc(function (backend) { return backend.add($a, $b); }, { a: $a, b: $b }, der, 'Add');
    }
    /**
     * 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 der = function (dy) {
            var ders = {};
            $tensors.forEach(function (t, i) {
                ders[i] = function () { return dy.clone(); };
            });
            return ders;
        };
        var inputs = $tensors;
        return ENGINE.runKernelFunc(function (backend) { return backend.addN($tensors); }, inputs, der, 'AddN');
    }
    /**
     * Adds two `tf.Tensor`s element-wise, A + B.
     *
     * Inputs must be the same shape. For broadcasting support, use add() instead.
     *
     * @param a The first Tensor to add element-wise.
     * @param b The second Tensor to add element-wise.
     */
    function addStrict_(a, b) {
        var $a = convertToTensor(a, 'a', 'addStrict');
        var $b = convertToTensor(b, 'b', 'addStrict');
        assertShapesMatch($a.shape, $b.shape, 'Error in addStrict: ');
        return $a.add($b);
    }
    /**
     * Subtracts two `tf.Tensor`s element-wise, A - B. Supports broadcasting.
     *
     * We also expose `tf.subStrict` 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([10, 20, 30, 40]);
     * const b = tf.tensor1d([1, 2, 3, 4]);
     *
     * a.sub(b).print();  // or tf.sub(a, b)
     * ```
     *
     * ```js
     * // Broadcast subtract a with b.
     * const a = tf.tensor1d([10, 20, 30, 40]);
     * const b = tf.scalar(5);
     *
     * a.sub(b).print();  // or tf.sub(a, b)
     * ```
     * @param a The first `tf.Tensor` to subtract from.
     * @param b The second `tf.Tensor` to be subtracted. Must have the same dtype as
     * `a`.
     */
    /** @doc {heading: 'Operations', subheading: 'Arithmetic'} */
    function sub_(a, b) {
        var _a;
        var $a = convertToTensor(a, 'a', 'sub');
        var $b = convertToTensor(b, 'b', 'sub');
        _a = makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
        var outShape = assertAndGetBroadcastShape($a.shape, $b.shape);
        var der = function (dy) {
            var derA = function () {
                var res = dy;
                var reduceAxes = getReductionAxes($a.shape, outShape);
                if (reduceAxes.length > 0) {
                    res = res.sum(reduceAxes);
                }
                return res.reshape($a.shape);
            };
            var derB = function () {
                var res = dy;
                var reduceAxes = getReductionAxes($b.shape, outShape);
                if (reduceAxes.length > 0) {
                    res = res.sum(reduceAxes);
                }
                return res.neg().reshape($b.shape);
            };
            return { a: derA, b: derB };
        };
        return ENGINE.runKernelFunc(function (backend) { return backend.subtract($a, $b); }, { a: $a, b: $b }, der, 'Sub');
    }
    /**
     * Subtracts two `tf.Tensor`s element-wise, A - B. Inputs must
     * be the same shape.
     *
     * For broadcasting support, use `tf.sub` instead.
     *
     * @param a The first Tensor to subtract element-wise.
     * @param b The second Tensor to subtract element-wise.
     */
    function subStrict_(a, b) {
        var $a = convertToTensor(a, 'a', 'subStrict');
        var $b = convertToTensor(b, 'b', 'subStrict');
        assertShapesMatch($a.shape, $b.shape, 'Error in subStrict: ');
        return $a.sub($b);
    }
    /**
     * Computes the power of one `tf.Tensor` to another. Supports broadcasting.
     *
     * Given a `tf.Tensor` x and a `tf.Tensor` y, this operation computes x^y for
     * corresponding elements in x and y. The result's dtype will be the upcasted
     * type of the `base` and `exp` dtypes.
     *
     * ```js
     * const a = tf.tensor([[2, 3], [4, 5]])
     * const b = tf.tensor([[1, 2], [3, 0]]).toInt();
     *
     * a.pow(b).print();  // or tf.pow(a, b)
     * ```
     *
     * ```js
     * const a = tf.tensor([[1, 2], [3, 4]])
     * const b = tf.tensor(2).toInt();
     *
     * a.pow(b).print();  // or tf.pow(a, b)
     * ```
     * We also expose `powStrict` which has the same signature as this op and
     * asserts that `base` and `exp` are the same shape (does not broadcast).
     *
     * @param base The base `tf.Tensor` to pow element-wise.
     * @param exp The exponent `tf.Tensor` to pow element-wise.
     */
    /** @doc {heading: 'Operations', subheading: 'Arithmetic'} */
    function pow_(base, exp) {
        var _a;
        var $base = convertToTensor(base, 'base', 'pow');
        var $exp = convertToTensor(exp, 'exp', 'pow');
        _a = makeTypesMatch($base, $exp), $base = _a[0], $exp = _a[1];
        var outShape = assertAndGetBroadcastShape($base.shape, $exp.shape);
        var grad = function (dy, saved) {
            var $base = saved[0], $exp = saved[1], y = saved[2];
            var derBase = function () {
                var expFloat = $exp.toFloat();
                var res = dy.mul(expFloat.mul($base.pow(expFloat.sub(scalar(1)))));
                var reduceAxes = getReductionAxes($base.shape, outShape);
                if (reduceAxes.length > 0) {
                    res = res.sum(reduceAxes);
                }
                return res.reshape($base.shape);
            };
            var derExp = function () {
                var condition = $base.greater(0);
                var logBase = $base.log().where(condition, zerosLike($base));
                var res = dy.mul(y.mul(logBase));
                var reduceAxes = getReductionAxes($exp.shape, outShape);
                if (reduceAxes.length > 0) {
                    res = res.sum(reduceAxes);
                }
                return res.reshape($exp.shape);
            };
            return { a: derBase, b: derExp };
        };
        var attrs = {};
        var inputsToSave = [$base, $exp];
        var outputsToSave = [true];
        return ENGINE.runKernelFunc(function (backend, save) {
            var y = backend.pow($base, $exp);
            save([$base, $exp, y]);
            return y;
        }, { a: $base, b: $exp }, grad, 'Pow', attrs, inputsToSave, outputsToSave);
    }
    /**
     * Computes the power of one `tf.Tensor` to another. Inputs must
     * be the same shape.
     *
     * For broadcasting support, use `tf.pow` instead.
     *
     * @param base The base tensor to pow element-wise.
     * @param exp The exponent tensor to pow element-wise.
     */
    function powStrict_(base, exp) {
        assertShapesMatch(base.shape, exp.shape, 'Error in powStrict: ');
        return base.pow(exp);
    }
    /**
     * Multiplies two `tf.Tensor`s element-wise, A * B. Supports broadcasting.
     *
     * We also expose `tf.mulStrict` which has the same signature as this op and
     * asserts that `a` and `b` are the same shape (does not broadcast).
     *
     * ```js
     * const a = tf.tensor1d([1, 2, 3, 4]);
     * const b = tf.tensor1d([2, 3, 4, 5]);
     *
     * a.mul(b).print();  // or tf.mul(a, b)
     * ```
     *
     * ```js
     * // Broadcast mul a with b.
     * const a = tf.tensor1d([1, 2, 3, 4]);
     * const b = tf.scalar(5);
     *
     * a.mul(b).print();  // or tf.mul(a, b)
     * ```
     * @param a The first tensor to multiply.
     * @param b The second tensor to multiply. Must have the same dtype as `a`.
     */
    /** @doc {heading: 'Operations', subheading: 'Arithmetic'} */
    function mul_(a, b) {
        var _a;
        var $a = convertToTensor(a, 'a', 'mul');
        var $b = convertToTensor(b, 'b', 'mul');
        _a = makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
        var outShape = assertAndGetBroadcastShape($a.shape, $b.shape);
        var der = function (dy, saved) {
            var $a = saved[0], $b = saved[1];
            var derA = function () {
                var res = dy.mul($b.toFloat());
                var reduceAxes = getReductionAxes($a.shape, outShape);
                if (reduceAxes.length > 0) {
                    return res.sum(reduceAxes).reshape($a.shape);
                }
                return res;
            };
            var derB = function () {
                var res = dy.mul($a.toFloat());
                var reduceAxes = getReductionAxes($b.shape, outShape);
                if (reduceAxes.length > 0) {
                    return res.sum(reduceAxes).reshape($b.shape);
                }
                return res;
            };
            return { a: derA, b: derB };
        };
        return ENGINE.runKernelFunc(function (backend, save) {
            var res = backend.multiply($a, $b);
            save([$a, $b]);
            return res;
        }, { a: $a, b: $b }, der, 'Mul');
    }
    /**
     * Multiplies two `tf.Tensor`s element-wise, A * B.
     *
     * Inputs must be the same shape. For broadcasting support, use `tf.mul`.
     *
     * @param a The first tensor to multiply.
     * @param b The first tensor to multiply. Must have the same
     *    dtype as `a`.
     */
    function mulStrict_(a, b) {
        var $a = convertToTensor(a, 'a', 'mul');
        var $b = convertToTensor(b, 'b', 'mul');
        assertShapesMatch($a.shape, $b.shape, 'Error in multiplyStrict: ');
        return $a.mul($b);
    }
    /**
     * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting.
     *
     * We also expose `tf.divStrict` 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, 9, 16]);
     * const b = tf.tensor1d([1, 2, 3, 4]);
     *
     * a.div(b).print();  // or tf.div(a, b)
     * ```
     *
     * ```js
     * // Broadcast div a with b.
     * const a = tf.tensor1d([2, 4, 6, 8]);
     * const b = tf.scalar(2);
     *
     * a.div(b).print();  // or tf.div(a, b)
     * ```
     *
     * @param a The first tensor as the numerator.
     * @param b The second tensor as the denominator. Must have the same dtype as
     * `a`.
     */
    /** @doc {heading: 'Operations', subheading: 'Arithmetic'} */
    function div_(a, b) {
        var _a;
        var $a = convertToTensor(a, 'a', 'div');
        var $b = convertToTensor(b, 'b', 'div');
        _a = makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
        if ($a.dtype === 'int32' && $b.dtype === 'int32') {
            return floorDiv($a, $b);
        }
        var outShape = assertAndGetBroadcastShape($a.shape, $b.shape);
        var der = function (dy, saved) {
            var $a = saved[0], $b = saved[1];
            var derA = function () {
                var res = dy.div($b.toFloat());
                var reduceAxes = getReductionAxes($a.shape, outShape);
                if (reduceAxes.length > 0) {
                    return res.sum(reduceAxes).reshape($a.shape);
                }
                return res;
            };
            var derB = function () {
                var res = dy.mul($a.toFloat());
                var reduceAxes = getReductionAxes($b.shape, outShape);
                if (reduceAxes.length > 0) {
                    res = res.sum(reduceAxes).reshape($b.shape);
                }
                var tmp = $b.square();
                return res.div(tmp.toFloat()).neg();
            };
            return { a: derA, b: derB };
        };
        return ENGINE.runKernelFunc(function (backend, save) {
            var res = backend.realDivide($a, $b);
            save([$a, $b]);
            return res;
        }, { a: $a, b: $b }, der, 'Div');
    }
    /**
     * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting. Return 0
     * if denominator is 0.
     *
     * We also expose `tf.divStrict` 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, 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;
        var $a = convertToTensor(a, 'a', 'div');
        var $b = convertToTensor(b, 'b', 'div');
        _a = makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
        var divResult = div($a, $b);
        var zeros = zerosLike(divResult);
        var bEqualsZero = $b.equal(zeros);
        return where(bEqualsZero, zeros, divResult);
    }
    /**
     * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting.
     * The result is rounded with floor function.
     *
     *
     * ```js
     * const a = tf.tensor1d([1, 4, 9, 16]);
     * const b = tf.tensor1d([1, 2, 3, 4]);
     *
     * a.floorDiv(b).print();  // or tf.div(a, b)
     * ```
     *
     * ```js
     * // Broadcast div a with b.
     * const a = tf.tensor1d([2, 4, 6, 8]);
     * const b = tf.scalar(2);
     *
     * a.floorDiv(b).print();  // or tf.floorDiv(a, b)
     * ```
     *
     * @param a The first tensor as the numerator.
     * @param b The second tensor as the denominator. Must have the same dtype as
     * `a`.
     */
    /** @doc {heading: 'Operations', subheading: 'Arithmetic'} */
    function floorDiv_(a, b) {
        var _a;
        var $a = convertToTensor(a, 'a', 'floorDiv');
        var $b = convertToTensor(b, 'b', 'floorDiv');
        _a = makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
        var outShape = assertAndGetBroadcastShape($a.shape, $b.shape);
        var der = function (dy, saved) {
            var $a = saved[0], $b = saved[1];
            var derA = function () {
                var res = dy.div($b.toFloat());
                var reduceAxes = getReductionAxes($a.shape, outShape);
                if (reduceAxes.length > 0) {
                    return res.sum(reduceAxes).reshape($a.shape);
                }
                return res;
            };
            var derB = function () {
                var res = dy.mul($a.toFloat());
                var reduceAxes = getReductionAxes($b.shape, outShape);
                if (reduceAxes.length > 0) {
                    res = res.sum(reduceAxes).reshape($b.shape);
                }
                var tmp = $b.square();
                return res.div(tmp.toFloat()).neg();
            };
            return { a: derA, b: derB };
        };
        return ENGINE.runKernelFunc(function (backend, save) {
            var res = backend.floorDiv($a, $b);
            save([$a, $b]);
            return res;
        }, { a: $a, b: $b }, der, 'FloorDiv');
    }
    /**
     * Divides two `tf.Tensor`s element-wise, A / B. Inputs must
     * be the same shape.
     *
     * @param a The first tensor as the numerator for element-wise division.
     * @param b The second tensor as the denominator for element-wise division.
     */
    function divStrict_(a, b) {
        var $a = convertToTensor(a, 'a', 'div');
        var $b = convertToTensor(b, 'b', 'div');
        assertShapesMatch($a.shape, $b.shape, 'Error in divideStrict: ');
        return $a.div($b);
    }
    /**
     * Returns the mod of a and b element-wise.
     * `floor(x / y) * y + mod(x, y) = x`
     * Supports broadcasting.
     *
     * We also expose `tf.modStrict` which has the same signature as this op and
     * asserts that `a` and `b` are the same shape (does not broadcast).
     *
     * ```js
     * const a = tf.tensor1d([1, 4, 3, 16]);
     * const b = tf.tensor1d([1, 2, 9, 4]);
     *
     * a.mod(b).print();  // or tf.mod(a, b)
     * ```
     *
     * ```js
     * // Broadcast a mod b.
     * const a = tf.tensor1d([2, 4, 6, 8]);
     * const b = tf.scalar(5);
     *
     * a.mod(b).print();  // or tf.mod(a, b)
     * ```
     *
     * @param a The first tensor.
     * @param b The second tensor. Must have the same type as `a`.
     */
    /** @doc {heading: 'Operations', subheading: 'Arithmetic'} */
    function mod_(a, b) {
        var _a;
        var $a = convertToTensor(a, 'a', 'mod');
        var $b = convertToTensor(b, 'b', 'mod');
        _a = makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
        var outShape = assertAndGetBroadcastShape($a.shape, $b.shape);
        var der = function (dy, saved) {
            var $a = saved[0], $b = saved[1];
            var derA = function () {
                var reduceAxes = getReductionAxes($a.shape, outShape);
                if (reduceAxes.length > 0) {
                    return dy.sum(reduceAxes).reshape($a.shape);
                }
                return dy;
            };
            var derB = function () {
                var res = dy.mul($a.div($b).floor().neg());
                var reduceAxes = getReductionAxes($b.shape, outShape);
                if (reduceAxes.length > 0) {
                    return res.sum(reduceAxes).reshape($b.shape);
                }
                return res;
            };
            return { $a: derA, $b: derB };
        };
        return ENGINE.runKernelFunc(function (backend, save) {
            var res = backend.mod($a, $b);
            save([$a, $b]);
            return res;
        }, { $a: $a, $b: $b }, der);
    }
    /**
     * Returns the mod of a and b (`a < b ? a : b`) element-wise. Inputs must
     * be the same shape. For broadcasting support, use mod().
     *
     * @param a The first tensor.
     * @param b The second tensor. Must have the same dtype as `a`.
     */
    function modStrict_(a, b) {
        var $a = convertToTensor(a, 'a', 'modStrict');
        var $b = convertToTensor(b, 'b', 'modStrict');
        assertShapesMatch($a.shape, $b.shape, 'Error in modStrict: ');
        return $a.mod($b);
    }
    /**
     * Returns the min of a and b (`a < b ? a : b`) element-wise.
     * Supports broadcasting.
     *
     * We also expose `minimumStrict` which has the same signature as this op and
     * asserts that `a` and `b` are the same shape (does not broadcast).
     *
     * ```js
     * const a = tf.tensor1d([1, 4, 3, 16]);
     * const b = tf.tensor1d([1, 2, 9, 4]);
     *
     * a.minimum(b).print();  // or tf.minimum(a, b)
     * ```
     *
     * ```js
     * // Broadcast minimum a with b.
     * const a = tf.tensor1d([2, 4, 6, 8]);
     * const b = tf.scalar(5);
     *
     * a.minimum(b).print();  // or tf.minimum(a, b)
     * ```
     *
     * @param a The first tensor.
     * @param b The second tensor. Must have the same type as `a`.
     */
    /** @doc {heading: 'Operations', subheading: 'Arithmetic'} */
    function minimum_(a, b) {
        var _a;
        var $a = convertToTensor(a, 'a', 'minimum');
        var $b = convertToTensor(b, 'b', 'minimum');
        _a = makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
        if ($a.dtype === 'bool') {
            $a = $a.toInt();
            $b = $b.toInt();
        }
        assertAndGetBroadcastShape($a.shape, $b.shape);
        var der = function (dy, saved) {
            var $a = saved[0], $b = saved[1];
            var derA = function () { return dy.mul($a.lessEqual($b).toFloat()); };
            var derB = function () { return dy.mul($a.greater($b).toFloat()); };
            return { a: derA, b: derB };
        };
        return ENGINE.runKernelFunc(function (backend, save) {
            var res = backend.minimum($a, $b);
            save([$a, $b]);
            return res;
        }, { a: $a, b: $b }, der, 'Minimum');
    }
    /**
     * Returns the min of a and b (`a < b ? a : b`) element-wise. Inputs must
     * be the same shape. For broadcasting support, use minimum().
     *
     * @param a The first tensor.
     * @param b The second tensor. Must have the same dtype as `a`.
     */
    function minimumStrict_(a, b) {
        var $a = convertToTensor(a, 'a', 'minimumStrict');
        var $b = convertToTensor(b, 'b', 'minimumStrict');
        assertShapesMatch($a.shape, $b.shape, 'Error in minimumStrict: ');
        return $a.minimum($b);
    }
    /**
     * Returns the max of a and b (`a > b ? a : b`) element-wise.
     * Supports broadcasting.
     *
     * We also expose `tf.maximumStrict` which has the same signature as this op and
     * asserts that `a` and `b` are the same shape (does not broadcast).
     *
     * ```js
     * const a = tf.tensor1d([1, 4, 3, 16]);
     * const b = tf.tensor1d([1, 2, 9, 4]);
     *
     * a.maximum(b).print();  // or tf.maximum(a, b)
     * ```
     *
     * ```js
     * // Broadcast maximum a with b.
     * const a = tf.tensor1d([2, 4, 6, 8]);
     * const b = tf.scalar(5);
     *
     * a.maximum(b).print();  // or tf.maximum(a, b)
     * ```
     *
     * @param a The first tensor.
     * @param b The second tensor. Must have the same type as `a`.
     */
    /** @doc {heading: 'Operations', subheading: 'Arithmetic'} */
    function maximum_(a, b) {
        var _a;
        var $a = convertToTensor(a, 'a', 'maximum');
        var $b = convertToTensor(b, 'b', 'maximum');
        _a = makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
        if ($a.dtype === 'bool') {
            $a = $a.toInt();
            $b = $b.toInt();
        }
        assertAndGetBroadcastShape($a.shape, $b.shape);
        var der = function (dy, saved) {
            var $a = saved[0], $b = saved[1];
            var derA = function () { return dy.mul($a.greaterEqual($b).toFloat()); };
            var derB = function () { return dy.mul($a.less($b).toFloat()); };
            return { a: derA, b: derB };
        };
        return ENGINE.runKernelFunc(function (backend, save) {
            var res = backend.maximum($a, $b);
            save([$a, $b]);
            return res;
        }, { a: $a, b: $b }, der, 'Maximum');
    }
    /**
     * Returns the max of a and b (`a > b ? a : b`) element-wise. Inputs must
     * be the same shape. For broadcasting support, use maximum().
     *
     * @param a The first tensor.
     * @param b The second tensor. Must have the same dtype as `a`.
     */
    function maximumStrict_(a, b) {
        var $a = convertToTensor(a, 'a', 'maximumStrict');
        var $b = convertToTensor(b, 'b', 'maximumStrict');
        assertShapesMatch($a.shape, $b.shape, 'Error in maximumStrict: ');
        return $a.maximum($b);
    }
    /**
     * Returns (a - b) * (a - b) element-wise.
     *
     * Inputs must be the same shape. For broadcasting support, use
     * `tf.squaredDifference` instead.
     *
     * @param a The first tensor.
     * @param b The second tensor. Must have the same type as `a`.
     */
    function squaredDifferenceStrict_(a, b) {
        var $a = convertToTensor(a, 'a', 'squaredDifferenceStrict');
        var $b = convertToTensor(b, 'b', 'squaredDifferenceStrict');
        assertShapesMatch($a.shape, $b.shape, 'Error in squaredDifferenceStrict: ');
        return $a.squaredDifference($b);
    }
    /**
     * Computes arctangent of `tf.Tensor`s a / b element-wise: `atan2(a, b)`.
     * Supports broadcasting.
     *
     * ```js
     * const a = tf.tensor1d([1.0, 1.0, -1.0, .7]);
     * const b = tf.tensor1d([2.0, 13.0, 3.5, .21]);
     *
     * tf.atan2(a, b).print()
     * ```
     *
     * @param a The first tensor.
     * @param b The second tensor. Must have the same dtype as `a`.
     *
     */
    /** @doc {heading: 'Operations', subheading: 'Basic math'} */
    function atan2_(a, b) {
        var _a;
        var $a = convertToTensor(a, 'a', 'atan2');
        var $b = convertToTensor(b, 'b', 'atan2');
        _a = makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
        var outShape = assertAndGetBroadcastShape($a.shape, $b.shape);
        var der = function (dy, saved) {
            var $a = saved[0], $b = saved[1];
            var derA = function () {
                var d = add($a.square(), $b.square());
                var res = dy.mul($b.div(d));
                var reduceAxes = getReductionAxes($a.shape, outShape);
                if (reduceAxes.length > 0) {
                    res = res.sum(reduceAxes);
                }
                return res.reshape($a.shape);
            };
            var derB = function () {
                var d = add($a.square(), $b.square());
                var res = neg(dy.mul($a.div(d)));
                var reduceAxes = getReductionAxes($b.shape, outShape);
                if (reduceAxes.length > 0) {
                    res = res.sum(reduceAxes);
                }
                return res.reshape($b.shape);
            };
            return { $a: derA, $b: derB };
        };
        return ENGINE.runKernelFunc(function (backend, save) {
            var res = backend.atan2($a, $b);
            save([$a, $b]);
            return res;
        }, { $a: $a, $b: $b }, der);
    }
    var add = op({ add_: add_ });
    var addN = op({ addN_: addN_ });
    var addStrict = op({ addStrict_: addStrict_ });
    var atan2 = op({ atan2_: atan2_ });
    var div = op({ div_: div_ });
    var divNoNan = op({ divNoNan_: divNoNan_ });
    var divStrict = op({ divStrict_: divStrict_ });
    var floorDiv = op({ floorDiv_: floorDiv_ });
    var maximum = op({ maximum_: maximum_ });
    var maximumStrict = op({ maximumStrict_: maximumStrict_ });
    var minimum = op({ minimum_: minimum_ });
    var minimumStrict = op({ minimumStrict_: minimumStrict_ });
    var mod = op({ mod_: mod_ });
    var modStrict = op({ modStrict_: modStrict_ });
    var mul = op({ mul_: mul_ });
    var mulStrict = op({ mulStrict_: mulStrict_ });
    var pow = op({ pow_: pow_ });
    var powStrict = op({ powStrict_: powStrict_ });
    var squaredDifferenceStrict = op({ squaredDifferenceStrict_: squaredDifferenceStrict_ });
    var sub = op({ sub_: sub_ });
    var subStrict = op({ subStrict_: subStrict_ });

    /**
     * @license
     * Copyright 2018 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 truth value of (a != b) element-wise. Supports broadcasting.
     *
     * We also expose `tf.notEqualStrict` 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]);
     * 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');
        var $b = convertToTensor(b, 'b', 'notEqual');
        _a = makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
        assertAndGetBroadcastShape($a.shape, $b.shape);
        return ENGINE.runKernelFunc(function (backend) { return backend.notEqual($a, $b); }, { a: $a, b: $b }, null /* grad */, 'NotEqual');
    }
    /**
     * Strict version of `tf.notEqual` that forces `a` and `b` to be of the same
     * shape.
     *
     * @param a The first input tensor.
     * @param b The second input tensor. Must have the same shape and dtype as
     *     `a`.
     */
    function notEqualStrict_(a, b) {
        var $a = convertToTensor(a, 'a', 'notEqualStrict');
        var $b = convertToTensor(b, 'b', 'notEqualStrict');
        assertShapesMatch($a.shape, $b.shape, 'Error in notEqualStrict: ');
        return $a.notEqual($b);
    }
    /**
     * Returns the truth value of (a < b) element-wise. Supports broadcasting.
     *
     * We also expose `tf.lessStrict` 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]);
     * 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');
        var $b = convertToTensor(b, 'b', 'less');
        _a = makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
        assertAndGetBroadcastShape($a.shape, $b.shape);
        return ENGINE.runKernelFunc(function (backend) { return backend.less($a, $b); }, { a: $a, b: $b }, null /* grad */, 'Less');
    }
    /**
     * Strict version of `tf.less` that forces `a` and `b` to be of the same
     * shape.
     *
     * @param a The first input tensor.
     * @param b The second input tensor. Must have the same shape and dtype as
     *     `a`.
     */
    function lessStrict_(a, b) {
        var $a = convertToTensor(a, 'a', 'lessStrict');
        var $b = convertToTensor(b, 'b', 'lessStrict');
        assertShapesMatch($a.shape, $b.shape, 'Error in lessStrict: ');
        return $a.less($b);
    }
    /**
     * Returns the truth value of (a == b) element-wise. Supports broadcasting.
     *
     * We also expose `tf.equalStrict` 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]);
     * 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');
        var $b = convertToTensor(b, 'b', 'equal');
        _a = makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
        assertAndGetBroadcastShape($a.shape, $b.shape);
        return ENGINE.runKernelFunc(function (backend) { return backend.equal($a, $b); }, { $a: $a, $b: $b });
    }
    function equalStrict_(a, b) {
        var $a = convertToTensor(a, 'a', 'equalStrict');
        var $b = convertToTensor(b, 'b', 'equalStrict');
        assertShapesMatch($a.shape, $b.shape, 'Error in equalStrict: ');
        return $a.equal($b);
    }
    /**
     * Returns the truth value of (a <= b) element-wise. Supports broadcasting.
     *
     * We also expose `tf.lessEqualStrict` 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]);
     * 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');
        var $b = convertToTensor(b, 'b', 'lessEqual');
        _a = makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
        assertAndGetBroadcastShape($a.shape, $b.shape);
        return ENGINE.runKernelFunc(function (backend, save) {
            var res = backend.lessEqual($a, $b);
            save([$a, $b]);
            return res;
        }, { a: $a, b: $b }, null /* grad */, 'LessEqual');
    }
    function lessEqualStrict_(a, b) {
        var $a = convertToTensor(a, 'a', 'lessEqualStrict');
        var $b = convertToTensor(b, 'b', 'lessEqualStrict');
        assertShapesMatch($a.shape, $b.shape, 'Error in lessEqualStrict: ');
        return $a.lessEqual($b);
    }
    /**
     * Returns the truth value of (a > b) element-wise. Supports broadcasting.
     *
     * We also expose `tf.greaterStrict` 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]);
     * 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');
        var $b = convertToTensor(b, 'b', 'greater');
        _a = makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
        assertAndGetBroadcastShape($a.shape, $b.shape);
        return ENGINE.runKernelFunc(function (backend) { return backend.greater($a, $b); }, { a: $a, b: $b }, null /* grad */, 'Greater');
    }
    function greaterStrict_(a, b) {
        var $a = convertToTensor(a, 'a', 'greaterStrict');
        var $b = convertToTensor(b, 'b', 'greaterStrict');
        assertShapesMatch($a.shape, $b.shape, 'Error in greaterStrict: ');
        return $a.greater($b);
    }
    /**
     * Returns the truth value of (a >= b) element-wise. Supports broadcasting.
     *
     * We also expose `tf.greaterEqualStrict` 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]);
     * 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');
        var $b = convertToTensor(b, 'b', 'greaterEqual');
        _a = makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
        assertAndGetBroadcastShape($a.shape, $b.shape);
        var grad = function (dy, saved) {
            var $a = saved[0], $b = saved[1];
            return { a: function () { return zerosLike($a); }, b: function () { return zerosLike($b); } };
        };
        return ENGINE.runKernelFunc(function (backend, save) {
            var res = backend.greaterEqual($a, $b);
            save([$a, $b]);
            return res;
        }, { a: $a, b: $b }, grad, 'GreaterEqual');
    }
    function greaterEqualStrict_(a, b) {
        var $a = convertToTensor(a, 'a', 'greaterEqualStrict');
        var $b = convertToTensor(b, 'b', 'greaterEqualStrict');
        assertShapesMatch($a.shape, $b.shape, 'Error in greaterEqualStrict: ');
        return $a.greaterEqual($b);
    }
    var equal = op({ equal_: equal_ });
    var equalStrict = op({ equalStrict_: equalStrict_ });
    var greater = op({ greater_: greater_ });
    var greaterEqual = op({ greaterEqual_: greaterEqual_ });
    var greaterEqualStrict = op({ greaterEqualStrict_: greaterEqualStrict_ });
    var greaterStrict = op({ greaterStrict_: greaterStrict_ });
    var less = op({ less_: less_ });
    var lessEqual = op({ lessEqual_: lessEqual_ });
    var lessEqualStrict = op({ lessEqualStrict_: lessEqualStrict_ });
    var lessStrict = op({ lessStrict_: lessStrict_ });
    var notEqual = op({ notEqual_: notEqual_ });
    var notEqualStrict = op({ notEqualStrict_: notEqualStrict_ });

    /**
     * @license
     * Copyright 2018 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 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 gradFunc = function (dy, saved) {
            var $segmentIds = saved[0];
            var derX = function () {
                return gatherDropNegatives(dy, $segmentIds);
            };
            return { $x: derX };
        };
        return ENGINE.runKernelFunc(function (backend, save) {
            var res = backend.unsortedSegmentSum($x, $segmentIds, numSegments);
            save([$segmentIds]);
            return res;
        }, { $x: $x }, gradFunc);
    }
    /**
     * 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.
     */
    /** @doc {heading: 'Tensors', subheading: 'Slicing and Joining'} */
    function gather_(x, indices, axis) {
        if (axis === void 0) { axis = 0; }
        var $x = convertToTensor(x, 'x', 'gather');
        var $indices = convertToTensor(indices, 'indices', 'gather', 'int32');
        axis = parseAxisParam(axis, $x.shape)[0];
        var shapeInfo = collectGatherOpShapeInfo($x, $indices, axis);
        var grad = function (dy, saved) {
            var $indices = saved[0];
            var derX = function () {
                var paramsShape = $x.shape;
                var indicesSize = $indices.size;
                var outerShape = paramsShape.slice(0, axis);
                var outerDims = outerShape.length;
                var innerShape = paramsShape.slice(axis, paramsShape.length).slice(1);
                var innerDims = innerShape.length;
                var outerAxesIndices = arrayRange(0, outerDims);
                var innerAxesIndices = arrayRange(outerDims + 1, outerDims + 1 + innerDims);
                var valuesShape = arrayConcat([outerShape, [indicesSize], innerShape]);
                var values = dy.reshape(valuesShape);
                var reshapedIndices = $indices.reshape([indicesSize]);
                var transposeDims = arrayConcat([[outerDims], outerAxesIndices, innerAxesIndices]);
                var valuesTranspose = values.transpose(transposeDims);
                var paramsGrad = unsortedSegmentSum(valuesTranspose, reshapedIndices, $x.shape[axis]);
                var invertTransposeDims = getUndoAxesPermutation(transposeDims);
                paramsGrad = paramsGrad.transpose(invertTransposeDims);
                return paramsGrad;
            };
            return { x: derX, indices: function () { return $indices; } };
        };
        return (ENGINE.runKernelFunc(function (backend, save) {
            var res = backend.gather($x, $indices.flatten(), axis);
            save([$indices]);
            return res;
        }, { x: $x, indices: $indices }, grad, 'Gather', { axis: axis }))
            .reshape(shapeInfo.outputShape);
    }
    function arrayRange(start, stop) {
        var result = [];
        for (var i = start; i < stop; ++i) {
            result.push(i);
        }
        return result;
    }
    function arrayConcat(arrays) {
        var result = [];
        for (var i = 0; i < arrays.length; ++i) {
            for (var j = 0; j < arrays[i].length; ++j) {
                result.push(arrays[i][j]);
            }
        }
        return result;
    }
    function gatherDropNegatives(x, indices) {
        // Helper function for unsorted segment ops. Gathers params for
        // positive segment ids and gathers 0 for inputs with negative segment id.
        // Mirrors _GatherDropNegatives from tensorflow/python/ops/math_grad.py
        var zeroClippedIndices = maximum(indices, zerosLike(indices));
        var gathered = gather(x, zeroClippedIndices);
        var isPositive = greaterEqual(indices, scalar(0, 'int32'));
        var numIters = gathered.rank - isPositive.rank;
        for (var i = 0; i < numIters; ++i) {
            isPositive = expandDims(isPositive, i + 1);
        }
        isPositive = logicalAnd(isPositive, ones$1(gathered.shape, 'bool'));
        var zeroSlice = zerosLike(gathered);
        return where(isPositive, gathered, zeroSlice);
    }
    var gather = op({ gather_: gather_ });
    var unsortedSegmentSum = op({ unsortedSegmentSum_: unsortedSegmentSum_ });

    /**
     * @license
     * Copyright 2018 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.
     * =============================================================================
     */
    /**
     * 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 = $tensor.reshape(targetTensorShape);
                        reshapedMask = $mask.reshape([-1]);
                        return [4 /*yield*/, whereAsync(reshapedMask)];
                    case 1:
                        positivePositions = _a.sent();
                        indices = positivePositions.squeeze([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 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 a 1D convolution over the input x.
     *
     * @param x The input tensor, of rank 3 or rank 2, of shape
     *     `[batch, width, inChannels]`. If rank 2, batch of 1 is assumed.
     * @param filter The filter, rank 3, of shape
     *     `[filterWidth, inDepth, outDepth]`.
     * @param stride The number of entries by which the filter is moved right at
     *     each step.
     * @param pad The type of padding algorithm.
     *    - `same` and stride 1: output will be of same size as input,
     *       regardless of filter size.
     *    - `valid`: output will be smaller than input if filter is larger
     *       than 1x1.
     *   - For more info, see this guide:
     *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](
     *          https://www.tensorflow.org/api_guides/python/nn#Convolution)
     * @param dataFormat An optional string from "NWC", "NCW". Defaults to "NWC",
     *     the data is stored in the order of [batch, in_width, in_channels]. Only
     *     "NWC" is currently supported.
     * @param dilation The dilation rate in which we sample input values in
     *     atrous convolution. Defaults to `1`. If it is greater than 1, then
     *     stride must be `1`.
     * @param dimRoundingMode The rounding mode used when computing output
     *     dimensions if pad is a number. If none is provided, it will not round
     *     and error if the output is of fractional size.
     */
    /** @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 = $x.as3D(1, $x.shape[0], $x.shape[1]);
        }
        assert(x3D.rank === 3, function () { return "Error in conv1d: input must be rank 3, but got rank " + x3D.rank + "."; });
        assert($filter.rank === 3, function () { return "Error in conv1d: filter must be rank 3, but got rank " +
            ($filter.rank + "."); });
        if (dimRoundingMode != null) {
            assert(isInt(pad), function () { return "Error in conv1d: pad must be an integer when using, " +
                ("dimRoundingMode " + dimRoundingMode + " but got pad " + pad + "."); });
        }
        assert(x3D.shape[2] === $filter.shape[1], function () { return "Error in conv1d: depth of input (" + x3D.shape[2] + ") must match " +
            ("input depth for filter " + $filter.shape[1] + "."); });
        assert(eitherStridesOrDilationsAreOne(stride, dilation), function () { return 'Error in conv1D: Either stride or dilation must be 1. ' +
            ("Got stride " + stride + " and dilation '" + dilation + "'"); });
        assert(dataFormat === 'NWC', function () { return "Error in conv1d: got dataFormat of " + dataFormat + " but only NWC is currently supported."; });
        var filter4D = $filter.as4D(1, $filter.shape[0], $filter.shape[1], $filter.shape[2]);
        var input4D = x3D.as4D(x3D.shape[0], 1, x3D.shape[1], x3D.shape[2]);
        var strides = [1, stride];
        var dilations = [1, dilation];
        var conv2dDataFormat = 'NHWC';
        var res = conv2d(input4D, filter4D, strides, pad, conv2dDataFormat, dilations, dimRoundingMode);
        if (reshapedTo3D) {
            return res.as2D(res.shape[2], res.shape[3]);
        }
        return res.as3D(res.shape[0], res.shape[2], res.shape[3]);
    }
    /**
     * Computes a 2D convolution over the input x.
     *
     * @param x The input tensor, of rank 4 or rank 3, of shape
     *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is
     * assumed.
     * @param filter The filter, rank 4, of shape
     *     `[filterHeight, filterWidth, inDepth, outDepth]`.
     * @param strides The strides of the convolution: `[strideHeight,
     * strideWidth]`.
     * @param pad The type of padding algorithm.
     *    - `same` and stride 1: output will be of same size as input,
     *       regardless of filter size.
     *    - `valid`: output will be smaller than input if filter is larger
     *       than 1x1.
     *   - For more info, see this guide:
     *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](
     *          https://www.tensorflow.org/api_guides/python/nn#Convolution)
     * @param dataFormat: An optional string from: "NHWC", "NCHW". Defaults to
     *     "NHWC". Specify the data format of the input and output data. With the
     *     default format "NHWC", the data is stored in the order of: [batch,
     *     height, width, channels].
     * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`
     *     in which we sample input values across the height and width dimensions
     *     in atrous convolution. Defaults to `[1, 1]`. If `dilations` is a single
     *     number, then `dilationHeight == dilationWidth`. If it is greater than
     *     1, then all values of `strides` must be 1.
     * @param dimRoundingMode The rounding mode used when computing output
     *     dimensions if pad is a number. If none is provided, it will not round
     *     and error if the output is of fractional size.
     */
    /** @doc {heading: 'Operations', subheading: 'Convolution'} */
    function conv2d_(x, filter, strides, pad, dataFormat, dilations, dimRoundingMode) {
        if (dataFormat === void 0) { dataFormat = 'NHWC'; }
        if (dilations === void 0) { dilations = [1, 1]; }
        var $x = convertToTensor(x, 'x', 'conv2d');
        var $filter = convertToTensor(filter, 'filter', 'conv2d');
        var x4D = $x;
        var reshapedTo4D = false;
        if ($x.rank === 3) {
            reshapedTo4D = true;
            x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);
        }
        assert(x4D.rank === 4, function () { return "Error in conv2d: input must be rank 4, but got rank " + x4D.rank + "."; });
        assert($filter.rank === 4, function () { return "Error in conv2d: filter must be rank 4, but got rank " +
            ($filter.rank + "."); });
        if (dimRoundingMode != null) {
            assert(isInt(pad), function () { return "Error in conv2d: pad must be an integer when using, " +
                ("dimRoundingMode " + dimRoundingMode + " but got pad " + pad + "."); });
        }
        var inDepth = dataFormat === 'NHWC' ? x4D.shape[3] : x4D.shape[1];
        assert(inDepth === $filter.shape[2], function () { return "Error in conv2d: depth of input (" + inDepth + ") must match " +
            ("input depth for filter " + $filter.shape[2] + "."); });
        assert(eitherStridesOrDilationsAreOne(strides, dilations), function () { return 'Error in conv2D: Either strides or dilations must be 1. ' +
            ("Got strides " + strides + " and dilations '" + dilations + "'"); });
        var $dataFormat = convertConv2DDataFormat(dataFormat);
        var convInfo = computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode, false, $dataFormat);
        var grad = function (dy, saved) {
            var _a = saved, $filter = _a[0], x4D = _a[1];
            assert(tupleValuesAreOne(dilations), function () { return 'Error in gradient of conv2D: dilation rates greater than 1 ' +
                ("are not yet supported in gradients. Got dilations '" + dilations + "'"); });
            return {
                x: function () { return conv2dDerInput(x4D.shape, dy, $filter, strides, pad, dataFormat); },
                filter: function () {
                    return conv2dDerFilter(x4D, dy, $filter.shape, strides, pad, dataFormat);
                }
            };
        };
        var inputsToSave = [$filter, x4D];
        var res = ENGINE.runKernelFunc(function (backend, save) {
            var res = backend.conv2d(x4D, $filter, convInfo);
            save([$filter, x4D]);
            return res;
        }, { x: x4D, filter: $filter }, grad, 'Conv2D', convInfo, inputsToSave);
        if (reshapedTo4D) {
            return res.as3D(res.shape[1], res.shape[2], res.shape[3]);
        }
        return res;
    }
    /**
     * 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 The rounding mode used when computing output
     *     dimensions if pad is a number. If none is provided, it will not round
     *     and error if the output is of fractional size.
     */
    function conv2dDerInput_(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 = dy.as4D(1, dy.shape[0], dy.shape[1], dy.shape[2]);
            xShape4D = [1, xShape[0], xShape[1], xShape[2]];
        }
        assert(xShape4D.length === 4, function () {
            return "Error in conv2dDerInput: inShape must be length 4, but got length " +
                (xShape4D.length + ".");
        });
        assert(dy4D.rank === 4, function () { return "Error in conv2dDerInput: dy must be rank 4, but got " +
            ("rank " + dy4D.rank); });
        assert(filter.rank === 4, function () { return "Error in conv2dDerInput: filter must be rank 4, but got " +
            ("rank " + filter.rank); });
        var inDepth = dataFormat === 'NHWC' ? xShape4D[3] : xShape4D[1];
        var outDepth = dataFormat === 'NHWC' ? dy4D.shape[3] : dy4D.shape[1];
        assert(inDepth === filter.shape[2], function () { return "Error in conv2dDerInput: depth of input (" + inDepth + ") must " +
            ("match input depth for filter " + filter.shape[2] + "."); });
        assert(outDepth === filter.shape[3], function () { return "Error in conv2dDerInput: depth of output (" + outDepth + ") must " +
            ("match output depth for filter " + filter.shape[3] + "."); });
        if (dimRoundingMode != null) {
            assert(isInt(pad), function () { return "Error in conv2dDerInput: pad must be an integer when using, " +
                ("dimRoundingMode " + dimRoundingMode + " but got pad " + pad + "."); });
        }
        var dilations = 1;
        var grad = function (ddx, saved) {
            var filter = saved[0], dy4D = saved[1];
            return {
                dy4D: function () { return conv2d(ddx, filter, strides, pad, dataFormat, dilations, dimRoundingMode); },
                filter: function () { return conv2dDerFilter(ddx, dy4D, filter.shape, strides, pad, dataFormat, dimRoundingMode); }
            };
        };
        var $dataFormat = convertConv2DDataFormat(dataFormat);
        var convInfo = computeConv2DInfo(xShape4D, filter.shape, strides, dilations, pad, dimRoundingMode, false, $dataFormat);
        var res = ENGINE.runKernelFunc(function (backend, save) {
            var res = backend.conv2dDerInput(dy4D, filter, convInfo);
            save([filter, dy4D]);
            return res;
        }, { dy4D: dy4D, filter: filter }, grad);
        if (reshapedTo4D) {
            return res.as3D(res.shape[1], res.shape[2], res.shape[3]);
        }
        return res;
    }
    /**
     * 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'. The
     *     rounding mode used when computing output dimensions if pad is a
     *     number. If none is provided, it will not round and error if the output
     *     is of fractional size.
     */
    function conv2dDerFilter_(x, dy, filterShape, strides, pad, dataFormat, dimRoundingMode) {
        if (dataFormat === void 0) { dataFormat = 'NHWC'; }
        var x4D = x;
        if (x.rank === 3) {
            x4D = x.as4D(1, x.shape[0], x.shape[1], x.shape[2]);
        }
        var dy4D = dy;
        if (dy4D.rank === 3) {
            dy4D = dy.as4D(1, dy.shape[0], dy.shape[1], dy.shape[2]);
        }
        assert(x4D.rank === 4, function () { return "Error in conv2dDerFilter: input must be rank 4, but got shape " +
            (x4D.shape + "."); });
        assert(dy4D.rank === 4, function () { return "Error in conv2dDerFilter: dy must be rank 4, but got shape " +
            (dy4D.shape + "."); });
        assert(filterShape.length === 4, function () { return "Error in conv2dDerFilter: filterShape must be length 4, but got " +
            (filterShape + "."); });
        var inDepth = dataFormat === 'NHWC' ? x4D.shape[3] : x4D.shape[1];
        var outDepth = dataFormat === 'NHWC' ? dy4D.shape[3] : dy4D.shape[1];
        assert(inDepth === filterShape[2], function () { return "Error in conv2dDerFilter: depth of input " + inDepth + ") must " +
            ("match input depth in filter (" + filterShape[2] + "."); });
        assert(outDepth === filterShape[3], function () { return "Error in conv2dDerFilter: depth of dy (" + outDepth + ") must " +
            ("match output depth for filter (" + filterShape[3] + ")."); });
        if (dimRoundingMode != null) {
            assert(isInt(pad), function () { return "Error in conv2dDerFilter: pad must be an integer when using, " +
                ("dimRoundingMode " + dimRoundingMode + " but got pad " + pad + "."); });
        }
        var dilations = 1;
        var $dataFormat = convertConv2DDataFormat(dataFormat);
        var convInfo = computeConv2DInfo(x4D.shape, filterShape, strides, dilations, pad, dimRoundingMode, false, $dataFormat);
        return ENGINE.runKernelFunc(function (backend) { return backend.conv2dDerFilter(x4D, dy4D, convInfo); }, { x4D: x4D, dy4D: dy4D });
    }
    /**
     * 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 The rounding mode used when computing output
     *    dimensions if pad is a number. If none is provided, it will not round
     *    and error if the output is of fractional size.
     */
    /** @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 conv2dDerInput_(outputShape, $x, $filter, strides, pad, 'NHWC', dimRoundingMode);
    }
    /**
     * Depthwise 2D convolution.
     *
     * Given a 4D `input` array and a `filter` array of shape
     * `[filterHeight, filterWidth, inChannels, channelMultiplier]` containing
     * `inChannels` convolutional filters of depth 1, this op applies a
     * different filter to each input channel (expanding from 1 channel to
     * `channelMultiplier` channels for each), then concatenates the results
     * together. The output has `inChannels * channelMultiplier` channels.
     *
     * See
     * [https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d](
     *     https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d)
     * for more details.
     *
     * @param x The input tensor, of rank 4 or rank 3, of shape
     *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is
     * assumed.
     * @param filter The filter tensor, rank 4, of shape
     *     `[filterHeight, filterWidth, inChannels, channelMultiplier]`.
     * @param strides The strides of the convolution: `[strideHeight,
     * strideWidth]`. If strides is a single number, then `strideHeight ==
     * strideWidth`.
     * @param pad The type of padding algorithm.
     *   - `same` and stride 1: output will be of same size as input,
     *       regardless of filter size.
     *   - `valid`: output will be smaller than input if filter is larger
     *       than 1x1.
     *   - For more info, see this guide:
     *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](
     *          https://www.tensorflow.org/api_guides/python/nn#Convolution)
     * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`
     *     in which we sample input values across the height and width dimensions
     *     in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single
     *     number, then `dilationHeight == dilationWidth`. If it is greater than
     *     1, then all values of `strides` must be 1.
     * @param dataFormat: An optional string from: "NHWC", "NCHW". Defaults to
     *     "NHWC". Specify the data format of the input and output data. With the
     *     default format "NHWC", the data is stored in the order of: [batch,
     *     height, width, channels]. Only "NHWC" is currently supported.
     * @param dimRoundingMode The rounding mode used when computing output
     *     dimensions if pad is a number. If none is provided, it will not round
     *     and error if the output is of fractional size.
     */
    /** @doc {heading: 'Operations', subheading: 'Convolution'} */
    function depthwiseConv2d_(x, filter, strides, pad, dataFormat, dilations, dimRoundingMode) {
        if (dataFormat === void 0) { dataFormat = 'NHWC'; }
        if (dilations === void 0) { dilations = [1, 1]; }
        var $x = convertToTensor(x, 'x', 'depthwiseConv2d');
        var $filter = convertToTensor(filter, 'filter', 'depthwiseConv2d');
        var x4D = $x;
        var reshapedTo4D = false;
        if ($x.rank === 3) {
            reshapedTo4D = true;
            x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);
        }
        assert(x4D.rank === 4, function () { return "Error in depthwiseConv2d: input must be rank 4, but got " +
            ("rank " + x4D.rank + "."); });
        assert($filter.rank === 4, function () { return "Error in depthwiseConv2d: filter must be rank 4, but got rank " +
            ($filter.rank + "."); });
        assert(x4D.shape[3] === $filter.shape[2], function () { return "Error in depthwiseConv2d: number of input channels " +
            ("(" + x4D.shape[3] + ") must match the inChannels dimension in ") +
            ("filter " + $filter.shape[2] + "."); });
        if (dilations == null) {
            dilations = [1, 1];
        }
        assert(eitherStridesOrDilationsAreOne(strides, dilations), function () {
            return 'Error in depthwiseConv2d: Either strides or dilations must be 1. ' +
                ("Got strides " + strides + " and dilations '" + dilations + "'");
        });
        if (dimRoundingMode != null) {
            assert(isInt(pad), function () { return "Error in depthwiseConv2d: pad must be an integer when using, " +
                ("dimRoundingMode " + dimRoundingMode + " but got pad " + pad + "."); });
        }
        var convInfo = computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode, true /* depthwise */);
        var grad = function (dy, saved) {
            assert(tupleValuesAreOne(dilations), function () { return 'Error in gradient of depthwiseConv2d: dilation rates ' +
                "greater than 1 are not yet supported. Got dilations " +
                ("'" + dilations + "'"); });
            var x4D = saved[0], $filter = saved[1];
            return {
                x: function () { return depthwiseConv2dDerInput(x4D.shape, dy, $filter, convInfo); },
                filter: function () { return depthwiseConv2dDerFilter(x4D, dy, $filter.shape, convInfo); },
            };
        };
        var inputsToSave = [x4D, $filter];
        var res = ENGINE.runKernelFunc(function (backend, save) {
            var res = backend.depthwiseConv2D(x4D, $filter, convInfo);
            save([x4D, $filter]);
            return res;
        }, { x: x4D, filter: $filter }, grad, 'DepthwiseConv2dNative', convInfo, inputsToSave);
        if (reshapedTo4D) {
            return res.as3D(res.shape[1], res.shape[2], res.shape[3]);
        }
        return res;
    }
    /**
     * 2-D convolution with separable filters.
     *
     * Performs a depthwise convolution that acts separately on channels followed
     * by a pointwise convolution that mixes channels. Note that this is
     * separability between dimensions [1, 2] and 3, not spatial separability
     * between dimensions 1 and 2.
     *
     * See
     * [https://www.tensorflow.org/api_docs/python/tf/nn/separable_conv2d](
     *     https://www.tensorflow.org/api_docs/python/tf/nn/separable_conv2d)
     * for more details.
     *
     * @param x The input tensor, of rank 4 or rank 3, of shape
     *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is
     * assumed.
     * @param depthwiseFilter The depthwise filter tensor, rank 4, of shape
     *     `[filterHeight, filterWidth, inChannels, channelMultiplier]`. This is
     *     the filter used in the first step.
     * @param pointwiseFilter The pointwise filter tensor, rank 4, of shape
     *     `[1, 1, inChannels * channelMultiplier, outChannels]`. This is
     *     the filter used in the second step.
     * @param strides The strides of the convolution: `[strideHeight,
     * strideWidth]`. If strides is a single number, then `strideHeight ==
     * strideWidth`.
     * @param pad The type of padding algorithm.
     *   - `same` and stride 1: output will be of same size as input,
     *       regardless of filter size.
     *   - `valid`: output will be smaller than input if filter is larger
     *       than 1x1.
     *   - For more info, see this guide:
     *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](
     *          https://www.tensorflow.org/api_guides/python/nn#Convolution)
     * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`
     *     in which we sample input values across the height and width dimensions
     *     in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single
     *     number, then `dilationHeight == dilationWidth`. If it is greater than
     *     1, then all values of `strides` must be 1.
     * @param dataFormat: An optional string from: "NHWC", "NCHW". Defaults to
     *     "NHWC". Specify the data format of the input and output data. With the
     *     default format "NHWC", the data is stored in the order of: [batch,
     *     height, width, channels]. Only "NHWC" is currently supported.
     */
    /** @doc {heading: 'Operations', subheading: 'Convolution'} */
    function separableConv2d_(x, depthwiseFilter, pointwiseFilter, strides, pad, dilation, dataFormat) {
        if (dilation === void 0) { dilation = [1, 1]; }
        if (dataFormat === void 0) { dataFormat = 'NHWC'; }
        var $x = convertToTensor(x, 'x', 'separableConv2d');
        var $depthwiseFilter = convertToTensor(depthwiseFilter, 'depthwiseFilter', 'separableConv2d');
        var $pointwiseFilter = convertToTensor(pointwiseFilter, 'pointwiseFilter', 'separableConv2d');
        var x4D = $x;
        var reshapedTo4D = false;
        if ($x.rank === 3) {
            reshapedTo4D = true;
            x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);
        }
        if (dataFormat === 'NCHW') {
            throw new Error('separableConv2d currently does not support dataFormat NCHW; only ' +
                'NHWC is supported');
        }
        assert(x4D.rank === 4, function () { return "Error in separableConv2d: input must be rank 4, but got " +
            ("rank " + x4D.rank + "."); });
        assert($depthwiseFilter.rank === 4, function () { return "Error in separableConv2d: depthwise filter must be rank 4, but " +
            ("got rank " + $depthwiseFilter.rank + "."); });
        assert($pointwiseFilter.rank === 4, function () { return "Error in separableConv2d: pointwise filter must be rank 4, but " +
            ("got rank " + $depthwiseFilter.rank + "."); });
        assert($pointwiseFilter.shape[0] === 1, function () {
            return "Error in separableConv2d: the first dimension of pointwise filter " +
                (" must be 1, but got " + $pointwiseFilter.shape[0] + ".");
        });
        assert($pointwiseFilter.shape[1] === 1, function () { return "Error in separableConv2d: the second dimension of pointwise " +
            ("filter must be 1, but got " + $pointwiseFilter.shape[1] + "."); });
        var inChannels = $depthwiseFilter.shape[2];
        var channelMultiplier = $depthwiseFilter.shape[3];
        assert($pointwiseFilter.shape[2] === inChannels * channelMultiplier, function () {
            return "Error in separableConv2d: the third dimension of pointwise filter " +
                ("must be " + inChannels * channelMultiplier + ", ") +
                ("but got " + $pointwiseFilter.shape[2] + ".");
        });
        var depthwise = depthwiseConv2d(x4D, $depthwiseFilter, strides, pad, dataFormat, dilation);
        var pointwiseStride = 1;
        var res = conv2d(depthwise, $pointwiseFilter, pointwiseStride, 'valid', dataFormat);
        if (reshapedTo4D) {
            return res.as3D(res.shape[1], res.shape[2], res.shape[3]);
        }
        return res;
    }
    function parseTupleParam$1(param) {
        if (typeof param === 'number') {
            return [param, param, param];
        }
        if (param.length === 2) {
            return [param[0], param[1], 1];
        }
        return param;
    }
    function tupleValuesAreOne$1(param) {
        var _a = parseTupleParam$1(param), dimA = _a[0], dimB = _a[1], dimC = _a[2];
        return dimA === 1 && dimB === 1 && dimC === 1;
    }
    function eitherStridesOrDilationsAreOne$1(strides, dilations) {
        return tupleValuesAreOne$1(strides) || tupleValuesAreOne$1(dilations);
    }
    function depthwiseConv2dDerInput_(xShape, dy, filter, convInfo) {
        var dy4D = dy;
        var reshapedTo4D = false;
        if (dy.rank === 3) {
            reshapedTo4D = true;
            dy4D = dy.as4D(1, dy.shape[0], dy.shape[1], dy.shape[2]);
        }
        var res = ENGINE.runKernelFunc(function (backend) { return backend.depthwiseConv2DDerInput(dy4D, filter, convInfo); }, { dy4D: dy4D });
        if (reshapedTo4D) {
            return res.as3D(res.shape[1], res.shape[2], res.shape[3]);
        }
        return res;
    }
    function depthwiseConv2dDerFilter_(x, dy, filterShape, convInfo) {
        var x4D = x;
        if (x.rank === 3) {
            x4D = x.as4D(1, x.shape[0], x.shape[1], x.shape[2]);
        }
        var dy4D = dy;
        if (dy4D.rank === 3) {
            dy4D = dy.as4D(1, dy.shape[0], dy.shape[1], dy.shape[2]);
        }
        return ENGINE.runKernelFunc(function (backend) { return backend.depthwiseConv2DDerFilter(x4D, dy4D, convInfo); }, { x4D: x4D, dy4D: dy4D });
    }
    /**
     * Computes a 3D convolution over the input x.
     *
     * @param x The input tensor, of rank 5 or rank 4, of shape
     *     `[batch, depth, height, width, channels]`. If rank 4,
     * batch of 1 is assumed.
     * @param filter The filter, rank 5, of shape
     *     `[filterDepth, filterHeight, filterWidth, inChannels, outChannels]`.
     *      inChannels must match between input and filter.
     * @param strides The strides of the convolution: `[strideDepth, strideHeight,
     * strideWidth]`.
     * @param pad The type of padding algorithm.
     *    - `same` and stride 1: output will be of same size as input,
     *       regardless of filter size.
     *    - `valid`: output will be smaller than input if filter is larger
     *       than 1x1.
     *   - For more info, see this guide:
     *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](
     *          https://www.tensorflow.org/api_guides/python/nn#Convolution)
     * @param dataFormat: An optional string from: "NDHWC", "NCDHW". Defaults to
     *     "NDHWC". Specify the data format of the input and output data. With the
     *     default format "NDHWC", the data is stored in the order of: [batch,
     *     depth, height, width, channels]. Only "NDHWC" is currently supported.
     * @param dilations The dilation rates: `[dilationDepth, dilationHeight,
     *     dilationWidth]` in which we sample input values across the height
     *     and width dimensions in atrous convolution. Defaults to `[1, 1, 1]`.
     *     If `dilations` is a single number, then
     *     `dilationDepth == dilationHeight == dilationWidth`. If it is greater
     *     than 1, then all values of `strides` must be 1.
     */
    /** @doc {heading: 'Operations', subheading: 'Convolution'} */
    function conv3d_(x, filter, strides, pad, dataFormat, dilations) {
        if (dataFormat === void 0) { dataFormat = 'NDHWC'; }
        if (dilations === void 0) { dilations = [1, 1, 1]; }
        var $x = convertToTensor(x, 'x', 'conv3d');
        var $filter = convertToTensor(filter, 'filter', 'conv3d');
        var x5D = $x;
        var reshapedTo5D = false;
        if ($x.rank === 4) {
            reshapedTo5D = true;
            x5D = $x.as5D(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$1(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 convInfo = computeConv3DInfo(x5D.shape, $filter.shape, strides, dilations, pad);
        var grad = function (dy, saved) {
            assert(tupleValuesAreOne$1(dilations), function () {
                return 'Error in gradient of conv3D: dilation rates greater than 1 are ' +
                    ("not yet supported in gradients. Got dilations '" + dilations + "'");
            });
            var x5D = saved[0], $filter = saved[1];
            return {
                x: function () { return conv3dDerInput_(x5D.shape, dy, $filter, strides, pad); },
                $filter: function () { return conv3dDerFilter_(x5D, dy, $filter.shape, strides, pad); }
            };
        };
        var res = ENGINE.runKernelFunc(function (backend, save) {
            var res = backend.conv3d(x5D, $filter, convInfo);
            save([x5D, $filter]);
            return res;
        }, { x: x5D, $filter: $filter }, grad);
        if (reshapedTo5D) {
            return res.as4D(res.shape[1], res.shape[2], res.shape[3], res.shape[4]);
        }
        return res;
    }
    /**
     * 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 conv3dDerInput_(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 = dy.as5D(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 dilations = 1;
        var convInfo = computeConv3DInfo(xShape5D, filter.shape, strides, dilations, pad);
        var res = ENGINE.runKernelFunc(function (backend) { return backend.conv3dDerInput(dy5D, filter, convInfo); }, { dy5D: dy5D });
        if (reshapedTo5D) {
            return res.as4D(res.shape[1], res.shape[2], res.shape[3], res.shape[4]);
        }
        return res;
    }
    /**
     * Computes the derivative of the filter of a 3D convolution.
     *
     * @param x The input tensor, of rank 5 or rank 4 of shape
     *     [batch, depth, height, width, inChannels]. If rank 4, batch of 1 is
     *     assumed.
     * @param dy The dy image, of rank 5 or rank 4, of shape
     *     [batch, depth, height, width, outDepth]. If rank 4, batch of 1 is
     *     assumed.
     * @param filterShape The shape of the filter, length 5,
     *     [filterDepth, filterHeight, filterWidth, inDepth, outDepth].
     * @param strides The strides of the convolution: [strideDepth, strideHeight,
     * strideWidth].
     * @param pad A string from: 'same', 'valid'. The type of padding algorithm
     *     used in the forward prop of the op.
     */
    function conv3dDerFilter_(x, dy, filterShape, strides, pad) {
        var x5D = x;
        if (x.rank === 4) {
            x5D = x.as5D(1, x.shape[0], x.shape[1], x.shape[2], x.shape[3]);
        }
        var dy5D = dy;
        if (dy5D.rank === 4) {
            dy5D = dy.as5D(1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]);
        }
        assert(x5D.rank === 5, function () { return "Error in conv3dDerFilter: input must be rank 5, but got shape " +
            (x5D.shape + "."); });
        assert(dy5D.rank === 5, function () { return "Error in conv3dDerFilter: dy must be rank 5, but got shape " +
            (dy5D.shape + "."); });
        assert(filterShape.length === 5, function () { return "Error in conv3dDerFilter: filterShape must be length 5, but got " +
            (filterShape + "."); });
        assert(x5D.shape[4] === filterShape[3], function () { return "Error in conv3dDerFilter: depth of input " + x5D.shape[4] + ") must " +
            ("match input depth in filter (" + filterShape[3] + "."); });
        assert(dy5D.shape[4] === filterShape[4], function () { return "Error in conv3dDerFilter: depth of dy (" + dy5D.shape[4] + ") must " +
            ("match output depth for filter (" + filterShape[4] + ")."); });
        var dilations = 1;
        var convInfo = computeConv3DInfo(x5D.shape, filterShape, strides, dilations, pad);
        return ENGINE.runKernelFunc(function (backend) { return backend.conv3dDerFilter(x5D, dy5D, convInfo); }, { x5D: x5D, dy5D: dy5D });
    }
    /**
     * 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 conv3dDerInput_(outputShape, $x, $filter, strides, pad);
    }
    var conv1d = op({ conv1d_: conv1d_ });
    var conv2d = op({ conv2d_: conv2d_ });
    var conv3d = op({ conv3d_: conv3d_ });
    var conv2dDerFilter = op({ conv2dDerFilter_: conv2dDerFilter_ });
    var conv2dDerInput = op({ conv2dDerInput_: conv2dDerInput_ });
    var depthwiseConv2d = op({ depthwiseConv2d_: depthwiseConv2d_ });
    var depthwiseConv2dDerInput = op({ depthwiseConv2dDerInput_: depthwiseConv2dDerInput_ });
    var depthwiseConv2dDerFilter = op({ depthwiseConv2dDerFilter_: depthwiseConv2dDerFilter_ });
    var separableConv2d = op({ separableConv2d_: separableConv2d_ });
    var conv2dTranspose = op({ conv2dTranspose_: conv2dTranspose_ });
    var conv3dTranspose = op({ conv3dTranspose_: conv3dTranspose_ });

    /**
     * @license
     * Copyright 2018 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 dot product of two matrices, A * B. These must be matrices.
     *
     * ```js
     * const a = tf.tensor2d([1, 2], [1, 2]);
     * const b = tf.tensor2d([1, 2, 3, 4], [2, 2]);
     *
     * a.matMul(b).print();  // or tf.matMul(a, b)
     * ```
     * @param a First matrix in dot product operation.
     * @param b Second matrix in dot product operation.
     * @param transposeA If true, `a` is transposed before multiplication.
     * @param transposeB If true, `b` is transposed before multiplication.
     */
    /** @doc {heading: 'Operations', subheading: 'Matrices'} */
    function matMul_(a, b, transposeA, transposeB) {
        var _a;
        if (transposeA === void 0) { transposeA = false; }
        if (transposeB === void 0) { transposeB = false; }
        var $a = convertToTensor(a, 'a', 'matMul');
        var $b = convertToTensor(b, 'b', 'matMul');
        _a = makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
        var innerShapeA = transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1];
        var innerShapeB = transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2];
        var outerShapeA = transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2];
        var outerShapeB = transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1];
        var outerDimsA = $a.shape.slice(0, -2);
        var outerDimsB = $b.shape.slice(0, -2);
        var batchDimA = sizeFromShape(outerDimsA);
        var batchDimB = sizeFromShape(outerDimsB);
        assert($a.rank >= 2 && $b.rank >= 2 && $a.rank === $b.rank, function () { return "Error in matMul: inputs must have the same rank of at least 2, " +
            ("got ranks " + $a.rank + " and " + $b.rank + "."); });
        assert(arraysEqual(outerDimsA, outerDimsB), function () { return "Error in matMul: outer dimensions (" + outerDimsA + ") and (" +
            (outerDimsB + ") of Tensors with shapes " + $a.shape + " and ") +
            ($b.shape + " must match."); });
        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 outShape = $a.shape.slice(0, -2).concat([outerShapeA, outerShapeB]);
        var a3D = transposeA ? $a.as3D(batchDimA, innerShapeA, outerShapeA) :
            $a.as3D(batchDimA, outerShapeA, innerShapeA);
        var b3D = transposeB ? $b.as3D(batchDimB, outerShapeB, innerShapeB) :
            $b.as3D(batchDimB, innerShapeB, outerShapeB);
        var grad = function (dy, saved) {
            var _a = saved, a3D = _a[0], b3D = _a[1];
            if (!transposeA && !transposeB) {
                return {
                    a: function () { return dy.matMul(b3D, false, true); },
                    b: function () { return a3D.matMul(dy, true, false); }
                };
            }
            else if (!transposeA && transposeB) {
                return {
                    a: function () { return dy.matMul(b3D, false, false); },
                    b: function () { return dy.matMul(a3D, true, false); }
                };
            }
            else if (transposeA && !transposeB) {
                return {
                    a: function () { return b3D.matMul(dy, false, true); },
                    b: function () { return a3D.matMul(dy, false, false); }
                };
            }
            else {
                return {
                    a: function () { return b3D.matMul(dy, true, true); },
                    b: function () { return dy.matMul(a3D, true, true); }
                };
            }
        };
        var attrs = { transposeA: transposeA, transposeB: transposeB };
        var res = ENGINE.runKernelFunc(function (backend, save) {
            var res = backend.batchMatMul(a3D, b3D, transposeA, transposeB);
            save([a3D, b3D]);
            return res;
        }, { a: a3D, b: b3D }, grad, 'BatchMatMul', attrs);
        return res.reshape(outShape);
    }
    /**
     * 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 + "."); });
        return $v1.as2D(-1, 1).matMul($v2.as2D(1, -1));
    }
    /**
     * 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) {
            return $t1.as2D(1, -1).matMul($t2.as2D(-1, 1)).asScalar();
        }
        else if ($t1.rank === 1 && $t2.rank === 2) {
            return $t1.as2D(1, -1).matMul($t2.as2D($t2.shape[0], $t2.shape[1])).as1D();
        }
        else if ($t1.rank === 2 && $t2.rank === 1) {
            return $t1.matMul($t2.as2D(-1, 1)).as1D();
        }
        else {
            return $t1.matMul($t2.as2D($t2.shape[0], $t2.shape[1]));
        }
    }
    var matMul = op({ matMul_: matMul_ });
    var dot = op({ dot_: dot_ });
    var outerProduct = op({ outerProduct_: outerProduct_ });

    /**
     * @license
     * Copyright 2018 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.
     * =============================================================================
     */
    /**
     * 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);
    }
    /**
     * 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);
    }
    /**
     * 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);
    }
    /**
     * 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);
    }
    /**
     * 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 $x = convertToTensor(x, 'x', 'reverse');
        if ($x.rank === 0) {
            return $x.clone();
        }
        var axes = parseAxisParam(axis, $x.shape);
        var grad = function (dy) {
            return { $x: function () { return dy.reverse(axes); } };
        };
        var res = ENGINE.runKernelFunc(function (backend) { return backend.reverse($x, axes); }, { $x: $x }, grad);
        return res.reshapeAs($x);
    }
    var reverse = op({ reverse_: reverse_ });
    var reverse1d = op({ reverse1d_: reverse1d_ });
    var reverse2d = op({ reverse2d_: reverse2d_ });
    var reverse3d = op({ reverse3d_: reverse3d_ });
    var reverse4d = op({ reverse4d_: reverse4d_ });

    /**
     * @license
     * Copyright 2018 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 2D max pooling of an image.
     *
     * @param x The input tensor, of rank 4 or rank 3 of shape
     *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.
     * @param filterSize The filter size: `[filterHeight, filterWidth]`. If
     *     `filterSize` is a single number, then `filterHeight == filterWidth`.
     * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If
     *     `strides` is a single number, then `strideHeight == strideWidth`.
     * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`
     *     in which we sample input values across the height and width dimensions
     *     in dilated pooling. Defaults to `[1, 1]`. If `dilations` is a single
     *     number, then `dilationHeight == dilationWidth`. If it is greater than
     *     1, then all values of `strides` must be 1.
     * @param pad The type of padding algorithm.
     *    - `same` and stride 1: output will be of same size as input,
     *       regardless of filter size.
     *    - `valid`: output will be smaller than input if filter is larger
     *       than 1x1.
     *    - For more info, see this guide:
     *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](
     *          https://www.tensorflow.org/api_guides/python/nn#Convolution)
     * @param dimRoundingMode The rounding mode used when computing output
     *     dimensions if pad is a number. If none is provided, it will not round
     *     and error if the output is of fractional size.
     */
    function maxPoolImpl_(x, filterSize, strides, dilations, pad, dimRoundingMode) {
        var $x = convertToTensor(x, 'x', 'maxPool');
        var x4D = $x;
        var reshapedTo4D = false;
        if ($x.rank === 3) {
            reshapedTo4D = true;
            x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);
        }
        if (dilations == null) {
            dilations = [1, 1];
        }
        assert(x4D.rank === 4, function () { return "Error in maxPool: input must be rank 4 but got rank " + x4D.rank + "."; });
        assert(eitherStridesOrDilationsAreOne(strides, dilations), function () { return 'Error in maxPool: Either strides or dilations must be 1. ' +
            ("Got strides " + strides + " and dilations '" + dilations + "'"); });
        if (dimRoundingMode != null) {
            assert(isInt(pad), function () { return "Error in maxPool: pad must be an integer when using, " +
                ("dimRoundingMode " + dimRoundingMode + " but got pad " + pad + "."); });
        }
        var convInfo = computePool2DInfo(x4D.shape, filterSize, strides, dilations, pad, dimRoundingMode);
        if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&
            arraysEqual(convInfo.inShape, convInfo.outShape)) {
            return $x.clone();
        }
        var grad = function (dy, saved) {
            var x4D = saved[0], y = saved[1];
            return {
                x: function () { return maxPoolBackprop(dy, x4D, y, filterSize, strides, dilations, pad); }
            };
        };
        var inputsToSave = [x4D];
        var res = ENGINE.runKernelFunc(function (backend, save) {
            var y = backend.maxPool(x4D, convInfo);
            save([x4D, y]);
            return y;
        }, { x: x4D }, grad, 'MaxPool', convInfo, inputsToSave);
        if (reshapedTo4D) {
            return res.as3D(res.shape[1], res.shape[2], res.shape[3]);
        }
        return res;
    }
    /**
     * 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 pad The type of padding algorithm.
     *    - `same` and stride 1: output will be of same size as input,
     *       regardless of filter size.
     *    - `valid`: output will be smaller than input if filter is larger
     *       than 1x1.
     *    - For more info, see this guide:
     *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](
     *          https://www.tensorflow.org/api_guides/python/nn#Convolution)
     * @param dimRoundingMode The rounding mode used when computing output
     *     dimensions if pad is a number. If none is provided, it will not round
     *     and error if the output is of fractional size.
     */
    /** @doc {heading: 'Operations', subheading: 'Convolution'} */
    function maxPool_(x, filterSize, strides, pad, dimRoundingMode) {
        return maxPoolImpl_(x, filterSize, strides, 1, pad, dimRoundingMode);
    }
    /**
     * 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 dilations The dilation rates: `[dilationHeight, dilationWidth]`
     *     in which we sample input values across the height and width dimensions
     *     in dilated pooling. Defaults to `[1, 1]`. If `dilations` is a single
     *     number, then `dilationHeight == dilationWidth`. If it is greater than
     *     1, then all values of `strides` must be 1.
     * @param pad The type of padding algorithm:
     *    - `same` and stride 1: output will be of same size as input,
     *       regardless of filter size.
     *    - `valid`: output will be smaller than input if filter is larger
     *       than 1x1.
     *    - For more info, see this guide:
     *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](
     *         https://www.tensorflow.org/api_guides/python/nn#Convolution)
     * @param dimRoundingMode The rounding mode used when computing output
     *     dimensions if pad is a number. If none is provided, it will not round
     *     and error if the output is of fractional size.
     */
    function avgPoolImpl_(x, filterSize, strides, dilations, pad, dimRoundingMode) {
        var $x = convertToTensor(x, 'x', 'avgPool', 'float32');
        if (dilations == null) {
            dilations = [1, 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 = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);
        }
        assert(x4D.rank === 4, function () { return "Error in avgPool: x must be rank 4 but got rank " + x4D.rank + "."; });
        if (dimRoundingMode != null) {
            assert(isInt(pad), function () { return "Error in avgPool: pad must be an integer when using, " +
                ("dimRoundingMode " + dimRoundingMode + " but got pad " + pad + "."); });
        }
        var convInfo = computePool2DInfo(x4D.shape, filterSize, strides, dilations, pad, dimRoundingMode);
        if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&
            arraysEqual(convInfo.inShape, convInfo.outShape)) {
            return $x.clone();
        }
        var grad = function (dy) {
            return {
                x: function () { return avgPoolBackprop(dy, x4D, filterSize, strides, dilations, pad); }
            };
        };
        var res = ENGINE.runKernelFunc(function (backend) { return backend.avgPool(x4D, convInfo); }, { x: x4D }, grad, 'AvgPool', convInfo);
        res = res.cast($x.dtype);
        if (reshapedTo4D) {
            return res.as3D(res.shape[1], res.shape[2], res.shape[3]);
        }
        return res;
    }
    /**
     * Computes the 2D average pooling of an image.
     *
     * @param x The input tensor, of rank 4 or rank 3 of shape
     *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.
     * @param filterSize The filter size: `[filterHeight, filterWidth]`. If
     *     `filterSize` is a single number, then `filterHeight == filterWidth`.
     * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If
     *     `strides` is a single number, then `strideHeight == strideWidth`.
     * @param pad The type of padding algorithm:
     *    - `same` and stride 1: output will be of same size as input,
     *       regardless of filter size.
     *    - `valid`: output will be smaller than input if filter is larger
     *       than 1x1.
     *    - For more info, see this guide:
     *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](
     *         https://www.tensorflow.org/api_guides/python/nn#Convolution)
     * @param dimRoundingMode The rounding mode used when computing output
     *     dimensions if pad is a number. If none is provided, it will not round
     *     and error if the output is of fractional size.
     */
    /** @doc {heading: 'Operations', subheading: 'Convolution'} */
    function avgPool_(x, filterSize, strides, pad, dimRoundingMode) {
        return avgPoolImpl_(x, filterSize, strides, 1, pad, dimRoundingMode);
    }
    /**
     * Performs an N-D pooling operation
     *
     * @param input The input tensor, of rank 4 or rank 3 of shape
     *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.
     * @param windowShape The filter size: `[filterHeight, filterWidth]`. If
     *     `filterSize` is a single number, then `filterHeight == filterWidth`.
     * @param poolingType The type of pooling, either 'max' or 'avg'.
     * @param pad The type of padding algorithm:
     *    - `same` and stride 1: output will be of same size as input,
     *       regardless of filter size.
     *    - `valid`: output will be smaller than input if filter is larger
     *       than 1x1.
     *    - For more info, see this guide:
     *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](
     *         https://www.tensorflow.org/api_guides/python/nn#Convolution)
     * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`
     *     in which we sample input values across the height and width dimensions
     *     in dilated pooling. Defaults to `[1, 1]`. If `dilationRate` is a single
     *     number, then `dilationHeight == dilationWidth`. If it is greater than
     *     1, then all values of `strides` must be 1.
     * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If
     *     `strides` is a single number, then `strideHeight == strideWidth`.
     */
    /** @doc {heading: 'Operations', subheading: 'Convolution'} */
    function pool_(input, windowShape, poolingType, pad, dilations, strides) {
        if (dilations == null) {
            dilations = [1, 1];
        }
        if (strides == null) {
            strides = 1;
        }
        if (pad === 0) {
            pad = 'valid';
        }
        var $x = convertToTensor(input, 'x', 'maxPool');
        var x4D = $x;
        var reshapedTo4D = false;
        if ($x.rank === 3) {
            reshapedTo4D = true;
            x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);
        }
        assert(eitherStridesOrDilationsAreOne(strides, dilations), function () { return 'Error in pool: Either strides or dilations must be 1. ' +
            ("Got strides " + strides + " and dilations '" + dilations + "'"); });
        var convInfo = computePool2DInfo(x4D.shape, windowShape, strides, dilations, pad);
        var dilation = [convInfo.dilationHeight, convInfo.dilationWidth];
        // The following implementation does batchToSpace(pool(spaceToBatch(x)))
        // whenever dilation > 1 since the TF kernels do not support dilation > 1.
        // tslint:disable-next-line:max-line-length
        // https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/nn_ops.py#L1037
        var basePadding;
        if (pad === 'same') {
            basePadding = withSpaceToBatchBasePaddings([convInfo.filterHeight, convInfo.filterWidth], dilation);
        }
        else {
            basePadding = [[0, 0], [0, 0]];
        }
        var isDilationOne = dilation[0] === 1 && dilation[1] === 1;
        var _a = requiredSpaceToBatchPaddings([convInfo.inHeight, convInfo.inWidth], dilation, basePadding), adjustedPadding = _a[0], adjustedCrops = _a[1];
        var convertedPad = isDilationOne ? pad : 'valid';
        var convertedX = isDilationOne ? x4D : spaceToBatchND(x4D, dilation, adjustedPadding);
        var forwardOp = poolingType === 'avg' ?
            function () { return avgPoolImpl_(convertedX, windowShape, strides, 1 /* dilation */, convertedPad); } :
            function () { return maxPoolImpl_(convertedX, windowShape, strides, 1 /* dilation */, convertedPad); };
        var y = forwardOp();
        var res = isDilationOne ? y : batchToSpaceND(y, dilation, adjustedCrops);
        if (reshapedTo4D) {
            return res.as3D(res.shape[1], res.shape[2], res.shape[3]);
        }
        return res;
    }
    /**
     * Computes the backprop of a 2D max pool.
     *
     * @param dy The dy error, of rank 4 or rank 3 of shape
     *     [batchSize, height, width, channels]. If rank 3, batch of 1 is
     * assumed.
     * @param input The original input image, of rank 4, of shape
     *     [batchSize, height, width, channels].
     * @param output The original output image, of rank 4, of shape
     *     [batchSize, outHeight, outWidth, channels].
     * @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 A string from: 'same', 'valid'. The type of padding algorithm
     *     used in the forward prop of the op.
     * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. The
     *     rounding mode used when computing output dimensions if pad is a
     *     number. If none is provided, it will not round and error if the output
     *     is of fractional size.
     */
    function maxPoolBackprop(dy, input, output, filterSize, strides, dilations, pad, dimRoundingMode) {
        var $dy = convertToTensor(dy, 'dy', 'maxPoolBackprop');
        var $input = convertToTensor(input, 'input', 'maxPoolBackprop');
        var $output = convertToTensor(output, 'output', 'maxPoolBackprop');
        assert($input.rank === $dy.rank, function () { return "Rank of input (" + $input.rank + ") does not match rank of dy " +
            ("(" + $dy.rank + ")"); });
        if (dilations == null) {
            dilations = [1, 1];
        }
        assert(eitherStridesOrDilationsAreOne(strides, dilations), function () {
            return 'Error in maxPoolBackProp: Either strides or dilations must be 1. ' +
                ("Got strides " + strides + " and dilations '" + dilations + "'");
        });
        assert($dy.rank === 4, function () { return "Error in maxPoolBackprop: dy must be rank 4 but got rank " +
            ($dy.rank + "."); });
        assert($input.rank === 4, function () { return "Error in maxPoolBackprop: input must be rank 4 but got rank " +
            ($input.rank + "."); });
        if (dimRoundingMode != null) {
            assert(isInt(pad), function () { return "Error in maxPoolBackprop: pad must be an integer when using, " +
                ("dimRoundingMode " + dimRoundingMode + " but got pad " + pad + "."); });
        }
        var convInfo = computePool2DInfo($input.shape, filterSize, strides, dilations, pad, dimRoundingMode);
        var res = ENGINE.runKernelFunc(function (backend) { return backend.maxPoolBackprop($dy, $input, $output, convInfo); }, { $dy: $dy, $input: $input });
        return res;
    }
    /**
     * Computes the backprop of an 2D avg pool.
     *
     * @param dy The dy error, of rank 4 or rank 3 of shape
     *     [batchSize, height, width, channels]. If rank 3, batch of 1 is
     * assumed.
     * @param input The input image, of rank 4 or rank 3 of shape
     *     [batchSize, height, width, channels]. 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 A string from: 'same', 'valid'. The type of padding algorithm
     *     used in the forward prop of the op.
     */
    function avgPoolBackprop(dy, input, filterSize, strides, dilations, pad) {
        var $dy = convertToTensor(dy, 'dy', 'avgPoolBackprop');
        var $input = convertToTensor(input, 'input', 'avgPoolBackprop');
        assert($input.rank === $dy.rank, function () { return "Rank of input (" + $input.rank + ") does not match rank of dy (" + $dy.rank + ")"; });
        if (dilations == null) {
            dilations = [1, 1];
        }
        assert(eitherStridesOrDilationsAreOne(strides, dilations), function () {
            return 'Error in avgPoolBackprop: Either strides or dilations must be 1. ' +
                ("Got strides " + strides + " and dilations '" + dilations + "'");
        });
        var input4D = $input;
        var dy4D = $dy;
        var reshapedTo4D = false;
        if ($input.rank === 3) {
            reshapedTo4D = true;
            input4D = $input.as4D(1, $input.shape[0], $input.shape[1], $input.shape[2]);
            dy4D = $dy.as4D(1, $dy.shape[0], $dy.shape[1], $dy.shape[2]);
        }
        assert(dy4D.rank === 4, function () { return "Error in avgPoolBackprop: dy must be rank 4 but got rank " +
            (dy4D.rank + "."); });
        assert(input4D.rank === 4, function () { return "Error in avgPoolBackprop: input must be rank 4 but got rank " +
            (input4D.rank + "."); });
        var convInfo = computePool2DInfo(input4D.shape, filterSize, strides, dilations, pad);
        var res = ENGINE.runKernelFunc(function (backend) { return backend.avgPoolBackprop(dy4D, input4D, convInfo); }, { dy4D: dy4D, input4D: input4D });
        if (reshapedTo4D) {
            return res.as3D(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]];
        });
    }
    /**
     * Computes the 3D average pooling.
     *
     * ```js
     * const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);
     * const result = tf.avgPool3d(x, 2, 1, 'valid');
     * result.print();
     * ```
     *
     * @param x The input tensor, of rank 5 or rank 4 of shape
     *     `[batch, depth, height, width, inChannels]`.
     * @param filterSize The filter size:
     *     `[filterDepth, filterHeight, filterWidth]`.
     *     If `filterSize` is a single number,
     *     then `filterDepth == filterHeight == filterWidth`.
     * @param strides The strides of the pooling:
     *     `[strideDepth, strideHeight, strideWidth]`.
     *     If `strides` is a single number,
     *     then `strideDepth == strideHeight == strideWidth`.
     * @param pad The type of padding algorithm.
     *    - `same` and stride 1: output will be of same size as input,
     *       regardless of filter size.
     *    - `valid`: output will be smaller than input if filter is larger
     *       than 1*1x1.
     *    - For more info, see this guide:
     *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](
     *          https://www.tensorflow.org/api_guides/python/nn#Convolution)
     * @param dimRoundingMode The rounding mode used when computing output
     *     dimensions if pad is a number. If none is provided, it will not round
     *     and error if the output is of fractional size.
     * @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 depth, height and width
     *     dimensions in dilated pooling.
     *     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 avgPool3d_(x, filterSize, strides, pad, dimRoundingMode, dataFormat, dilations) {
        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 = $x.as5D(1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]);
        }
        if (dilations == null) {
            dilations = [1, 1, 1];
        }
        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); });
        assert(eitherStridesOrDilationsAreOne(strides, dilations), function () { return 'Error in avgPool3d: Either strides or dilations must be 1. ' +
            ("Got strides " + strides + " and dilations '" + dilations + "'"); });
        if (dimRoundingMode != null) {
            assert(isInt(pad), function () { return "Error in avgPool3d: pad must be an integer when using, " +
                ("dimRoundingMode " + dimRoundingMode + " but got pad " + pad + "."); });
        }
        var convInfo = computePool3DInfo(x5D.shape, filterSize, strides, dilations, pad, dimRoundingMode, dataFormat);
        var grad = function (dy) {
            return {
                x: function () { return avgPool3dBackprop(dy, x5D, filterSize, strides, dilations, pad, dimRoundingMode); }
            };
        };
        var res = ENGINE.runKernelFunc(function (backend) { return backend.avgPool3d(x5D, convInfo); }, { x: x5D }, grad);
        res = res.cast(x5D.dtype);
        if (reshapedTo5D) {
            return res.as4D(res.shape[1], res.shape[2], res.shape[3], res.shape[4]);
        }
        return res;
    }
    /**
     * Computes the backprop of a 3d avg pool.
     *
     * @param dy The dy error, of rank 5 of shape
     *     [batchSize, depth, height, width, channels].
     * assumed.
     * @param input The original input image, of rank 5 or rank4 of shape
     *     [batchSize, depth, height, width, channels].
     * @param filterSize The filter size:
     *     `[filterDepth, filterHeight, filterWidth]`.
     *     `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 `strideHeight == strideWidth`.
     * @param dilations The dilation rates:
     *     `[dilationDepth, dilationHeight, dilationWidth]`
     *     in which we sample input values across the depth, height and width
     *     dimensions in dilated pooling.
     *     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.
     * @param pad A string from: 'same', 'valid'. The type of padding algorithm
     *     used in the forward prop of the op.
     * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. The
     *     rounding mode used when computing output dimensions if pad is a
     *     number. If none is provided, it will not round and error if the output
     *     is of fractional size.
     */
    function avgPool3dBackprop(dy, input, filterSize, strides, dilations, pad, dimRoundingMode) {
        var $dy = convertToTensor(dy, 'dy', 'avgPool3dBackprop');
        var $input = convertToTensor(input, 'input', 'avgPool3dBackprop');
        var dy5D = $dy;
        var input5D = $input;
        var reshapedTo5D = false;
        if ($input.rank === 4) {
            reshapedTo5D = true;
            dy5D = $dy.as5D(1, $dy.shape[0], $dy.shape[1], $dy.shape[2], $dy.shape[3]);
            input5D = $input.as5D(1, $input.shape[0], $input.shape[1], $input.shape[2], $input.shape[3]);
        }
        assert(dy5D.rank === 5, function () { return "Error in avgPool3dBackprop: dy must be rank 5 but got rank " +
            (dy5D.rank + "."); });
        assert(input5D.rank === 5, function () { return "Error in avgPool3dBackprop: input must be rank 5 but got rank " +
            (input5D.rank + "."); });
        if (dilations == null) {
            dilations = [1, 1, 1];
        }
        assert(eitherStridesOrDilationsAreOne(strides, dilations), function () { return 'Error in avgPool3dBackprop: Either strides or dilations ' +
            ("must be 1. Got strides " + strides + " and dilations '" + dilations + "'"); });
        if (dimRoundingMode != null) {
            assert(isInt(pad), function () { return "Error in maxPool3dBackprop: pad must be an integer when " +
                ("using, dimRoundingMode " + dimRoundingMode + " but got pad " + pad + "."); });
        }
        var convInfo = computePool3DInfo(input5D.shape, filterSize, strides, dilations, pad, dimRoundingMode);
        var res = ENGINE.runKernelFunc(function (backend) { return backend.avgPool3dBackprop(dy5D, input5D, convInfo); }, { dy5D: dy5D, input5D: input5D });
        if (reshapedTo5D) {
            return res.as4D(res.shape[1], res.shape[2], res.shape[3], res.shape[4]);
        }
        return res;
    }
    /**
     * Computes the 3D max pooling.
     *
     * ```js
     * const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);
     * const result = tf.maxPool3d(x, 2, 1, 'valid');
     * result.print();
     * ```
     *
     * @param x The input tensor, of rank 5 or rank 4 of shape
     *     `[batch, depth, height, width, inChannels]`.
     * @param filterSize The filter size:
     *     `[filterDepth, filterHeight, filterWidth]`.
     *     If `filterSize` is a single number,
     *     then `filterDepth == filterHeight == filterWidth`.
     * @param strides The strides of the pooling:
     *     `[strideDepth, strideHeight, strideWidth]`.
     *     If `strides` is a single number,
     *     then `strideDepth == strideHeight == strideWidth`.
     * @param pad The type of padding algorithm.
     *    - `same` and stride 1: output will be of same size as input,
     *       regardless of filter size.
     *    - `valid`: output will be smaller than input if filter is larger
     *       than 1*1x1.
     *    - For more info, see this guide:
     *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](
     *          https://www.tensorflow.org/api_guides/python/nn#Convolution)
     * @param dimRoundingMode The rounding mode used when computing output
     *     dimensions if pad is a number. If none is provided, it will not round
     *     and error if the output is of fractional size.
     * @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 depth, height and width
     *     dimensions in dilated pooling.
     *     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 maxPool3d_(x, filterSize, strides, pad, dimRoundingMode, dataFormat, dilations) {
        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 = $x.as5D(1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]);
        }
        if (dilations == null) {
            dilations = [1, 1, 1];
        }
        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); });
        assert(eitherStridesOrDilationsAreOne(strides, dilations), function () { return 'Error in maxPool3d: Either strides or dilations must be 1. ' +
            ("Got strides " + strides + " and dilations '" + dilations + "'"); });
        if (dimRoundingMode != null) {
            assert(isInt(pad), function () { return "Error in maxPool3d: pad must be an integer when using, " +
                ("dimRoundingMode " + dimRoundingMode + " but got pad " + pad + "."); });
        }
        var convInfo = computePool3DInfo(x5D.shape, filterSize, strides, dilations, pad, dimRoundingMode, dataFormat);
        var grad = function (dy, saved) {
            var x5D = saved[0], y = saved[1];
            return {
                x: function () { return maxPool3dBackprop(dy, x5D, y, filterSize, strides, dilations, pad, dimRoundingMode); }
            };
        };
        var res = ENGINE.runKernelFunc(function (backend, save) {
            var y = backend.maxPool3d(x5D, convInfo);
            save([x5D, y]);
            return y;
        }, { x: x5D }, grad);
        if (reshapedTo5D) {
            return res.as4D(res.shape[1], res.shape[2], res.shape[3], res.shape[4]);
        }
        return res;
    }
    /**
     * Computes the backprop of a 3d max pool.
     *
     * @param dy The dy error, of rank 5 of shape
     *     [batchSize, depth, height, width, channels].
     * assumed.
     * @param input The original input image, of rank 5 or rank 4 of shape
     *     [batchSize, depth, height, width, channels].
     * @param output The original output image, of rank 5 of shape
     *     [batchSize, outDepth, outHeight, outWidth, channels].
     * @param filterSize The filter size:
     *     `[filterDepth, filterHeight, filterWidth]`.
     *     `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 `strideHeight == strideWidth`.
     * @param dilations The dilation rates:
     *     `[dilationDepth, dilationHeight, dilationWidth]`
     *     in which we sample input values across the depth, height and width
     *     dimensions in dilated pooling.
     *     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.
     * @param pad A string from: 'same', 'valid'. The type of padding algorithm
     *     used in the forward prop of the op.
     * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. The
     *     rounding mode used when computing output dimensions if pad is a
     *     number. If none is provided, it will not round and error if the output
     *     is of fractional size.
     */
    function maxPool3dBackprop(dy, input, output, filterSize, strides, dilations, pad, dimRoundingMode) {
        var $dy = convertToTensor(dy, 'dy', 'maxPool3dBackprop');
        var $input = convertToTensor(input, 'input', 'maxPool3dBackprop');
        var $output = convertToTensor(output, 'output', 'maxPool3dBackprop');
        var dy5D = $dy;
        var input5D = $input;
        var output5D = $output;
        var reshapedTo5D = false;
        if ($input.rank === 4) {
            reshapedTo5D = true;
            dy5D = $dy.as5D(1, $dy.shape[0], $dy.shape[1], $dy.shape[2], $dy.shape[3]);
            input5D = $input.as5D(1, $input.shape[0], $input.shape[1], $input.shape[2], $input.shape[3]);
            output5D = $output.as5D(1, $output.shape[0], $output.shape[1], $output.shape[2], $output.shape[3]);
        }
        assert(dy5D.rank === 5, function () { return "Error in maxPool3dBackprop: dy must be rank 5 but got rank " +
            (dy5D.rank + "."); });
        assert(input5D.rank === 5, function () { return "Error in maxPool3dBackprop: input must be rank 5 but got rank " +
            (input5D.rank + "."); });
        assert(output5D.rank === 5, function () { return "Error in maxPool3dBackprop: output must be rank 5 but got rank " +
            (output5D.rank + "."); });
        if (dilations == null) {
            dilations = [1, 1, 1];
        }
        assert(eitherStridesOrDilationsAreOne(strides, dilations), function () { return 'Error in maxPool3dBackprop: Either strides or dilations ' +
            ("must be 1. Got strides " + strides + " and dilations '" + dilations + "'"); });
        if (dimRoundingMode != null) {
            assert(isInt(pad), function () { return "Error in maxPool3dBackprop: pad must be an integer when " +
                ("using, dimRoundingMode " + dimRoundingMode + " but got pad " + pad + "."); });
        }
        var convInfo = computePool3DInfo(input5D.shape, filterSize, strides, dilations, pad, dimRoundingMode);
        var res = ENGINE.runKernelFunc(function (backend) { return backend.maxPool3dBackprop(dy5D, input5D, output5D, convInfo); }, { dy5D: dy5D, input5D: input5D });
        if (reshapedTo5D) {
            return res.as4D(res.shape[1], res.shape[2], res.shape[3], res.shape[4]);
        }
        return res;
    }
    var maxPool = op({ maxPool_: maxPool_ });
    var avgPool = op({ avgPool_: avgPool_ });
    var pool = op({ pool_: pool_ });
    var maxPool3d = op({ maxPool3d_: maxPool3d_ });
    var avgPool3d = op({ avgPool3d_: avgPool3d_ });

    /**
     * @license
     * Copyright 2018 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.
     * =============================================================================
     */
    /**
     * 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]);
    }
    /**
     * 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);
    }
    /**
     * 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);
    }
    /**
     * 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);
    }
    /**
     * 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 $x = convertToTensor(x, 'x', 'slice');
        if ($x.rank === 0) {
            throw new Error('Slicing scalar is not possible');
        }
        // The following logic allows for more ergonomic calls.
        var begin_;
        if (typeof begin === 'number') {
            begin_ = [begin].concat(new Array($x.rank - 1).fill(0));
        }
        else if (begin.length < $x.rank) {
            begin_ = begin.concat(new Array($x.rank - 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($x.rank).fill(-1);
        }
        else if (typeof size === 'number') {
            size_ = [size].concat(new Array($x.rank - 1).fill(-1));
        }
        else if (size.length < $x.rank) {
            size_ = size.concat(new Array($x.rank - 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 $x.shape[i] - begin_[i];
            }
        });
        assertParamsValid($x, begin_, size_);
        var inputShape = $x.shape;
        var grad = function (dy) {
            // Create an Nx2 padding where the first column represents how many
            // zeros are prepended (at start) for each dimension, and the second
            // column indicates how many zeros are appended (at end).
            // The number of zeros to append is the shape of the input
            // elementwise-subtracted by both the begin vector and sizes vector.
            var paddings = [];
            for (var i = 0; i < dy.rank; i++) {
                paddings.push([begin_[i], inputShape[i] - begin_[i] - size_[i]]);
            }
            return { x: function () { return dy.pad(paddings); } };
        };
        var attrs = { begin: begin_, size: size_ };
        return ENGINE.runKernelFunc(function (backend) { return backend.slice($x, begin_, size_); }, { x: $x }, grad, 'Slice', attrs);
    }
    var slice = op({ slice_: slice_ });
    var slice1d = op({ slice1d_: slice1d_ });
    var slice2d = op({ slice2d_: slice2d_ });
    var slice3d = op({ slice3d_: slice3d_ });
    var slice4d = op({ slice4d_: slice4d_ });

    /**
     * @license
     * Copyright 2018 Google Inc. All Rights Reserved.
     * Licensed under the Apache License, Version 2.0 (the "License");
     * you may not use this file except in compliance with the License.
     * You may obtain a copy of the License at
     *
     * http://www.apache.org/licenses/LICENSE-2.0
     *
     * Unless required by applicable law or agreed to in writing, software
     * distributed under the License is distributed on an "AS IS" BASIS,
     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
     * See the License for the specific language governing permissions and
     * limitations under the License.
     * =============================================================================
     */
    /**
     * Computes the log(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 = $x.max(axes, true /* keepDims */);
        var a = $x.sub(xMax);
        var b = a.exp();
        var c = b.sum(axes);
        var d = c.log();
        var res = xMax.reshape(d.shape).add(d);
        if (keepDims) {
            var newShape = expandShapeToKeepDim(res.shape, axes);
            return res.reshape(newShape);
        }
        return res;
    }
    /**
     * 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 = $x.toInt();
        }
        var axes = parseAxisParam(axis, $x.shape);
        // Use a custom gradient to bypass 2 gradient backprops since sum is used
        // extremely often.
        var customOp = customGrad(function (x) {
            var permutation = getAxesPermutation(axes, x.rank);
            var reductionAxes = axes;
            var permutedX = x;
            if (permutation != null) {
                permutedX = x.transpose(permutation);
                reductionAxes = getInnerMostAxes(reductionAxes.length, x.rank);
            }
            var gradFunc = function (dy) {
                var expandedDyShape = x.shape.slice();
                axes.forEach(function (axis) {
                    expandedDyShape[axis] = 1;
                });
                var expandedDy = dy.reshape(expandedDyShape);
                var derX = expandedDy.mul(ones$1(x.shape, 'float32'));
                return derX;
            };
            var gradInputs = function (dy) {
                return { x: function () { return gradFunc(dy); } };
            };
            var attrs = { axes: reductionAxes };
            var value = ENGINE.runKernelFunc(function (backend) { return backend.sum(permutedX, reductionAxes); }, { x: permutedX }, gradInputs, 'Sum', attrs);
            if (keepDims) {
                var newShape = expandShapeToKeepDim(value.shape, axes);
                value = value.reshape(newShape);
            }
            return { value: value, gradFunc: gradFunc };
        });
        return customOp($x);
    }
    /**
     * 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') {
            $x = $x.toInt();
        }
        var axes = parseAxisParam(axis, $x.shape);
        var permutation = getAxesPermutation(axes, $x.rank);
        var reductionAxes = axes;
        var permutedX = $x;
        if (permutation != null) {
            permutedX = $x.transpose(permutation);
            reductionAxes = getInnerMostAxes(reductionAxes.length, $x.rank);
        }
        var value = ENGINE.runKernelFunc(function (backend) { return backend.prod(permutedX, reductionAxes); }, { permutedX: permutedX });
        if (keepDims) {
            var newShape = expandShapeToKeepDim(value.shape, axes);
            value = value.reshape(newShape);
        }
        return value;
    }
    /**
     * 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 axes = parseAxisParam(axis, $x.shape);
        var shapes = computeOutAndReduceShapes($x.shape, axes);
        var reduceShape = shapes[1];
        var reduceSize = sizeFromShape(reduceShape);
        // Use a custom gradient to bypass 2 gradient backprops since mean is used
        // extremely often.
        var customOp = customGrad(function (x) {
            var reduceSizeScalar = scalar(reduceSize);
            // Cast if needed.
            var xReduce = reduceSizeScalar.dtype === x.dtype ? x : x.cast(reduceSizeScalar.dtype);
            var res = xReduce.div(reduceSizeScalar);
            var value = res.sum(axis, keepDims);
            var gradFunc = function (dy) {
                var expandedDyShape = x.shape.slice();
                axes.forEach(function (axis) {
                    expandedDyShape[axis] = 1;
                });
                var expandedDy = dy.reshape(expandedDyShape);
                var derX = expandedDy.mul(ones$1(x.shape, 'float32')).div(reduceSize);
                return derX;
            };
            return { value: value, gradFunc: gradFunc };
        });
        return customOp($x);
    }
    /**
     * Gradient helper function for the min and max operations.
     */
    function gradForMinAndMax(dy, y, xOrig, origAxes, permutedAxes) {
        if (y.rank < xOrig.rank) {
            y = y.reshape(expandShapeToKeepDim(y.shape, origAxes));
        }
        if (dy.rank < xOrig.rank) {
            dy = dy.reshape(expandShapeToKeepDim(dy.shape, origAxes));
        }
        return {
            x: function () {
                var dx = dy.mul(xOrig.equal(y).cast(dy.dtype));
                return permutedAxes == null ? dx : dx.transpose(permutedAxes);
            }
        };
    }
    /**
     * 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 xOrig = $x;
        var origAxes = parseAxisParam(axis, $x.shape);
        var axes = origAxes;
        var permutedAxes = getAxesPermutation(axes, $x.rank);
        if (permutedAxes != null) {
            $x = $x.transpose(permutedAxes);
            axes = getInnerMostAxes(axes.length, $x.rank);
        }
        var grad = function (dy, saved) {
            return gradForMinAndMax(dy, saved[1], saved[0], origAxes, permutedAxes);
        };
        var inputsToSave = [$x];
        var outputsToSave = [true];
        var res = ENGINE.runKernelFunc(function (backend, save) {
            var y = backend.min($x, axes);
            save([xOrig, y]);
            return y;
        }, { x: $x }, grad, 'Min', { axes: axes }, inputsToSave, outputsToSave);
        if (keepDims) {
            var newShape = expandShapeToKeepDim(res.shape, origAxes);
            res = res.reshape(newShape);
        }
        return res;
    }
    /**
     * 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 xOrig = $x;
        var origAxes = parseAxisParam(axis, $x.shape);
        var axes = origAxes;
        var permutedAxes = getAxesPermutation(axes, $x.rank);
        if (permutedAxes != null) {
            $x = $x.transpose(permutedAxes);
            axes = getInnerMostAxes(axes.length, $x.rank);
        }
        var grad = function (dy, saved) {
            return gradForMinAndMax(dy, saved[1], saved[0], origAxes, permutedAxes);
        };
        var inputsToSave = [$x];
        var outputsToSave = [true];
        var res = ENGINE.runKernelFunc(function (backend, save) {
            var y = backend.max($x, axes);
            save([xOrig, y]);
            return y;
        }, { x: $x }, grad, 'Max', { axes: axes }, inputsToSave, outputsToSave);
        if (keepDims) {
            var newShape = expandShapeToKeepDim(res.shape, origAxes);
            res = res.reshape(newShape);
        }
        return res;
    }
    /**
     * 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');
        if (axis == null) {
            axis = 0;
        }
        var axes = parseAxisParam(axis, $x.shape);
        var permutedAxes = getAxesPermutation(axes, $x.rank);
        if (permutedAxes != null) {
            $x = $x.transpose(permutedAxes);
            axes = getInnerMostAxes(axes.length, $x.rank);
        }
        var grad = function (dy, saved) {
            var $x = saved[0];
            return { $x: function () { return zerosLike($x); } };
        };
        return ENGINE.runKernelFunc(function (backend, save) {
            var res = backend.argMin($x, axes[0]);
            save([$x]);
            return res;
        }, { $x: $x }, grad);
    }
    /**
     * 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');
        if (axis == null) {
            axis = 0;
        }
        var axes = parseAxisParam(axis, $x.shape);
        var permutedAxes = getAxesPermutation(axes, $x.rank);
        if (permutedAxes != null) {
            $x = $x.transpose(permutedAxes);
            axes = getInnerMostAxes(axes.length, $x.rank);
        }
        var grad = function (dy, saved) {
            var $x = saved[0];
            return { x: function () { return zerosLike($x); } };
        };
        var attrs = { axis: axes[0] };
        var inputsToSave = [$x];
        return ENGINE.runKernelFunc(function (backend, save) {
            var res = backend.argMax($x, axes[0]);
            save([$x]);
            return res;
        }, { x: $x }, grad, 'ArgMax', attrs, inputsToSave);
    }
    /**
     * 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 origAxes = parseAxisParam(axis, $x.shape);
        var axes = origAxes;
        var permutedAxes = getAxesPermutation(axes, $x.rank);
        if (permutedAxes != null) {
            $x = $x.transpose(permutedAxes);
            axes = getInnerMostAxes(axes.length, $x.rank);
        }
        var res = ENGINE.runKernelFunc(function (backend) { return backend.all($x, axes); }, { $x: $x });
        if (keepDims) {
            var newShape = expandShapeToKeepDim(res.shape, origAxes);
            return res.reshape(newShape);
        }
        return res;
    }
    /**
     * 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 origAxes = parseAxisParam(axis, $x.shape);
        var axes = origAxes;
        var permutedAxes = getAxesPermutation(axes, $x.rank);
        if (permutedAxes != null) {
            $x = $x.transpose(permutedAxes);
            axes = getInnerMostAxes(axes.length, $x.rank);
        }
        var res = ENGINE.runKernelFunc(function (backend) { return backend.any($x, axes); }, { $x: $x });
        if (keepDims) {
            var newShape = expandShapeToKeepDim(res.shape, origAxes);
            return res.reshape(newShape);
        }
        return res;
    }
    /**
     * 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 mean = x.mean(axes, keepDims);
        var keepDimsShape = mean.shape;
        if (!keepDims) {
            keepDimsShape = expandShapeToKeepDim(mean.shape, axes);
        }
        var devSquared = x.toFloat().sub(mean.reshape(keepDimsShape)).square();
        var variance = devSquared.mean(axes, keepDims);
        return { mean: mean, variance: variance };
    }
    var all = op({ all_: all_ });
    // tslint:disable-next-line:variable-name
    var any = op({ any_: any_ });
    var argMax = op({ argMax_: argMax_ });
    var argMin = op({ argMin_: argMin_ });
    var logSumExp = op({ logSumExp_: logSumExp_ });
    var max = op({ max_: max_ });
    var mean = op({ mean_: mean_ });
    var min = op({ min_: min_ });
    var moments = op({ moments_: moments_ });
    var sum$1 = op({ sum_: sum_ });
    var prod = op({ prod_: prod_ });

    /**
     * @license
     * Copyright 2018 Google LLC. All Rights Reserved.
     * Licensed under the Apache License, Version 2.0 (the "License");
     * you may not use this file except in compliance with the License.
     * You may obtain a copy of the License at
     *
     * http://www.apache.org/licenses/LICENSE-2.0
     *
     * Unless required by applicable law or agreed to in writing, software
     * distributed under the License is distributed on an "AS IS" BASIS,
     * WITHOUT 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 $x = convertToTensor(x, 'x', 'relu');
        if ($x.dtype === 'bool') {
            return $x.toInt();
        }
        var grad = function (dy, saved) {
            var $x = saved[0];
            return { x: function () { return dy.mulStrict($x.step().toFloat()); } };
        };
        return ENGINE.runKernelFunc(function (backend, save) {
            var res = backend.relu($x);
            save([$x]);
            return res;
        }, { x: $x }, grad, 'Relu');
    }
    /**
     * 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 $x = convertToTensor(x, 'x', 'relu6');
        if ($x.dtype === 'bool') {
            return $x.toInt();
        }
        var grad = function (dy, saved) {
            var $x = saved[0];
            var mask = $x.lessEqual(6).mul($x.step());
            return { x: function () { return dy.mulStrict(mask.toFloat()); } };
        };
        return ENGINE.runKernelFunc(function (backend, save) {
            var res = backend.relu6($x);
            save([$x]);
            return res;
        }, { x: $x }, grad, 'Relu6');
    }
    /**
     * Computes exponential linear element-wise: `x > 0 ? e ^ x - 1 : 0`.
     *
     * ```js
     * const x = tf.tensor1d([-1, 1, -3, 2]);
     *
     * x.elu().print();  // or tf.elu(x)
     * ```
     * @param x The input tensor.
     */
    /** @doc {heading: 'Operations', subheading: 'Basic math'} */
    function elu_(x) {
        var $x = convertToTensor(x, 'x', 'elu');
        var grad = function (dy, saved) {
            var y = saved[0];
            return {
                $x: function () {
                    return ENGINE.runKernelFunc(function (backend) { return backend.eluDer(dy, y); }, { dy: dy, y: y });
                }
            };
        };
        return ENGINE.runKernelFunc(function (backend, save) {
            var y = backend.elu($x);
            save([y]);
            return y;
        }, { $x: $x }, grad);
    }
    /**
     * 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 grad = function (dy, saved) {
            var $x = saved[0];
            return {
                $x: function () {
                    var mask = $x.greater(scalar(0));
                    var scaleAlpha = scalar(SELU_SCALEALPHA);
                    var scale = scalar(SELU_SCALE);
                    var greaterThanZeroDer = dy.mul(scale);
                    var lessEqualZeroDer = dy.mul(scaleAlpha).mul($x.toFloat().exp());
                    return where(mask, greaterThanZeroDer, lessEqualZeroDer);
                }
            };
        };
        return ENGINE.runKernelFunc(function (backend, save) {
            var res = backend.selu($x);
            save([$x]);
            return res;
        }, { $x: $x }, grad);
    }
    /**
     * 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 $x = convertToTensor(x, 'x', 'leakyRelu');
        return maximum(scalar(alpha).mul($x), $x);
    }
    /**
     * 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 grad = function (dy, saved) {
            var $x = saved[0], $alpha = saved[1];
            var mask = $x.greater(0);
            return {
                x: function () { return where(mask, dy, dy.mul($alpha)); },
                alpha: function () {
                    var res = where(mask, zerosLike(dy), dy.mul($x));
                    var reduceAxes = getReductionAxes($alpha.shape, dy.shape);
                    if (reduceAxes.length > 0) {
                        res = res.sum(reduceAxes);
                    }
                    return res.reshape($alpha.shape);
                }
            };
        };
        return ENGINE.runKernelFunc(function (backend, save) {
            var res = backend.prelu($x, $alpha);
            save([$x, $alpha]);
            return res;
        }, { x: $x, alpha: $alpha }, grad, 'Prelu');
    }
    var elu = op({ elu_: elu_ });
    var leakyRelu = op({ leakyRelu_: leakyRelu_ });
    var prelu = op({ prelu_: prelu_ });
    var relu = op({ relu_: relu_ });
    var relu6 = op({ relu6_: relu6_ });
    var selu = op({ selu_: selu_ });

    /**
     * @license
     * Copyright 2018 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.
     * =============================================================================
     */
    /**
     * 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 $x.clone();
        }
        var der = function (dy) {
            var undoPerm = getUndoAxesPermutation(perm);
            return { x: function () { return dy.transpose(undoPerm); } };
        };
        var attrs = { perm: perm };
        return ENGINE.runKernelFunc(function (backend) { return backend.transpose($x, perm); }, { x: $x }, der, 'Transpose', attrs);
    }
    var transpose = op({ transpose_: transpose_ });

    /**
     * @license
     * Copyright 2018 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.
     * =============================================================================
     */
    /**
     * 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 = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);
        }
        var backward = function (dy, saved) {
            var x4D = saved[0], y = saved[1];
            return {
                x4D: function () { return ENGINE.runKernelFunc(function (backend) { return backend.LRNGrad(dy, x4D, y, depthRadius, bias, alpha, beta); }, {}); }
            };
        };
        var res = ENGINE.runKernelFunc(function (backend, save) {
            var y = backend.localResponseNormalization4D(x4D, depthRadius, bias, alpha, beta);
            save([x4D, y]);
            return y;
        }, { x4D: x4D }, backward);
        if (reshapedTo4D) {
            return res.as3D(res.shape[1], res.shape[2], res.shape[3]);
        }
        else {
            return res;
        }
    }
    var localResponseNormalization = op({ localResponseNormalization_: localResponseNormalization_ });

    /**
     * @license
     * Copyright 2018 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 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 norm.reshape(keepDimsShape);
    }
    function normImpl(x, p, axis) {
        if (axis === void 0) { axis = null; }
        if (x.rank === 0) {
            return x.abs();
        }
        // consider vector when no axis is specified
        if (x.rank !== 1 && axis === null) {
            return normImpl(x.reshape([-1]), p, axis);
        }
        // vector
        if (x.rank === 1 || typeof axis === 'number' ||
            Array.isArray(axis) && axis.length === 1) {
            if (p === 1) {
                return x.abs().sum(axis);
            }
            if (p === Infinity) {
                return x.abs().max(axis);
            }
            if (p === -Infinity) {
                return x.abs().min(axis);
            }
            if (p === 'euclidean' || p === 2) {
                // norm(x, 2) = sum(abs(xi) ^ 2) ^ 1/2
                return x.abs().pow(scalar(2, 'int32')).sum(axis).sqrt();
            }
            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 x.abs().sum(axis[0]).max(axis[1] - 1);
            }
            if (p === Infinity) {
                return x.abs().sum(axis[1]).max(axis[0]);
            }
            if (p === -Infinity) {
                return x.abs().sum(axis[1]).min(axis[0]);
            }
            if (p === 'fro' || p === 'euclidean') {
                // norm(x) = sqrt(sum(pow(x, 2)))
                return x.square().sum(axis).sqrt();
            }
            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 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 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];
    }
    /**
     * 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 = $data.concat($h, 1);
        var weighted = combined.matMul($lstmKernel);
        var res = weighted.add($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 = res.slice([0, 0], sliceSize);
        var j = res.slice([0, sliceCols], sliceSize);
        var f = res.slice([0, sliceCols * 2], sliceSize);
        var o = res.slice([0, sliceCols * 3], sliceSize);
        var newC = i.sigmoid().mulStrict(j.tanh()).addStrict($c.mulStrict($forgetBias.add(f).sigmoid()));
        var newH = newC.tanh().mulStrict(o.sigmoid());
        return [newC, newH];
    }
    var basicLSTMCell = op({ basicLSTMCell_: basicLSTMCell_ });
    var multiRNNCell = op({ multiRNNCell_: multiRNNCell_ });

    /**
     * @license
     * Copyright 2018 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.
     * =============================================================================
     */
    /**
     * 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, $x.shape), function () { return 'Shape mismatch in v and x'; });
        var one = scalar(1);
        var oneMinusDecay = one.sub($decay);
        var update = $x.sub($v).mul(oneMinusDecay);
        if (zeroDebias) {
            assert(step != null, function () { return 'When using zeroDebias: true, step is required.'; });
            var $step = convertToTensor(step, 'step', 'movingAverage');
            update = update.div(one.sub(pow($decay, $step)));
        }
        return $v.add(update);
    }
    var movingAverage = op({ movingAverage_: movingAverage_ });

    /**
     * @license
     * Copyright 2018 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.
     * =============================================================================
     */
    /**
     * 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; }
        if (strides == null) {
            strides = new Array(begin.length);
        }
        if (ellipsisMask !== 0) {
            throw new Error('ellipsis mask is not yet supported');
        }
        var $x = convertToTensor(x, 'x', 'stridedSlice');
        // Expand the dims of x based on the newAxisMask.
        var expandAxes = maskToAxes(newAxisMask);
        var newShape = $x.shape.slice();
        expandAxes.forEach(function (axis) {
            begin[axis] = 0;
            end[axis] = 1;
            newShape.splice(axis, 0, 1);
        });
        $x = $x.reshape(newShape);
        // Normalize the start, end and strides.
        for (var axis = 0; axis < $x.rank; axis++) {
            begin[axis] = startForAxis(beginMask, begin, strides, $x.shape, axis);
            end[axis] = stopForAxis(endMask, end, strides, $x.shape, axis);
            strides[axis] = strides[axis] || 1;
        }
        var shrinkAxes = maskToAxes(shrinkAxisMask);
        // Adjust the ends based on the shrink mask.
        shrinkAxes.forEach(function (axis) {
            end[axis] = begin[axis] + 1;
            strides[axis] = 1;
        });
        // Figure out the output shape.
        var size = computeOutShape$2(begin, end, strides);
        // Remove the axes based on shrinkMask.
        var outShape = size.filter(function (_, axis) { return shrinkAxes.indexOf(axis) === -1; });
        var nonStrided = strides.every(function (v) { return v === 1; });
        if (nonStrided) {
            return slice($x, begin, size).reshape(outShape);
        }
        var res = ENGINE.runKernelFunc(function (backend) { return backend.stridedSlice($x, begin, end, strides); }, { $x: $x });
        return res.reshape(outShape);
    }
    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.
     * =============================================================================
     */
    /**
     * Finds the values and indices of the `k` largest entries along the last
     * dimension.
     *
     * If the input is a vector (rank=1), finds the k largest entries in the vector
     * and outputs their values and indices as vectors. Thus values[j] is the j-th
     * largest entry in input, and its index is indices[j].
     * For higher rank inputs, computes the top k entries along the last dimension.
     *
     * If two elements are equal, the lower-index element appears first.
     *
     * ```js
     * const a = tf.tensor2d([[1, 5], [4, 3]]);
     * const {values, indices} = tf.topk(a);
     * values.print();
     * indices.print();
     * ```
     * @param x 1-D or higher `tf.Tensor` with last dimension being at least `k`.
     * @param k Number of top elements to look for along the last dimension.
     * @param sorted If true, the resulting `k` elements will be sorted by the
     *     values in descending order.
     */
    /** @doc {heading: 'Operations', subheading: 'Evaluation'} */
    function topk_(x, k, sorted) {
        if (k === void 0) { k = 1; }
        if (sorted === void 0) { sorted = true; }
        var $x = convertToTensor(x, 'x', 'topk');
        if ($x.rank === 0) {
            throw new Error('topk() expects the input to be of rank 1 or higher');
        }
        var lastDim = $x.shape[$x.shape.length - 1];
        if (k > lastDim) {
            throw new Error("'k' passed to topk() must be <= the last dimension (" + lastDim + ") " +
                ("but got " + k));
        }
        var _a = ENGINE.runKernelFunc(function (b) { return b.topk($x, k, sorted); }, { $x: $x }), values = _a[0], indices = _a[1];
        return { values: values, indices: indices };
    }
    var topk = op({ topk_: topk_ });

    /**
     * @license
     * Copyright 2018 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 a new tensor by applying sparse updates to individual
     * values or slices within a zero tensor of the given shape tensor according to
     * indices. This operator is the inverse of the `tf.gatherND` operator which
     * extracts values or slices from a given tensor.
     *
     * ```js
     * const indices = tf.tensor2d([4, 3, 1, 7], [4, 1], 'int32');
     * const updates = tf.tensor1d([9, 10, 11, 12]);
     * const shape = [8];
     * tf.scatterND(indices, updates, shape).print() //[0, 11, 0, 10, 9, 0, 0, 12]
     * ```
     *
     * @param indices The tensor contains the indices into the output tensor.
     * @param updates The tensor contains the value for the indices.
     * @param shape: The shape of the output tensor.
     */
    /** @doc {heading: 'Operations', subheading: 'Slicing and Joining'} */
    function scatterND_(indices, updates, shape) {
        var $indices = convertToTensor(indices, 'indices', 'scatterND', 'int32');
        var $updates = convertToTensor(updates, 'updates', 'scatterND');
        validateInput($updates, $indices, shape);
        return ENGINE.runKernelFunc(function (backend) { return backend.scatterND($indices, $updates, shape); }, { indices: $indices, updates: $updates }, null /* backward */, 'ScatterNd', { shape: shape });
    }
    var scatterND = op({ scatterND_: scatterND_ });

    /**
     * @license
     * Copyright 2018 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.
     * =============================================================================
     */
    /**
     * 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 + "."); });
        // Collapse all outer dimensions to a single batch dimension.
        var innerDimensionSize = input.shape[input.shape.length - 1];
        var batch = input.size / innerDimensionSize;
        var input2D = input.as2D(batch, innerDimensionSize);
        var ret = ENGINE.runKernelFunc(function (backend) { return backend.fft(input2D); }, { input: input });
        return ret.reshape(input.shape);
    }
    /**
     * 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 + "."); });
        // Collapse all outer dimensions to a single batch dimension.
        var innerDimensionSize = input.shape[input.shape.length - 1];
        var batch = input.size / innerDimensionSize;
        var input2D = input.as2D(batch, innerDimensionSize);
        var ret = ENGINE.runKernelFunc(function (backend) { return backend.ifft(input2D); }, { input: input });
        return ret.reshape(input.shape);
    }
    /**
     * 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 = input.slice(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 = input.concat(zeros(zerosShape), input.shape.length - 1);
            innerDimensionSize = fftLength;
        }
        else {
            adjustedInput = input;
        }
        // Complement the input with zero imaginary numbers.
        var zerosInput = adjustedInput.zerosLike();
        var complexInput = complex(adjustedInput, zerosInput).as2D(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 = realValues.split([half, innerDimensionSize - half], realValues.shape.length - 1);
        var imagComplexConjugate = imagValues.split([half, innerDimensionSize - half], imagValues.shape.length - 1);
        var outputShape = adjustedInput.shape.slice();
        outputShape[adjustedInput.shape.length - 1] = half;
        return complex(realComplexConjugate[0], imagComplexConjugate[0])
            .reshape(outputShape);
    }
    /**
     * 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;
        if (innerDimensionSize <= 2) {
            var complexInput = input.as2D(batch, innerDimensionSize);
            var ret = ifft(complexInput);
            return real(ret);
        }
        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 = real(input).as2D(batch, innerDimensionSize);
            var imagInput = imag(input).as2D(batch, innerDimensionSize);
            var realConjugate = realInput.slice([0, 1], [batch, innerDimensionSize - 2]).reverse(1);
            var imagConjugate = imagInput.slice([0, 1], [batch, innerDimensionSize - 2])
                .reverse(1)
                .mul(scalar(-1));
            var r = realInput.concat(realConjugate, 1);
            var i = imagInput.concat(imagConjugate, 1);
            var complexInput = complex(r, i).as2D(outputShape[0], outputShape[1]);
            var ret = ifft(complexInput);
            return real(ret);
        }
    }
    var fft = op({ fft_: fft_ });
    var ifft = op({ ifft_: ifft_ });
    var rfft = op({ rfft_: rfft_ });
    var irfft = op({ irfft_: irfft_ });

    var spectral_ops = /*#__PURE__*/Object.freeze({
        fft: fft,
        ifft: ifft,
        rfft: rfft,
        irfft: irfft
    });

    /**
     * Validate sparseToDense inputs.
     *
     * @param sparseIndices A 0-D, 1-D, or 2-D Tensor of type int32.
     * sparseIndices[i] contains the complete index where sparseValues[i] will be
     * placed.
     * @param sparseValues A 0-D or 1-D Tensor. Values
     * corresponding to each row of sparseIndices, or a scalar value to be used for
     * all sparse indices.
     * @param outputShape number[]. Shape of the dense output tensor.
     * @param validateIndices boolean. indice validation is not supported, error
     * will be thrown if it is set.
     */
    function validateInput$1(sparseIndices, sparseValues, outputShape, defaultValues) {
        if (sparseIndices.dtype !== 'int32') {
            throw new Error('tf.sparseToDense() expects the indices to be int32 type,' +
                (" but the dtype was " + sparseIndices.dtype + "."));
        }
        if (sparseIndices.rank > 2) {
            throw new Error('sparseIndices should be a scalar, vector, or matrix,' +
                (" but got shape " + sparseIndices.shape + "."));
        }
        var numElems = sparseIndices.rank > 0 ? sparseIndices.shape[0] : 1;
        var numDims = sparseIndices.rank > 1 ? sparseIndices.shape[1] : 1;
        if (outputShape.length !== numDims) {
            throw new Error('outputShape has incorrect number of elements:,' +
                (" " + outputShape.length + ", should be: " + numDims + "."));
        }
        var numValues = sparseValues.size;
        if (!(sparseValues.rank === 0 ||
            sparseValues.rank === 1 && numValues === numElems)) {
            throw new Error('sparseValues has incorrect shape ' +
                (sparseValues.shape + ", should be [] or [" + numElems + "]"));
        }
        if (sparseValues.dtype !== defaultValues.dtype) {
            throw new Error('sparseValues.dtype must match defaultValues.dtype');
        }
    }

    /**
     * @license
     * Copyright 2018 Google 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.
     * =============================================================================
     */
    /**
     * Converts a sparse representation into a dense tensor.
     *
     * Builds an array dense with shape outputShape such that:
     *
     * // If sparseIndices is scalar
     * dense[i] = (i == sparseIndices ? sparseValues : defaultValue)
     *
     * // If sparseIndices is a vector, then for each i
     * dense[sparseIndices[i]] = sparseValues[i]
     *
     * // If sparseIndices is an n by d matrix, then for each i in [0, n)
     * dense[sparseIndices[i][0], ..., sparseIndices[i][d-1]] = sparseValues[i]
     * All other values in dense are set to defaultValue. If sparseValues is a
     * scalar, all sparse indices are set to this single value.
     *
     * If indices are repeated the final value is summed over all values for those
     * indices.
     *
     * ```js
     * const indices = tf.tensor1d([4, 5, 6, 1, 2, 3], 'int32');
     * const values = tf.tensor1d([10, 11, 12, 13, 14, 15], 'float32');
     * const shape = [8];
     * tf.sparseToDense(indices, values, shape).print();
     * ```
     *
     * @param sparseIndices A 0-D, 1-D, or 2-D Tensor of type int32.
     * sparseIndices[i] contains the complete index where sparseValues[i] will be
     * placed.
     * @param sparseValues A 0-D or 1-D Tensor. Values
     * corresponding to each row of sparseIndices, or a scalar value to be used for
     * all sparse indices.
     * @param outputShape Shape of the dense output tensor. the type is inferred.
     * @param defaultValue Scalar. Value to set for indices not specified in
     * sparseIndices. Defaults to zero.
     */
    /** @doc {heading: 'Operations', subheading: 'Normalization'} */
    function sparseToDense_(sparseIndices, sparseValues, outputShape, defaultValue) {
        if (defaultValue === void 0) { defaultValue = 0; }
        var $sparseIndices = convertToTensor(sparseIndices, 'sparseIndices', 'sparseToDense', 'int32');
        var $sparseValues = convertToTensor(sparseValues, 'sparseValues', 'sparseToDense');
        var $defaultValue = convertToTensor(defaultValue, 'defaultValue', 'sparseToDense', $sparseValues.dtype);
        validateInput$1($sparseIndices, $sparseValues, outputShape, $defaultValue);
        return ENGINE.runKernelFunc(function (backend) { return backend.sparseToDense($sparseIndices, $sparseValues, outputShape, $defaultValue); }, { $sparseIndices: $sparseIndices, $sparseValues: $sparseValues, $defaultValue: $defaultValue });
    }
    var sparseToDense = op({ sparseToDense_: sparseToDense_ });

    /**
     * @license
     * Copyright 2018 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.
     * =============================================================================
     */
    /**
     * 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 $x = convertToTensor(x, 'x', 'gatherND');
        return ENGINE.runKernelFunc(function (backend) { return backend.gatherND($x, $indices); }, { x: $x, indices: $indices }, null /* backward */, 'GatherNd');
    }
    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.
     * =============================================================================
     */
    /**
     * Returns a diagonal tensor with a given diagonal values.
     *
     * Given a diagonal, this operation returns a tensor with the diagonal and
     * everything else padded with zeros.
     *
     * Assume the input has dimensions `[D1,..., Dk]`, then the output is a tensor
     * of rank 2k with dimensions `[D1,..., Dk, D1,..., Dk]`
     *
     * ```js
     * const x = tf.tensor1d([1, 2, 3, 4]);
     *
     * tf.diag(x).print()
     * ```
     * ```js
     * const x = tf.tensor1d([1, 2, 3, 4, 5, 6, 6, 8], [4, 2])
     *
     * tf.diag(x).print()
     * ```
     * @param x The input tensor.
     */
    function diag_(x) {
        var $x = convertToTensor(x, 'x', 'diag').flatten();
        var outShape = x.shape.concat(x.shape);
        return ENGINE.runKernelFunc(function (backend) { return backend.diag($x); }, { $x: $x })
            .reshape(outShape);
    }
    var diag = op({ diag_: diag_ });

    /**
     * @license
     * Copyright 2019 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.
     * =============================================================================
     */
    /**
     * 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 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 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 = randomUniform($noiseShape, 0, 1, 'float32', seed)
            .add(keepProb)
            .floor()
            .div(keepProb);
        return $x.mul(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.
     * =============================================================================
     */
    /**
     * 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);
    }
    /**
     * 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);
    }
    /**
     * 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 concat(output).as2D(output.length, frameLength);
    }
    /**
     * 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));
        var output = [];
        for (var i = 0; i < framedSignal.shape[0]; i++) {
            output.push(rfft(windowedSignal.slice([i, 0], [1, frameLength]), fftLength));
        }
        return concat(output);
    }
    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');
    }
    var hannWindow = op({ hannWindow_: hannWindow_ });
    var hammingWindow = op({ hammingWindow_: hammingWindow_ });
    var frame = op({ frame_: frame_ });
    var stft = op({ stft_: stft_ });

    var signal_ops = /*#__PURE__*/Object.freeze({
        hannWindow: hannWindow,
        hammingWindow: hammingWindow,
        frame: frame,
        stft: stft
    });

    /**
     * @license
     * Copyright 2019 Google LLC. All Rights Reserved.
     * Licensed under the Apache License, Version 2.0 (the "License");
     * you may not use this file except in compliance with the License.
     * You may obtain a copy of the License at
     *
     * http://www.apache.org/licenses/LICENSE-2.0
     *
     * Unless required by applicable law or agreed to in writing, software
     * distributed under the License is distributed on an "AS IS" BASIS,
     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
     * See the License for the specific language governing permissions and
     * limitations under the License.
     * =============================================================================
     */
    /**
     * Returns whether the targets are in the top K predictions.
     *
     * ```js
     * const predictions = tf.tensor2d([[20, 10, 40, 30], [30, 50, -20, 10]]);
     * const targets = tf.tensor1d([2, 0]);
     * const precision = await tf.inTopKAsync(predictions, targets);
     * precision.print();
     * ```
     * @param predictions 2-D or higher `tf.Tensor` with last dimension being
     *     at least `k`.
     * @param targets 1-D or higher `tf.Tensor`.
     * @param k Optional Number of top elements to look at for computing precision,
     *     default to 1.
     */
    /** @doc {heading: 'Operations', subheading: 'Evaluation'} */
    function inTopKAsync_(predictions, targets, k) {
        if (k === void 0) { k = 1; }
        return __awaiter(this, void 0, void 0, function () {
            var $predictions, $targets, lastDim, predictionsVals, targetsVals, _a, batch, size, precision, b, offset, vals, valAndInd, i, i;
            return __generator(this, function (_b) {
                switch (_b.label) {
                    case 0:
                        $predictions = convertToTensor(predictions, 'predictions', 'inTopK');
                        $targets = convertToTensor(targets, 'targets', 'inTopK');
                        assert($predictions.rank > 1, function () { return 'inTopK() expects the predictions to be of rank 2 or higher, ' +
                            ("but got " + $predictions.rank); });
                        assert($predictions.rank - 1 === $targets.rank, function () { return "predictions rank should be 1 larger than " +
                            "targets rank, but got predictions rank " +
                            ($predictions.rank + " and targets rank " + $targets.rank); });
                        assertShapesMatch($predictions.shape.slice(0, $predictions.shape.length - 1), $targets.shape, "predictions's shape should be align with the targets' shape, " +
                            'except the last dimension.');
                        lastDim = $predictions.shape[$predictions.shape.length - 1];
                        assert(k > 0 && k <= lastDim, function () { return "'k' passed to inTopK() must be > 0 && <= the predictions last " +
                            ("dimension (" + lastDim + "), but got " + k); });
                        return [4 /*yield*/, $predictions.data()];
                    case 1:
                        predictionsVals = _b.sent();
                        return [4 /*yield*/, $targets.data()];
                    case 2:
                        targetsVals = _b.sent();
                        _a = [predictionsVals.length / lastDim, lastDim], batch = _a[0], size = _a[1];
                        precision = getTypedArrayFromDType('bool', batch);
                        for (b = 0; b < batch; b++) {
                            offset = b * size;
                            vals = predictionsVals.subarray(offset, offset + size);
                            valAndInd = [];
                            for (i = 0; i < vals.length; i++) {
                                valAndInd.push({ value: vals[i], index: i });
                            }
                            valAndInd.sort(function (a, b) { return b.value - a.value; });
                            precision[b] = 0;
                            for (i = 0; i < k; i++) {
                                if (valAndInd[i].index === targetsVals[b]) {
                                    precision[b] = 1;
                                    break;
                                }
                            }
                        }
                        if (predictions !== $predictions) {
                            $predictions.dispose();
                        }
                        if (targets !== $targets) {
                            $targets.dispose();
                        }
                        // Output precision has the same shape as targets.
                        return [2 /*return*/, tensor(precision, $targets.shape, 'bool')];
                }
            });
        });
    }
    var inTopKAsync = inTopKAsync_;

    /**
     * @license
     * Copyright 2018 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.
     * =============================================================================
     */
    (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 : $losses.mul($weights);
        if (reduction === exports.Reduction.NONE) {
            return weightedLoss;
        }
        if (reduction === exports.Reduction.SUM) {
            return weightedLoss.sum();
        }
        if (reduction === exports.Reduction.MEAN) {
            if ($weights == null) {
                return weightedLoss.mean();
            }
            else {
                var broadcastFactor = $losses.size / $weights.size;
                var result = weightedLoss.sum().div($weights.sum());
                return broadcastFactor > 1 ? result.div(scalar(broadcastFactor)) :
                    result;
            }
        }
        if (reduction === exports.Reduction.SUM_BY_NONZERO_WEIGHTS) {
            if ($weights == null) {
                return weightedLoss.sum().div(scalar($losses.size));
            }
            else {
                var broadcastedWeights = $weights.mul(ones$1($losses.shape));
                var numNonZeros = broadcastedWeights.notEqual(scalar(0)).sum().toFloat();
                return weightedLoss.sum().div(numNonZeros);
            }
        }
        throw Error("Unknown reduction: " + reduction);
    }
    /**
     * 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 = $labels.sub($predictions).abs();
        return computeWeightedLoss(losses, $weights, reduction);
    }
    /**
     * 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 = $labels.squaredDifference($predictions);
        return computeWeightedLoss(losses, $weights, reduction);
    }
    /**
     * 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, $predictions.shape, 'Error in cosineDistance: ');
        var one = scalar(1);
        var losses = one.sub($labels.mul($predictions).sum(axis, true));
        return computeWeightedLoss(losses, $weights, reduction);
    }
    /**
     * 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)
        $labels = scalar(2).mul($labels).sub(one);
        var losses = one.sub($labels.mul($predictions)).relu();
        return computeWeightedLoss(losses, $weights, reduction);
    }
    /**
     * 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 losses = $labels.mul($predictions.add(epsilonScalar).log())
            .neg()
            .sub(one.sub($labels).mul(one.sub($predictions).add(epsilonScalar).log()));
        return computeWeightedLoss(losses, $weights, reduction);
    }
    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 = $logits.relu();
        var outputXTarget = $logits.mul($labels);
        var sigmoidOutput = $logits.abs().neg().exp().log1p();
        return maxOutput.sub(outputXTarget).add(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 = $multiClassLabels.mul(one.sub(labelSmoothingScalar))
                .add(half.mul(labelSmoothingScalar));
        }
        var losses = sigmoidCrossEntropyWithLogits_($multiClassLabels, $logits);
        return computeWeightedLoss(losses, $weights, reduction);
    }
    /**
     * 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 = $predictions.sub($labels).abs();
        var quadratic = minimum(error, deltaScalar);
        var linear = error.sub(quadratic);
        var losses = scalar(0.5).mul(quadratic.square()).add(deltaScalar.mul(linear));
        return computeWeightedLoss(losses, $weights, reduction);
    }
    /**
     * 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 = logits.logSumExp([dim], keepDims);
            var logResult = logits.toFloat().sub(lse);
            save([labels, logResult]);
            var costVector = logResult.mul(labels).neg();
            var value = costVector.sum([dim]);
            var gradFunc = function (dy, saved) {
                var labels = saved[0], logResult = saved[1];
                var dyShape = expandShapeToKeepDim(dy.shape, [dim]);
                return [
                    dy.reshape(dyShape).mul(labels.toFloat().sub(logResult.exp())),
                    dy.reshape(dyShape).mul(logResult.exp().sub(labels.toFloat())),
                ];
            };
            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 = $onehotLabels.mul(one.sub(labelSmoothingScalar))
                .add(labelSmoothingScalar.div(numClasses));
        }
        var losses = softmaxCrossEntropyWithLogits_($onehotLabels, $logits);
        return computeWeightedLoss(losses, $weights, reduction);
    }
    var absoluteDifference = op({ absoluteDifference_: absoluteDifference_ });
    var computeWeightedLoss = op({ computeWeightedLoss_: computeWeightedLoss_ });
    var cosineDistance = op({ cosineDistance_: cosineDistance_ });
    var hingeLoss = op({ hingeLoss_: hingeLoss_ });
    var huberLoss = op({ huberLoss_: huberLoss_ });
    var logLoss = op({ logLoss_: logLoss_ });
    var meanSquaredError = op({ meanSquaredError_: meanSquaredError_ });
    var sigmoidCrossEntropy = op({ sigmoidCrossEntropy_: sigmoidCrossEntropy_ });
    var softmaxCrossEntropy = op({ softmaxCrossEntropy_: softmaxCrossEntropy_ });

    var loss_ops = /*#__PURE__*/Object.freeze({
        get Reduction () { return exports.Reduction; },
        absoluteDifference: absoluteDifference,
        computeWeightedLoss: computeWeightedLoss,
        cosineDistance: cosineDistance,
        hingeLoss: hingeLoss,
        huberLoss: huberLoss,
        logLoss: logLoss,
        meanSquaredError: meanSquaredError,
        sigmoidCrossEntropy: sigmoidCrossEntropy,
        softmaxCrossEntropy: softmaxCrossEntropy
    });

    /**
     * @license
     * Copyright 2018 Google LLC. All Rights Reserved.
     * Licensed under the Apache License, Version 2.0 (the "License");
     * you may not use this file except in compliance with the License.
     * You may obtain a copy of the License at
     *
     * http://www.apache.org/licenses/LICENSE-2.0
     *
     * Unless required by applicable law or agreed to in writing, software
     * distributed under the License is distributed on an "AS IS" BASIS,
     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
     * See the License for the specific language governing permissions and
     * limitations under the License.
     * =============================================================================
     */
    /**
     * Copy a tensor setting everything outside a central band in each innermost
     * matrix to zero.
     *
     * The band part is computed as follows: Assume input has `k` dimensions
     * `[I, J, K, ..., M, N]`, then the output is a tensor with the same shape where
     * `band[i, j, k, ..., m, n] = in_band(m, n) * input[i, j, k, ..., m, n]`.
     * The indicator function
     * `in_band(m, n) = (num_lower < 0 || (m-n) <= num_lower))`
     * `&& (num_upper < 0 || (n-m) <= num_upper)`
     *
     * ```js
     * const x = tf.tensor2d([[ 0,  1,  2, 3],
     *                        [-1,  0,  1, 2],
     *                        [-2, -1,  0, 1],
     *                        [-3, -2, -1, 0]]);
     * let y = tf.linalg.bandPart(x, 1, -1);
     * y.print(); // [[ 0,  1,  2, 3],
     *            //  [-1,  0,  1, 2],
     *            //  [ 0, -1,  0, 1],
     *            //  [ 0, 0 , -1, 0]]
     * let z = tf.linalg.bandPart(x, 2, 1);
     * z.print(); // [[ 0,  1,  0, 0],
     *            //  [-1,  0,  1, 0],
     *            //  [-2, -1,  0, 1],
     *            //  [ 0, -2, -1, 0]]
     * ```
     *
     * @param x Rank `k` tensor
     * @param numLower Number of subdiagonals to keep.
     *   If negative, keep entire lower triangle.
     * @param numUpper Number of subdiagonals to keep.
     *   If negative, keep entire upper triangle.
     * @returns Rank `k` tensor of the same shape as input.
     *   The extracted banded tensor.
     */
    /**
     * @doc {heading:'Operations',
     *       subheading:'Linear Algebra',
     *       namespace:'linalg'}
     */
    function bandPart_(a, numLower, numUpper) {
        if (numLower % 1 !== 0) {
            throw new Error("bandPart(): numLower must be an integer, got " + numLower + ".");
        }
        if (numUpper % 1 !== 0) {
            throw new Error("bandPart(): numUpper must be an integer, got " + numUpper + ".");
        }
        var $a = convertToTensor(a, 'a', 'bandPart');
        if ($a.rank < 2) {
            throw new Error("bandPart(): Rank must be at least 2, got " + $a.rank + ".");
        }
        var shape = $a.shape, _a = $a.shape.slice(-2), M = _a[0], N = _a[1];
        if (!(numLower <= M)) {
            throw new Error("bandPart(): numLower (" + numLower + ")" +
                (" must not be greater than the number of rows (" + M + ")."));
        }
        if (!(numUpper <= N)) {
            throw new Error("bandPart(): numUpper (" + numUpper + ")" +
                (" must not be greater than the number of columns (" + N + ")."));
        }
        if (numLower < 0) {
            numLower = M;
        }
        if (numUpper < 0) {
            numUpper = N;
        }
        var i = range(0, M, 1, 'int32').reshape([-1, 1]), j = range(0, N, 1, 'int32'), ij = sub(i, j);
        var inBand = logicalAnd(ij.lessEqual(scalar(+numLower, 'int32')), ij.greaterEqual(scalar(-numUpper, 'int32')));
        var zero = zeros([M, N], $a.dtype);
        return stack(unstack($a.reshape([-1, M, N])).map(function (mat) { return where(inBand, mat, zero); })).reshape(shape);
    }
    /**
     * 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 = sum$1(ys[j].mulStrict(x)).mul(ys[j]);
                        x = x.sub(proj);
                    }
                }
                return x.div(norm(x, 'euclidean'));
            }));
        };
        for (var i = 0; i < xs.length; ++i) {
            _loop_2(i);
        }
        if (inputIsTensor2D) {
            return stack(ys, 0);
        }
        else {
            return ys;
        }
    }
    /**
     * 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; }
        if (x.rank < 2) {
            throw new Error("qr() requires input tensor to have a rank >= 2, but got rank " + x.rank);
        }
        else 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(x.reshape([
                outerDimsProd, x.shape[x.shape.length - 2],
                x.shape[x.shape.length - 1]
            ]), 0);
            var q2ds_1 = [];
            var r2ds_1 = [];
            x2ds.forEach(function (x2d) {
                var _a = qr2d(x2d, fullMatrices), q2d = _a[0], r2d = _a[1];
                q2ds_1.push(q2d);
                r2ds_1.push(r2d);
            });
            var q = stack(q2ds_1, 0).reshape(x.shape);
            var r = stack(r2ds_1, 0).reshape(x.shape);
            return [q, r];
        }
    }
    function qr2d(x, fullMatrices) {
        if (fullMatrices === void 0) { fullMatrices = false; }
        return ENGINE.tidy(function () {
            if (x.shape.length !== 2) {
                throw new Error("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 = x.clone(); // Transformed matrix so far.
            var one2D = tensor2d([[1]], [1, 1]);
            var w = one2D.clone();
            var iters = m >= n ? n : m;
            var _loop_3 = function (j) {
                var _a;
                // This tidy within the for-loop ensures we clean up temporary
                // tensors as soon as they are no longer needed.
                var rTemp = r;
                var wTemp = w;
                var qTemp = q;
                _a = ENGINE.tidy(function () {
                    // Find H = I - tau * w * w', to put zeros below R(j, j).
                    var rjEnd1 = r.slice([j, j], [m - j, 1]);
                    var normX = rjEnd1.norm();
                    var rjj = r.slice([j, j], [1, 1]);
                    // The sign() function returns 0 on 0, which causes division by zero.
                    var s = tensor2d([[-1]]).where(rjj.greater(0), tensor2d([[1]]));
                    var u1 = rjj.sub(s.mul(normX));
                    var wPre = rjEnd1.div(u1);
                    if (wPre.shape[0] === 1) {
                        w = one2D.clone();
                    }
                    else {
                        w = one2D.concat(wPre.slice([1, 0], [wPre.shape[0] - 1, wPre.shape[1]]), 0);
                    }
                    var tau = s.matMul(u1).div(normX).neg();
                    // -- R := HR, Q := QH.
                    var rjEndAll = r.slice([j, 0], [m - j, n]);
                    var tauTimesW = tau.mul(w);
                    if (j === 0) {
                        r = rjEndAll.sub(tauTimesW.matMul(w.transpose().matMul(rjEndAll)));
                    }
                    else {
                        var rTimesTau = rjEndAll.sub(tauTimesW.matMul(w.transpose().matMul(rjEndAll)));
                        r = r.slice([0, 0], [j, n]).concat(rTimesTau, 0);
                    }
                    var qAllJEnd = q.slice([0, j], [m, q.shape[1] - j]);
                    if (j === 0) {
                        q = qAllJEnd.sub(qAllJEnd.matMul(w).matMul(tauTimesW.transpose()));
                    }
                    else {
                        var qTimesTau = qAllJEnd.sub(qAllJEnd.matMul(w).matMul(tauTimesW.transpose()));
                        q = q.slice([0, 0], [m, j]).concat(qTimesTau, 1);
                    }
                    return [w, r, q];
                }), w = _a[0], r = _a[1], q = _a[2];
                dispose([rTemp, wTemp, qTemp]);
            };
            for (var j = 0; j < iters; ++j) {
                _loop_3(j);
            }
            if (!fullMatrices && m > n) {
                q = q.slice([0, 0], [m, n]);
                r = r.slice([0, 0], [n, n]);
            }
            return [q, r];
        });
    }
    var bandPart = op({ bandPart_: bandPart_ });
    var gramSchmidt = op({ gramSchmidt_: gramSchmidt_ });
    var qr = op({ qr_: qr_ });

    var linalg_ops = /*#__PURE__*/Object.freeze({
        bandPart: bandPart,
        gramSchmidt: gramSchmidt,
        qr: qr
    });

    /**
     * @license
     * Copyright 2018 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.
     * =============================================================================
     */
    /**
     * Bilinear 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.
     */
    /** @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */
    function resizeBilinear_(images, size, alignCorners) {
        if (alignCorners === void 0) { alignCorners = 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 + "."); });
        var batchImages = $images;
        var reshapedTo4D = false;
        if ($images.rank === 3) {
            reshapedTo4D = true;
            batchImages =
                $images.as4D(1, $images.shape[0], $images.shape[1], $images.shape[2]);
        }
        var newHeight = size[0], newWidth = size[1];
        var forward = function (backend, save) {
            save([batchImages]);
            return backend.resizeBilinear(batchImages, newHeight, newWidth, alignCorners);
        };
        var backward = function (dy, saved) {
            return {
                x: function () { return ENGINE.runKernelFunc(function (backend) { return backend.resizeBilinearBackprop(dy, saved[0], alignCorners); }, {}); }
            };
        };
        var res = ENGINE.runKernelFunc(forward, { x: batchImages }, backward, 'ResizeBilinear', { alignCorners: alignCorners, newHeight: newHeight, newWidth: newWidth });
        if (reshapedTo4D) {
            return res.as3D(res.shape[1], res.shape[2], res.shape[3]);
        }
        return res;
    }
    /**
     * 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.
     */
    /** @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */
    function resizeNearestNeighbor_(images, size, alignCorners) {
        if (alignCorners === void 0) { alignCorners = 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'; });
        var batchImages = $images;
        var reshapedTo4D = false;
        if ($images.rank === 3) {
            reshapedTo4D = true;
            batchImages =
                $images.as4D(1, $images.shape[0], $images.shape[1], $images.shape[2]);
        }
        var newHeight = size[0], newWidth = size[1];
        var forward = function (backend, save) {
            save([batchImages]);
            return backend.resizeNearestNeighbor(batchImages, newHeight, newWidth, alignCorners);
        };
        var backward = function (dy, saved) {
            return {
                batchImages: function () { return ENGINE.runKernelFunc(function (backend) { return backend.resizeNearestNeighborBackprop(dy, saved[0], alignCorners); }, {}); }
            };
        };
        var res = ENGINE.runKernelFunc(forward, { batchImages: batchImages }, backward);
        if (reshapedTo4D) {
            return res.as3D(res.shape[1], res.shape[2], res.shape[3]);
        }
        return res;
    }
    /**
     * Performs non maximum suppression of bounding boxes based on
     * iou (intersection over union).
     *
     * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is
     *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of
     *     the bounding box.
     * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.
     * @param maxOutputSize The maximum number of boxes to be selected.
     * @param iouThreshold A float representing the threshold for deciding whether
     *     boxes overlap too much with respect to IOU. Must be between [0, 1].
     *     Defaults to 0.5 (50% box overlap).
     * @param scoreThreshold A threshold for deciding when to remove boxes based
     *     on score. Defaults to -inf, which means any score is accepted.
     * @return A 1D tensor with the selected box indices.
     */
    /** @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */
    function nonMaxSuppression_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
        if (iouThreshold === void 0) { iouThreshold = 0.5; }
        if (scoreThreshold === void 0) { scoreThreshold = Number.NEGATIVE_INFINITY; }
        var $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression');
        var $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression');
        var inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);
        maxOutputSize = inputs.maxOutputSize;
        iouThreshold = inputs.iouThreshold;
        scoreThreshold = inputs.scoreThreshold;
        var attrs = { maxOutputSize: maxOutputSize, iouThreshold: iouThreshold, scoreThreshold: scoreThreshold };
        return ENGINE.runKernelFunc(function (b) { return b.nonMaxSuppression($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold); }, { boxes: $boxes, scores: $scores }, null /* grad */, 'NonMaxSuppressionV3', attrs);
    }
    /** This is the async version of `nonMaxSuppression` */
    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, res;
            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(), $scores.data()])];
                    case 1:
                        boxesAndScores = _a.sent();
                        boxesVals = boxesAndScores[0];
                        scoresVals = boxesAndScores[1];
                        res = nonMaxSuppressionV3(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);
                        if ($boxes !== boxes) {
                            $boxes.dispose();
                        }
                        if ($scores !== scores) {
                            $scores.dispose();
                        }
                        return [2 /*return*/, res];
                }
            });
        });
    }
    /**
     * 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 $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression');
        var inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);
        maxOutputSize = inputs.maxOutputSize;
        iouThreshold = inputs.iouThreshold;
        scoreThreshold = inputs.scoreThreshold;
        softNmsSigma = inputs.softNmsSigma;
        var attrs = { maxOutputSize: maxOutputSize, iouThreshold: iouThreshold, scoreThreshold: scoreThreshold, softNmsSigma: softNmsSigma };
        var result = ENGINE.runKernel('NonMaxSuppressionV5', { boxes: $boxes, scores: $scores }, attrs);
        return { selectedIndices: result[0], selectedScores: result[1] };
    }
    /** This is the async version of `nonMaxSuppressionWithScore` */
    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, inputs, boxesAndScores, boxesVals, scoresVals, res;
            return __generator(this, function (_a) {
                switch (_a.label) {
                    case 0:
                        $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync');
                        $scores = convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync');
                        inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);
                        maxOutputSize = inputs.maxOutputSize;
                        iouThreshold = inputs.iouThreshold;
                        scoreThreshold = inputs.scoreThreshold;
                        softNmsSigma = inputs.softNmsSigma;
                        return [4 /*yield*/, Promise.all([$boxes.data(), $scores.data()])];
                    case 1:
                        boxesAndScores = _a.sent();
                        boxesVals = boxesAndScores[0];
                        scoresVals = boxesAndScores[1];
                        res = nonMaxSuppressionV5(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);
                        if ($boxes !== boxes) {
                            $boxes.dispose();
                        }
                        if ($scores !== scores) {
                            $scores.dispose();
                        }
                        return [2 /*return*/, res];
                }
            });
        });
    }
    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 };
    }
    /**
     * 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 crop_size.
     *
     * @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) {
        var $image = convertToTensor(image, 'image', 'cropAndResize');
        var $boxes = convertToTensor(boxes, 'boxes', 'cropAndResize', 'float32');
        var $boxInd = convertToTensor(boxInd, 'boxInd', 'cropAndResize', 'int32');
        method = method || 'bilinear';
        extrapolationValue = extrapolationValue || 0;
        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 " + $boxes.shape + "."); });
        assert(cropSize.length === 2, function () { return "Error in cropAndResize: cropSize must be of length 2, but got " +
            ("length " + cropSize.length + "."); });
        assert(cropSize[0] >= 1 && cropSize[1] >= 1, function () { return "cropSize must be atleast [1,1], but was " + cropSize; });
        assert(method === 'bilinear' || method === 'nearest', function () { return "method must be bilinear or nearest, but was " + method; });
        var forward = function (backend, save) {
            return backend.cropAndResize($image, $boxes, $boxInd, cropSize, method, extrapolationValue);
        };
        var res = ENGINE.runKernelFunc(forward, { images: $image, boxes: $boxes, boxInd: $boxInd }, null /* der */, 'CropAndResize', { method: method, extrapolationValue: extrapolationValue, cropSize: cropSize });
        return res;
    }
    var resizeBilinear = op({ resizeBilinear_: resizeBilinear_ });
    var resizeNearestNeighbor = op({ resizeNearestNeighbor_: resizeNearestNeighbor_ });
    var nonMaxSuppression = op({ nonMaxSuppression_: nonMaxSuppression_ });
    var nonMaxSuppressionAsync = nonMaxSuppressionAsync_;
    var nonMaxSuppressionWithScore = op({ nonMaxSuppressionWithScore_: nonMaxSuppressionWithScore_ });
    var nonMaxSuppressionWithScoreAsync = nonMaxSuppressionWithScoreAsync_;
    var cropAndResize = op({ cropAndResize_: cropAndResize_ });

    var image_ops = /*#__PURE__*/Object.freeze({
        resizeBilinear: resizeBilinear,
        resizeNearestNeighbor: resizeNearestNeighbor,
        nonMaxSuppression: nonMaxSuppression,
        nonMaxSuppressionAsync: nonMaxSuppressionAsync,
        nonMaxSuppressionWithScore: nonMaxSuppressionWithScore,
        nonMaxSuppressionWithScoreAsync: nonMaxSuppressionWithScoreAsync,
        cropAndResize: cropAndResize
    });

    /**
     * @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.
     * =============================================================================
     */
    // Whether we should call fused ops.
    var shouldFuse = function (gradientDepth, activation) {
        var gradientMode = gradientDepth > 0;
        return !gradientMode || activation === 'linear';
    };

    /**
     * @license
     * Copyright 2019 Google LLC. All Rights Reserved.
     * Licensed under the Apache License, Version 2.0 (the "License");
     * you may not use this file except in compliance with the License.
     * You may obtain a copy of the License at
     *
     * http://www.apache.org/licenses/LICENSE-2.0
     *
     * Unless required by applicable law or agreed to in writing, software
     * distributed under the License is distributed on an "AS IS" BASIS,
     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
     * See the License for the specific language governing permissions and
     * limitations under the License.
     * =============================================================================
     */
    // Returns gradient for fused activation.
    var getFusedDyActivation = function (dy, y, activation) {
        if (activation == null || activation === 'linear') {
            return dy;
        }
        if (activation === 'relu') {
            return dy.mul(y.step());
        }
        throw new Error("Gradient for activation " + activation + " has not been " +
            "implemented yet.");
    };
    // Returns gradient for fused bias.
    var getFusedBiasGradient = function (bias, dyActivation) {
        var res = dyActivation;
        var reduceAxes = getReductionAxes(bias.shape, dyActivation.shape);
        if (reduceAxes.length > 0) {
            res = res.sum(reduceAxes);
        }
        return res.reshape(bias.shape);
    };
    var applyActivation = function (x, activation, preluActivationWeights) {
        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);
        }
        throw new Error("Unknown fused activation " + activation + ".");
    };
    /**
     * 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.
     */
    function fusedMatMul_(_a) {
        var _b;
        var a = _a.a, b = _a.b, _c = _a.transposeA, transposeA = _c === void 0 ? false : _c, _d = _a.transposeB, transposeB = _d === void 0 ? false : _d, bias = _a.bias, _e = _a.activation, activation = _e === void 0 ? 'linear' : _e, preluActivationWeights = _a.preluActivationWeights;
        if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {
            var result = matMul(a, b, transposeA, transposeB);
            if (bias != null) {
                result = add(result, bias);
            }
            return applyActivation(result, activation, preluActivationWeights);
        }
        var $a = convertToTensor(a, 'a', 'fused matMul');
        var $b = convertToTensor(b, 'b', 'fused matMul');
        _b = makeTypesMatch($a, $b), $a = _b[0], $b = _b[1];
        var innerShapeA = transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1];
        var innerShapeB = transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2];
        var outerShapeA = transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2];
        var outerShapeB = transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1];
        var outerDimsA = $a.shape.slice(0, -2);
        var outerDimsB = $b.shape.slice(0, -2);
        var batchDimA = sizeFromShape(outerDimsA);
        var batchDimB = sizeFromShape(outerDimsB);
        assert($a.rank >= 2 && $b.rank >= 2 && $a.rank === $b.rank, function () {
            return "Error in fused matMul: inputs must have the same rank of at least " +
                ("2, got ranks " + $a.rank + " and " + $b.rank + ".");
        });
        assert(arraysEqual(outerDimsA, outerDimsB), function () { return "Error in fused matMul: outer dimensions (" + outerDimsA + ") and (" +
            (outerDimsB + ") of Tensors with shapes " + $a.shape + " and ") +
            ($b.shape + " must match."); });
        assert(innerShapeA === innerShapeB, function () { return "Error in fused matMul: inner shapes (" + innerShapeA + ") and (" +
            (innerShapeB + ") of Tensors with shapes " + $a.shape + " and ") +
            ($b.shape + " and transposeA=" + transposeA) +
            (" and transposeB=" + transposeB + " must match."); });
        var outShape = $a.shape.slice(0, -2).concat([outerShapeA, outerShapeB]);
        var a3D = transposeA ? $a.as3D(batchDimA, innerShapeA, outerShapeA) :
            $a.as3D(batchDimA, outerShapeA, innerShapeA);
        var b3D = transposeB ? $b.as3D(batchDimB, outerShapeB, innerShapeB) :
            $b.as3D(batchDimB, innerShapeB, outerShapeB);
        var $bias;
        if (bias != null) {
            $bias = convertToTensor(bias, 'bias', 'fused matMul');
            $bias = makeTypesMatch($bias, $a)[0];
            assertAndGetBroadcastShape(outShape, $bias.shape);
        }
        var $preluActivationWeights;
        if (preluActivationWeights != null) {
            $preluActivationWeights = convertToTensor(preluActivationWeights, 'prelu weights', 'fused matMul');
        }
        var grad = function (dy, saved) {
            var a3D = saved[0], b3D = saved[1], y = saved[2];
            var dyActivation = getFusedDyActivation(dy, y, activation);
            var biasGradient = {};
            if (bias != null) {
                biasGradient = { bias: function () { return getFusedBiasGradient($bias, dyActivation); } };
            }
            if (!transposeA && !transposeB) {
                return Object.assign({
                    a: function () { return dyActivation.matMul(b3D, false, true); },
                    b: function () { return a3D.matMul(dyActivation, true, false); }
                }, biasGradient);
            }
            else if (!transposeA && transposeB) {
                return Object.assign({
                    a: function () { return dyActivation.matMul(b3D, false, false); },
                    b: function () { return dyActivation.matMul(a3D, true, false); }
                }, biasGradient);
            }
            else if (transposeA && !transposeB) {
                return Object.assign({
                    a: function () { return b3D.matMul(dyActivation, false, true); },
                    b: function () { return a3D.matMul(dyActivation, false, false); }
                }, biasGradient);
            }
            else {
                return Object.assign({
                    a: function () { return b3D.matMul(dyActivation, true, true); },
                    b: function () { return dyActivation.matMul(a3D, true, true); }
                }, biasGradient);
            }
        };
        var inputs = { a: a3D, b: b3D };
        if (bias != null) {
            inputs.bias = $bias;
        }
        if (preluActivationWeights != null) {
            inputs.preluActivationWeights = $preluActivationWeights;
        }
        var inputsToSave = [a3D, b3D];
        var outputsToSave = [true];
        var res = ENGINE.runKernelFunc(function (backend, save) {
            var y = backend.fusedBatchMatMul({
                a: a3D,
                b: b3D,
                transposeA: transposeA,
                transposeB: transposeB,
                bias: $bias,
                activation: activation,
                preluActivationWeights: $preluActivationWeights
            });
            save([a3D, b3D, y]);
            return y;
        }, inputs, grad, '_FusedMatMul', { transposeA: transposeA, transposeB: transposeB, activation: activation }, inputsToSave, outputsToSave);
        return res.reshape(outShape);
    }
    /**
     * Computes a 2D convolution over the input x, optionally fused with adding a
     * bias and applying an activation.
     *
     * ```js
     * const inputDepth = 2;
     * const inShape = [2, 2, 2, inputDepth];
     * const outputDepth = 2;
     * const fSize = 1;
     * const pad = 0;
     * const strides = 1;
     *
     * const x = tf.tensor4d( [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
     * 16], inShape);
     * const w = tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth,
     * outputDepth]);
     *
     * tf.fused.conv2d({ x, filter: w, strides, pad, dataFormat: 'NHWC',
     * dilations: [1, 1], bias: tf.scalar(5), activation: 'relu' }).print();
     * ```
     *
     * @param obj An object with the following properties:
     * @param x The input tensor, of rank 4 or rank 3, of shape
     *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is
     * assumed.
     * @param filter The filter, rank 4, of shape
     *     `[filterHeight, filterWidth, inDepth, outDepth]`.
     * @param strides The strides of the convolution: `[strideHeight,
     * strideWidth]`.
     * @param pad The type of padding algorithm.
     *   - `same` and stride 1: output will be of same size as input,
     *       regardless of filter size.
     *   - `valid` output will be smaller than input if filter is larger
     *       than 1x1.
     *   - For more info, see this guide:
     *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](
     *          https://www.tensorflow.org/api_guides/python/nn#Convolution)
     * @param dataFormat An optional string from: "NHWC", "NCHW". Defaults to
     *     "NHWC". Specify the data format of the input and output data. With the
     *     default format "NHWC", the data is stored in the order of: [batch,
     *     height, width, channels]. Only "NHWC" is currently supported.
     * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`
     *     in which we sample input values across the height and width dimensions
     *     in atrous convolution. Defaults to `[1, 1]`. If `dilations` is a single
     *     number, then `dilationHeight == dilationWidth`. If it is greater than
     *     1, then all values of `strides` must be 1.
     * @param dimRoundingMode The rounding mode used when computing output
     *     dimensions if pad is a number. If none is provided, it will not round
     *     and error if the output is of fractional size.
     * @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`.
     */
    function fusedConv2d_(_a) {
        var x = _a.x, filter = _a.filter, strides = _a.strides, pad = _a.pad, _b = _a.dataFormat, dataFormat = _b === void 0 ? 'NHWC' : _b, _c = _a.dilations, dilations = _c === void 0 ? [1, 1] : _c, dimRoundingMode = _a.dimRoundingMode, bias = _a.bias, _d = _a.activation, activation = _d === void 0 ? 'linear' : _d, preluActivationWeights = _a.preluActivationWeights;
        activation = activation || 'linear';
        if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {
            var result = conv2d(x, filter, strides, pad, dataFormat, dilations, dimRoundingMode);
            if (bias != null) {
                result = add(result, bias);
            }
            return applyActivation(result, activation, preluActivationWeights);
        }
        var $x = convertToTensor(x, 'x', 'conv2d');
        var $filter = convertToTensor(filter, 'filter', 'conv2d');
        var x4D = $x;
        var reshapedTo4D = false;
        if ($x.rank === 3) {
            reshapedTo4D = true;
            x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);
        }
        assert(x4D.rank === 4, function () { return "Error in fused conv2d: input must be rank 4, but got rank " +
            (x4D.rank + "."); });
        assert($filter.rank === 4, function () { return "Error in fused conv2d: filter must be rank 4, but got rank " +
            ($filter.rank + "."); });
        if (dimRoundingMode != null) {
            assert(isInt(pad), function () { return "Error in fused conv2d: pad must be an integer when using, " +
                ("dimRoundingMode " + dimRoundingMode + " but got pad " + pad + "."); });
        }
        assert(x4D.shape[3] === $filter.shape[2], function () { return "Error in conv2d: depth of input (" + x4D.shape[3] + ") must match " +
            ("input depth for filter " + $filter.shape[2] + "."); });
        assert(eitherStridesOrDilationsAreOne(strides, dilations), function () { return 'Error in conv2D: Either strides or dilations must be 1. ' +
            ("Got strides " + strides + " and dilations '" + dilations + "'"); });
        assert(dataFormat === 'NHWC', function () { return "Error in conv2d: got dataFormat of " + dataFormat + " but only NHWC is currently supported."; });
        var convInfo = computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode);
        var $bias;
        if (bias != null) {
            $bias = convertToTensor(bias, 'bias', 'fused conv2d');
            $bias = makeTypesMatch($bias, $x)[0];
            assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);
        }
        var $preluActivationWeights;
        if (preluActivationWeights != null) {
            $preluActivationWeights = convertToTensor(preluActivationWeights, 'prelu weights', 'fused conv2d');
        }
        var grad = function (dy, saved) {
            var _a = saved, $filter = _a[0], x4D = _a[1], y = _a[2];
            var dyActivation = getFusedDyActivation(dy, y, activation);
            assert(tupleValuesAreOne(dilations), function () { return 'Error in gradient of fused conv2D: ' +
                "dilation rates greater than 1 " +
                ("are not yet supported in gradients. Got dilations '" + dilations + "'"); });
            var biasGradient = {};
            if (bias != null) {
                biasGradient = { bias: function () { return getFusedBiasGradient($bias, dyActivation); } };
            }
            return Object.assign({
                x: function () {
                    return conv2dDerInput(x4D.shape, dyActivation, $filter, strides, pad);
                },
                filter: function () {
                    return conv2dDerFilter(x4D, dyActivation, $filter.shape, strides, pad);
                }
            }, biasGradient);
        };
        var inputs = { x: x4D, filter: $filter };
        if (bias != null) {
            inputs.bias = $bias;
        }
        if (preluActivationWeights != null) {
            inputs.preluActivationWeights = $preluActivationWeights;
        }
        var inputsToSave = [$filter, x4D];
        var outputsToSave = [true]; // Save the only output.
        var res = ENGINE.runKernelFunc(function (backend, save) {
            var res = backend.fusedConv2d({
                input: x4D,
                filter: $filter,
                convInfo: convInfo,
                bias: $bias,
                activation: activation,
                preluActivationWeights: $preluActivationWeights
            });
            save([$filter, x4D, res]);
            return res;
        }, inputs, grad, 'FusedConv2D', { convInfo: convInfo, activation: activation }, inputsToSave, outputsToSave);
        if (reshapedTo4D) {
            return res.as3D(res.shape[1], res.shape[2], res.shape[3]);
        }
        return res;
    }
    /**
     * Computes depthwise 2D convolution, optionally fused with adding a
     * bias and applying an activation.
     *
     * Given a 4D `input` array and a `filter` array of shape
     * `[filterHeight, filterWidth, inChannels, channelMultiplier]` containing
     * `inChannels` convolutional filters of depth 1, this op applies a
     * different filter to each input channel (expanding from 1 channel to
     * `channelMultiplier` channels for each), then concatenates the results
     * together. The output has `inChannels * channelMultiplier` channels.
     *
     * See
     * [https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d](
     *     https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d)
     * for more details.
     *
     * @param obj An object with the following properties:
     * @param x The input tensor, of rank 4 or rank 3, of shape
     *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is
     * assumed.
     * @param filter The filter tensor, rank 4, of shape
     *     `[filterHeight, filterWidth, inChannels, channelMultiplier]`.
     * @param strides The strides of the convolution: `[strideHeight,
     * strideWidth]`. If strides is a single number, then `strideHeight ==
     * strideWidth`.
     * @param pad The type of padding algorithm.
     *   - `same` and stride 1: output will be of same size as input,
     *       regardless of filter size.
     *   - `valid`: output will be smaller than input if filter is larger
     *       than 1x1.
     *   - For more info, see this guide:
     *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](
     *          https://www.tensorflow.org/api_guides/python/nn#Convolution)
     * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`
     *     in which we sample input values across the height and width dimensions
     *     in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single
     *     number, then `dilationHeight == dilationWidth`. If it is greater than
     *     1, then all values of `strides` must be 1.
     * @param dataFormat: An optional string from: "NHWC", "NCHW". Defaults to
     *     "NHWC". Specify the data format of the input and output data. With the
     *     default format "NHWC", the data is stored in the order of: [batch,
     *     height, width, channels]. Only "NHWC" is currently supported.
     * @param dimRoundingMode The rounding mode used when computing output
     *     dimensions if pad is a number. If none is provided, it will not round
     *     and error if the output is of fractional size.
     * @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`.
     */
    function fusedDepthwiseConv2d_(_a) {
        var x = _a.x, filter = _a.filter, strides = _a.strides, pad = _a.pad, _b = _a.dataFormat, dataFormat = _b === void 0 ? 'NHWC' : _b, _c = _a.dilations, dilations = _c === void 0 ? [1, 1] : _c, dimRoundingMode = _a.dimRoundingMode, bias = _a.bias, _d = _a.activation, activation = _d === void 0 ? 'linear' : _d, preluActivationWeights = _a.preluActivationWeights;
        if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {
            var result = depthwiseConv2d(x, filter, strides, pad, dataFormat, dilations, dimRoundingMode);
            if (bias != null) {
                result = add(result, bias);
            }
            return applyActivation(result, activation, preluActivationWeights);
        }
        var $x = convertToTensor(x, 'x', 'depthwiseConv2d');
        var $filter = convertToTensor(filter, 'filter', 'depthwiseConv2d');
        var x4D = $x;
        var reshapedTo4D = false;
        if ($x.rank === 3) {
            reshapedTo4D = true;
            x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);
        }
        assert(x4D.rank === 4, function () { return "Error in fused depthwiseConv2d: input must be rank 4, but got " +
            ("rank " + x4D.rank + "."); });
        assert($filter.rank === 4, function () { return "Error in fused depthwiseConv2d: filter must be rank 4, " +
            ("but got rank " + $filter.rank + "."); });
        assert(x4D.shape[3] === $filter.shape[2], function () { return "Error in fused depthwiseConv2d: number of input channels " +
            ("(" + x4D.shape[3] + ") must match the inChannels dimension in ") +
            ("filter " + $filter.shape[2] + "."); });
        if (dilations == null) {
            dilations = [1, 1];
        }
        assert(eitherStridesOrDilationsAreOne(strides, dilations), function () {
            return 'Error in fused depthwiseConv2d: Either strides or dilations must ' +
                ("be 1. Got strides " + strides + " and dilations '" + dilations + "'");
        });
        if (dimRoundingMode != null) {
            assert(isInt(pad), function () { return "Error in fused depthwiseConv2d: pad must be an integer when " +
                ("using dimRoundingMode " + dimRoundingMode + " but got pad " + pad + "."); });
        }
        var convInfo = computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode, true /* depthwise */);
        var $bias;
        if (bias != null) {
            $bias = convertToTensor(bias, 'bias', 'fused conv2d');
            $bias = makeTypesMatch($bias, $x)[0];
            assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);
        }
        var $preluActivationWeights;
        if (preluActivationWeights != null) {
            $preluActivationWeights = convertToTensor(preluActivationWeights, 'prelu weights', 'fused depthwiseConv2d');
        }
        var grad = function (dy, saved) {
            assert(tupleValuesAreOne(dilations), function () { return 'Error in gradient of fused depthwiseConv2d: dilation rates ' +
                "greater than 1 are not yet supported. Got dilations " +
                ("'" + dilations + "'"); });
            var $filter = saved[0], x4D = saved[1], y = saved[2];
            var dyActivation = getFusedDyActivation(dy, y, activation);
            var biasGradient = {};
            if (bias != null) {
                biasGradient = { bias: function () { return getFusedBiasGradient($bias, dyActivation); } };
            }
            return Object.assign({
                x: function () { return depthwiseConv2dDerInput(x4D.shape, dyActivation, $filter, convInfo); },
                filter: function () { return depthwiseConv2dDerFilter(x4D, dyActivation, $filter.shape, convInfo); },
            }, biasGradient);
        };
        var inputs = { x: x4D, filter: $filter };
        if (bias != null) {
            inputs.bias = $bias;
        }
        if (preluActivationWeights != null) {
            inputs.preluActivationWeights = $preluActivationWeights;
        }
        var inputsToSave = [$filter, x4D];
        var outputsToSave = [true];
        var res = ENGINE.runKernelFunc(function (backend, save) {
            var res = backend.fusedDepthwiseConv2D({
                input: x4D,
                filter: $filter,
                convInfo: convInfo,
                bias: $bias,
                activation: activation,
                preluActivationWeights: $preluActivationWeights
            });
            save([$filter, x4D, res]);
            return res;
        }, inputs, grad, 'FusedDepthwiseConv2D', { convInfo: convInfo, activation: activation }, inputsToSave, outputsToSave);
        if (reshapedTo4D) {
            return res.as3D(res.shape[1], res.shape[2], res.shape[3]);
        }
        return res;
    }
    var matMul$1 = op({ fusedMatMul_: fusedMatMul_ });
    var conv2d$1 = op({ fusedConv2d_: fusedConv2d_ });
    var depthwiseConv2d$1 = op({ fusedDepthwiseConv2d_: fusedDepthwiseConv2d_ });

    var fused_ops = /*#__PURE__*/Object.freeze({
        matMul: matMul$1,
        conv2d: conv2d$1,
        depthwiseConv2d: depthwiseConv2d$1
    });

    /**
     * @license
     * Copyright 2018 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.
     * =============================================================================
     */

    var ops = /*#__PURE__*/Object.freeze({
        image: image_ops,
        linalg: linalg_ops,
        losses: loss_ops,
        spectral: spectral_ops,
        fused: fused_ops,
        signal: signal_ops,
        square: square,
        squaredDifference: squaredDifference,
        conv1d: conv1d,
        conv2d: conv2d,
        conv3d: conv3d,
        depthwiseConv2d: depthwiseConv2d,
        separableConv2d: separableConv2d,
        conv2dTranspose: conv2dTranspose,
        conv3dTranspose: conv3dTranspose,
        op: op,
        batchNormalization2d: batchNormalization2d,
        batchNormalization3d: batchNormalization3d,
        batchNormalization4d: batchNormalization4d,
        batchNormalization: batchNormalization,
        batchNorm: batchNorm,
        batchNorm2d: batchNorm2d,
        batchNorm3d: batchNorm3d,
        batchNorm4d: batchNorm4d,
        booleanMaskAsync: booleanMaskAsync,
        complex: complex,
        real: real,
        imag: imag,
        concat: concat,
        concat1d: concat1d,
        concat2d: concat2d,
        concat3d: concat3d,
        concat4d: concat4d,
        split: split,
        matMul: matMul,
        dot: dot,
        outerProduct: outerProduct,
        reverse: reverse,
        reverse1d: reverse1d,
        reverse2d: reverse2d,
        reverse3d: reverse3d,
        reverse4d: reverse4d,
        maxPool: maxPool,
        avgPool: avgPool,
        pool: pool,
        maxPool3d: maxPool3d,
        avgPool3d: avgPool3d,
        slice: slice,
        slice1d: slice1d,
        slice2d: slice2d,
        slice3d: slice3d,
        slice4d: slice4d,
        abs: abs,
        acos: acos,
        acosh: acosh,
        asin: asin,
        asinh: asinh,
        atan: atan,
        atanh: atanh,
        ceil: ceil,
        clipByValue: clipByValue,
        cos: cos,
        cosh: cosh,
        erf: erf,
        exp: exp,
        expm1: expm1,
        floor: floor,
        log: log,
        log1p: log1p,
        logSigmoid: logSigmoid,
        neg: neg,
        reciprocal: reciprocal,
        round: round,
        rsqrt: rsqrt,
        sigmoid: sigmoid,
        sign: sign,
        isNaN: isNaN$1,
        isInf: isInf,
        isFinite: isFinite$1,
        sin: sin,
        sinh: sinh,
        softplus: softplus,
        sqrt: sqrt,
        step: step,
        tan: tan,
        tanh: tanh$1,
        all: all,
        any: any,
        argMax: argMax,
        argMin: argMin,
        logSumExp: logSumExp,
        max: max,
        mean: mean,
        min: min,
        moments: moments,
        sum: sum$1,
        prod: prod,
        equal: equal,
        equalStrict: equalStrict,
        greater: greater,
        greaterEqual: greaterEqual,
        greaterEqualStrict: greaterEqualStrict,
        greaterStrict: greaterStrict,
        less: less,
        lessEqual: lessEqual,
        lessEqualStrict: lessEqualStrict,
        lessStrict: lessStrict,
        notEqual: notEqual,
        notEqualStrict: notEqualStrict,
        add: add,
        addN: addN,
        addStrict: addStrict,
        atan2: atan2,
        div: div,
        divNoNan: divNoNan,
        divStrict: divStrict,
        floorDiv: floorDiv,
        maximum: maximum,
        maximumStrict: maximumStrict,
        minimum: minimum,
        minimumStrict: minimumStrict,
        mod: mod,
        modStrict: modStrict,
        mul: mul,
        mulStrict: mulStrict,
        pow: pow,
        powStrict: powStrict,
        squaredDifferenceStrict: squaredDifferenceStrict,
        sub: sub,
        subStrict: subStrict,
        elu: elu,
        leakyRelu: leakyRelu,
        prelu: prelu,
        relu: relu,
        relu6: relu6,
        selu: selu,
        logicalAnd: logicalAnd,
        logicalNot: logicalNot,
        logicalOr: logicalOr,
        logicalXor: logicalXor,
        where: where,
        whereAsync: whereAsync,
        buffer: buffer,
        print: print,
        batchToSpaceND: batchToSpaceND,
        broadcastTo: broadcastTo,
        cast: cast,
        clone: clone,
        cumsum: cumsum,
        depthToSpace: depthToSpace,
        expandDims: expandDims,
        eye: eye,
        multinomial: multinomial,
        oneHot: oneHot,
        pad: pad,
        pad1d: pad1d,
        pad2d: pad2d,
        pad3d: pad3d,
        pad4d: pad4d,
        rand: rand,
        randomNormal: randomNormal,
        randomGamma: randomGamma,
        randomUniform: randomUniform,
        reshape: reshape,
        spaceToBatchND: spaceToBatchND,
        squeeze: squeeze,
        stack: stack,
        tile: tile,
        truncatedNormal: truncatedNormal,
        unstack: unstack,
        setdiff1dAsync: setdiff1dAsync,
        fill: fill,
        linspace: linspace,
        ones: ones$1,
        range: range,
        scalar: scalar,
        tensor: tensor,
        tensor1d: tensor1d,
        tensor2d: tensor2d,
        tensor3d: tensor3d,
        tensor4d: tensor4d,
        tensor5d: tensor5d,
        tensor6d: tensor6d,
        variable: variable,
        zeros: zeros,
        onesLike: onesLike,
        zerosLike: zerosLike,
        transpose: transpose,
        softmax: softmax,
        logSoftmax: logSoftmax,
        localResponseNormalization: localResponseNormalization,
        norm: norm,
        gather: gather,
        unsortedSegmentSum: unsortedSegmentSum,
        basicLSTMCell: basicLSTMCell,
        multiRNNCell: multiRNNCell,
        movingAverage: movingAverage,
        stridedSlice: stridedSlice,
        topk: topk,
        scatterND: scatterND,
        fft: fft,
        ifft: ifft,
        rfft: rfft,
        irfft: irfft,
        sparseToDense: sparseToDense,
        gatherND: gatherND,
        diag: diag,
        dropout: dropout,
        hannWindow: hannWindow,
        hammingWindow: hammingWindow,
        frame: frame,
        stft: stft,
        inTopKAsync: inTopKAsync
    });

    /**
     * @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) {
                assert(t.dtype !== 'complex64', function () { return opName + " does not support complex64 tensors."; });
            }
        });
    }

    /**
     * @license
     * Copyright 2017 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.
     * =============================================================================
     */
    function mapActivation(backend, x, activation, preluActivationWeights) {
        if (activation === 'linear') {
            return backend.linear(x);
        }
        else if (activation === 'relu') {
            return backend.relu(x);
        }
        else if (activation === 'elu') {
            return backend.elu(x);
        }
        else if (activation === 'relu6') {
            return backend.relu6(x);
        }
        else if (activation === 'prelu') {
            return backend.prelu(x, preluActivationWeights);
        }
        throw new Error("Activation " + activation + " has not been implemented for the CPU backend.");
    }
    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 DataStorage(_this, ENGINE);
            return _this;
        }
        MathBackendCPU.prototype.write = function (values, shape, dtype) {
            if (this.firstUse) {
                this.firstUse = false;
                if (env().get('IS_NODE')) {
                    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 = {};
            this.data.set(dataId, { values: values, dtype: dtype });
            return dataId;
        };
        MathBackendCPU.prototype.move = function (dataId, values, shape, dtype) {
            this.data.set(dataId, { values: values, dtype: dtype });
        };
        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 (_a) {
                    return [2 /*return*/, this.readSync(dataId)];
                });
            });
        };
        MathBackendCPU.prototype.readSync = function (dataId) {
            var _a = this.data.get(dataId), dtype = _a.dtype, complexTensors = _a.complexTensors;
            if (dtype === 'complex64') {
                var realValues = this.readSync(complexTensors.real.dataId);
                var imagValues = this.readSync(complexTensors.imag.dataId);
                return 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 decodeString(d); });
                }
                catch (_a) {
                    throw new Error('Failed to decode encoded string bytes into utf-8');
                }
            }
            return buffer(t.shape, t.dtype, decodedData);
        };
        MathBackendCPU.prototype.makeOutput = function (values, shape, dtype) {
            var dataId = this.write(values, shape, dtype);
            return ENGINE.makeTensorFromDataId(dataId, shape, dtype, this);
        };
        MathBackendCPU.prototype.disposeData = function (dataId) {
            if (this.data.has(dataId)) {
                var complexTensors = this.data.get(dataId).complexTensors;
                if (complexTensors != null) {
                    complexTensors.real.dispose();
                    complexTensors.imag.dispose();
                }
                this.data.delete(dataId);
            }
        };
        MathBackendCPU.prototype.time = function (f) {
            return __awaiter(this, void 0, void 0, function () {
                var start, kernelMs;
                return __generator(this, function (_a) {
                    start = now();
                    f();
                    kernelMs = 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.complex = function (real, imag) {
            var result = this.makeOutput(null, real.shape, 'complex64');
            var resultData = this.data.get(result.dataId);
            // The backend owns the reference to the underlying real and imaginary
            // clones. These will explicitly get disposed when the complex tensor is
            // disposed.
            resultData.complexTensors = {
                real: ENGINE.keep(real.clone()),
                imag: ENGINE.keep(imag.clone())
            };
            return result;
        };
        MathBackendCPU.prototype.real = function (input) {
            var resultData = this.data.get(input.dataId);
            return resultData.complexTensors.real.clone();
        };
        MathBackendCPU.prototype.imag = function (input) {
            var resultData = this.data.get(input.dataId);
            return resultData.complexTensors.imag.clone();
        };
        MathBackendCPU.prototype.slice = function (x, begin, size) {
            assertNotComplex(x, 'slice');
            var isContinous = isSliceContinous(x.shape, begin, size);
            if (isContinous) {
                var flatOffset = computeFlatOffset(begin, x.strides);
                var length_1 = sizeFromShape(size);
                var vals = this.readSync(x.dataId);
                return tensor(vals.subarray(flatOffset, flatOffset + length_1), size, x.dtype);
            }
            var buffer$1 = buffer(size, x.dtype);
            var xBuf = this.bufferSync(x);
            for (var i = 0; i < buffer$1.size; ++i) {
                var loc = buffer$1.indexToLoc(i);
                var xLoc = loc.map(function (idx, j) { return idx + begin[j]; });
                buffer$1.values[i] = xBuf.get.apply(xBuf, xLoc);
            }
            return buffer$1.toTensor();
        };
        MathBackendCPU.prototype.stridedSlice = function (x, begin, end, strides) {
            assertNotComplex(x, 'stridedSlice');
            var outShape = computeOutShape$2(begin, end, strides);
            if (outShape.some(function (axis) { return axis === 0; })) {
                return tensor([], outShape);
            }
            var buffer$1 = buffer(outShape, x.dtype);
            var xBuf = this.bufferSync(x);
            for (var i = 0; i < buffer$1.size; i++) {
                var loc = buffer$1.indexToLoc(i);
                var newLoc = new Array(loc.length);
                for (var j = 0; j < newLoc.length; j++) {
                    newLoc[j] = loc[j] * strides[j] + begin[j];
                }
                buffer$1.set.apply(buffer$1, [xBuf.get.apply(xBuf, newLoc)].concat(loc));
            }
            return buffer$1.toTensor();
        };
        MathBackendCPU.prototype.diag = function (x) {
            var xVals = this.readSync(x.dataId);
            var buffer$1 = buffer([x.size, x.size], x.dtype);
            var vals = buffer$1.values;
            for (var i = 0; i < xVals.length; i++) {
                vals[i * x.size + i] = xVals[i];
            }
            return buffer$1.toTensor();
        };
        MathBackendCPU.prototype.unstack = function (x, axis) {
            var num = x.shape[axis];
            var outShape = new Array(x.rank - 1);
            var outIndex = 0;
            for (var i = 0; i < x.rank; i++) {
                if (i !== axis) {
                    outShape[outIndex++] = x.shape[i];
                }
            }
            var begin = new Array(x.rank).fill(0);
            var size = x.shape.slice();
            size[axis] = 1;
            var res = new Array(num);
            for (var i = 0; i < res.length; i++) {
                begin[axis] = i;
                res[i] = this.slice(x, begin, size).reshape(outShape);
            }
            return res;
        };
        MathBackendCPU.prototype.reverse = function (x, axis) {
            assertNotComplex(x, 'reverse');
            var buffer$1 = buffer(x.shape, x.dtype);
            var xBuf = this.bufferSync(x);
            var _loop_1 = function (i) {
                var outLoc = buffer$1.indexToLoc(i);
                var inLoc = outLoc.slice();
                axis.forEach(function (ax) { return inLoc[ax] = x.shape[ax] - 1 - inLoc[ax]; });
                buffer$1.set.apply(buffer$1, [xBuf.get.apply(xBuf, inLoc)].concat(outLoc));
            };
            for (var i = 0; i < buffer$1.size; i++) {
                _loop_1(i);
            }
            return buffer$1.toTensor();
        };
        MathBackendCPU.prototype.concat = function (tensors, axis) {
            var _this = this;
            if (tensors[0].dtype === 'complex64') {
                var reals = tensors.map(function (t) { return real(t); });
                var imags = tensors.map(function (t) { return imag(t); });
                return complex(this.concat(reals, axis), this.concat(imags, axis));
            }
            var tensors2D = tensors.map(function (t) {
                var innerSize = sizeFromShape(t.shape.slice(axis));
                return t.as2D(-1, innerSize);
            });
            var outShape = computeOutShape(tensors2D.map(function (t) { return t.shape; }), 1 /* axis */);
            var values = buffer(outShape, tensors[0].dtype)
                .values;
            if (tensors2D[0].shape[0] === 1) {
                // Use built-in TypedArray.set() method for speed.
                var offset_1 = 0;
                tensors2D.forEach(function (t) {
                    values.set(_this.readSync(t.dataId), offset_1);
                    offset_1 += t.size;
                });
            }
            else {
                var colOffset_1 = 0;
                tensors2D.forEach(function (t) {
                    var tVals = _this.readSync(t.dataId);
                    var tIdx = 0;
                    for (var row = 0; row < t.shape[0]; ++row) {
                        var resIdx = row * outShape[1] + colOffset_1;
                        for (var col = 0; col < t.shape[1]; ++col) {
                            values[resIdx + col] = tVals[tIdx++];
                        }
                    }
                    colOffset_1 += t.shape[1];
                });
            }
            var finalOutShape = computeOutShape(tensors.map(function (t) { return t.shape; }), axis);
            return tensor(values, finalOutShape, tensors[0].dtype);
        };
        MathBackendCPU.prototype.neg = function (x) {
            assertNotComplex(x, 'neg');
            return this.multiply(scalar(-1), x);
        };
        MathBackendCPU.prototype.add = function (a, b) {
            if (a.dtype === 'complex64' || b.dtype === 'complex64') {
                return this.broadcastedBinaryComplexOp(a.cast('complex64'), b.cast('complex64'), function (aReal, aImag, bReal, bImag) {
                    return { real: aReal + bReal, imag: aImag + bImag };
                });
            }
            return this.broadcastedBinaryOp(a, b, upcastType(a.dtype, b.dtype), function (aValue, bValue) { return aValue + bValue; });
        };
        MathBackendCPU.prototype.addN = function (tensors) {
            var _this = this;
            assertNotComplex(tensors, 'addN');
            var vals = tensors.map(function (t) { return _this.readSync(t.dataId); });
            var result = buffer(tensors[0].shape, tensors[0].dtype);
            var resultVals = result.values;
            for (var i = 0; i < tensors.length; i++) {
                var currVals = vals[i];
                for (var j = 0; j < resultVals.length; j++) {
                    resultVals[j] += currVals[j];
                }
            }
            return result.toTensor();
        };
        MathBackendCPU.prototype.softmax = function (logits, dim) {
            var axes = parseAxisParam([dim], logits.shape);
            var maxLogit = this.max(logits, axes);
            var expandedShape = expandShapeToKeepDim(maxLogit.shape, axes);
            var a = this.subtract(logits, maxLogit.reshape(expandedShape));
            var b = this.exp(a);
            var sumExp = this.sum(b, axes).reshape(expandedShape);
            return this.realDivide(b, sumExp);
        };
        MathBackendCPU.prototype.subtract = function (a, b) {
            if (a.dtype === 'complex64' || b.dtype === 'complex64') {
                return this.broadcastedBinaryComplexOp(a.cast('complex64'), b.cast('complex64'), function (aReal, aImag, bReal, bImag) {
                    return { real: aReal - bReal, imag: aImag - bImag };
                });
            }
            return this.broadcastedBinaryOp(a, b, upcastType(a.dtype, b.dtype), function (aValue, bValue) { return aValue - bValue; });
        };
        MathBackendCPU.prototype.pow = function (a, b) {
            assertNotComplex([a, b], 'pow');
            return this.broadcastedBinaryOp(a, b, a.dtype, function (aValue, bValue) { return Math.pow(aValue, bValue); });
        };
        MathBackendCPU.prototype.batchMatMul = function (a, b, transposeA, transposeB) {
            assertNotComplex([a, b], 'matMul');
            var sharedDim = transposeA ? a.shape[1] : a.shape[2];
            var leftDim = transposeA ? a.shape[2] : a.shape[1];
            var rightDim = transposeB ? b.shape[1] : b.shape[2];
            var batchDim = a.shape[0];
            var aValues = this.readSync(a.dataId);
            var bValues = this.readSync(b.dataId);
            var _a = transposeA ?
                [a.strides[0], 1, a.strides[1]] :
                [a.strides[0], a.strides[1], 1], aBatch = _a[0], aOuterStep = _a[1], aInnerStep = _a[2];
            var _b = transposeB ?
                [1, b.strides[1], b.strides[0]] :
                [b.strides[1], 1, b.strides[0]], bInnerStep = _b[0], bOuterStep = _b[1], bBatch = _b[2];
            var size = leftDim * rightDim;
            var result = buffer([batchDim, leftDim, rightDim], a.dtype);
            var resVals = result.values;
            var blockSize = this.blockSize;
            for (var b_1 = 0; b_1 < batchDim; b_1++) {
                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++) {
                                        sum += aValues[b_1 * aBatch + i * aOuterStep + k * aInnerStep] *
                                            bValues[k * bInnerStep + j * bOuterStep + b_1 * bBatch];
                                    }
                                    resVals[b_1 * size + (i * rightDim + j)] += sum;
                                }
                            }
                        }
                    }
                }
            }
            return result.toTensor();
        };
        MathBackendCPU.prototype.fusedBatchMatMul = function (_a) {
            var a = _a.a, b = _a.b, transposeA = _a.transposeA, transposeB = _a.transposeB, bias = _a.bias, activation = _a.activation, preluActivationWeights = _a.preluActivationWeights;
            var result = this.batchMatMul(a, b, transposeA, transposeB);
            if (bias) {
                result = this.add(result, bias);
            }
            if (activation) {
                result =
                    mapActivation(this, result, activation, preluActivationWeights);
            }
            return result;
        };
        MathBackendCPU.prototype.multiply = function (a, b) {
            if (a.dtype === 'complex64' || b.dtype === 'complex64') {
                return this.broadcastedBinaryComplexOp(a.cast('complex64'), b.cast('complex64'), function (aReal, aImag, bReal, bImag) {
                    return {
                        real: aReal * bReal - aImag * bImag,
                        imag: aReal * bImag + aImag * bReal
                    };
                });
            }
            return this.broadcastedBinaryOp(a, b, upcastType(a.dtype, b.dtype), function (aValue, bValue) { return aValue * bValue; });
        };
        MathBackendCPU.prototype.realDivide = function (a, b) {
            assertNotComplex([a, b], 'realDivide');
            var op = function (a, b) { return a / b; };
            var outputDtype = 'float32';
            return this.broadcastedBinaryOp(a, b, outputDtype, op);
        };
        MathBackendCPU.prototype.floorDiv = function (a, b) {
            assertNotComplex([a, b], 'floorDiv');
            var op = function (a, b) { return Math.floor(a / b); };
            var outputDtype = 'int32';
            return this.broadcastedBinaryOp(a, b, outputDtype, op);
        };
        MathBackendCPU.prototype.sum = function (x, axes) {
            assertNotComplex(x, 'sum');
            assertAxesAreInnerMostDims('sum', axes, x.rank);
            var _a = computeOutAndReduceShapes(x.shape, axes), outShape = _a[0], reduceShape = _a[1];
            var resultDtype = upcastType(x.dtype, 'int32');
            var result = zeros(outShape, resultDtype);
            var reduceSize = sizeFromShape(reduceShape);
            var vals = this.readSync(result.dataId);
            var aVals = this.readSync(x.dataId);
            for (var i = 0; i < vals.length; ++i) {
                var offset = i * reduceSize;
                var sum = 0;
                for (var j = 0; j < reduceSize; ++j) {
                    sum += aVals[offset + j];
                }
                vals[i] = sum;
            }
            return result;
        };
        MathBackendCPU.prototype.prod = function (x, axes) {
            assertNotComplex(x, 'sum');
            var _a = computeOutAndReduceShapes(x.shape, axes), outShape = _a[0], reduceShape = _a[1];
            var resultDtype = upcastType(x.dtype, 'int32');
            var result = zeros(outShape, resultDtype);
            var reduceSize = sizeFromShape(reduceShape);
            var vals = this.readSync(result.dataId);
            var aVals = this.readSync(x.dataId);
            for (var i = 0; i < vals.length; ++i) {
                var offset = i * reduceSize;
                var prod = 1;
                for (var j = 0; j < reduceSize; ++j) {
                    prod *= aVals[offset + j];
                }
                vals[i] = prod;
            }
            return result;
        };
        MathBackendCPU.prototype.unsortedSegmentSum = function (x, segmentIds, numSegments) {
            assertNotComplex(x, 'unsortedSegmentSum');
            var res = [];
            // Reshape the segment id's so that they can be broadcast with
            // x. The new shape should be [segmentIds.shape, 1, ..., 1]
            var numIters = x.rank - segmentIds.rank;
            for (var i = 0; i < numIters; ++i) {
                segmentIds = segmentIds.expandDims(i + 1);
            }
            for (var i = 0; i < numSegments; ++i) {
                var segmentId = scalar(i, 'int32');
                var mask = equal(segmentId, segmentIds).asType('float32');
                var sum = mask.mul(x).sum(0);
                res.push(sum);
            }
            return stack(res);
        };
        MathBackendCPU.prototype.argMin = function (x, axis) {
            assertNotComplex(x, 'argMin');
            var axes = [axis];
            assertAxesAreInnerMostDims('argMin', axes, x.rank);
            var _a = computeOutAndReduceShapes(x.shape, axes), outShape = _a[0], reduceShape = _a[1];
            var result = zeros(outShape, 'int32');
            var reduceSize = sizeFromShape(reduceShape);
            var vals = this.readSync(result.dataId);
            var aVals = this.readSync(x.dataId);
            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;
            }
            return result;
        };
        MathBackendCPU.prototype.argMax = function (x, axis) {
            assertNotComplex(x, 'argMax');
            var axes = [axis];
            assertAxesAreInnerMostDims('argMax', axes, x.rank);
            var _a = computeOutAndReduceShapes(x.shape, axes), outShape = _a[0], reduceShape = _a[1];
            var result = zeros(outShape, 'int32');
            var reduceSize = sizeFromShape(reduceShape);
            var vals = this.readSync(result.dataId);
            var aVals = this.readSync(x.dataId);
            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;
            }
            return result;
        };
        MathBackendCPU.prototype.cumsum = function (x, axis, exclusive, reverse) {
            assertNotComplex(x, 'cumsum');
            if (axis !== x.rank - 1) {
                throw new Error("backend.cumsum in CPU expects an inner-most axis=" + (x.rank - 1) + " " +
                    ("but got axis=" + axis));
            }
            var resultDtype = upcastType(x.dtype, 'int32');
            var result = zeros(x.shape, resultDtype);
            var vals = this.readSync(result.dataId);
            var aVals = this.readSync(x.dataId);
            var finalDim = x.shape[x.rank - 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];
                    }
                }
            }
            return result;
        };
        MathBackendCPU.prototype.equal = function (a, b) {
            assertNotComplex([a, b], 'equal');
            return this.broadcastedBinaryOp(a, b, 'bool', function (aVal, bVal) {
                return (aVal === bVal) ? 1 : 0;
            });
        };
        MathBackendCPU.prototype.notEqual = function (a, b) {
            assertNotComplex([a, b], 'notEqual');
            return this.broadcastedBinaryOp(a, b, 'bool', function (aVal, bVal) {
                return (aVal !== bVal) ? 1 : 0;
            });
        };
        MathBackendCPU.prototype.less = function (a, b) {
            assertNotComplex([a, b], 'less');
            return this.broadcastedBinaryOp(a, b, 'bool', function (aVal, bVal) {
                return (aVal < bVal) ? 1 : 0;
            });
        };
        MathBackendCPU.prototype.lessEqual = function (a, b) {
            assertNotComplex([a, b], 'lessEqual');
            return this.broadcastedBinaryOp(a, b, 'bool', function (aVal, bVal) {
                return (aVal <= bVal) ? 1 : 0;
            });
        };
        MathBackendCPU.prototype.greater = function (a, b) {
            assertNotComplex([a, b], 'greater');
            return this.broadcastedBinaryOp(a, b, 'bool', function (aVal, bVal) {
                return (aVal > bVal) ? 1 : 0;
            });
        };
        MathBackendCPU.prototype.greaterEqual = function (a, b) {
            assertNotComplex([a, b], 'greaterEqual');
            return this.broadcastedBinaryOp(a, b, 'bool', function (aVal, bVal) {
                return (aVal >= bVal) ? 1 : 0;
            });
        };
        MathBackendCPU.prototype.logicalNot = function (x) {
            assertNotComplex(x, 'logicalNot');
            var values = this.readSync(x.dataId);
            var newValues = new Uint8Array(values.length);
            for (var i = 0; i < values.length; ++i) {
                newValues[i] = values[i] ? 0 : 1;
            }
            return this.makeOutput(newValues, x.shape, 'bool');
        };
        MathBackendCPU.prototype.logicalAnd = function (a, b) {
            assertNotComplex([a, b], 'logicalAnd');
            return this.broadcastedBinaryOp(a, b, 'bool', function (aVal, bVal) {
                return aVal && bVal;
            });
        };
        MathBackendCPU.prototype.logicalOr = function (a, b) {
            assertNotComplex([a, b], 'logicalOr');
            return this.broadcastedBinaryOp(a, b, 'bool', function (aVal, bVal) {
                return aVal || bVal;
            });
        };
        MathBackendCPU.prototype.select = function (condition, a, b) {
            assertNotComplex([condition, a, b], 'select');
            var values = this.readSync(condition.dataId);
            var aValues = this.readSync(a.dataId);
            var bValues = this.readSync(b.dataId);
            var result = zeros(a.shape, upcastType(a.dtype, b.dtype));
            var newValues = this.readSync(result.dataId);
            var index = 0;
            var offset = condition.rank === 0 || condition.rank > 1 || a.rank === 1 ?
                1 :
                sizeFromShape(a.shape.slice(1));
            for (var i = 0; i < values.length; i++) {
                for (var j = 0; j < offset; j++) {
                    if (values[i] === 1) {
                        newValues[index++] = aValues[i];
                    }
                    else {
                        newValues[index++] = bValues[i];
                    }
                }
            }
            return result;
        };
        MathBackendCPU.prototype.where = function (condition) {
            assertNotComplex([condition], 'where');
            var condVals = this.readSync(condition.dataId);
            return whereImpl(condition.shape, condVals);
        };
        MathBackendCPU.prototype.topk = function (x, k, sorted) {
            assertNotComplex(x, 'topk');
            var xVals = this.readSync(x.dataId);
            return topkImpl(xVals, x.shape, x.dtype, k, sorted);
        };
        MathBackendCPU.prototype.min = function (x, axes) {
            assertNotComplex(x, 'min');
            assertAxesAreInnerMostDims('min', axes, x.rank);
            var _a = computeOutAndReduceShapes(x.shape, axes), outShape = _a[0], reduceShape = _a[1];
            var result = zeros(outShape, x.dtype);
            var reduceSize = sizeFromShape(reduceShape);
            var vals = this.readSync(result.dataId);
            var aVals = this.readSync(x.dataId);
            for (var i = 0; i < vals.length; ++i) {
                var offset = i * reduceSize;
                var min = aVals[offset];
                for (var j = 0; j < reduceSize; ++j) {
                    var value = aVals[offset + j];
                    if (value < min) {
                        min = value;
                    }
                }
                vals[i] = min;
            }
            return result;
        };
        MathBackendCPU.prototype.minimum = function (a, b) {
            assertNotComplex([a, b], 'minimum');
            return this.broadcastedBinaryOp(a, b, a.dtype, function (aVal, bVal) { return Math.min(aVal, bVal); });
        };
        MathBackendCPU.prototype.mod = function (a, b) {
            assertNotComplex([a, b], 'mod');
            return this.broadcastedBinaryOp(a, b, a.dtype, function (aVal, bVal) {
                var rem = aVal % bVal;
                if ((aVal < 0 && bVal < 0) || (aVal >= 0 && bVal >= 0)) {
                    return rem;
                }
                else {
                    return (rem + bVal) % bVal;
                }
            });
        };
        MathBackendCPU.prototype.max = function (x, axes) {
            assertNotComplex(x, 'max');
            assertAxesAreInnerMostDims('max', axes, x.rank);
            var _a = computeOutAndReduceShapes(x.shape, axes), outShape = _a[0], reduceShape = _a[1];
            var result = zeros(outShape, x.dtype);
            var reduceSize = sizeFromShape(reduceShape);
            var vals = this.readSync(result.dataId);
            var aVals = this.readSync(x.dataId);
            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 (value > max) {
                        max = value;
                    }
                }
                vals[i] = max;
            }
            return result;
        };
        MathBackendCPU.prototype.maximum = function (a, b) {
            assertNotComplex([a, b], 'maximum');
            return this.broadcastedBinaryOp(a, b, a.dtype, function (aVal, bVal) { return Math.max(aVal, bVal); });
        };
        MathBackendCPU.prototype.all = function (x, axes) {
            assertNotComplex(x, 'all');
            assertAxesAreInnerMostDims('all', axes, x.rank);
            var _a = computeOutAndReduceShapes(x.shape, axes), outShape = _a[0], reduceShape = _a[1];
            var result = zeros(outShape, x.dtype);
            var reduceSize = sizeFromShape(reduceShape);
            var vals = this.readSync(result.dataId);
            var aVals = this.readSync(x.dataId);
            for (var i = 0; i < vals.length; ++i) {
                var offset = i * reduceSize;
                var all = aVals[offset];
                for (var j = 0; j < reduceSize; ++j) {
                    var value = aVals[offset + j];
                    all = all && value;
                }
                vals[i] = all;
            }
            return result;
        };
        MathBackendCPU.prototype.any = function (x, axes) {
            assertNotComplex(x, 'any');
            assertAxesAreInnerMostDims('any', axes, x.rank);
            var _a = computeOutAndReduceShapes(x.shape, axes), outShape = _a[0], reduceShape = _a[1];
            var result = zeros(outShape, x.dtype);
            var reduceSize = sizeFromShape(reduceShape);
            var vals = this.readSync(result.dataId);
            var aVals = this.readSync(x.dataId);
            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;
            }
            return result;
        };
        MathBackendCPU.prototype.squaredDifference = function (a, b) {
            assertNotComplex([a, b], 'squaredDifference');
            return this.broadcastedBinaryOp(a, b, a.dtype, function (aVal, bVal) {
                var diff = aVal - bVal;
                return diff * diff;
            });
        };
        MathBackendCPU.prototype.ceil = function (x) {
            assertNotComplex(x, 'ceil');
            var values = this.readSync(x.dataId);
            var newValues = new Float32Array(values.length);
            for (var i = 0; i < values.length; ++i) {
                newValues[i] = Math.ceil(values[i]);
            }
            return this.makeOutput(newValues, x.shape, 'float32');
        };
        MathBackendCPU.prototype.floor = function (x) {
            assertNotComplex(x, 'floor');
            var values = this.readSync(x.dataId);
            var newValues = new Float32Array(values.length);
            for (var i = 0; i < values.length; ++i) {
                newValues[i] = Math.floor(values[i]);
            }
            return this.makeOutput(newValues, x.shape, 'float32');
        };
        MathBackendCPU.prototype.sign = function (x) {
            assertNotComplex(x, 'x');
            var values = this.readSync(x.dataId);
            var newValues = new Float32Array(values.length);
            for (var i = 0; i < values.length; ++i) {
                if (values[i] < 0) {
                    newValues[i] = -1;
                }
                else if (values[i] > 0) {
                    newValues[i] = 1;
                }
                else {
                    newValues[i] = 0;
                }
            }
            return this.makeOutput(newValues, x.shape, 'float32');
        };
        MathBackendCPU.prototype.isNaN = function (x) {
            assertNotComplex(x, 'x');
            var values = this.readSync(x.dataId);
            var newValues = new Uint8Array(values.length);
            for (var i = 0; i < values.length; ++i) {
                if (Number.isNaN(values[i])) {
                    newValues[i] = 1;
                }
            }
            return this.makeOutput(newValues, x.shape, 'bool');
        };
        MathBackendCPU.prototype.isInf = function (x) {
            assertNotComplex(x, 'x');
            var values = this.readSync(x.dataId);
            var newValues = new Uint8Array(values.length);
            for (var i = 0; i < values.length; ++i) {
                if (Math.abs(values[i]) === Infinity) {
                    newValues[i] = 1;
                }
            }
            return this.makeOutput(newValues, x.shape, 'bool');
        };
        MathBackendCPU.prototype.isFinite = function (x) {
            assertNotComplex(x, 'x');
            var values = this.readSync(x.dataId);
            var newValues = new Uint8Array(values.length);
            for (var i = 0; i < values.length; ++i) {
                if (Number.isFinite(values[i])) {
                    newValues[i] = 1;
                }
            }
            return this.makeOutput(newValues, x.shape, 'bool');
        };
        MathBackendCPU.prototype.round = function (x) {
            assertNotComplex(x, 'round');
            var values = this.readSync(x.dataId);
            var newValues = new Float32Array(values.length);
            for (var i = 0; i < values.length; ++i) {
                // The algorithm is based on banker's rounding.
                var base = Math.floor(values[i]);
                if (values[i] - base < 0.5) {
                    newValues[i] = Math.floor(values[i]);
                }
                else if (values[i] - base > 0.5) {
                    newValues[i] = Math.ceil(values[i]);
                }
                else {
                    if (base % 2.0 === 0.0) {
                        newValues[i] = base;
                    }
                    else {
                        newValues[i] = base + 1.0;
                    }
                }
            }
            return this.makeOutput(newValues, x.shape, 'float32');
        };
        MathBackendCPU.prototype.exp = function (x) {
            assertNotComplex(x, 'exp');
            var values = this.readSync(x.dataId);
            var newValues = new Float32Array(values.length);
            for (var i = 0; i < values.length; ++i) {
                newValues[i] = Math.exp(values[i]);
            }
            return this.makeOutput(newValues, x.shape, 'float32');
        };
        MathBackendCPU.prototype.expm1 = function (x) {
            assertNotComplex(x, 'expm1');
            var values = this.readSync(x.dataId);
            var newValues = new Float32Array(values.length);
            for (var i = 0; i < values.length; ++i) {
                newValues[i] = Math.expm1(values[i]);
            }
            return this.makeOutput(newValues, x.shape, 'float32');
        };
        MathBackendCPU.prototype.log = function (x) {
            assertNotComplex(x, 'log');
            var values = this.readSync(x.dataId);
            var newValues = new Float32Array(values.length);
            for (var i = 0; i < values.length; ++i) {
                var value = values[i];
                newValues[i] = Math.log(value);
            }
            return this.makeOutput(newValues, x.shape, 'float32');
        };
        MathBackendCPU.prototype.log1p = function (x) {
            assertNotComplex(x, 'log1p');
            var values = this.readSync(x.dataId);
            var newValues = new Float32Array(values.length);
            for (var i = 0; i < values.length; ++i) {
                var value = values[i];
                newValues[i] = Math.log1p(value);
            }
            return this.makeOutput(newValues, x.shape, 'float32');
        };
        MathBackendCPU.prototype.sqrt = function (x) {
            assertNotComplex(x, 'sqrt');
            var values = this.readSync(x.dataId);
            var newValues = new Float32Array(values.length);
            for (var i = 0; i < values.length; ++i) {
                var value = values[i];
                newValues[i] = Math.sqrt(value);
            }
            return this.makeOutput(newValues, x.shape, 'float32');
        };
        MathBackendCPU.prototype.rsqrt = function (x) {
            assertNotComplex(x, 'rsqrt');
            var values = this.readSync(x.dataId);
            var newValues = new Float32Array(values.length);
            for (var i = 0; i < values.length; ++i) {
                var value = values[i];
                newValues[i] = 1 / Math.sqrt(value);
            }
            return this.makeOutput(newValues, x.shape, 'float32');
        };
        MathBackendCPU.prototype.reciprocal = function (x) {
            assertNotComplex(x, 'reciprocal');
            var values = this.readSync(x.dataId);
            var newValues = new Float32Array(values.length);
            for (var i = 0; i < values.length; ++i) {
                newValues[i] = 1 / values[i];
            }
            return this.makeOutput(newValues, x.shape, 'float32');
        };
        MathBackendCPU.prototype.linear = function (x) {
            return x;
        };
        MathBackendCPU.prototype.relu = function (x) {
            assertNotComplex(x, 'relu');
            var res = zeros(x.shape, x.dtype);
            var resVals = this.readSync(res.dataId);
            var inVals = this.readSync(x.dataId);
            for (var i = 0; i < inVals.length; ++i) {
                resVals[i] = Math.max(0, inVals[i]);
            }
            return res;
        };
        MathBackendCPU.prototype.relu6 = function (x) {
            assertNotComplex(x, 'relu');
            var res = zeros(x.shape, x.dtype);
            var resVals = this.readSync(res.dataId);
            var inVals = this.readSync(x.dataId);
            for (var i = 0; i < inVals.length; ++i) {
                resVals[i] = Math.min(Math.max(0, inVals[i]), 6);
            }
            return res;
        };
        MathBackendCPU.prototype.prelu = function (x, a) {
            assertNotComplex([x, a], 'prelu');
            return this.broadcastedBinaryOp(x, a, x.dtype, function (xValue, aValue) { return xValue < 0 ? aValue * xValue : xValue; });
        };
        MathBackendCPU.prototype.elu = function (x) {
            assertNotComplex(x, 'elu');
            var resultValues = new Float32Array(x.size);
            var values = this.readSync(x.dataId);
            for (var i = 0; i < values.length; ++i) {
                var v = values[i];
                if (v >= 0) {
                    resultValues[i] = v;
                }
                else {
                    resultValues[i] = (Math.exp(v) - 1);
                }
            }
            return this.makeOutput(resultValues, x.shape, 'float32');
        };
        MathBackendCPU.prototype.eluDer = function (dy, y) {
            assertNotComplex([dy, y], 'eluDer');
            var resultValues = new Float32Array(y.size);
            var values = this.readSync(y.dataId);
            var dyValues = this.readSync(dy.dataId);
            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 this.makeOutput(resultValues, y.shape, 'float32');
        };
        MathBackendCPU.prototype.selu = function (x) {
            assertNotComplex(x, 'selu');
            // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
            // see: https://arxiv.org/abs/1706.02515
            var scaleAlpha = SELU_SCALEALPHA;
            var scale = SELU_SCALE;
            var resultValues = new Float32Array(x.size);
            var values = this.readSync(x.dataId);
            for (var i = 0; i < values.length; ++i) {
                var v = values[i];
                if (v >= 0) {
                    resultValues[i] = scale * v;
                }
                else {
                    resultValues[i] = scaleAlpha * (Math.exp(v) - 1);
                }
            }
            return this.makeOutput(resultValues, x.shape, 'float32');
        };
        MathBackendCPU.prototype.clip = function (x, min, max) {
            assertNotComplex(x, 'clip');
            var resultValues = new Float32Array(x.size);
            var values = this.readSync(x.dataId);
            for (var i = 0; i < values.length; ++i) {
                var v = values[i];
                resultValues[i] = v > max ? max : (v < min ? min : v);
            }
            return this.makeOutput(resultValues, x.shape, 'float32');
        };
        MathBackendCPU.prototype.abs = function (x) {
            var resultValues = new Float32Array(x.size);
            var values = this.readSync(x.dataId);
            for (var i = 0; i < values.length; ++i) {
                resultValues[i] = Math.abs(values[i]);
            }
            return this.makeOutput(resultValues, x.shape, 'float32');
        };
        MathBackendCPU.prototype.complexAbs = function (x) {
            var resultValues = new Float32Array(x.size);
            var values = this.readSync(x.dataId);
            for (var i = 0; i < x.size; ++i) {
                var real_1 = values[i * 2];
                var imag_1 = values[i * 2 + 1];
                resultValues[i] = Math.hypot(real_1, imag_1);
            }
            return this.makeOutput(resultValues, x.shape, 'float32');
        };
        MathBackendCPU.prototype.int = function (x) {
            assertNotComplex(x, 'int');
            var resultValues = new Int32Array(x.size);
            var values = this.readSync(x.dataId);
            for (var i = 0; i < values.length; ++i) {
                resultValues[i] = values[i];
            }
            return this.makeOutput(resultValues, x.shape, 'int32');
        };
        MathBackendCPU.prototype.sigmoid = function (x) {
            assertNotComplex(x, 'sigmoid');
            var resultValues = new Float32Array(x.size);
            var values = this.readSync(x.dataId);
            for (var i = 0; i < values.length; ++i) {
                resultValues[i] = 1 / (1 + Math.exp(-values[i]));
            }
            return this.makeOutput(resultValues, x.shape, 'float32');
        };
        MathBackendCPU.prototype.softplus = function (x) {
            assertNotComplex(x, 'softplus');
            // 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 resultValues = new Float32Array(x.size);
            var values = this.readSync(x.dataId);
            for (var i = 0; i < values.length; ++i) {
                // Value above which exp(x) may overflow, but softplus(x) == x
                // is within machine epsilon.
                var tooLarge = values[i] > -threshold;
                // Value below which exp(x) may underflow, but softplus(x) == exp(x)
                // is within machine epsilon.
                var tooSmall = values[i] < threshold;
                var expX = Math.exp(values[i]);
                var result = void 0;
                if (tooSmall) {
                    result = expX;
                }
                else if (tooLarge) {
                    result = values[i];
                }
                else {
                    result = Math.log(1.0 + expX);
                }
                resultValues[i] = result;
            }
            return this.makeOutput(resultValues, x.shape, 'float32');
        };
        MathBackendCPU.prototype.sin = function (x) {
            assertNotComplex(x, 'sin');
            var resultValues = new Float32Array(x.size);
            var values = this.readSync(x.dataId);
            for (var i = 0; i < values.length; ++i) {
                resultValues[i] = Math.sin(values[i]);
            }
            return this.makeOutput(resultValues, x.shape, 'float32');
        };
        MathBackendCPU.prototype.cos = function (x) {
            assertNotComplex(x, 'cos');
            var resultValues = new Float32Array(x.size);
            var values = this.readSync(x.dataId);
            for (var i = 0; i < values.length; ++i) {
                resultValues[i] = Math.cos(values[i]);
            }
            return this.makeOutput(resultValues, x.shape, 'float32');
        };
        MathBackendCPU.prototype.tan = function (x) {
            assertNotComplex(x, 'tan');
            var resultValues = new Float32Array(x.size);
            var values = this.readSync(x.dataId);
            for (var i = 0; i < values.length; ++i) {
                resultValues[i] = Math.tan(values[i]);
            }
            return this.makeOutput(resultValues, x.shape, 'float32');
        };
        MathBackendCPU.prototype.asin = function (x) {
            assertNotComplex(x, 'asin');
            var resultValues = new Float32Array(x.size);
            var values = this.readSync(x.dataId);
            for (var i = 0; i < values.length; ++i) {
                resultValues[i] = Math.asin(values[i]);
            }
            return this.makeOutput(resultValues, x.shape, 'float32');
        };
        MathBackendCPU.prototype.acos = function (x) {
            assertNotComplex(x, 'acos');
            var resultValues = new Float32Array(x.size);
            var values = this.readSync(x.dataId);
            for (var i = 0; i < values.length; ++i) {
                resultValues[i] = Math.acos(values[i]);
            }
            return this.makeOutput(resultValues, x.shape, 'float32');
        };
        MathBackendCPU.prototype.atan = function (x) {
            assertNotComplex(x, 'atan');
            var resultValues = new Float32Array(x.size);
            var values = this.readSync(x.dataId);
            for (var i = 0; i < values.length; ++i) {
                resultValues[i] = Math.atan(values[i]);
            }
            return this.makeOutput(resultValues, x.shape, 'float32');
        };
        MathBackendCPU.prototype.atan2 = function (a, b) {
            assertNotComplex([a, b], 'atan2');
            return this.broadcastedBinaryOp(a, b, a.dtype, function (aValue, bValue) { return Math.atan2(aValue, bValue); });
        };
        MathBackendCPU.prototype.sinh = function (x) {
            assertNotComplex(x, 'sinh');
            var resultValues = new Float32Array(x.size);
            var values = this.readSync(x.dataId);
            for (var i = 0; i < values.length; ++i) {
                resultValues[i] = Math.sinh(values[i]);
            }
            return this.makeOutput(resultValues, x.shape, 'float32');
        };
        MathBackendCPU.prototype.cosh = function (x) {
            assertNotComplex(x, 'cosh');
            var resultValues = new Float32Array(x.size);
            var values = this.readSync(x.dataId);
            for (var i = 0; i < values.length; ++i) {
                resultValues[i] = Math.cosh(values[i]);
            }
            return this.makeOutput(resultValues, x.shape, 'float32');
        };
        MathBackendCPU.prototype.tanh = function (x) {
            assertNotComplex(x, 'tanh');
            var resultValues = new Float32Array(x.size);
            var values = this.readSync(x.dataId);
            for (var i = 0; i < values.length; ++i) {
                resultValues[i] = tanh(values[i]);
            }
            return this.makeOutput(resultValues, x.shape, 'float32');
        };
        MathBackendCPU.prototype.asinh = function (x) {
            assertNotComplex(x, 'asinh');
            var resultValues = new Float32Array(x.size);
            var values = this.readSync(x.dataId);
            for (var i = 0; i < values.length; ++i) {
                resultValues[i] = Math.asinh(values[i]);
            }
            return this.makeOutput(resultValues, x.shape, 'float32');
        };
        MathBackendCPU.prototype.acosh = function (x) {
            assertNotComplex(x, 'acosh');
            var resultValues = new Float32Array(x.size);
            var values = this.readSync(x.dataId);
            for (var i = 0; i < values.length; ++i) {
                resultValues[i] = Math.acosh(values[i]);
            }
            return this.makeOutput(resultValues, x.shape, 'float32');
        };
        MathBackendCPU.prototype.atanh = function (x) {
            assertNotComplex(x, 'atanh');
            var resultValues = new Float32Array(x.size);
            var values = this.readSync(x.dataId);
            for (var i = 0; i < values.length; ++i) {
                resultValues[i] = Math.atanh(values[i]);
            }
            return this.makeOutput(resultValues, x.shape, 'float32');
        };
        MathBackendCPU.prototype.erf = function (x) {
            assertNotComplex(x, 'erf');
            var resultValues = new Float32Array(x.size);
            var values = this.readSync(x.dataId);
            var p = ERF_P;
            var a1 = ERF_A1;
            var a2 = ERF_A2;
            var a3 = ERF_A3;
            var a4 = ERF_A4;
            var a5 = ERF_A5;
            for (var i = 0; i < values.length; ++i) {
                var sign = Math.sign(values[i]);
                var v = Math.abs(values[i]);
                var t = 1.0 / (1.0 + p * v);
                resultValues[i] = sign *
                    (1.0 -
                        (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t *
                            Math.exp(-v * v));
            }
            return this.makeOutput(resultValues, x.shape, 'float32');
        };
        MathBackendCPU.prototype.step = function (x, alpha) {
            if (alpha === void 0) { alpha = 0; }
            assertNotComplex(x, 'step');
            var resultValues = new Float32Array(x.size);
            var values = this.readSync(x.dataId);
            for (var i = 0; i < values.length; ++i) {
                var value = values[i];
                if (isNaN(value)) {
                    resultValues[i] = NaN;
                }
                else {
                    resultValues[i] = value > 0 ? 1 : alpha;
                }
            }
            return this.makeOutput(resultValues, x.shape, 'float32');
        };
        MathBackendCPU.prototype.fusedConv2d = function (_a) {
            var input = _a.input, filter = _a.filter, convInfo = _a.convInfo, bias = _a.bias, activation = _a.activation, preluActivationWeights = _a.preluActivationWeights;
            var result = this.conv2d(input, filter, convInfo);
            if (bias) {
                result = this.add(result, bias);
            }
            if (activation) {
                result =
                    mapActivation(this, result, activation, preluActivationWeights);
            }
            return result;
        };
        MathBackendCPU.prototype.conv2d = function (x, filter, convInfo) {
            assertNotComplex([x, filter], 'conv2d');
            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 = buffer(convInfo.outShape, x.dtype);
            var xBatchStride = x.strides[0];
            var xRowStride = isChannelsLast ? x.strides[1] : x.strides[2];
            var xColStride = isChannelsLast ? x.strides[2] : 1;
            var xChannelStride = isChannelsLast ? 1 : x.strides[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 = this.readSync(x.dataId);
            var wVals = this.readSync(filter.dataId);
            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 * filter.strides[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 * filter.strides[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 y.toTensor();
        };
        MathBackendCPU.prototype.conv3d = function (x, filter, convInfo) {
            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 padFront = convInfo.padInfo.front;
            var padLeft = convInfo.padInfo.left;
            var padTop = convInfo.padInfo.top;
            var y = buffer(convInfo.outShape, x.dtype);
            var xVals = this.readSync(x.dataId);
            var wVals = this.readSync(filter.dataId);
            var yVals = y.values;
            for (var b = 0; b < convInfo.batchSize; ++b) {
                var xOffset1 = b * x.strides[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 * filter.strides[0];
                        var xOffset2 = xOffset1 + xF * x.strides[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 * filter.strides[1];
                                var xOffset3 = xOffset2 + xR * x.strides[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 * filter.strides[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 y.toTensor();
        };
        MathBackendCPU.prototype.conv2dDerInput = function (dy, filter, convInfo) {
            assertNotComplex([dy, filter], 'conv2dDerInput');
            var dx = buffer(convInfo.inShape, 'float32');
            var dxValues = dx.values;
            var dyValues = this.readSync(dy.dataId);
            var fltValues = this.readSync(filter.dataId);
            var _a = filter.strides, 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 = dy.strides[0];
            var yRowStride = isChannelsLast ? dy.strides[1] : dy.strides[2];
            var yColStride = isChannelsLast ? dy.strides[2] : 1;
            var yChannelStride = isChannelsLast ? 1 : dy.strides[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 dx.toTensor();
        };
        MathBackendCPU.prototype.conv3dDerInput = function (dy, filter, convInfo) {
            var dx = buffer(convInfo.inShape, 'float32');
            var dxValues = dx.values;
            var _a = dx.strides, dxS0 = _a[0], dxS1 = _a[1], dxS2 = _a[2], dxS3 = _a[3];
            var dyValues = this.readSync(dy.dataId);
            var _b = dy.strides, dyS0 = _b[0], dyS1 = _b[1], dyS2 = _b[2], dyS3 = _b[3];
            var fltValues = this.readSync(filter.dataId);
            var _c = filter.strides, 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 dx.toTensor();
        };
        MathBackendCPU.prototype.conv2dDerFilter = function (x, dy, convInfo) {
            assertNotComplex([x, dy], 'conv2dDerFilter');
            var strideHeight = convInfo.strideHeight;
            var strideWidth = convInfo.strideWidth;
            var filterHeight = convInfo.filterHeight;
            var filterWidth = convInfo.filterWidth;
            var isChannelsLast = convInfo.dataFormat === 'channelsLast';
            var dW = buffer(convInfo.filterShape, 'float32');
            var leftPad = convInfo.padInfo.left;
            var topPad = convInfo.padInfo.top;
            var xBuf = this.bufferSync(x);
            var dyBuf = this.bufferSync(dy);
            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) {
                            // Need to convolve.
                            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 dW.toTensor();
        };
        MathBackendCPU.prototype.conv3dDerFilter = function (x, dy, convInfo) {
            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 = buffer(convInfo.filterShape, 'float32');
            var dwValues = dw.values;
            var _a = dw.strides, dwS0 = _a[0], dwS1 = _a[1], dwS2 = _a[2], dwS3 = _a[3];
            var dyValues = this.readSync(dy.dataId);
            var _b = dy.strides, dyS0 = _b[0], dyS1 = _b[1], dyS2 = _b[2], dyS3 = _b[3];
            var xValues = this.readSync(x.dataId);
            var _c = x.strides, 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 dw.toTensor();
        };
        MathBackendCPU.prototype.fusedDepthwiseConv2D = function (_a) {
            var input = _a.input, filter = _a.filter, convInfo = _a.convInfo, bias = _a.bias, activation = _a.activation, preluActivationWeights = _a.preluActivationWeights;
            var result = this.depthwiseConv2D(input, filter, convInfo);
            if (bias) {
                result = this.add(result, bias);
            }
            if (activation) {
                result =
                    mapActivation(this, result, activation, preluActivationWeights);
            }
            return result;
        };
        MathBackendCPU.prototype.depthwiseConv2D = function (x, filter, convInfo) {
            assertNotComplex([x, filter], 'depthwiseConv2D');
            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 chMul = convInfo.outChannels / convInfo.inChannels;
            var y = buffer(convInfo.outShape, x.dtype);
            var xVals = this.readSync(x.dataId);
            var wVals = this.readSync(filter.dataId);
            var yVals = y.values;
            for (var b = 0; b < convInfo.batchSize; ++b) {
                var xOffset1 = b * x.strides[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 - padLeft;
                    for (var wR = 0; wR < filterHeight; ++wR) {
                        var xR = xRCorner + wR * dilationHeight;
                        if (xR < 0 || xR >= convInfo.inHeight) {
                            continue;
                        }
                        var wOffset1 = wR * filter.strides[0];
                        var xOffset2 = xOffset1 + xR * x.strides[1];
                        for (var yC = 0; yC < convInfo.outWidth; ++yC) {
                            var yOffset3 = yOffset2 + yC * y.strides[2];
                            var xCCorner = yC * convInfo.strideWidth - padTop;
                            for (var wC = 0; wC < filterWidth; ++wC) {
                                var xC = xCCorner + wC * dilationWidth;
                                if (xC < 0 || xC >= convInfo.inWidth) {
                                    continue;
                                }
                                var wOffset2 = wOffset1 + wC * filter.strides[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 y.toTensor();
        };
        MathBackendCPU.prototype.depthwiseConv2DDerInput = function (dy, filter, convInfo) {
            assertNotComplex([dy, filter], 'depthwiseConv2DDerInput');
            var dx = buffer(convInfo.inShape, 'float32');
            var dxValues = dx.values;
            var _a = dx.strides, dxS0 = _a[0], dxS1 = _a[1], dxS2 = _a[2];
            var dyValues = this.readSync(dy.dataId);
            var _b = dy.strides, dyS0 = _b[0], dyS1 = _b[1], dyS2 = _b[2];
            var fltValues = this.readSync(filter.dataId);
            var _c = filter.strides, 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 dx.toTensor();
        };
        MathBackendCPU.prototype.depthwiseConv2DDerFilter = function (x, dy, convInfo) {
            assertNotComplex([x, dy], 'depthwiseConv2DDerFilter');
            var strideHeight = convInfo.strideHeight;
            var strideWidth = convInfo.strideWidth;
            var filterHeight = convInfo.filterHeight;
            var filterWidth = convInfo.filterWidth;
            var dW = buffer(convInfo.filterShape, 'float32');
            var leftPad = convInfo.padInfo.left;
            var topPad = convInfo.padInfo.top;
            var chMul = convInfo.outChannels / convInfo.inChannels;
            var xBuf = this.bufferSync(x);
            var dyBuf = this.bufferSync(dy);
            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 dW.toTensor();
        };
        MathBackendCPU.prototype.tile = function (x, reps) {
            assertNotComplex(x, 'tile');
            return tile$1(this.bufferSync(x), reps);
        };
        MathBackendCPU.prototype.pad = function (x, paddings, 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 xBuffer = this.bufferSync(x);
            var buffer$1 = buffer(outShape, x.dtype);
            if (constantValue !== 0) {
                buffer$1.values.fill(constantValue);
            }
            for (var i = 0; i < x.size; i++) {
                var coords = xBuffer.indexToLoc(i);
                var outCoords = coords.map(function (c, i) { return c + start[i]; });
                buffer$1.set.apply(buffer$1, [xBuffer.get.apply(xBuffer, coords)].concat(outCoords));
            }
            return buffer$1.toTensor();
        };
        MathBackendCPU.prototype.transpose = function (x, perm) {
            assertNotComplex(x, 'transpose');
            var newShape = new Array(x.rank);
            for (var i = 0; i < newShape.length; i++) {
                newShape[i] = x.shape[perm[i]];
            }
            var values = this.readSync(x.dataId);
            var result = buffer(newShape, x.dtype);
            var xBuf = this.bufferSync(x);
            for (var i = 0; i < x.size; ++i) {
                var loc = xBuf.indexToLoc(i);
                // 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 = result.locToIndex(newLoc);
                result.values[newIndex] = values[i];
            }
            return result.toTensor();
        };
        MathBackendCPU.prototype.gather = function (x, indices, axis) {
            assertNotComplex([x, indices], 'gather');
            var newShape = x.shape.slice();
            var indicesValues = this.readSync(indices.dataId);
            newShape[axis] = indicesValues.length;
            var result = buffer(newShape, x.dtype);
            var xBuf = this.bufferSync(x);
            for (var i = 0; i < result.size; ++i) {
                var newLoc = result.indexToLoc(i);
                var originalLoc = newLoc.slice();
                originalLoc[axis] = indicesValues[newLoc[axis]];
                var originalIndex = xBuf.locToIndex(originalLoc);
                result.values[i] = xBuf.values[originalIndex];
            }
            return result.toTensor();
        };
        MathBackendCPU.prototype.batchToSpaceND = function (x, blockShape, crops) {
            assertNotComplex([x], 'batchToSpaceND');
            var prod = blockShape.reduce(function (a, b) { return a * b; });
            var reshaped = getReshaped(x.shape, blockShape, prod);
            var permuted = getPermuted(reshaped.length, blockShape.length);
            var reshapedPermuted = getReshapedPermuted(x.shape, blockShape, prod);
            var sliceBeginCoords = getSliceBeginCoords(crops, blockShape.length);
            var sliceSize = getSliceSize(reshapedPermuted, crops, blockShape.length);
            return x.reshape(reshaped)
                .transpose(permuted)
                .reshape(reshapedPermuted)
                .slice(sliceBeginCoords, sliceSize);
        };
        MathBackendCPU.prototype.spaceToBatchND = function (x, blockShape, paddings) {
            assertNotComplex([x], 'spaceToBatchND');
            var prod = blockShape.reduce(function (a, b) { return a * b; });
            var completePaddings = [[0, 0]];
            completePaddings.push.apply(completePaddings, paddings);
            for (var i = 1 + blockShape.length; i < x.shape.length; ++i) {
                completePaddings.push([0, 0]);
            }
            var paddedX = x.pad(completePaddings);
            var reshapedPaddedShape = getReshaped(paddedX.shape, blockShape, prod, false);
            var permutedReshapedPaddedPermutation = getPermuted(reshapedPaddedShape.length, blockShape.length, false);
            var flattenShape = getReshapedPermuted(paddedX.shape, blockShape, prod, false);
            return paddedX.reshape(reshapedPaddedShape)
                .transpose(permutedReshapedPaddedPermutation)
                .reshape(flattenShape);
        };
        MathBackendCPU.prototype.pool = function (x, convInfo, poolType) {
            assertNotComplex(x, 'pool');
            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 xValues = this.readSync(x.dataId);
            var output = buffer(convInfo.outShape, x.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 * x.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 * x.strides[1];
                                for (var xC = xCMin; xC < xCMax; xC += dilationWidth) {
                                    var xCOffset = xROffset + xC * x.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.toTensor();
        };
        MathBackendCPU.prototype.maxPool = function (x, convInfo) {
            return this.pool(x, convInfo, 'max');
        };
        MathBackendCPU.prototype.maxPoolPositions = function (x, convInfo) {
            var maxPositions = 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 = this.bufferSync(x);
            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;
                                        maxPosition = wR * effectiveFilterWidth + wC;
                                    }
                                }
                            }
                            maxPositions.set(maxPosition, b, yR, yC, d);
                        }
                    }
                }
            }
            return maxPositions.toTensor();
        };
        MathBackendCPU.prototype.maxPoolBackprop = function (dy, x, y, convInfo) {
            assertNotComplex([x, y], 'maxPoolBackprop');
            var maxPositions = this.maxPoolPositions(x, convInfo);
            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 = buffer(x.shape, 'float32');
            var maxPosBuf = this.bufferSync(maxPositions);
            var dyBuf = this.bufferSync(dy);
            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 dx.toTensor();
        };
        MathBackendCPU.prototype.avgPoolBackprop = function (dy, x, convInfo) {
            assertNotComplex([dy, x], 'avgPoolBackprop');
            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 = buffer(x.shape, 'float32');
            var avgMultiplier = 1 / (filterHeight * filterWidth);
            var dyBuf = this.bufferSync(dy);
            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 dx.toTensor();
        };
        MathBackendCPU.prototype.pool3d = function (x, convInfo, poolType) {
            assertNotComplex(x, 'pool3d');
            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 xValues = this.readSync(x.dataId);
            var output = buffer(convInfo.outShape, x.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 * x.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 * x.strides[1];
                                    for (var xRow = xRowMin; xRow < xRowMax; xRow += dilationHeight) {
                                        var xRowOffset = xDepthOffset + xRow * x.strides[2];
                                        for (var xCol = xColMin; xCol < xColMax; xCol += dilationWidth) {
                                            var xColOffset = xRowOffset + xCol * x.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.toTensor();
        };
        MathBackendCPU.prototype.avgPool3d = function (x, convInfo) {
            assertNotComplex(x, 'avgPool3d');
            return this.pool3d(x, convInfo, 'avg').toFloat();
        };
        MathBackendCPU.prototype.avgPool3dBackprop = function (dy, x, convInfo) {
            assertNotComplex([dy, x], 'avgPool3dBackprop');
            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 = buffer(x.shape, 'float32');
            var avgMultiplier = 1 / (filterDepth * filterHeight * filterWidth);
            var dyBuf = this.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 dx.toTensor();
        };
        MathBackendCPU.prototype.maxPool3d = function (x, convInfo) {
            assertNotComplex(x, 'maxPool3d');
            return this.pool3d(x, convInfo, 'max').toFloat();
        };
        MathBackendCPU.prototype.maxPool3dPositions = function (x, convInfo) {
            var maxPositions = 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;
            var xBuf = this.bufferSync(x);
            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.toTensor();
        };
        MathBackendCPU.prototype.maxPool3dBackprop = function (dy, x, y, convInfo) {
            assertNotComplex([x, y], 'maxPool3dBackprop');
            var maxPositions = this.maxPool3dPositions(x, 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 = buffer(x.shape, 'float32');
            var maxPosBuf = this.bufferSync(maxPositions);
            var dyBuf = this.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 dx.toTensor();
        };
        MathBackendCPU.prototype.cast = function (x, dtype) {
            return castTensor(x, dtype, this);
        };
        MathBackendCPU.prototype.reshape = function (x, shape) {
            return reshapeTensor(x, shape);
        };
        MathBackendCPU.prototype.avgPool = function (x, convInfo) {
            assertNotComplex(x, 'avgPool');
            return this.pool(x, convInfo, 'avg').toFloat();
        };
        MathBackendCPU.prototype.resizeBilinear = function (x, newHeight, newWidth, alignCorners) {
            assertNotComplex(x, 'resizeBilinear');
            var _a = x.shape, batch = _a[0], oldHeight = _a[1], oldWidth = _a[2], numChannels = _a[3];
            var xValues = this.readSync(x.dataId);
            var result = new Float32Array(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 = effectiveRowSizeRatio * r;
                    var sourceRowFloor = Math.floor(sourceFracRow);
                    var rowFrac = sourceFracRow - sourceRowFloor;
                    var sourceRowCeil = Math.min(oldHeight - 1, Math.ceil(sourceFracRow));
                    var topRowOffset = b * x.strides[0] + sourceRowFloor * x.strides[1];
                    var botRowOffset = b * x.strides[0] + sourceRowCeil * x.strides[1];
                    for (var c = 0; c < newWidth; c++) {
                        var sourceFracCol = effectiveColSizeRatio * c;
                        var sourceColFloor = Math.floor(sourceFracCol);
                        var colFrac = sourceFracCol - sourceColFloor;
                        var sourceColCeil = Math.min(oldWidth - 1, Math.ceil(sourceFracCol));
                        var topLeftOffest = topRowOffset + sourceColFloor * x.strides[2];
                        var botLeftOffset = botRowOffset + sourceColFloor * x.strides[2];
                        var topRightOffset = topRowOffset + sourceColCeil * x.strides[2];
                        var botRightOffest = botRowOffset + sourceColCeil * x.strides[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_1 = topLeft + (topRight - topLeft) * colFrac;
                            var bottom = bottomLeft + (bottomRight - bottomLeft) * colFrac;
                            var newValue = top_1 + (bottom - top_1) * rowFrac;
                            result[outputIdx++] = newValue;
                        }
                    }
                }
            }
            return tensor(result, [batch, newHeight, newWidth, numChannels]);
        };
        MathBackendCPU.prototype.resizeBilinearBackprop = function (dy, x, alignCorners) {
            assertNotComplex([dy, x], 'resizeBilinearBackprop');
            var _a = x.shape, batch = _a[0], xHeight = _a[1], xWidth = _a[2], depth = _a[3];
            var _b = dy.shape, 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 = this.readSync(dy.dataId);
            var offset = 0;
            for (var b = 0; b < batch; b++) {
                var bOffset = b * x.strides[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 * x.strides[1];
                    var bottomDxROffset = bOffset + bottomDxRIndex * x.strides[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 * x.strides[2];
                        var topRightRCOffset = topDxROffset + rightDxCIndex * x.strides[2];
                        var bottomLeftRCOffset = bottomDxROffset + leftDxCIndex * x.strides[2];
                        var bottomRightRCOffset = bottomDxROffset + rightDxCIndex * x.strides[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 tensor4d(output, [batch, xWidth, xHeight, depth], x.dtype);
        };
        MathBackendCPU.prototype.resizeNearestNeighbor = function (x, newHeight, newWidth, alignCorners) {
            assertNotComplex(x, 'resizeNearestNeighbor');
            var _a = x.shape, batch = _a[0], oldHeight = _a[1], oldWidth = _a[2], numChannels = _a[3];
            var xValues = this.readSync(x.dataId);
            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 * x.strides[0];
                for (var r = 0; r < newHeight; r++) {
                    var sourceFracRow = effectiveRowSizeRatio * r;
                    var sourceNearestRow = Math.min(oldHeight - 1, alignCorners ? Math.round(sourceFracRow) :
                        Math.floor(sourceFracRow));
                    var rowOffset = batchOffset + sourceNearestRow * x.strides[1];
                    for (var c = 0; c < newWidth; c++) {
                        var sourceFracCol = effectiveColSizeRatio * c;
                        var sourceNearestCol = Math.min(oldWidth - 1, alignCorners ? Math.round(sourceFracCol) :
                            Math.floor(sourceFracCol));
                        var colOffset = rowOffset + sourceNearestCol * x.strides[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 tensor(output, [batch, newHeight, newWidth, numChannels], x.dtype);
        };
        MathBackendCPU.prototype.resizeNearestNeighborBackprop = function (dy, x, alignCorners) {
            assertNotComplex([dy, x], 'resizeNearestNeighborBackprop');
            var _a = x.shape, batch = _a[0], xHeight = _a[1], xWidth = _a[2], depth = _a[3];
            var _b = dy.shape, yHeight = _b[1], yWidth = _b[2];
            var output = new Float32Array(batch * xHeight * xWidth * depth);
            var dyValues = this.readSync(dy.dataId);
            // 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 * x.strides[0];
                for (var r = 0; r < xHeight; r++) {
                    var rowOffset = batchOffset + r * x.strides[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 * x.strides[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 * dy.strides[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 * dy.strides[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 tensor4d(output, x.shape, x.dtype);
        };
        MathBackendCPU.prototype.batchNormalization = function (x, mean, variance, varianceEpsilon, scale, offset) {
            assertNotComplex([x, mean, variance, scale, offset], 'batchNorm');
            var xVals = this.readSync(x.dataId);
            var mVals = this.readSync(mean.dataId);
            var varVals = this.readSync(variance.dataId);
            var sVals = scale ? this.readSync(scale.dataId) :
                new Float32Array([1]);
            var offVals = offset ? this.readSync(offset.dataId) :
                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 tensor4d(outVals, x.shape);
        };
        MathBackendCPU.prototype.localResponseNormalization4D = function (x, depthRadius, bias, alpha, beta) {
            assertNotComplex(x, 'localResponseNormalization4D');
            var channels = x.shape[3];
            var maxD = channels - 1;
            var xValues = this.readSync(x.dataId);
            var size = x.size;
            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 tensor4d(result, x.shape);
        };
        MathBackendCPU.prototype.LRNGrad = function (dy, inputImage, outputImage, depthRadius, bias, alpha, beta) {
            assertNotComplex(dy, 'LRNGrad');
            var channels = dy.shape[3];
            var dyValues = this.readSync(dy.dataId);
            var inputImageValues = this.readSync(inputImage.dataId);
            var outputImageValues = this.readSync(outputImage.dataId);
            var result = new Float32Array(dy.size);
            var size = dy.size;
            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(inputImageValues[k], 2);
                }
                norm = alpha * norm + bias;
                for (var k = depthBegin; k < depthEnd; k++) {
                    var dyi = -2 * alpha * beta * inputImageValues[k] *
                        outputImageValues[offset] / norm;
                    if (offset === k) {
                        dyi += Math.pow(norm, -beta);
                    }
                    dyi *= dyValues[offset];
                    result[k] += dyi;
                }
            }
            return tensor4d(result, dy.shape);
        };
        MathBackendCPU.prototype.multinomial = function (logits, normalized, numSamples, seed) {
            assertNotComplex(logits, 'multinomial');
            var probabilities = normalized ? logits : softmax(logits);
            var batchSize = probabilities.shape[0];
            var numEvents = probabilities.shape[1];
            var res = zeros([batchSize, numSamples], 'int32');
            var resVals = this.readSync(res.dataId);
            var probVals = this.readSync(probabilities.dataId);
            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 = 1; event_1 < cdf.length; ++event_1) {
                    cdf[event_1] = cdf[event_1 - 1] + probVals[offset + event_1];
                }
                var random = seedrandom_1(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_2 = 0; event_2 < cdf.length; event_2++) {
                        if (r < cdf[event_2]) {
                            resVals[outOffset + sampleId] = event_2;
                            break;
                        }
                    }
                }
            }
            return res;
        };
        MathBackendCPU.prototype.oneHot = function (indices, depth, onValue, offValue) {
            assertNotComplex(indices, 'oneHot');
            var res = new Float32Array(indices.size * depth);
            res.fill(offValue);
            var indicesVal = this.readSync(indices.dataId);
            for (var event_3 = 0; event_3 < indices.size; ++event_3) {
                if (indicesVal[event_3] >= 0 && indicesVal[event_3] < depth) {
                    res[event_3 * depth + indicesVal[event_3]] = onValue;
                }
            }
            return tensor2d(res, [indices.size, depth], 'int32');
        };
        MathBackendCPU.prototype.nonMaxSuppression = function (boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
            assertNotComplex(boxes, 'nonMaxSuppression');
            var boxesVals = this.readSync(boxes.dataId);
            var scoresVals = this.readSync(scores.dataId);
            return nonMaxSuppressionV3(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);
        };
        MathBackendCPU.prototype.fft = function (x) {
            return this.fftBatch(x, false);
        };
        MathBackendCPU.prototype.ifft = function (x) {
            return this.fftBatch(x, true);
        };
        /**
         * Calculate FFT of inner most elements of batch tensor.
         */
        MathBackendCPU.prototype.fftBatch = function (x, inverse) {
            var batch = x.shape[0];
            var innerDim = x.shape[1];
            // Collects real and imaginary values separately.
            var realResult = buffer(x.shape, 'float32');
            var imagResult = buffer(x.shape, 'float32');
            var real$1 = real(x).as2D(batch, innerDim);
            var imag$1 = imag(x).as2D(batch, innerDim);
            for (var b = 0; b < batch; b++) {
                // TODO: Support slice ops for complex type.
                var r = real$1.slice([b, 0], [1, innerDim]);
                var i = imag$1.slice([b, 0], [1, innerDim]);
                var input = complex(r, i);
                // Run FFT by batch element.
                var res = this.readSync(this.fftImpl(input, inverse).dataId);
                for (var d = 0; d < innerDim; d++) {
                    var c = getComplexWithIndex(res, d);
                    realResult.values[b * innerDim + d] = c.real;
                    imagResult.values[b * innerDim + d] = c.imag;
                }
            }
            var t = complex(realResult.toTensor(), imagResult.toTensor());
            return t.as2D(batch, innerDim);
        };
        MathBackendCPU.prototype.fftImpl = function (x, inverse) {
            var x1D = x.as1D();
            var n = x1D.size;
            if (this.isExponentOf2(n)) {
                var result = this.fftRadix2(x1D, n, inverse).as2D(x.shape[0], x.shape[1]);
                if (inverse) {
                    result = complex(real(result).div(scalar(n)), imag(result).div(scalar(n)));
                }
                return result;
            }
            else {
                var data = this.readSync(x.dataId);
                var rawOutput = this.fourierTransformByMatmul(data, n, inverse);
                var output = splitRealAndImagArrays(rawOutput);
                return complex(output.real, output.imag).as2D(x.shape[0], x.shape[1]);
            }
        };
        MathBackendCPU.prototype.isExponentOf2 = function (size) {
            return (size & size - 1) === 0;
        };
        // FFT using Cooley-Tukey algorithm on radix 2 dimensional input.
        MathBackendCPU.prototype.fftRadix2 = function (input, size, inverse) {
            if (size === 1) {
                return input;
            }
            var data = this.readSync(input.dataId);
            var half = size / 2;
            var evenComplex = complexWithEvenIndex(data);
            var evenTensor = complex(evenComplex.real, evenComplex.imag).as1D();
            var oddComplex = complexWithOddIndex(data);
            var oddTensor = complex(oddComplex.real, oddComplex.imag).as1D();
            // Recursive call for half part of original input.
            evenTensor = this.fftRadix2(evenTensor, half, inverse);
            oddTensor = this.fftRadix2(oddTensor, half, inverse);
            var e = exponents(size, inverse);
            var exponent = complex(e.real, e.imag).mul(oddTensor);
            var addPart = evenTensor.add(exponent);
            var subPart = evenTensor.sub(exponent);
            var realTensor = real(addPart).concat(real(subPart));
            var imagTensor = imag(addPart).concat(imag(subPart));
            return complex(realTensor, imagTensor).as1D();
        };
        // Calculate fourier transform by multplying sinusoid matrix.
        MathBackendCPU.prototype.fourierTransformByMatmul = function (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 = exponent(r * c, size, inverse);
                    var term = 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;
                }
                assignToTypedArray(ret, real_2, imag_2, r);
            }
            return ret;
        };
        MathBackendCPU.prototype.depthToSpace = function (x, blockSize, dataFormat) {
            assert(dataFormat === 'NHWC', function () { return "Only NHWC dataFormat supported on CPU for depthToSpace. Got " + dataFormat; });
            assert(blockSize > 1, function () {
                return "blockSize should be > 1 for depthToSpace, but was: " + blockSize;
            });
            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 = this.readSync(x.dataId);
            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 tensor4d(result, [batchSize, outputHeight, outputWidth, outputDepth]);
        };
        MathBackendCPU.prototype.broadcastedBinaryOp = function (a, b, dtype, op) {
            var newShape = assertAndGetBroadcastShape(a.shape, b.shape);
            var result = buffer(newShape, dtype);
            var aVals = this.readSync(a.dataId);
            var bVals = this.readSync(b.dataId);
            var aBroadcastDims = getBroadcastDims(a.shape, newShape);
            var bBroadcastDims = getBroadcastDims(b.shape, newShape);
            var resVals = result.values;
            if (aBroadcastDims.length + bBroadcastDims.length === 0) {
                for (var i = 0; i < resVals.length; ++i) {
                    resVals[i] = op(aVals[i % aVals.length], bVals[i % bVals.length]);
                }
            }
            else {
                var aBuf = this.bufferSync(a);
                var bBuf = this.bufferSync(b);
                var _loop_2 = function (i) {
                    var loc = result.indexToLoc(i);
                    var aLoc = loc.slice(-a.rank);
                    aBroadcastDims.forEach(function (d) { return aLoc[d] = 0; });
                    var aIndex = aBuf.locToIndex(aLoc);
                    var bLoc = loc.slice(-b.rank);
                    bBroadcastDims.forEach(function (d) { return bLoc[d] = 0; });
                    var bIndex = bBuf.locToIndex(bLoc);
                    resVals[i] = op(aVals[aIndex], bVals[bIndex]);
                };
                for (var i = 0; i < resVals.length; ++i) {
                    _loop_2(i);
                }
            }
            return result.toTensor();
        };
        MathBackendCPU.prototype.broadcastedBinaryComplexOp = function (a, b, op) {
            var newShape = assertAndGetBroadcastShape(a.shape, b.shape);
            var realResult = buffer(newShape, 'float32');
            var imagResult = buffer(newShape, 'float32');
            var aVals = this.readSync(a.dataId);
            var bVals = this.readSync(b.dataId);
            var aBroadcastDims = getBroadcastDims(a.shape, newShape);
            var bBroadcastDims = getBroadcastDims(b.shape, newShape);
            var realVals = realResult.values;
            var imagVals = imagResult.values;
            if (aBroadcastDims.length + bBroadcastDims.length === 0) {
                for (var i = 0; i < realVals.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]);
                    realVals[i] = result.real;
                    imagVals[i] = result.imag;
                }
            }
            else {
                var aRealBuf = this.bufferSync(this.data.get(a.dataId).complexTensors.real);
                var bRealBuf = this.bufferSync(this.data.get(b.dataId).complexTensors.real);
                var _loop_3 = function (i) {
                    var loc = realResult.indexToLoc(i);
                    var aLoc = loc.slice(-a.rank);
                    aBroadcastDims.forEach(function (d) { return aLoc[d] = 0; });
                    var aIndex = aRealBuf.locToIndex(aLoc);
                    var bLoc = loc.slice(-b.rank);
                    bBroadcastDims.forEach(function (d) { return bLoc[d] = 0; });
                    var bIndex = bRealBuf.locToIndex(bLoc);
                    var opResult = op(aVals[aIndex * 2], aVals[aIndex * 2 + 1], bVals[bIndex * 2], bVals[bIndex * 2 + 1]);
                    realVals[i] = opResult.real;
                    imagVals[i] = opResult.imag;
                };
                for (var i = 0; i < realVals.length; i++) {
                    _loop_3(i);
                }
            }
            return this.complex(realResult.toTensor(), imagResult.toTensor());
        };
        MathBackendCPU.prototype.split = function (x, sizeSplits, axis) {
            return split$1(x, sizeSplits, axis);
        };
        MathBackendCPU.prototype.dispose = function () { };
        MathBackendCPU.prototype.floatPrecision = function () {
            return 32;
        };
        /** Returns the smallest representable number.  */
        MathBackendCPU.prototype.epsilon = function () {
            return EPSILON_FLOAT32;
        };
        MathBackendCPU.prototype.cropAndResize = function (images, boxes, boxIndex, cropSize, method, extrapolationValue) {
            var _a = images.shape, batch = _a[0], imageHeight = _a[1], imageWidth = _a[2], numChannels = _a[3];
            var numBoxes = boxes.shape[0];
            var cropHeight = cropSize[0], cropWidth = cropSize[1];
            var output = buffer([numBoxes, cropHeight, cropWidth, numChannels], 'float32');
            var boxVals = this.readSync(boxes.dataId);
            var boxIndVals = this.readSync(boxIndex.dataId);
            var imageVals = this.readSync(images.dataId);
            var inStride = images.strides; // to calculate flat indexes into image
            var outStride = output.strides; // 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_2 = 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_2 + ((bottom - top_2) * 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 output.toTensor();
        };
        MathBackendCPU.prototype.sparseToDense = function (sparseIndices, sparseValues, outputShape, defaultValue) {
            var _a = calculateShapes(sparseValues, sparseIndices, outputShape), sliceRank = _a.sliceRank, numUpdates = _a.numUpdates, sliceSize = _a.sliceSize, strides = _a.strides, outputSize = _a.outputSize;
            var sumDupeIndices = false;
            return this.scatter(sparseIndices, sparseValues, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, defaultValue, sumDupeIndices);
        };
        MathBackendCPU.prototype.gatherND = function (x, indices) {
            var indicesShape = indices.shape;
            var sliceRank = indicesShape[indicesShape.length - 1];
            var _a = prepareAndValidate(x, indices), resultShape = _a[0], numSlices = _a[1], sliceSize = _a[2], strides = _a[3];
            if (numSlices === 0) {
                return tensor([], resultShape, x.dtype);
            }
            var buffer = new TensorBuffer([numSlices, sliceSize], x.dtype);
            var indicesData = this.readSync(indices.dataId);
            var xData = this.readSync(x.dataId);
            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 >= x.size / sliceSize) {
                    throw new Error("Invalid indices: " + index + " does not index into " + x.shape);
                }
                for (var k = 0; k < sliceSize; k++) {
                    buffer.values[i * sliceSize + k] = xData[flattenIndex * sliceSize + k];
                }
            }
            return buffer.toTensor().reshape(resultShape);
        };
        MathBackendCPU.prototype.scatterND = function (indices, updates, shape) {
            var _a = calculateShapes(updates, indices, shape), sliceRank = _a.sliceRank, numUpdates = _a.numUpdates, sliceSize = _a.sliceSize, strides = _a.strides, outputSize = _a.outputSize;
            var defaultValue = scalar(0);
            var sumDupeIndices = true;
            return this.scatter(indices, updates, shape, outputSize, sliceSize, numUpdates, sliceRank, strides, defaultValue, sumDupeIndices);
        };
        MathBackendCPU.prototype.fill = function (shape, value, dtype) {
            dtype = dtype || inferDtype(value);
            var values = getArrayFromDType(dtype, sizeFromShape(shape));
            values.fill(value);
            return ENGINE.makeTensor(values, shape, dtype, this);
        };
        MathBackendCPU.prototype.onesLike = function (x) {
            if (x.dtype === 'string') {
                throw new Error('onesLike is not supported for string tensors');
            }
            else {
                return this.fill(x.shape, 1, x.dtype);
            }
        };
        MathBackendCPU.prototype.zerosLike = function (x) {
            var values = getArrayFromDType(x.dtype, sizeFromShape(x.shape));
            return this.makeOutput(values, x.shape, x.dtype);
        };
        MathBackendCPU.prototype.linspace = function (start, stop, num) {
            return linspaceImpl(start, stop, num);
        };
        MathBackendCPU.prototype.scatter = function (indices, updates, shape, outputSize, sliceSize, numUpdates, sliceRank, strides, defaultValue, sumDupeIndices) {
            var flattenShape = [outputSize / sliceSize, sliceSize];
            var indicesData = this.readSync(indices.dataId);
            var updatesData = this.readSync(updates.dataId);
            if (outputSize === 0) {
                return tensor([], shape, updates.dtype);
            }
            var buffer = new TensorBuffer(flattenShape, updates.dtype);
            buffer.values.fill(this.readSync(defaultValue.dataId)[0]);
            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) {
                        buffer.values[flattenIndex * sliceSize + k] +=
                            updatesData[i * sliceSize + k];
                    }
                    else {
                        buffer.values[flattenIndex * sliceSize + k] = updates.rank === 0 ?
                            updatesData[0] :
                            updatesData[i * sliceSize + k];
                    }
                }
            }
            return buffer.toTensor().reshape(shape);
        };
        return MathBackendCPU;
    }(KernelBackend));
    ENGINE.registerBackend('cpu', function () { return new MathBackendCPU(); }, 1 /* priority */);

    /**
     * @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 nonMaxSuppressionV5Config = {
        kernelName: NonMaxSuppressionV5,
        backendName: 'cpu',
        kernelFunc: function (_a) {
            var inputs = _a.inputs, backend = _a.backend, attrs = _a.attrs;
            var _b = inputs, boxes = _b.boxes, scores = _b.scores;
            var _c = attrs, maxOutputSize = _c.maxOutputSize, iouThreshold = _c.iouThreshold, scoreThreshold = _c.scoreThreshold, softNmsSigma = _c.softNmsSigma;
            var cpuBackend = backend;
            assertNotComplex(boxes, 'NonMaxSuppressionWithScore');
            var boxesVals = cpuBackend.data.get(boxes.dataId).values;
            var scoresVals = cpuBackend.data.get(scores.dataId).values;
            var maxOutputSizeVal = maxOutputSize;
            var iouThresholdVal = iouThreshold;
            var scoreThresholdVal = scoreThreshold;
            var softNmsSigmaVal = softNmsSigma;
            var _d = nonMaxSuppressionV5(boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal, scoreThresholdVal, softNmsSigmaVal), selectedIndices = _d.selectedIndices, selectedScores = _d.selectedScores;
            return [selectedIndices, selectedScores];
        }
    };

    /**
     * @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: 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.
     * =============================================================================
     */
    function broadcastedBinaryOp(aShape, bShape, aVals, bVals, dtype, op) {
        var newShape = assertAndGetBroadcastShape(aShape, bShape);
        var resultRank = newShape.length;
        var resultStrides = computeStrides(newShape);
        var resultSize = sizeFromShape(newShape);
        var result = getTypedArrayFromDType(dtype, resultSize);
        var aRank = aShape.length;
        var bRank = bShape.length;
        var aStrides = computeStrides(aShape);
        var bStrides = computeStrides(bShape);
        var aBroadcastDims = getBroadcastDims(aShape, newShape);
        var bBroadcastDims = 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 = indexToLoc(i, resultRank, resultStrides);
                var aLoc = loc.slice(-aRank);
                aBroadcastDims.forEach(function (d) { return aLoc[d] = 0; });
                var aIndex = locToIndex(aLoc, aRank, aStrides);
                var bLoc = loc.slice(-bRank);
                bBroadcastDims.forEach(function (d) { return bLoc[d] = 0; });
                var bIndex = 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.
     * =============================================================================
     */
    var squaredDifferenceConfig = {
        kernelName: SquaredDifference,
        backendName: 'cpu',
        kernelFunc: function (_a) {
            var inputs = _a.inputs, backend = _a.backend;
            var _b = inputs, a = _b.a, b = _b.b;
            var cpuBackend = backend;
            assertNotComplex([a, b], SquaredDifference);
            var aVals = cpuBackend.data.get(a.dataId).values;
            var bVals = cpuBackend.data.get(b.dataId).values;
            var _c = broadcastedBinaryOp(a.shape, b.shape, aVals, bVals, a.dtype, function (aVal, bVal) {
                var diff = aVal - bVal;
                return diff * diff;
            }), resultData = _c[0], resultShape = _c[1];
            var dataId = cpuBackend.write(resultData, resultShape, a.dtype);
            return { dataId: dataId, shape: resultShape, dtype: a.dtype };
        }
    };

    /**
     * @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.
     * =============================================================================
     */
    // List all kernel configs here
    var kernelConfigs = [
        nonMaxSuppressionV5Config,
        squareConfig,
        squaredDifferenceConfig,
    ];
    for (var _i = 0, kernelConfigs_1 = kernelConfigs; _i < kernelConfigs_1.length; _i++) {
        var kernelConfig = kernelConfigs_1[_i];
        registerKernel(kernelConfig);
    }

    /**
     * @license
     * Copyright 2018 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.
     * =============================================================================
     */
    var FromPixelsProgram = /** @class */ (function () {
        function FromPixelsProgram(outputShape) {
            this.variableNames = ['A'];
            var glsl = getGlslDifferences();
            var height = outputShape[0], width = outputShape[1];
            this.outputShape = outputShape;
            this.userCode = "\n      void main() {\n        ivec3 coords = getOutputCoords();\n        int texR = coords[0];\n        int texC = coords[1];\n        int depth = coords[2];\n        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(" + width + ".0, " + height + ".0);\n\n        vec4 values = " + glsl.texture2D + "(A, uv);\n        float value;\n        if (depth == 0) {\n          value = values.r;\n        } else if (depth == 1) {\n          value = values.g;\n        } else if (depth == 2) {\n          value = values.b;\n        } else if (depth == 3) {\n          value = values.a;\n        }\n\n        setOutput(floor(value * 255.0 + 0.5));\n      }\n    ";
        }
        return FromPixelsProgram;
    }());

    /**
     * @license
     * Copyright 2018 Google 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.
     * =============================================================================
     */
    var FromPixelsPackedProgram = /** @class */ (function () {
        function FromPixelsPackedProgram(outputShape) {
            this.variableNames = ['A'];
            this.packedInputs = false;
            this.packedOutput = true;
            var glsl = getGlslDifferences();
            var height = outputShape[0], width = outputShape[1];
            this.outputShape = outputShape;
            this.userCode = "\n      void main() {\n        ivec3 coords = getOutputCoords();\n        int texR = coords[0];\n        int texC = coords[1];\n        int depth = coords[2];\n\n        vec4 result = vec4(0.);\n\n        for(int row=0; row<=1; row++) {\n          for(int col=0; col<=1; col++) {\n            texC = coords[1] + row;\n            depth = coords[2] + col;\n\n            vec2 uv = (vec2(texC, texR) + halfCR) /\n                       vec2(" + width + ".0, " + height + ".0);\n            vec4 values = " + glsl.texture2D + "(A, uv);\n            float value;\n            if (depth == 0) {\n              value = values.r;\n            } else if (depth == 1) {\n              value = values.g;\n            } else if (depth == 2) {\n              value = values.b;\n            } else if (depth == 3) {\n              value = values.a;\n            }\n\n            result[row * 2 + col] = floor(value * 255.0 + 0.5);\n          }\n        }\n\n        " + glsl.output + " = result;\n      }\n    ";
        }
        return FromPixelsPackedProgram;
    }());

    /**
     * @license
     * Copyright 2019 Google LLC. All Rights Reserved.
     * Licensed under the Apache License, Version 2.0 (the "License");
     * you may not use this file except in compliance with the License.
     * You may obtain a copy of the License at
     *
     * http://www.apache.org/licenses/LICENSE-2.0
     *
     * Unless required by applicable law or agreed to in writing, software
     * distributed under the License is distributed on an "AS IS" BASIS,
     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
     * See the License for the specific language governing permissions and
     * limitations under the License.
     * =============================================================================
     */
    var fromPixelsConfig = {
        kernelName: FromPixels,
        backendName: 'webgl',
        kernelFunc: fromPixels,
    };
    var fromPixels2DContext;
    function fromPixels(args) {
        var inputs = args.inputs, backend = args.backend, attrs = args.attrs;
        var pixels = inputs.pixels;
        var numChannels = attrs.numChannels;
        var isVideo = typeof (HTMLVideoElement) !== 'undefined' &&
            pixels instanceof HTMLVideoElement;
        var isImage = typeof (HTMLImageElement) !== 'undefined' &&
            pixels instanceof HTMLImageElement;
        var _a = isVideo ?
            [
                pixels.videoWidth,
                pixels.videoHeight
            ] :
            [pixels.width, pixels.height], width = _a[0], height = _a[1];
        var texShape = [height, width];
        var outShape = [height, width, numChannels];
        if (isImage || isVideo) {
            if (fromPixels2DContext == null) {
                fromPixels2DContext = document.createElement('canvas').getContext('2d');
            }
            fromPixels2DContext.canvas.width = width;
            fromPixels2DContext.canvas.height = height;
            fromPixels2DContext.drawImage(pixels, 0, 0, width, height);
            pixels = fromPixels2DContext.canvas;
        }
        var tempPixelHandle = backend.makeTensorInfo(texShape, 'int32');
        // This is a byte texture with pixels.
        backend.texData.get(tempPixelHandle.dataId).usage = TextureUsage.PIXELS;
        backend.gpgpu.uploadPixelDataToTexture(backend.getTexture(tempPixelHandle.dataId), pixels);
        var program = env().getBool('WEBGL_PACK') ?
            new FromPixelsPackedProgram(outShape) :
            new FromPixelsProgram(outShape);
        var res = backend.runWebGLProgram(program, [tempPixelHandle], 'int32');
        backend.disposeData(tempPixelHandle.dataId);
        return res;
    }

    /**
     * @license
     * Copyright 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 nonMaxSuppressionV5Config$1 = {
        kernelName: NonMaxSuppressionV5,
        backendName: 'webgl',
        kernelFunc: function (_a) {
            var inputs = _a.inputs, backend = _a.backend, attrs = _a.attrs;
            warn('tf.nonMaxSuppression() in webgl locks the UI thread. ' +
                'Call tf.nonMaxSuppressionAsync() instead');
            var _b = inputs, boxes = _b.boxes, scores = _b.scores;
            var _c = attrs, maxOutputSize = _c.maxOutputSize, iouThreshold = _c.iouThreshold, scoreThreshold = _c.scoreThreshold, softNmsSigma = _c.softNmsSigma;
            var gpuBackend = backend;
            var boxesVals = gpuBackend.readSync(boxes.dataId);
            var scoresVals = gpuBackend.readSync(scores.dataId);
            var maxOutputSizeVal = maxOutputSize;
            var iouThresholdVal = iouThreshold;
            var scoreThresholdVal = scoreThreshold;
            var softNmsSigmaVal = softNmsSigma;
            var _d = nonMaxSuppressionV5(boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal, scoreThresholdVal, softNmsSigmaVal), selectedIndices = _d.selectedIndices, selectedScores = _d.selectedScores;
            return [selectedIndices, selectedScores];
        }
    };

    /**
     * @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$1 = {
        kernelName: Square,
        backendName: 'webgl',
        kernelFunc: function (_a) {
            var inputs = _a.inputs, backend = _a.backend;
            var x = inputs.x;
            var webglBackend = backend;
            var program = new UnaryOpProgram(x.shape, SQUARE);
            return webglBackend.runWebGLProgram(program, [x], x.dtype);
        }
    };

    /**
     * @license
     * Copyright 2020 Google LLC. All Rights Reserved.
     * Licensed under the Apache License, Version 2.0 (the "License");
     * you may not use this file except in compliance with the License.
     * You may obtain a copy of the License at
     *
     * http://www.apache.org/licenses/LICENSE-2.0
     *
     * Unless required by applicable law or agreed to in writing, software
     * distributed under the License is distributed on an "AS IS" BASIS,
     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
     * See the License for the specific language governing permissions and
     * limitations under the License.
     * =============================================================================
     */
    var squaredDifferenceConfig$1 = {
        kernelName: SquaredDifference,
        backendName: 'webgl',
        kernelFunc: function (_a) {
            var inputs = _a.inputs, backend = _a.backend;
            var _b = inputs, a = _b.a, b = _b.b;
            var SQUARED_DIFFERENCE = 'return (a - b) * (a - b);';
            var webGLBackend = backend;
            var program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?
                new BinaryOpPackedProgram(SQUARED_DIFFERENCE, a.shape, b.shape) :
                new BinaryOpProgram(SQUARED_DIFFERENCE, a.shape, b.shape);
            return webGLBackend.compileAndRun(program, [a, b]);
        }
    };

    /**
     * @license
     * Copyright 2020 Google LLC. All Rights Reserved.
     * Licensed under the Apache License, Version 2.0 (the "License");
     * you may not use this file except in compliance with the License.
     * You may obtain a copy of the License at
     *
     * http://www.apache.org/licenses/LICENSE-2.0
     *
     * Unless required by applicable law or agreed to in writing, software
     * distributed under the License is distributed on an "AS IS" BASIS,
     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
     * See the License for the specific language governing permissions and
     * limitations under the License.
     * =============================================================================
     */
    // List all kernel configs here
    var kernelConfigs$1 = [
        fromPixelsConfig,
        nonMaxSuppressionV5Config$1,
        squareConfig$1,
        squaredDifferenceConfig$1,
    ];
    for (var _i$1 = 0, kernelConfigs_1$1 = kernelConfigs$1; _i$1 < kernelConfigs_1$1.length; _i$1++) {
        var kernelConfig$1 = kernelConfigs_1$1[_i$1];
        registerKernel(kernelConfig$1);
    }

    /**
     * @license
     * Copyright 2019 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.
     * =============================================================================
     */
    var squareGradConfig = {
        kernelName: Square,
        gradFunc: function (dy, saved) {
            var x = saved[0];
            return { x: function () { return dy.mul(x.toFloat().mul(2)); } };
        }
    };

    /**
     * @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.
     * =============================================================================
     */
    var squaredDifferenceGradConfig = {
        kernelName: SquaredDifference,
        gradFunc: function (dy, saved) {
            var a = saved[0], b = saved[1];
            var two = scalar(2);
            var derA = function () { return mul(dy, mul(two, sub(a, b))); };
            var derB = function () { return mul(dy, mul(two, sub(b, a))); };
            return { a: derA, b: derB };
        }
    };

    /**
     * @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.
     * =============================================================================
     */
    // Export all kernel configs here so that the package can auto register them
    var gradConfigs = [
        squareGradConfig,
        squaredDifferenceGradConfig,
    ];
    for (var _i$2 = 0, gradConfigs_1 = gradConfigs; _i$2 < gradConfigs_1.length; _i$2++) {
        var gradientConfig = gradConfigs_1[_i$2];
        registerGradient(gradientConfig);
    }

    /**
     * @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());
    }

    /**
     * @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 2018 Google LLC. All Rights Reserved.
     * Licensed under the Apache License, Version 2.0 (the "License");
     * you may not use this file except in compliance with the License.
     * You may obtain a copy of the License at
     *
     * http://www.apache.org/licenses/LICENSE-2.0
     *
     * Unless required by applicable law or agreed to in writing, software
     * distributed under the License is distributed on an "AS IS" BASIS,
     * WITHOUT 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,
        'int32': 4,
        'uint16': 2,
        'uint8': 1,
        'bool': 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.
     * =============================================================================
     */
    /** Number of bytes reserved for the length of the string. (32bit integer). */
    var NUM_BYTES_STRING_LENGTH = 4;
    /**
     * Encode a map from names to weight values as an ArrayBuffer, along with an
     * `Array` of `WeightsManifestEntry` as specification of the encoded weights.
     *
     * This function does not perform sharding.
     *
     * This function is the reverse of `decodeWeights`.
     *
     * @param tensors A map ("dict") from names to tensors.
     * @param group Group to which the weights belong (optional).
     * @returns A `Promise` of
     *   - A flat `ArrayBuffer` with all the binary values of the `Tensor`s
     *     concatenated.
     *   - An `Array` of `WeightManifestEntry`s, carrying information including
     *     tensor names, `dtype`s and shapes.
     * @throws Error: on unsupported tensor `dtype`.
     */
    function encodeWeights(tensors, group) {
        return __awaiter(this, void 0, void 0, function () {
            var specs, dataPromises, names, _loop_1, i, tensorValues;
            var _this = this;
            return __generator(this, function (_a) {
                switch (_a.label) {
                    case 0:
                        specs = [];
                        dataPromises = [];
                        names = Array.isArray(tensors) ?
                            tensors.map(function (tensor) { return tensor.name; }) :
                            Object.keys(tensors);
                        _loop_1 = function (i) {
                            var name_1 = names[i];
                            var t = Array.isArray(tensors) ? tensors[i].tensor : tensors[name_1];
                            if (t.dtype !== 'float32' && t.dtype !== 'int32' && t.dtype !== 'bool' &&
                                t.dtype !== 'string') {
                                throw new Error("Unsupported dtype in weight '" + name_1 + "': " + t.dtype);
                            }
                            var spec = { name: name_1, shape: t.shape, dtype: t.dtype };
                            if (t.dtype === 'string') {
                                var utf8bytes = new Promise(function (resolve) { return __awaiter(_this, void 0, void 0, function () {
                                    var vals, totalNumBytes, bytes, offset, i_1, val, bytesOfLength;
                                    return __generator(this, function (_a) {
                                        switch (_a.label) {
                                            case 0: return [4 /*yield*/, t.bytes()];
                                            case 1:
                                                vals = _a.sent();
                                                totalNumBytes = vals.reduce(function (p, c) { return p + c.length; }, 0) +
                                                    NUM_BYTES_STRING_LENGTH * vals.length;
                                                bytes = new Uint8Array(totalNumBytes);
                                                offset = 0;
                                                for (i_1 = 0; i_1 < vals.length; i_1++) {
                                                    val = vals[i_1];
                                                    bytesOfLength = new Uint8Array(new Uint32Array([val.length]).buffer);
                                                    bytes.set(bytesOfLength, offset);
                                                    offset += NUM_BYTES_STRING_LENGTH;
                                                    bytes.set(val, offset);
                                                    offset += val.length;
                                                }
                                                resolve(bytes);
                                                return [2 /*return*/];
                                        }
                                    });
                                }); });
                                dataPromises.push(utf8bytes);
                            }
                            else {
                                dataPromises.push(t.data());
                            }
                            if (group != null) {
                                spec.group = group;
                            }
                            specs.push(spec);
                        };
                        for (i = 0; i < names.length; ++i) {
                            _loop_1(i);
                        }
                        return [4 /*yield*/, Promise.all(dataPromises)];
                    case 1:
                        tensorValues = _a.sent();
                        return [2 /*return*/, { data: concatenateTypedArrays(tensorValues), specs: specs }];
                }
            });
        });
    }
    /**
     * Decode flat ArrayBuffer as weights.
     *
     * This function does not handle sharding.
     *
     * This function is the reverse of `encodeWeights`.
     *
     * @param buffer A flat ArrayBuffer carrying the binary values of the tensors
     *   concatenated in the order specified in `specs`.
     * @param specs Specifications of the names, dtypes and shapes of the tensors
     *   whose value are encoded by `buffer`.
     * @return A map from tensor name to tensor value, with the names corresponding
     *   to names in `specs`.
     * @throws Error, if any of the tensors has unsupported dtype.
     */
    function decodeWeights(buffer, specs) {
        // TODO(adarob, cais): Support quantization.
        var out = {};
        var offset = 0;
        var _loop_2 = function (spec) {
            var name_2 = spec.name;
            var dtype = spec.dtype;
            var shape = spec.shape;
            var size = sizeFromShape(shape);
            var values = void 0;
            if ('quantization' in spec) {
                var quantization_1 = spec.quantization;
                if (quantization_1.dtype !== 'uint8' && quantization_1.dtype !== 'uint16') {
                    throw new Error("Weight " + spec.name + " has unknown " +
                        ("quantization dtype " + quantization_1.dtype + ". ") +
                        "Supported quantization dtypes are: 'uint8' and 'uint16'.");
                }
                var quantizationSizeFactor = DTYPE_VALUE_SIZE_MAP[quantization_1.dtype];
                var byteBuffer = buffer.slice(offset, offset + size * quantizationSizeFactor);
                var quantizedArray = (quantization_1.dtype === 'uint8') ?
                    new Uint8Array(byteBuffer) :
                    new Uint16Array(byteBuffer);
                if (dtype === 'float32') {
                    values = Float32Array.from(quantizedArray, function (v) { return v * quantization_1.scale + quantization_1.min; });
                }
                else if (dtype === 'int32') {
                    values = Int32Array.from(quantizedArray, function (v) { return Math.round(v * quantization_1.scale + quantization_1.min); });
                }
                else {
                    throw new Error("Unsupported dtype in weight '" + name_2 + "': " + dtype);
                }
                offset += size * quantizationSizeFactor;
            }
            else if (dtype === 'string') {
                var size_1 = sizeFromShape(spec.shape);
                values = [];
                for (var i = 0; i < size_1; i++) {
                    var byteLength = new Uint32Array(buffer.slice(offset, offset + NUM_BYTES_STRING_LENGTH))[0];
                    offset += NUM_BYTES_STRING_LENGTH;
                    var bytes = new Uint8Array(buffer.slice(offset, offset + byteLength));
                    values.push(bytes);
                    offset += byteLength;
                }
            }
            else {
                var dtypeFactor = DTYPE_VALUE_SIZE_MAP[dtype];
                var byteBuffer = buffer.slice(offset, offset + size * dtypeFactor);
                if (dtype === 'float32') {
                    values = new Float32Array(byteBuffer);
                }
                else if (dtype === 'int32') {
                    values = new Int32Array(byteBuffer);
                }
                else if (dtype === 'bool') {
                    values = new Uint8Array(byteBuffer);
                }
                else {
                    throw new Error("Unsupported dtype in weight '" + name_2 + "': " + dtype);
                }
                offset += size * dtypeFactor;
            }
            out[name_2] = tensor(values, shape, dtype);
        };
        for (var _i = 0, specs_1 = specs; _i < specs_1.length; _i++) {
            var spec = specs_1[_i];
            _loop_2(spec);
        }
        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) {
        var totalByteLength = 0;
        buffers.forEach(function (buffer) {
            totalByteLength += buffer.byteLength;
        });
        var temp = new Uint8Array(totalByteLength);
        var offset = 0;
        buffers.forEach(function (buffer) {
            temp.set(new Uint8Array(buffer), offset);
            offset += buffer.byteLength;
        });
        return temp.buffer;
    }
    /**
     * Get the basename of a path.
     *
     * Behaves in a way analogous to Linux's basename command.
     *
     * @param path
     */
    function basename(path) {
        var SEPARATOR = '/';
        path = path.trim();
        while (path.endsWith(SEPARATOR)) {
            path = path.slice(0, path.length - 1);
        }
        var items = path.split(SEPARATOR);
        return items[items.length - 1];
    }
    /**
     * Populate ModelArtifactsInfo fields for a model with JSON topology.
     * @param modelArtifacts
     * @returns A ModelArtifactsInfo object.
     */
    function getModelArtifactsInfoForJSON(modelArtifacts) {
        if (modelArtifacts.modelTopology instanceof ArrayBuffer) {
            throw new Error('Expected JSON model topology, received ArrayBuffer.');
        }
        return {
            dateSaved: new Date(),
            modelTopologyType: 'JSON',
            modelTopologyBytes: modelArtifacts.modelTopology == null ?
                0 :
                stringByteLength(JSON.stringify(modelArtifacts.modelTopology)),
            weightSpecsBytes: modelArtifacts.weightSpecs == null ?
                0 :
                stringByteLength(JSON.stringify(modelArtifacts.weightSpecs)),
            weightDataBytes: modelArtifacts.weightData == null ?
                0 :
                modelArtifacts.weightData.byteLength,
        };
    }

    /**
     * @license
     * Copyright 2018 Google LLC. All Rights Reserved.
     * Licensed under the Apache License, Version 2.0 (the "License");
     * you may not use this file except in compliance with the License.
     * You may obtain a copy of the License at
     *
     * http://www.apache.org/licenses/LICENSE-2.0
     *
     * Unless required by applicable law or agreed to in writing, software
     * distributed under the License is distributed on an "AS IS" BASIS,
     * WITHOUT WARRANTIES 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 onProgress Optional, progress callback function, fired periodically
         *   before the load is completed.
         * @returns All valid handlers for `url`, given the currently registered
         *   handler routers.
         */
        IORouterRegistry.getLoadHandlers = function (url, onProgress) {
            return IORouterRegistry.getHandlers(url, 'load', onProgress);
        };
        IORouterRegistry.getHandlers = function (url, handlerType, onProgress) {
            var validHandlers = [];
            var routers = handlerType === 'load' ?
                IORouterRegistry.getInstance().loadRouters :
                IORouterRegistry.getInstance().saveRouters;
            routers.forEach(function (router) {
                var handler = router(url, onProgress);
                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, onProgress) {
        return IORouterRegistry.getLoadHandlers(url, onProgress);
    };

    /**
     * @license
     * Copyright 2018 Google LLC. All Rights Reserved.
     * Licensed under the Apache License, Version 2.0 (the "License");
     * you may not use this file except in compliance with the License.
     * You may obtain a copy of the License at
     *
     * http://www.apache.org/licenses/LICENSE-2.0
     *
     * Unless required by applicable law or agreed to in writing, software
     * distributed under the License is distributed on an "AS IS" BASIS,
     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
     * See the License for the specific language governing permissions and
     * limitations under the License.
     * =============================================================================
     */
    var URL_SCHEME_SUFFIX = '://';
    var ModelStoreManagerRegistry = /** @class */ (function () {
        function ModelStoreManagerRegistry() {
            this.managers = {};
        }
        ModelStoreManagerRegistry.getInstance = function () {
            if (ModelStoreManagerRegistry.instance == null) {
                ModelStoreManagerRegistry.instance = new ModelStoreManagerRegistry();
            }
            return ModelStoreManagerRegistry.instance;
        };
        /**
         * Register a save-handler router.
         *
         * @param saveRouter A function that maps a URL-like string onto an instance
         * of `IOHandler` with the `save` method defined or `null`.
         */
        ModelStoreManagerRegistry.registerManager = function (scheme, manager) {
            assert(scheme != null, function () { return 'scheme must not be undefined or null.'; });
            if (scheme.endsWith(URL_SCHEME_SUFFIX)) {
                scheme = scheme.slice(0, scheme.indexOf(URL_SCHEME_SUFFIX));
            }
            assert(scheme.length > 0, function () { return 'scheme must not be an empty string.'; });
            var registry = ModelStoreManagerRegistry.getInstance();
            assert(registry.managers[scheme] == null, function () { return "A model store manager is already registered for scheme '" + scheme + "'."; });
            registry.managers[scheme] = manager;
        };
        ModelStoreManagerRegistry.getManager = function (scheme) {
            var manager = this.getInstance().managers[scheme];
            if (manager == null) {
                throw new Error("Cannot find model manager for scheme '" + scheme + "'");
            }
            return manager;
        };
        ModelStoreManagerRegistry.getSchemes = function () {
            return Object.keys(this.getInstance().managers);
        };
        return ModelStoreManagerRegistry;
    }());
    /**
     * Helper method for parsing a URL string into a scheme and a path.
     *
     * @param url E.g., 'localstorage://my-model'
     * @returns A dictionary with two fields: scheme and path.
     *   Scheme: e.g., 'localstorage' in the example above.
     *   Path: e.g., 'my-model' in the example above.
     */
    function parseURL(url) {
        if (url.indexOf(URL_SCHEME_SUFFIX) === -1) {
            throw new Error("The url string provided does not contain a scheme. " +
                "Supported schemes are: " +
                ("" + ModelStoreManagerRegistry.getSchemes().join(',')));
        }
        return {
            scheme: url.split(URL_SCHEME_SUFFIX)[0],
            path: url.split(URL_SCHEME_SUFFIX)[1],
        };
    }
    function cloneModelInternal(sourceURL, destURL, deleteSource) {
        if (deleteSource === void 0) { deleteSource = false; }
        return __awaiter(this, void 0, void 0, function () {
            var loadHandlers, loadHandler, saveHandlers, saveHandler, sourceScheme, sourcePath, sameMedium, modelArtifacts, saveResult;
            return __generator(this, function (_a) {
                switch (_a.label) {
                    case 0:
                        assert(sourceURL !== destURL, function () { return "Old path and new path are the same: '" + sourceURL + "'"; });
                        loadHandlers = IORouterRegistry.getLoadHandlers(sourceURL);
                        assert(loadHandlers.length > 0, function () { return "Copying failed because no load handler is found for source URL " + sourceURL + "."; });
                        assert(loadHandlers.length < 2, function () { return "Copying failed because more than one (" + loadHandlers.length + ") " +
                            ("load handlers for source URL " + sourceURL + "."); });
                        loadHandler = loadHandlers[0];
                        saveHandlers = IORouterRegistry.getSaveHandlers(destURL);
                        assert(saveHandlers.length > 0, function () { return "Copying failed because no save handler is found for destination " +
                            ("URL " + destURL + "."); });
                        assert(saveHandlers.length < 2, function () { return "Copying failed because more than one (" + loadHandlers.length + ") " +
                            ("save handlers for destination URL " + destURL + "."); });
                        saveHandler = saveHandlers[0];
                        sourceScheme = parseURL(sourceURL).scheme;
                        sourcePath = parseURL(sourceURL).path;
                        sameMedium = sourceScheme === parseURL(sourceURL).scheme;
                        return [4 /*yield*/, loadHandler.load()];
                    case 1:
                        modelArtifacts = _a.sent();
                        if (!(deleteSource && sameMedium)) return [3 /*break*/, 3];
                        return [4 /*yield*/, ModelStoreManagerRegistry.getManager(sourceScheme)
                                .removeModel(sourcePath)];
                    case 2:
                        _a.sent();
                        _a.label = 3;
                    case 3: return [4 /*yield*/, saveHandler.save(modelArtifacts)];
                    case 4:
                        saveResult = _a.sent();
                        if (!(deleteSource && !sameMedium)) return [3 /*break*/, 6];
                        return [4 /*yield*/, ModelStoreManagerRegistry.getManager(sourceScheme)
                                .removeModel(sourcePath)];
                    case 5:
                        _a.sent();
                        _a.label = 6;
                    case 6: return [2 /*return*/, saveResult.modelArtifactsInfo];
                }
            });
        });
    }
    /**
     * List all models stored in registered storage mediums.
     *
     * For a web browser environment, the registered mediums are Local Storage and
     * IndexedDB.
     *
     * ```js
     * // First create and save a model.
     * const model = tf.sequential();
     * model.add(tf.layers.dense(
     *     {units: 1, inputShape: [10], activation: 'sigmoid'}));
     * await model.save('localstorage://demo/management/model1');
     *
     * // Then list existing models.
     * console.log(JSON.stringify(await tf.io.listModels()));
     *
     * // Delete the model.
     * await tf.io.removeModel('localstorage://demo/management/model1');
     *
     * // List models again.
     * console.log(JSON.stringify(await tf.io.listModels()));
     * ```
     *
     * @returns A `Promise` of a dictionary mapping URLs of existing models to
     * their model artifacts info. URLs include medium-specific schemes, e.g.,
     *   'indexeddb://my/model/1'. Model artifacts info include type of the
     * model's topology, byte sizes of the topology, weights, etc.
     */
    /**
     * @doc {
     *   heading: 'Models',
     *   subheading: 'Management',
     *   namespace: 'io',
     *   ignoreCI: true
     * }
     */
    function listModels() {
        return __awaiter(this, void 0, void 0, function () {
            var schemes, out, _i, schemes_1, scheme, schemeOut, path, url;
            return __generator(this, function (_a) {
                switch (_a.label) {
                    case 0:
                        schemes = ModelStoreManagerRegistry.getSchemes();
                        out = {};
                        _i = 0, schemes_1 = schemes;
                        _a.label = 1;
                    case 1:
                        if (!(_i < schemes_1.length)) return [3 /*break*/, 4];
                        scheme = schemes_1[_i];
                        return [4 /*yield*/, ModelStoreManagerRegistry.getManager(scheme).listModels()];
                    case 2:
                        schemeOut = _a.sent();
                        for (path in schemeOut) {
                            url = scheme + URL_SCHEME_SUFFIX + path;
                            out[url] = schemeOut[path];
                        }
                        _a.label = 3;
                    case 3:
                        _i++;
                        return [3 /*break*/, 1];
                    case 4: return [2 /*return*/, out];
                }
            });
        });
    }
    /**
     * Remove a model specified by URL from a reigstered storage medium.
     *
     * ```js
     * // First create and save a model.
     * const model = tf.sequential();
     * model.add(tf.layers.dense(
     *     {units: 1, inputShape: [10], activation: 'sigmoid'}));
     * await model.save('localstorage://demo/management/model1');
     *
     * // Then list existing models.
     * console.log(JSON.stringify(await tf.io.listModels()));
     *
     * // Delete the model.
     * await tf.io.removeModel('localstorage://demo/management/model1');
     *
     * // List models again.
     * console.log(JSON.stringify(await tf.io.listModels()));
     * ```
     *
     * @param url A URL to a stored model, with a scheme prefix, e.g.,
     *   'localstorage://my-model-1', 'indexeddb://my/model/2'.
     * @returns ModelArtifactsInfo of the deleted model (if and only if deletion
     *   is successful).
     * @throws Error if deletion fails, e.g., if no model exists at `path`.
     */
    /**
     * @doc {
     *   heading: 'Models',
     *   subheading: 'Management',
     *   namespace: 'io',
     *   ignoreCI: true
     * }
     */
    function removeModel(url) {
        return __awaiter(this, void 0, void 0, function () {
            var schemeAndPath, manager;
            return __generator(this, function (_a) {
                schemeAndPath = parseURL(url);
                manager = ModelStoreManagerRegistry.getManager(schemeAndPath.scheme);
                return [2 /*return*/, manager.removeModel(schemeAndPath.path)];
            });
        });
    }
    /**
     * Copy a model from one URL to another.
     *
     * This function supports:
     *
     * 1. Copying within a storage medium, e.g.,
     *    `tf.io.copyModel('localstorage://model-1', 'localstorage://model-2')`
     * 2. Copying between two storage mediums, e.g.,
     *    `tf.io.copyModel('localstorage://model-1', 'indexeddb://model-1')`
     *
     * ```js
     * // First create and save a model.
     * const model = tf.sequential();
     * model.add(tf.layers.dense(
     *     {units: 1, inputShape: [10], activation: 'sigmoid'}));
     * await model.save('localstorage://demo/management/model1');
     *
     * // Then list existing models.
     * console.log(JSON.stringify(await tf.io.listModels()));
     *
     * // Copy the model, from Local Storage to IndexedDB.
     * await tf.io.copyModel(
     *     'localstorage://demo/management/model1',
     *     'indexeddb://demo/management/model1');
     *
     * // List models again.
     * console.log(JSON.stringify(await tf.io.listModels()));
     *
     * // Remove both models.
     * await tf.io.removeModel('localstorage://demo/management/model1');
     * await tf.io.removeModel('indexeddb://demo/management/model1');
     * ```
     *
     * @param sourceURL Source URL of copying.
     * @param destURL Destination URL of copying.
     * @returns ModelArtifactsInfo of the copied model (if and only if copying
     *   is successful).
     * @throws Error if copying fails, e.g., if no model exists at `sourceURL`, or
     *   if `oldPath` and `newPath` are identical.
     */
    /**
     * @doc {
     *   heading: 'Models',
     *   subheading: 'Management',
     *   namespace: 'io',
     *   ignoreCI: true
     * }
     */
    function copyModel(sourceURL, destURL) {
        return __awaiter(this, void 0, void 0, function () {
            var deleteSource;
            return __generator(this, function (_a) {
                deleteSource = false;
                return [2 /*return*/, cloneModelInternal(sourceURL, destURL, deleteSource)];
            });
        });
    }
    /**
     * Move a model from one URL to another.
     *
     * This function supports:
     *
     * 1. Moving within a storage medium, e.g.,
     *    `tf.io.moveModel('localstorage://model-1', 'localstorage://model-2')`
     * 2. Moving between two storage mediums, e.g.,
     *    `tf.io.moveModel('localstorage://model-1', 'indexeddb://model-1')`
     *
     * ```js
     * // First create and save a model.
     * const model = tf.sequential();
     * model.add(tf.layers.dense(
     *     {units: 1, inputShape: [10], activation: 'sigmoid'}));
     * await model.save('localstorage://demo/management/model1');
     *
     * // Then list existing models.
     * console.log(JSON.stringify(await tf.io.listModels()));
     *
     * // Move the model, from Local Storage to IndexedDB.
     * await tf.io.moveModel(
     *     'localstorage://demo/management/model1',
     *     'indexeddb://demo/management/model1');
     *
     * // List models again.
     * console.log(JSON.stringify(await tf.io.listModels()));
     *
     * // Remove the moved model.
     * await tf.io.removeModel('indexeddb://demo/management/model1');
     * ```
     *
     * @param sourceURL Source URL of moving.
     * @param destURL Destination URL of moving.
     * @returns ModelArtifactsInfo of the copied model (if and only if copying
     *   is successful).
     * @throws Error if moving fails, e.g., if no model exists at `sourceURL`, or
     *   if `oldPath` and `newPath` are identical.
     */
    /**
     * @doc {
     *   heading: 'Models',
     *   subheading: 'Management',
     *   namespace: 'io',
     *   ignoreCI: true
     * }
     */
    function moveModel(sourceURL, destURL) {
        return __awaiter(this, void 0, void 0, function () {
            var deleteSource;
            return __generator(this, function (_a) {
                deleteSource = true;
                return [2 /*return*/, cloneModelInternal(sourceURL, destURL, deleteSource)];
            });
        });
    }

    /**
     * @license
     * Copyright 2018 Google LLC. All Rights Reserved.
     * Licensed under the Apache License, Version 2.0 (the "License");
     * you may not use this file except in compliance with the License.
     * You may obtain a copy of the License at
     *
     * http://www.apache.org/licenses/LICENSE-2.0
     *
     * Unless required by applicable law or agreed to in writing, software
     * distributed under the License is distributed on an "AS IS" BASIS,
     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
     * See the License for the specific language governing permissions and
     * limitations under the License.
     * =============================================================================
     */
    var DATABASE_NAME = 'tensorflowjs';
    var DATABASE_VERSION = 1;
    // Model data and ModelArtifactsInfo (metadata) are stored in two separate
    // stores for efficient access of the list of stored models and their metadata.
    // 1. The object store for model data: topology, weights and weight manifests.
    var MODEL_STORE_NAME = 'models_store';
    // 2. The object store for ModelArtifactsInfo, including meta-information such
    //    as the type of topology (JSON vs binary), byte size of the topology, byte
    //    size of the weights, etc.
    var INFO_STORE_NAME = 'model_info_store';
    function getIndexedDBFactory() {
        if (!env().getBool('IS_BROWSER')) {
            // TODO(cais): Add more info about what IOHandler subtypes are available.
            //   Maybe point to a doc page on the web and/or automatically determine
            //   the available IOHandlers and print them in the error message.
            throw new Error('Failed to obtain IndexedDB factory because the current environment' +
                'is not a web browser.');
        }
        // tslint:disable-next-line:no-any
        var theWindow = window || self;
        var factory = theWindow.indexedDB || theWindow.mozIndexedDB ||
            theWindow.webkitIndexedDB || theWindow.msIndexedDB ||
            theWindow.shimIndexedDB;
        if (factory == null) {
            throw new Error('The current browser does not appear to support IndexedDB.');
        }
        return factory;
    }
    function setUpDatabase(openRequest) {
        var db = openRequest.result;
        db.createObjectStore(MODEL_STORE_NAME, { keyPath: 'modelPath' });
        db.createObjectStore(INFO_STORE_NAME, { keyPath: 'modelPath' });
    }
    /**
     * IOHandler subclass: Browser IndexedDB.
     *
     * See the doc string of `browserIndexedDB` for more details.
     */
    var BrowserIndexedDB = /** @class */ (function () {
        function BrowserIndexedDB(modelPath) {
            this.indexedDB = getIndexedDBFactory();
            if (modelPath == null || !modelPath) {
                throw new Error('For IndexedDB, modelPath must not be null, undefined or empty.');
            }
            this.modelPath = modelPath;
        }
        BrowserIndexedDB.prototype.save = function (modelArtifacts) {
            return __awaiter(this, void 0, void 0, function () {
                return __generator(this, function (_a) {
                    // TODO(cais): Support saving GraphDef models.
                    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {
                        throw new Error('BrowserLocalStorage.save() does not support saving model topology ' +
                            'in binary formats yet.');
                    }
                    return [2 /*return*/, this.databaseAction(this.modelPath, modelArtifacts)];
                });
            });
        };
        BrowserIndexedDB.prototype.load = function () {
            return __awaiter(this, void 0, void 0, function () {
                return __generator(this, function (_a) {
                    return [2 /*return*/, this.databaseAction(this.modelPath)];
                });
            });
        };
        /**
         * Perform database action to put model artifacts into or read model artifacts
         * from IndexedDB object store.
         *
         * Whether the action is put or get depends on whether `modelArtifacts` is
         * specified. If it is specified, the action will be put; otherwise the action
         * will be get.
         *
         * @param modelPath A unique string path for the model.
         * @param modelArtifacts If specified, it will be the model artifacts to be
         *   stored in IndexedDB.
         * @returns A `Promise` of `SaveResult`, if the action is put, or a `Promise`
         *   of `ModelArtifacts`, if the action is get.
         */
        BrowserIndexedDB.prototype.databaseAction = function (modelPath, modelArtifacts) {
            var _this = this;
            return new Promise(function (resolve, reject) {
                var openRequest = _this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);
                openRequest.onupgradeneeded = function () { return setUpDatabase(openRequest); };
                openRequest.onsuccess = function () {
                    var db = openRequest.result;
                    if (modelArtifacts == null) {
                        // Read model out from object store.
                        var modelTx = db.transaction(MODEL_STORE_NAME, 'readonly');
                        var modelStore = modelTx.objectStore(MODEL_STORE_NAME);
                        var getRequest_1 = modelStore.get(_this.modelPath);
                        getRequest_1.onsuccess = function () {
                            if (getRequest_1.result == null) {
                                db.close();
                                return reject(new Error("Cannot find model with path '" + _this.modelPath + "' " +
                                    "in IndexedDB."));
                            }
                            else {
                                resolve(getRequest_1.result.modelArtifacts);
                            }
                        };
                        getRequest_1.onerror = function (error) {
                            db.close();
                            return reject(getRequest_1.error);
                        };
                        modelTx.oncomplete = function () { return db.close(); };
                    }
                    else {
                        // Put model into object store.
                        var modelArtifactsInfo_1 = getModelArtifactsInfoForJSON(modelArtifacts);
                        // First, put ModelArtifactsInfo into info store.
                        var infoTx_1 = db.transaction(INFO_STORE_NAME, 'readwrite');
                        var infoStore_1 = infoTx_1.objectStore(INFO_STORE_NAME);
                        var putInfoRequest_1 = infoStore_1.put({ modelPath: _this.modelPath, modelArtifactsInfo: modelArtifactsInfo_1 });
                        var modelTx_1;
                        putInfoRequest_1.onsuccess = function () {
                            // Second, put model data into model store.
                            modelTx_1 = db.transaction(MODEL_STORE_NAME, 'readwrite');
                            var modelStore = modelTx_1.objectStore(MODEL_STORE_NAME);
                            var putModelRequest = modelStore.put({
                                modelPath: _this.modelPath,
                                modelArtifacts: modelArtifacts,
                                modelArtifactsInfo: modelArtifactsInfo_1
                            });
                            putModelRequest.onsuccess = function () { return resolve({ modelArtifactsInfo: modelArtifactsInfo_1 }); };
                            putModelRequest.onerror = function (error) {
                                // If the put-model request fails, roll back the info entry as
                                // well.
                                infoStore_1 = infoTx_1.objectStore(INFO_STORE_NAME);
                                var deleteInfoRequest = infoStore_1.delete(_this.modelPath);
                                deleteInfoRequest.onsuccess = function () {
                                    db.close();
                                    return reject(putModelRequest.error);
                                };
                                deleteInfoRequest.onerror = function (error) {
                                    db.close();
                                    return reject(putModelRequest.error);
                                };
                            };
                        };
                        putInfoRequest_1.onerror = function (error) {
                            db.close();
                            return reject(putInfoRequest_1.error);
                        };
                        infoTx_1.oncomplete = function () {
                            if (modelTx_1 == null) {
                                db.close();
                            }
                            else {
                                modelTx_1.oncomplete = function () { return db.close(); };
                            }
                        };
                    }
                };
                openRequest.onerror = function (error) { return reject(openRequest.error); };
            });
        };
        BrowserIndexedDB.URL_SCHEME = 'indexeddb://';
        return BrowserIndexedDB;
    }());
    var indexedDBRouter = function (url) {
        if (!env().getBool('IS_BROWSER')) {
            return null;
        }
        else {
            if (!Array.isArray(url) && url.startsWith(BrowserIndexedDB.URL_SCHEME)) {
                return browserIndexedDB(url.slice(BrowserIndexedDB.URL_SCHEME.length));
            }
            else {
                return null;
            }
        }
    };
    IORouterRegistry.registerSaveRouter(indexedDBRouter);
    IORouterRegistry.registerLoadRouter(indexedDBRouter);
    /**
     * Creates a browser IndexedDB IOHandler for saving and loading models.
     *
     * ```js
     * const model = tf.sequential();
     * model.add(
     *     tf.layers.dense({units: 1, inputShape: [100], activation: 'sigmoid'}));
     *
     * const saveResult = await model.save('indexeddb://MyModel'));
     * console.log(saveResult);
     * ```
     *
     * @param modelPath A unique identifier for the model to be saved. Must be a
     *   non-empty string.
     * @returns An instance of `BrowserIndexedDB` (sublcass of `IOHandler`),
     *   which can be used with, e.g., `tf.Model.save`.
     */
    function browserIndexedDB(modelPath) {
        return new BrowserIndexedDB(modelPath);
    }
    function maybeStripScheme(key) {
        return key.startsWith(BrowserIndexedDB.URL_SCHEME) ?
            key.slice(BrowserIndexedDB.URL_SCHEME.length) :
            key;
    }
    var BrowserIndexedDBManager = /** @class */ (function () {
        function BrowserIndexedDBManager() {
            this.indexedDB = getIndexedDBFactory();
        }
        BrowserIndexedDBManager.prototype.listModels = function () {
            return __awaiter(this, void 0, void 0, function () {
                var _this = this;
                return __generator(this, function (_a) {
                    return [2 /*return*/, new Promise(function (resolve, reject) {
                            var openRequest = _this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);
                            openRequest.onupgradeneeded = function () { return setUpDatabase(openRequest); };
                            openRequest.onsuccess = function () {
                                var db = openRequest.result;
                                var tx = db.transaction(INFO_STORE_NAME, 'readonly');
                                var store = tx.objectStore(INFO_STORE_NAME);
                                // tslint:disable:max-line-length
                                // Need to cast `store` as `any` here because TypeScript's DOM
                                // library does not have the `getAll()` method even though the
                                // method is supported in the latest version of most mainstream
                                // browsers:
                                // https://developer.mozilla.org/en-US/docs/Web/API/IDBObjectStore/getAll
                                // tslint:enable:max-line-length
                                // tslint:disable-next-line:no-any
                                var getAllInfoRequest = store.getAll();
                                getAllInfoRequest.onsuccess = function () {
                                    var out = {};
                                    for (var _i = 0, _a = getAllInfoRequest.result; _i < _a.length; _i++) {
                                        var item = _a[_i];
                                        out[item.modelPath] = item.modelArtifactsInfo;
                                    }
                                    resolve(out);
                                };
                                getAllInfoRequest.onerror = function (error) {
                                    db.close();
                                    return reject(getAllInfoRequest.error);
                                };
                                tx.oncomplete = function () { return db.close(); };
                            };
                            openRequest.onerror = function (error) { return reject(openRequest.error); };
                        })];
                });
            });
        };
        BrowserIndexedDBManager.prototype.removeModel = function (path) {
            return __awaiter(this, void 0, void 0, function () {
                var _this = this;
                return __generator(this, function (_a) {
                    path = maybeStripScheme(path);
                    return [2 /*return*/, new Promise(function (resolve, reject) {
                            var openRequest = _this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);
                            openRequest.onupgradeneeded = function () { return setUpDatabase(openRequest); };
                            openRequest.onsuccess = function () {
                                var db = openRequest.result;
                                var infoTx = db.transaction(INFO_STORE_NAME, 'readwrite');
                                var infoStore = infoTx.objectStore(INFO_STORE_NAME);
                                var getInfoRequest = infoStore.get(path);
                                var modelTx;
                                getInfoRequest.onsuccess = function () {
                                    if (getInfoRequest.result == null) {
                                        db.close();
                                        return reject(new Error("Cannot find model with path '" + path + "' " +
                                            "in IndexedDB."));
                                    }
                                    else {
                                        // First, delete the entry in the info store.
                                        var deleteInfoRequest = infoStore.delete(path);
                                        var deleteModelData_1 = function () {
                                            // Second, delete the entry in the model store.
                                            modelTx = db.transaction(MODEL_STORE_NAME, 'readwrite');
                                            var modelStore = modelTx.objectStore(MODEL_STORE_NAME);
                                            var deleteModelRequest = modelStore.delete(path);
                                            deleteModelRequest.onsuccess = function () {
                                                return resolve(getInfoRequest.result.modelArtifactsInfo);
                                            };
                                            deleteModelRequest.onerror = function (error) {
                                                return reject(getInfoRequest.error);
                                            };
                                        };
                                        // Proceed with deleting model data regardless of whether deletion
                                        // of info data succeeds or not.
                                        deleteInfoRequest.onsuccess = deleteModelData_1;
                                        deleteInfoRequest.onerror = function (error) {
                                            deleteModelData_1();
                                            db.close();
                                            return reject(getInfoRequest.error);
                                        };
                                    }
                                };
                                getInfoRequest.onerror = function (error) {
                                    db.close();
                                    return reject(getInfoRequest.error);
                                };
                                infoTx.oncomplete = function () {
                                    if (modelTx == null) {
                                        db.close();
                                    }
                                    else {
                                        modelTx.oncomplete = function () { return db.close(); };
                                    }
                                };
                            };
                            openRequest.onerror = function (error) { return reject(openRequest.error); };
                        })];
                });
            });
        };
        return BrowserIndexedDBManager;
    }());
    if (env().getBool('IS_BROWSER')) {
        // Wrap the construction and registration, to guard against browsers that
        // don't support Local Storage.
        try {
            ModelStoreManagerRegistry.registerManager(BrowserIndexedDB.URL_SCHEME, new BrowserIndexedDBManager());
        }
        catch (err) {
        }
    }

    /**
     * @license
     * Copyright 2018 Google LLC. All Rights Reserved.
     * Licensed under the Apache License, Version 2.0 (the "License");
     * you may not use this file except in compliance with the License.
     * You may obtain a copy of the License at
     *
     * http://www.apache.org/licenses/LICENSE-2.0
     *
     * Unless required by applicable law or agreed to in writing, software
     * distributed under the License is distributed on an "AS IS" BASIS,
     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
     * See the License for the specific language governing permissions and
     * limitations under the License.
     * =============================================================================
     */
    var PATH_SEPARATOR = '/';
    var PATH_PREFIX = 'tensorflowjs_models';
    var INFO_SUFFIX = 'info';
    var MODEL_TOPOLOGY_SUFFIX = 'model_topology';
    var WEIGHT_SPECS_SUFFIX = 'weight_specs';
    var WEIGHT_DATA_SUFFIX = 'weight_data';
    var MODEL_METADATA_SUFFIX = 'model_metadata';
    function getModelKeys(path) {
        return {
            info: [PATH_PREFIX, path, INFO_SUFFIX].join(PATH_SEPARATOR),
            topology: [PATH_PREFIX, path, MODEL_TOPOLOGY_SUFFIX].join(PATH_SEPARATOR),
            weightSpecs: [PATH_PREFIX, path, WEIGHT_SPECS_SUFFIX].join(PATH_SEPARATOR),
            weightData: [PATH_PREFIX, path, WEIGHT_DATA_SUFFIX].join(PATH_SEPARATOR),
            modelMetadata: [PATH_PREFIX, path, MODEL_METADATA_SUFFIX].join(PATH_SEPARATOR)
        };
    }
    /**
     * Get model path from a local-storage key.
     *
     * E.g., 'tensorflowjs_models/my/model/1/info' --> 'my/model/1'
     *
     * @param key
     */
    function getModelPathFromKey(key) {
        var items = key.split(PATH_SEPARATOR);
        if (items.length < 3) {
            throw new Error("Invalid key format: " + key);
        }
        return items.slice(1, items.length - 1).join(PATH_SEPARATOR);
    }
    function maybeStripScheme$1(key) {
        return key.startsWith(BrowserLocalStorage.URL_SCHEME) ?
            key.slice(BrowserLocalStorage.URL_SCHEME.length) :
            key;
    }
    /**
     * IOHandler subclass: Browser Local Storage.
     *
     * See the doc string to `browserLocalStorage` for more details.
     */
    var BrowserLocalStorage = /** @class */ (function () {
        function BrowserLocalStorage(modelPath) {
            if (!env().getBool('IS_BROWSER') ||
                typeof window === 'undefined' ||
                typeof window.localStorage === 'undefined') {
                // TODO(cais): Add more info about what IOHandler subtypes are
                // available.
                //   Maybe point to a doc page on the web and/or automatically determine
                //   the available IOHandlers and print them in the error message.
                throw new Error('The current environment does not support local storage.');
            }
            this.LS = window.localStorage;
            if (modelPath == null || !modelPath) {
                throw new Error('For local storage, modelPath must not be null, undefined or empty.');
            }
            this.modelPath = modelPath;
            this.keys = getModelKeys(this.modelPath);
        }
        /**
         * Save model artifacts to browser local storage.
         *
         * See the documentation to `browserLocalStorage` for details on the saved
         * artifacts.
         *
         * @param modelArtifacts The model artifacts to be stored.
         * @returns An instance of SaveResult.
         */
        BrowserLocalStorage.prototype.save = function (modelArtifacts) {
            return __awaiter(this, void 0, void 0, function () {
                var topology, weightSpecs, modelArtifactsInfo;
                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));
                            this.LS.setItem(this.keys.modelMetadata, JSON.stringify({
                                format: modelArtifacts.format,
                                generatedBy: modelArtifacts.generatedBy,
                                convertedBy: modelArtifacts.convertedBy,
                                userDefinedMetadata: modelArtifacts.userDefinedMetadata
                            }));
                            return [2 /*return*/, { modelArtifactsInfo: modelArtifactsInfo }];
                        }
                        catch (err) {
                            // If saving failed, clean up all items saved so far.
                            this.LS.removeItem(this.keys.info);
                            this.LS.removeItem(this.keys.topology);
                            this.LS.removeItem(this.keys.weightSpecs);
                            this.LS.removeItem(this.keys.weightData);
                            this.LS.removeItem(this.keys.modelMetadata);
                            throw new Error("Failed to save model '" + this.modelPath + "' to local storage: " +
                                "size quota being exceeded is a possible cause of this failure: " +
                                ("modelTopologyBytes=" + modelArtifactsInfo.modelTopologyBytes + ", ") +
                                ("weightSpecsBytes=" + modelArtifactsInfo.weightSpecsBytes + ", ") +
                                ("weightDataBytes=" + modelArtifactsInfo.weightDataBytes + "."));
                        }
                    }
                    return [2 /*return*/];
                });
            });
        };
        /**
         * Load a model from local storage.
         *
         * See the documentation to `browserLocalStorage` for details on the saved
         * artifacts.
         *
         * @returns The loaded model (if loading succeeds).
         */
        BrowserLocalStorage.prototype.load = function () {
            return __awaiter(this, void 0, void 0, function () {
                var info, out, topology, weightSpecs, metadataString, metadata, weightDataBase64;
                return __generator(this, function (_a) {
                    info = JSON.parse(this.LS.getItem(this.keys.info));
                    if (info == null) {
                        throw new Error("In local storage, there is no model with name '" + this.modelPath + "'");
                    }
                    if (info.modelTopologyType !== 'JSON') {
                        throw new Error('BrowserLocalStorage does not support loading non-JSON model ' +
                            'topology yet.');
                    }
                    out = {};
                    topology = JSON.parse(this.LS.getItem(this.keys.topology));
                    if (topology == null) {
                        throw new Error("In local storage, the topology of model '" + this.modelPath + "' " +
                            "is missing.");
                    }
                    out.modelTopology = topology;
                    weightSpecs = JSON.parse(this.LS.getItem(this.keys.weightSpecs));
                    if (weightSpecs == null) {
                        throw new Error("In local storage, the weight specs of model '" + this.modelPath + "' " +
                            "are missing.");
                    }
                    out.weightSpecs = weightSpecs;
                    metadataString = this.LS.getItem(this.keys.modelMetadata);
                    if (metadataString != null) {
                        metadata = JSON.parse(metadataString);
                        out.format = metadata['format'];
                        out.generatedBy = metadata['generatedBy'];
                        out.convertedBy = metadata['convertedBy'];
                        out.userDefinedMetadata = metadata['userDefinedMetadata'];
                    }
                    weightDataBase64 = this.LS.getItem(this.keys.weightData);
                    if (weightDataBase64 == null) {
                        throw new Error("In local storage, the binary weight values of model " +
                            ("'" + this.modelPath + "' are missing."));
                    }
                    out.weightData = base64StringToArrayBuffer(weightDataBase64);
                    return [2 /*return*/, out];
                });
            });
        };
        BrowserLocalStorage.URL_SCHEME = 'localstorage://';
        return BrowserLocalStorage;
    }());
    var localStorageRouter = function (url) {
        if (!env().getBool('IS_BROWSER')) {
            return null;
        }
        else {
            if (!Array.isArray(url) && url.startsWith(BrowserLocalStorage.URL_SCHEME)) {
                return browserLocalStorage(url.slice(BrowserLocalStorage.URL_SCHEME.length));
            }
            else {
                return null;
            }
        }
    };
    IORouterRegistry.registerSaveRouter(localStorageRouter);
    IORouterRegistry.registerLoadRouter(localStorageRouter);
    /**
     * Factory function for local storage IOHandler.
     *
     * This `IOHandler` supports both `save` and `load`.
     *
     * For each model's saved artifacts, four items are saved to local storage.
     *   - `${PATH_SEPARATOR}/${modelPath}/info`: Contains meta-info about the
     *     model, such as date saved, type of the topology, size in bytes, etc.
     *   - `${PATH_SEPARATOR}/${modelPath}/topology`: Model topology. For Keras-
     *     style models, this is a stringized JSON.
     *   - `${PATH_SEPARATOR}/${modelPath}/weight_specs`: Weight specs of the
     *     model, can be used to decode the saved binary weight values (see
     *     item below).
     *   - `${PATH_SEPARATOR}/${modelPath}/weight_data`: Concatenated binary
     *     weight values, stored as a base64-encoded string.
     *
     * Saving may throw an `Error` if the total size of the artifacts exceed the
     * browser-specific quota.
     *
     * @param modelPath A unique identifier for the model to be saved. Must be a
     *   non-empty string.
     * @returns An instance of `IOHandler`, which can be used with, e.g.,
     *   `tf.Model.save`.
     */
    function browserLocalStorage(modelPath) {
        return new BrowserLocalStorage(modelPath);
    }
    var BrowserLocalStorageManager = /** @class */ (function () {
        function BrowserLocalStorageManager() {
            assert(env().getBool('IS_BROWSER'), function () { return 'Current environment is not a web browser'; });
            assert(typeof window === 'undefined' ||
                typeof window.localStorage !== 'undefined', function () { return 'Current browser does not appear to support localStorage'; });
            this.LS = window.localStorage;
        }
        BrowserLocalStorageManager.prototype.listModels = function () {
            return __awaiter(this, void 0, void 0, function () {
                var out, prefix, suffix, i, key, modelPath;
                return __generator(this, function (_a) {
                    out = {};
                    prefix = PATH_PREFIX + PATH_SEPARATOR;
                    suffix = PATH_SEPARATOR + INFO_SUFFIX;
                    for (i = 0; i < this.LS.length; ++i) {
                        key = this.LS.key(i);
                        if (key.startsWith(prefix) && key.endsWith(suffix)) {
                            modelPath = getModelPathFromKey(key);
                            out[modelPath] = JSON.parse(this.LS.getItem(key));
                        }
                    }
                    return [2 /*return*/, out];
                });
            });
        };
        BrowserLocalStorageManager.prototype.removeModel = function (path) {
            return __awaiter(this, void 0, void 0, function () {
                var keys, info;
                return __generator(this, function (_a) {
                    path = maybeStripScheme$1(path);
                    keys = getModelKeys(path);
                    if (this.LS.getItem(keys.info) == null) {
                        throw new Error("Cannot find model at path '" + path + "'");
                    }
                    info = JSON.parse(this.LS.getItem(keys.info));
                    this.LS.removeItem(keys.info);
                    this.LS.removeItem(keys.topology);
                    this.LS.removeItem(keys.weightSpecs);
                    this.LS.removeItem(keys.weightData);
                    return [2 /*return*/, info];
                });
            });
        };
        return BrowserLocalStorageManager;
    }());
    if (env().getBool('IS_BROWSER')) {
        // Wrap the construction and registration, to guard against browsers that
        // don't support Local Storage.
        try {
            ModelStoreManagerRegistry.registerManager(BrowserLocalStorage.URL_SCHEME, new BrowserLocalStorageManager());
        }
        catch (err) {
        }
    }

    /**
     * @license
     * Copyright 2018 Google LLC. All Rights Reserved.
     * Licensed under the Apache License, Version 2.0 (the "License");
     * you may not use this file except in compliance with the License.
     * You may obtain a copy of the License at
     *
     * http://www.apache.org/licenses/LICENSE-2.0
     *
     * Unless required by applicable law or agreed to in writing, software
     * distributed under the License is distributed on an "AS IS" BASIS,
     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
     * See the License for the specific language governing permissions and
     * limitations under the License.
     * =============================================================================
     */
    var DEFAULT_FILE_NAME_PREFIX = 'model';
    var DEFAULT_JSON_EXTENSION_NAME = '.json';
    var DEFAULT_WEIGHT_DATA_EXTENSION_NAME = '.weights.bin';
    function defer(f) {
        return new Promise(function (resolve) { return setTimeout(resolve); }).then(f);
    }
    var BrowserDownloads = /** @class */ (function () {
        function BrowserDownloads(fileNamePrefix) {
            if (!env().getBool('IS_BROWSER')) {
                // TODO(cais): Provide info on what IOHandlers are available under the
                //   current environment.
                throw new Error('browserDownloads() cannot proceed because the current environment ' +
                    'is not a browser.');
            }
            if (fileNamePrefix.startsWith(BrowserDownloads.URL_SCHEME)) {
                fileNamePrefix = fileNamePrefix.slice(BrowserDownloads.URL_SCHEME.length);
            }
            if (fileNamePrefix == null || fileNamePrefix.length === 0) {
                fileNamePrefix = DEFAULT_FILE_NAME_PREFIX;
            }
            this.modelTopologyFileName = fileNamePrefix + DEFAULT_JSON_EXTENSION_NAME;
            this.weightDataFileName =
                fileNamePrefix + DEFAULT_WEIGHT_DATA_EXTENSION_NAME;
        }
        BrowserDownloads.prototype.save = function (modelArtifacts) {
            return __awaiter(this, void 0, void 0, function () {
                var weightsURL, weightsManifest, modelTopologyAndWeightManifest, modelTopologyAndWeightManifestURL, jsonAnchor_1, weightDataAnchor_1;
                return __generator(this, function (_a) {
                    switch (_a.label) {
                        case 0:
                            if (typeof (document) === 'undefined') {
                                throw new Error('Browser downloads are not supported in ' +
                                    'this environment since `document` is not present');
                            }
                            weightsURL = window.URL.createObjectURL(new Blob([modelArtifacts.weightData], { type: 'application/octet-stream' }));
                            if (!(modelArtifacts.modelTopology instanceof ArrayBuffer)) return [3 /*break*/, 1];
                            throw new Error('BrowserDownloads.save() does not support saving model topology ' +
                                'in binary formats yet.');
                        case 1:
                            weightsManifest = [{
                                    paths: ['./' + this.weightDataFileName],
                                    weights: modelArtifacts.weightSpecs
                                }];
                            modelTopologyAndWeightManifest = {
                                modelTopology: modelArtifacts.modelTopology,
                                format: modelArtifacts.format,
                                generatedBy: modelArtifacts.generatedBy,
                                convertedBy: modelArtifacts.convertedBy,
                                weightsManifest: weightsManifest
                            };
                            modelTopologyAndWeightManifestURL = window.URL.createObjectURL(new Blob([JSON.stringify(modelTopologyAndWeightManifest)], { type: 'application/json' }));
                            jsonAnchor_1 = this.jsonAnchor == null ? document.createElement('a') :
                                this.jsonAnchor;
                            jsonAnchor_1.download = this.modelTopologyFileName;
                            jsonAnchor_1.href = modelTopologyAndWeightManifestURL;
                            // Trigger downloads by evoking a click event on the download anchors.
                            // When multiple downloads are started synchronously, Firefox will only
                            // save the last one.
                            return [4 /*yield*/, defer(function () { return jsonAnchor_1.dispatchEvent(new MouseEvent('click')); })];
                        case 2:
                            // Trigger downloads by evoking a click event on the download anchors.
                            // When multiple downloads are started synchronously, Firefox will only
                            // save the last one.
                            _a.sent();
                            if (!(modelArtifacts.weightData != null)) return [3 /*break*/, 4];
                            weightDataAnchor_1 = this.weightDataAnchor == null ?
                                document.createElement('a') :
                                this.weightDataAnchor;
                            weightDataAnchor_1.download = this.weightDataFileName;
                            weightDataAnchor_1.href = weightsURL;
                            return [4 /*yield*/, defer(function () { return weightDataAnchor_1.dispatchEvent(new MouseEvent('click')); })];
                        case 3:
                            _a.sent();
                            _a.label = 4;
                        case 4: return [2 /*return*/, { modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts) }];
                    }
                });
            });
        };
        BrowserDownloads.URL_SCHEME = 'downloads://';
        return BrowserDownloads;
    }());
    var BrowserFiles = /** @class */ (function () {
        function BrowserFiles(files) {
            if (files == null || files.length < 1) {
                throw new Error("When calling browserFiles, at least 1 file is required, " +
                    ("but received " + files));
            }
            this.files = files;
        }
        BrowserFiles.prototype.load = function () {
            return __awaiter(this, void 0, void 0, function () {
                var jsonFile, weightFiles;
                var _this = this;
                return __generator(this, function (_a) {
                    jsonFile = this.files[0];
                    weightFiles = this.files.slice(1);
                    return [2 /*return*/, new Promise(function (resolve, reject) {
                            var jsonReader = new FileReader();
                            jsonReader.onload = function (event) {
                                // tslint:disable-next-line:no-any
                                var modelJSON = JSON.parse(event.target.result);
                                var modelTopology = modelJSON.modelTopology;
                                if (modelTopology == null) {
                                    reject(new Error("modelTopology field is missing from file " + jsonFile.name));
                                    return;
                                }
                                if (weightFiles.length === 0) {
                                    resolve({ modelTopology: modelTopology });
                                }
                                var weightsManifest = modelJSON.weightsManifest;
                                if (weightsManifest == null) {
                                    reject(new Error("weightManifest field is missing from file " + jsonFile.name));
                                    return;
                                }
                                var pathToFile;
                                try {
                                    pathToFile =
                                        _this.checkManifestAndWeightFiles(weightsManifest, weightFiles);
                                }
                                catch (err) {
                                    reject(err);
                                    return;
                                }
                                var weightSpecs = [];
                                var paths = [];
                                var perFileBuffers = [];
                                weightsManifest.forEach(function (weightsGroup) {
                                    weightsGroup.paths.forEach(function (path) {
                                        paths.push(path);
                                        perFileBuffers.push(null);
                                    });
                                    weightSpecs.push.apply(weightSpecs, weightsGroup.weights);
                                });
                                weightsManifest.forEach(function (weightsGroup) {
                                    weightsGroup.paths.forEach(function (path) {
                                        var weightFileReader = new FileReader();
                                        weightFileReader.onload = function (event) {
                                            // tslint:disable-next-line:no-any
                                            var weightData = event.target.result;
                                            var index = paths.indexOf(path);
                                            perFileBuffers[index] = weightData;
                                            if (perFileBuffers.indexOf(null) === -1) {
                                                resolve({
                                                    modelTopology: modelTopology,
                                                    weightSpecs: weightSpecs,
                                                    weightData: concatenateArrayBuffers(perFileBuffers),
                                                    format: modelJSON.format,
                                                    generatedBy: modelJSON.generatedBy,
                                                    convertedBy: modelJSON.convertedBy,
                                                    userDefinedMetadata: modelJSON.userDefinedMetadata
                                                });
                                            }
                                        };
                                        weightFileReader.onerror = function (error) {
                                            return reject("Failed to weights data from file of path '" + path + "'.");
                                        };
                                        weightFileReader.readAsArrayBuffer(pathToFile[path]);
                                    });
                                });
                            };
                            jsonReader.onerror = function (error) { return reject("Failed to read model topology and weights manifest JSON " +
                                ("from file '" + jsonFile.name + "'. BrowserFiles supports loading ") +
                                "Keras-style tf.Model artifacts only."); };
                            jsonReader.readAsText(jsonFile);
                        })];
                });
            });
        };
        /**
         * Check the compatibility between weights manifest and weight files.
         */
        BrowserFiles.prototype.checkManifestAndWeightFiles = function (manifest, files) {
            var basenames = [];
            var fileNames = files.map(function (file) { return basename(file.name); });
            var pathToFile = {};
            for (var _i = 0, manifest_1 = manifest; _i < manifest_1.length; _i++) {
                var group = manifest_1[_i];
                group.paths.forEach(function (path) {
                    var pathBasename = basename(path);
                    if (basenames.indexOf(pathBasename) !== -1) {
                        throw new Error("Duplicate file basename found in weights manifest: " +
                            ("'" + pathBasename + "'"));
                    }
                    basenames.push(pathBasename);
                    if (fileNames.indexOf(pathBasename) === -1) {
                        throw new Error("Weight file with basename '" + pathBasename + "' is not provided.");
                    }
                    else {
                        pathToFile[path] = files[fileNames.indexOf(pathBasename)];
                    }
                });
            }
            if (basenames.length !== files.length) {
                throw new Error("Mismatch in the number of files in weights manifest " +
                    ("(" + basenames.length + ") and the number of weight files provided ") +
                    ("(" + files.length + ")."));
            }
            return pathToFile;
        };
        return BrowserFiles;
    }());
    var browserDownloadsRouter = function (url) {
        if (!env().getBool('IS_BROWSER')) {
            return null;
        }
        else {
            if (!Array.isArray(url) && url.startsWith(BrowserDownloads.URL_SCHEME)) {
                return browserDownloads(url.slice(BrowserDownloads.URL_SCHEME.length));
            }
            else {
                return null;
            }
        }
    };
    IORouterRegistry.registerSaveRouter(browserDownloadsRouter);
    /**
     * Creates an IOHandler that triggers file downloads from the browser.
     *
     * The returned `IOHandler` instance can be used as model exporting methods such
     * as `tf.Model.save` and supports only saving.
     *
     * ```js
     * const model = tf.sequential();
     * model.add(tf.layers.dense(
     *     {units: 1, inputShape: [10], activation: 'sigmoid'}));
     * const saveResult = await model.save('downloads://mymodel');
     * // This will trigger downloading of two files:
     * //   'mymodel.json' and 'mymodel.weights.bin'.
     * console.log(saveResult);
     * ```
     *
     * @param fileNamePrefix Prefix name of the files to be downloaded. For use with
     *   `tf.Model`, `fileNamePrefix` should follow either of the following two
     *   formats:
     *   1. `null` or `undefined`, in which case the default file
     *      names will be used:
     *      - 'model.json' for the JSON file containing the model topology and
     *        weights manifest.
     *      - 'model.weights.bin' for the binary file containing the binary weight
     *        values.
     *   2. A single string or an Array of a single string, as the file name prefix.
     *      For example, if `'foo'` is provided, the downloaded JSON
     *      file and binary weights file will be named 'foo.json' and
     *      'foo.weights.bin', respectively.
     * @param config Additional configuration for triggering downloads.
     * @returns An instance of `BrowserDownloads` `IOHandler`.
     */
    /**
     * @doc {
     *   heading: 'Models',
     *   subheading: 'Loading',
     *   namespace: 'io',
     *   ignoreCI: true
     * }
     */
    function browserDownloads(fileNamePrefix) {
        if (fileNamePrefix === void 0) { fileNamePrefix = 'model'; }
        return new BrowserDownloads(fileNamePrefix);
    }
    /**
     * Creates an IOHandler that loads model artifacts from user-selected files.
     *
     * This method can be used for loading from files such as user-selected files
     * in the browser.
     * When used in conjunction with `tf.loadLayersModel`, an instance of
     * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts.
     *
     * ```js
     * // Note: This code snippet won't run properly without the actual file input
     * //   elements in the HTML DOM.
     *
     * // Suppose there are two HTML file input (`<input type="file" ...>`)
     * // elements.
     * const uploadJSONInput = document.getElementById('upload-json');
     * const uploadWeightsInput = document.getElementById('upload-weights');
     * const model = await tf.loadLayersModel(tf.io.browserFiles(
     *     [uploadJSONInput.files[0], uploadWeightsInput.files[0]]));
     * ```
     *
     * @param files `File`s to load from. Currently, this function supports only
     *   loading from files that contain Keras-style models (i.e., `tf.Model`s), for
     *   which an `Array` of `File`s is expected (in that order):
     *   - A JSON file containing the model topology and weight manifest.
     *   - Optionally, One or more binary files containing the binary weights.
     *     These files must have names that match the paths in the `weightsManifest`
     *     contained by the aforementioned JSON file, or errors will be thrown
     *     during loading. These weights files have the same format as the ones
     *     generated by `tensorflowjs_converter` that comes with the `tensorflowjs`
     *     Python PIP package. If no weights files are provided, only the model
     *     topology will be loaded from the JSON file above.
     * @returns An instance of `Files` `IOHandler`.
     */
    /**
     * @doc {
     *   heading: 'Models',
     *   subheading: 'Loading',
     *   namespace: 'io',
     *   ignoreCI: true
     * }
     */
    function browserFiles(files) {
        return new BrowserFiles(files);
    }

    /**
     * @license
     * Copyright 2019 Google 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.
     * =============================================================================
     */
    /**
     * Monitor Promise.all progress, fire onProgress callback function.
     *
     * @param promises Promise list going to be monitored
     * @param onProgress Callback function. Fired when a promise resolved.
     * @param startFraction Optional fraction start. Default to 0.
     * @param endFraction Optional fraction end. Default to 1.
     */
    function monitorPromisesProgress(promises, onProgress, startFraction, endFraction) {
        checkPromises(promises);
        startFraction = startFraction == null ? 0 : startFraction;
        endFraction = endFraction == null ? 1 : endFraction;
        checkFraction(startFraction, endFraction);
        var resolvedPromise = 0;
        var registerMonitor = function (promise) {
            promise.then(function (value) {
                var fraction = startFraction +
                    ++resolvedPromise / promises.length * (endFraction - startFraction);
                // pass fraction as parameter to callback function.
                onProgress(fraction);
                return value;
            });
            return promise;
        };
        function checkPromises(promises) {
            assert(promises != null && Array.isArray(promises) && promises.length > 0, function () { return 'promises must be a none empty array'; });
        }
        function checkFraction(startFraction, endFraction) {
            assert(startFraction >= 0 && startFraction <= 1, function () { return "Progress fraction must be in range [0, 1], but " +
                ("got startFraction " + startFraction); });
            assert(endFraction >= 0 && endFraction <= 1, function () { return "Progress fraction must be in range [0, 1], but " +
                ("got endFraction " + endFraction); });
            assert(endFraction >= startFraction, function () { return "startFraction must be no more than endFraction, but " +
                ("got startFraction " + startFraction + " and endFraction ") +
                ("" + endFraction); });
        }
        return Promise.all(promises.map(registerMonitor));
    }

    /**
     * @license
     * Copyright 2018 Google LLC. All Rights Reserved.
     * Licensed under the Apache License, Version 2.0 (the "License");
     * you may not use this file except in compliance with the License.
     * You may obtain a copy of the License at
     *
     * http://www.apache.org/licenses/LICENSE-2.0
     *
     * Unless required by applicable law or agreed to in writing, software
     * distributed under the License is distributed on an "AS IS" BASIS,
     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
     * See the License for the specific language governing permissions and
     * limitations under the License.
     * =============================================================================
     */
    /**
     * Reads binary weights data from a number of URLs.
     *
     * @param fetchURLs URLs to send the HTTP requests at, using `fetch` calls.
     * @param requestOptions RequestInit (options) for the HTTP requests.
     * @param fetchFunc Optional overriding value for the `window.fetch` function.
     * @param onProgress Optional, progress callback function, fired periodically
     *   before the load is completed.
     * @returns A `Promise` of an Array of `ArrayBuffer`. The Array has the same
     *   length as `fetchURLs`.
     */
    function loadWeightsAsArrayBuffer(fetchURLs, loadOptions) {
        return __awaiter(this, void 0, void 0, function () {
            var fetchFunc, requests, fetchStartFraction, fetchEndFraction, responses, _a, bufferPromises, bufferStartFraction, bufferEndFraction, buffers, _b;
            return __generator(this, function (_c) {
                switch (_c.label) {
                    case 0:
                        if (loadOptions == null) {
                            loadOptions = {};
                        }
                        fetchFunc = loadOptions.fetchFunc == null ? env().platform.fetch :
                            loadOptions.fetchFunc;
                        requests = fetchURLs.map(function (fetchURL) {
                            return fetchFunc(fetchURL, loadOptions.requestInit, { isBinary: true });
                        });
                        fetchStartFraction = 0;
                        fetchEndFraction = 0.5;
                        if (!(loadOptions.onProgress == null)) return [3 /*break*/, 2];
                        return [4 /*yield*/, Promise.all(requests)];
                    case 1:
                        _a = _c.sent();
                        return [3 /*break*/, 4];
                    case 2: return [4 /*yield*/, monitorPromisesProgress(requests, loadOptions.onProgress, fetchStartFraction, fetchEndFraction)];
                    case 3:
                        _a = _c.sent();
                        _c.label = 4;
                    case 4:
                        responses = _a;
                        bufferPromises = responses.map(function (response) { return response.arrayBuffer(); });
                        bufferStartFraction = 0.5;
                        bufferEndFraction = 1;
                        if (!(loadOptions.onProgress == null)) return [3 /*break*/, 6];
                        return [4 /*yield*/, Promise.all(bufferPromises)];
                    case 5:
                        _b = _c.sent();
                        return [3 /*break*/, 8];
                    case 6: return [4 /*yield*/, monitorPromisesProgress(bufferPromises, loadOptions.onProgress, bufferStartFraction, bufferEndFraction)];
                    case 7:
                        _b = _c.sent();
                        _c.label = 8;
                    case 8:
                        buffers = _b;
                        return [2 /*return*/, buffers];
                }
            });
        });
    }
    /**
     * Reads a weights manifest JSON configuration, fetches the weights and
     * returns them as `Tensor`s.
     *
     * @param manifest The weights manifest JSON.
     * @param filePathPrefix The path prefix for filenames given in the manifest.
     *     Defaults to the empty string.
     * @param weightNames The names of the weights to be fetched.
     */
    function loadWeights(manifest, filePathPrefix, weightNames, requestInit) {
        if (filePathPrefix === void 0) { filePathPrefix = ''; }
        return __awaiter(this, void 0, void 0, function () {
            var fetchWeights, loadWeights;
            return __generator(this, function (_a) {
                fetchWeights = function (fetchUrls) {
                    return loadWeightsAsArrayBuffer(fetchUrls, { requestInit: requestInit });
                };
                loadWeights = weightsLoaderFactory(fetchWeights);
                return [2 /*return*/, loadWeights(manifest, filePathPrefix, weightNames)];
            });
        });
    }
    /**
     * Creates a function, which reads a weights manifest JSON configuration,
     * fetches the weight files using the specified function and returns them as
     * `Tensor`s.
     *
     * ```js
     * // example for creating a nodejs weight loader, which reads the weight files
     * // from disk using fs.readFileSync
     *
     * import * as fs from 'fs'
     *
     * const fetchWeightsFromDisk = (filePaths: string[]) =>
     *   filePaths.map(filePath => fs.readFileSync(filePath).buffer)
     *
     * const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)
     *
     * const manifest = JSON.parse(
     *   fs.readFileSync('./my_model-weights_manifest').toString()
     * )
     * const weightMap = await loadWeights(manifest, './')
     * ```
     * @param fetchWeightsFunction The function used for fetching the weight files.
     * @returns Weight loading function.
     */
    function weightsLoaderFactory(fetchWeightsFunction) {
        var _this = this;
        return function (manifest, filePathPrefix, weightNames) {
            if (filePathPrefix === void 0) { filePathPrefix = ''; }
            return __awaiter(_this, void 0, void 0, function () {
                var groupIndicesToFetchMap, groupWeightsToFetch, weightsFound, allManifestWeightNames, weightsNotFound, groupIndicesToFetch, fetchUrls, buffers, weightsTensorMap, bufferIndexOffset;
                return __generator(this, function (_a) {
                    switch (_a.label) {
                        case 0:
                            groupIndicesToFetchMap = manifest.map(function () { return false; });
                            groupWeightsToFetch = {};
                            weightsFound = weightNames != null ? weightNames.map(function () { return false; }) : [];
                            allManifestWeightNames = [];
                            manifest.forEach(function (manifestGroupConfig, groupIndex) {
                                var groupOffset = 0;
                                manifestGroupConfig.weights.forEach(function (weightsEntry) {
                                    var rawDtype = ('quantization' in weightsEntry) ?
                                        weightsEntry.quantization.dtype :
                                        weightsEntry.dtype;
                                    var weightsBytes = DTYPE_VALUE_SIZE_MAP[rawDtype] *
                                        sizeFromShape(weightsEntry.shape);
                                    var enqueueWeightsForFetchingFn = function () {
                                        groupIndicesToFetchMap[groupIndex] = true;
                                        if (groupWeightsToFetch[groupIndex] == null) {
                                            groupWeightsToFetch[groupIndex] = [];
                                        }
                                        groupWeightsToFetch[groupIndex].push({
                                            manifestEntry: weightsEntry,
                                            groupOffset: groupOffset,
                                            sizeBytes: weightsBytes
                                        });
                                    };
                                    if (weightNames != null) {
                                        weightNames.forEach(function (weightName, weightIndex) {
                                            if (weightName === weightsEntry.name) {
                                                enqueueWeightsForFetchingFn();
                                                weightsFound[weightIndex] = true;
                                            }
                                        });
                                    }
                                    else {
                                        enqueueWeightsForFetchingFn();
                                    }
                                    allManifestWeightNames.push(weightsEntry.name);
                                    groupOffset += weightsBytes;
                                });
                            });
                            if (!weightsFound.every(function (found) { return found; })) {
                                weightsNotFound = weightNames.filter(function (_, i) { return !weightsFound[i]; });
                                throw new Error("Could not find weights in manifest with names: " +
                                    (weightsNotFound.join(', ') + ". \n") +
                                    "Manifest JSON has weights with names: " +
                                    (allManifestWeightNames.join(', ') + "."));
                            }
                            groupIndicesToFetch = groupIndicesToFetchMap.reduce(function (accumulator, shouldFetch, i) {
                                if (shouldFetch) {
                                    accumulator.push(i);
                                }
                                return accumulator;
                            }, []);
                            fetchUrls = [];
                            groupIndicesToFetch.forEach(function (i) {
                                manifest[i].paths.forEach(function (filepath) {
                                    var fetchUrl = filePathPrefix +
                                        (!filePathPrefix.endsWith('/') ? '/' : '') + filepath;
                                    fetchUrls.push(fetchUrl);
                                });
                            });
                            return [4 /*yield*/, fetchWeightsFunction(fetchUrls)];
                        case 1:
                            buffers = _a.sent();
                            weightsTensorMap = {};
                            bufferIndexOffset = 0;
                            groupIndicesToFetch.forEach(function (i) {
                                var numBuffers = manifest[i].paths.length;
                                var groupBytes = 0;
                                for (var i_1 = 0; i_1 < numBuffers; i_1++) {
                                    groupBytes += buffers[bufferIndexOffset + i_1].byteLength;
                                }
                                // Create a buffer for the whole group.
                                var groupBuffer = new ArrayBuffer(groupBytes);
                                var groupByteBuffer = new Uint8Array(groupBuffer);
                                var groupBufferOffset = 0;
                                for (var i_2 = 0; i_2 < numBuffers; i_2++) {
                                    var buffer = new Uint8Array(buffers[bufferIndexOffset + i_2]);
                                    groupByteBuffer.set(buffer, groupBufferOffset);
                                    groupBufferOffset += buffer.byteLength;
                                }
                                var weightsEntries = groupWeightsToFetch[i];
                                weightsEntries.forEach(function (weightsEntry) {
                                    var byteBuffer = groupBuffer.slice(weightsEntry.groupOffset, weightsEntry.groupOffset + weightsEntry.sizeBytes);
                                    var nameToTensorMap = decodeWeights(byteBuffer, [weightsEntry.manifestEntry]);
                                    for (var name_1 in nameToTensorMap) {
                                        weightsTensorMap[name_1] = nameToTensorMap[name_1];
                                    }
                                });
                                bufferIndexOffset += numBuffers;
                            });
                            return [2 /*return*/, weightsTensorMap];
                    }
                });
            });
        };
    }

    /**
     * @license
     * Copyright 2018 Google LLC. All Rights Reserved.
     * Licensed under the Apache License, Version 2.0 (the "License");
     * you may not use this file except in compliance with the License.
     * You may obtain a copy of the License at
     *
     * http://www.apache.org/licenses/LICENSE-2.0
     *
     * Unless required by applicable law or agreed to in writing, software
     * distributed under the License is distributed on an "AS IS" BASIS,
     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
     * See the License for the specific language governing permissions and
     * limitations under the License.
     * =============================================================================
     */
    var OCTET_STREAM_MIME_TYPE = 'application/octet-stream';
    var JSON_TYPE = 'application/json';
    var HTTPRequest = /** @class */ (function () {
        function HTTPRequest(path, loadOptions) {
            this.DEFAULT_METHOD = 'POST';
            if (loadOptions == null) {
                loadOptions = {};
            }
            this.weightPathPrefix = loadOptions.weightPathPrefix;
            this.onProgress = loadOptions.onProgress;
            if (loadOptions.fetchFunc != null) {
                assert(typeof loadOptions.fetchFunc === 'function', function () { return 'Must pass a function that matches the signature of ' +
                    '`fetch` (see ' +
                    'https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)'; });
                this.fetch = loadOptions.fetchFunc;
            }
            else {
                this.fetch = env().platform.fetch;
            }
            assert(path != null && path.length > 0, function () { return 'URL path for http must not be null, undefined or ' +
                'empty.'; });
            if (Array.isArray(path)) {
                assert(path.length === 2, function () { return 'URL paths for http must have a length of 2, ' +
                    ("(actual length is " + path.length + ")."); });
            }
            this.path = path;
            if (loadOptions.requestInit != null &&
                loadOptions.requestInit.body != null) {
                throw new Error('requestInit is expected to have no pre-existing body, but has one.');
            }
            this.requestInit = loadOptions.requestInit || {};
        }
        HTTPRequest.prototype.save = function (modelArtifacts) {
            return __awaiter(this, void 0, void 0, function () {
                var init, weightsManifest, modelTopologyAndWeightManifest, response;
                return __generator(this, function (_a) {
                    switch (_a.label) {
                        case 0:
                            if (modelArtifacts.modelTopology instanceof ArrayBuffer) {
                                throw new Error('BrowserHTTPRequest.save() does not support saving model topology ' +
                                    'in binary formats yet.');
                            }
                            init = Object.assign({ method: this.DEFAULT_METHOD }, this.requestInit);
                            init.body = new FormData();
                            weightsManifest = [{
                                    paths: ['./model.weights.bin'],
                                    weights: modelArtifacts.weightSpecs,
                                }];
                            modelTopologyAndWeightManifest = {
                                modelTopology: modelArtifacts.modelTopology,
                                format: modelArtifacts.format,
                                generatedBy: modelArtifacts.generatedBy,
                                convertedBy: modelArtifacts.convertedBy,
                                userDefinedMetadata: modelArtifacts.userDefinedMetadata,
                                weightsManifest: 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 + "."));
                            }
                            return [2 /*return*/];
                    }
                });
            });
        };
        /**
         * Load model artifacts via HTTP request(s).
         *
         * See the documentation to `tf.io.http` for details on the saved
         * artifacts.
         *
         * @returns The loaded model artifacts (if loading succeeds).
         */
        HTTPRequest.prototype.load = function () {
            return __awaiter(this, void 0, void 0, function () {
                var modelConfigRequest, modelConfig, e_1, message, modelTopology, weightsManifest, generatedBy, convertedBy, format, userDefinedMetadata, weightSpecs, weightData, results;
                return __generator(this, function (_a) {
                    switch (_a.label) {
                        case 0: return [4 /*yield*/, this.fetch(this.path, this.requestInit)];
                        case 1:
                            modelConfigRequest = _a.sent();
                            if (!modelConfigRequest.ok) {
                                throw new Error("Request to " + this.path + " failed with status code " +
                                    (modelConfigRequest.status + ". Please verify this URL points to ") +
                                    "the model JSON of the model to load.");
                            }
                            _a.label = 2;
                        case 2:
                            _a.trys.push([2, 4, , 5]);
                            return [4 /*yield*/, modelConfigRequest.json()];
                        case 3:
                            modelConfig = _a.sent();
                            return [3 /*break*/, 5];
                        case 4:
                            e_1 = _a.sent();
                            message = "Failed to parse model JSON of response from " + this.path + ".";
                            // TODO(nsthorat): Remove this after some time when we're comfortable that
                            // .pb files are mostly gone.
                            if (this.path.endsWith('.pb')) {
                                message += ' Your path contains a .pb file extension. ' +
                                    'Support for .pb models have been removed in TensorFlow.js 1.0 ' +
                                    'in favor of .json models. You can re-convert your Python ' +
                                    'TensorFlow model using the TensorFlow.js 1.0 conversion scripts ' +
                                    'or you can convert your.pb models with the \'pb2json\'' +
                                    'NPM script in the tensorflow/tfjs-converter repository.';
                            }
                            else {
                                message += ' Please make sure the server is serving valid ' +
                                    'JSON for this request.';
                            }
                            throw new Error(message);
                        case 5:
                            modelTopology = modelConfig.modelTopology;
                            weightsManifest = modelConfig.weightsManifest;
                            generatedBy = modelConfig.generatedBy;
                            convertedBy = modelConfig.convertedBy;
                            format = modelConfig.format;
                            userDefinedMetadata = modelConfig.userDefinedMetadata;
                            // We do not allow both modelTopology and weightsManifest to be missing.
                            if (modelTopology == null && weightsManifest == null) {
                                throw new Error("The JSON from HTTP path " + this.path + " contains neither model " +
                                    "topology or manifest for weights.");
                            }
                            if (!(weightsManifest != null)) return [3 /*break*/, 7];
                            return [4 /*yield*/, this.loadWeights(weightsManifest)];
                        case 6:
                            results = _a.sent();
                            weightSpecs = results[0], weightData = results[1];
                            _a.label = 7;
                        case 7: return [2 /*return*/, {
                                modelTopology: modelTopology,
                                weightSpecs: weightSpecs,
                                weightData: weightData,
                                userDefinedMetadata: userDefinedMetadata,
                                generatedBy: generatedBy,
                                convertedBy: convertedBy,
                                format: format
                            }];
                    }
                });
            });
        };
        HTTPRequest.prototype.loadWeights = function (weightsManifest) {
            return __awaiter(this, void 0, void 0, function () {
                var weightPath, _a, prefix, suffix, pathPrefix, weightSpecs, _i, weightsManifest_1, entry, fetchURLs, buffers;
                return __generator(this, function (_b) {
                    switch (_b.label) {
                        case 0:
                            weightPath = Array.isArray(this.path) ? this.path[1] : this.path;
                            _a = parseUrl(weightPath), prefix = _a[0], suffix = _a[1];
                            pathPrefix = this.weightPathPrefix || prefix;
                            weightSpecs = [];
                            for (_i = 0, weightsManifest_1 = weightsManifest; _i < weightsManifest_1.length; _i++) {
                                entry = weightsManifest_1[_i];
                                weightSpecs.push.apply(weightSpecs, entry.weights);
                            }
                            fetchURLs = [];
                            weightsManifest.forEach(function (weightsGroup) {
                                weightsGroup.paths.forEach(function (path) {
                                    fetchURLs.push(pathPrefix + path + suffix);
                                });
                            });
                            return [4 /*yield*/, loadWeightsAsArrayBuffer(fetchURLs, {
                                    requestInit: this.requestInit,
                                    fetchFunc: this.fetch,
                                    onProgress: this.onProgress
                                })];
                        case 1:
                            buffers = _b.sent();
                            return [2 /*return*/, [weightSpecs, concatenateArrayBuffers(buffers)]];
                    }
                });
            });
        };
        HTTPRequest.URL_SCHEME_REGEX = /^https?:\/\//;
        return HTTPRequest;
    }());
    /**
     * Extract the prefix and suffix of the url, where the prefix is the path before
     * the last file, and suffix is the search params after the last file.
     * ```
     * const url = 'http://tfhub.dev/model/1/tensorflowjs_model.pb?tfjs-format=file'
     * [prefix, suffix] = parseUrl(url)
     * // prefix = 'http://tfhub.dev/model/1/'
     * // suffix = '?tfjs-format=file'
     * ```
     * @param url the model url to be parsed.
     */
    function parseUrl(url) {
        var lastSlash = url.lastIndexOf('/');
        var lastSearchParam = url.lastIndexOf('?');
        var prefix = url.substring(0, lastSlash);
        var suffix = lastSearchParam > lastSlash ? url.substring(lastSearchParam) : '';
        return [prefix + '/', suffix];
    }
    function isHTTPScheme(url) {
        return url.match(HTTPRequest.URL_SCHEME_REGEX) != null;
    }
    var httpRouter = function (url, onProgress) {
        if (typeof fetch === 'undefined') {
            // `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, { onProgress: onProgress });
            }
        }
        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 = /*#__PURE__*/Object.freeze({
        browserFiles: browserFiles,
        browserHTTPRequest: browserHTTPRequest,
        concatenateArrayBuffers: concatenateArrayBuffers,
        decodeWeights: decodeWeights,
        encodeWeights: encodeWeights,
        fromMemory: fromMemory,
        getLoadHandlers: getLoadHandlers,
        getModelArtifactsInfoForJSON: getModelArtifactsInfoForJSON,
        getSaveHandlers: getSaveHandlers,
        http: http,
        isHTTPScheme: isHTTPScheme,
        loadWeights: loadWeights,
        registerLoadRouter: registerLoadRouter,
        registerSaveRouter: registerSaveRouter,
        weightsLoaderFactory: weightsLoaderFactory,
        withSaveHandler: withSaveHandler,
        copyModel: copyModel,
        listModels: listModels,
        moveModel: moveModel,
        removeModel: removeModel
    });

    /**
     * @license
     * Copyright 2018 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 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. " + $predictions.shape[0] + ". ") +
            "Labels and predictions should have the same number of elements."; });
        assert(numClasses > 0 && Number.isInteger(numClasses), function () { return "numClasses is required to be a positive integer, but got " +
            ("" + numClasses); });
        // TODO(cais): In the future, if oneHot supports tensors inputs for
        //   `numClasses`, `confusionMatrix` can make `numClasses` optional.
        var oneHotLabels = oneHot($labels.asType('int32'), numClasses);
        var oneHotPredictions = oneHot($predictions.asType('int32'), numClasses);
        return oneHotLabels.transpose().matMul(oneHotPredictions).asType('int32');
    }
    var confusionMatrix = op({ confusionMatrix_: confusionMatrix_ });

    /**
     * @license
     * Copyright 2018 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.
     * =============================================================================
     */

    var math = /*#__PURE__*/Object.freeze({
        confusionMatrix: confusionMatrix
    });

    /**
     * @license
     * Copyright 2019 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.
     * =============================================================================
     */
    var fromPixels2DContext$1;
    /**
     * 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).
     */
    /** @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;
        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 {
            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) {
            return ENGINE.runKernel('FromPixels', { pixels: pixels }, { numChannels: numChannels });
        }
        var _a = isVideo ?
            [
                pixels.videoWidth,
                pixels.videoHeight
            ] :
            [pixels.width, pixels.height], width = _a[0], height = _a[1];
        var vals;
        if (isCanvasLike) {
            vals =
                // tslint:disable-next-line:no-any
                pixels.getContext('2d').getImageData(0, 0, width, height).data;
        }
        else if (isImageData || isPixelData) {
            vals = pixels.data;
        }
        else if (isImage || isVideo) {
            if (fromPixels2DContext$1 == null) {
                fromPixels2DContext$1 = document.createElement('canvas').getContext('2d');
            }
            fromPixels2DContext$1.canvas.width = width;
            fromPixels2DContext$1.canvas.height = height;
            fromPixels2DContext$1.drawImage(pixels, 0, 0, width, height);
            vals = fromPixels2DContext$1.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');
    }
    /**
     * 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 or rank-3 tensor. 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, _a, height, width, depth, data, minTensor, maxTensor, vals, minVals, maxVals, min, max, multiplier, bytes, i, r, g, b, a, j, ctx, imageData;
            return __generator(this, function (_b) {
                switch (_b.label) {
                    case 0:
                        $img = convertToTensor(img, 'img', 'toPixels');
                        if (!(img instanceof Tensor)) {
                            // Assume int32 if user passed a native array.
                            $img = $img.toInt();
                        }
                        if ($img.rank !== 2 && $img.rank !== 3) {
                            throw new Error("toPixels only supports rank 2 or 3 tensors, got rank " + $img.rank + ".");
                        }
                        _a = $img.shape.slice(0, 2), height = _a[0], width = _a[1];
                        depth = $img.rank === 2 ? 1 : $img.shape[2];
                        if (depth > 4 || depth === 2) {
                            throw new Error("toPixels only supports depth of size " +
                                ("1, 3 or 4 but got " + depth));
                        }
                        return [4 /*yield*/, $img.data()];
                    case 1:
                        data = _b.sent();
                        minTensor = $img.min();
                        maxTensor = $img.max();
                        return [4 /*yield*/, Promise.all([minTensor.data(), maxTensor.data()])];
                    case 2:
                        vals = _b.sent();
                        minVals = vals[0];
                        maxVals = vals[1];
                        min = minVals[0];
                        max = maxVals[0];
                        minTensor.dispose();
                        maxTensor.dispose();
                        if ($img.dtype === 'float32') {
                            if (min < 0 || max > 1) {
                                throw new Error("Tensor values for a float32 Tensor must be in the " +
                                    ("range [0 - 1] but got range [" + min + " - " + max + "]."));
                            }
                        }
                        else if ($img.dtype === 'int32') {
                            if (min < 0 || max > 255) {
                                throw new Error("Tensor values for a int32 Tensor must be in the " +
                                    ("range [0 - 255] but got range [" + min + " - " + max + "]."));
                            }
                        }
                        else {
                            throw new Error("Unsupported type for toPixels: " + $img.dtype + "." +
                                " Please use float32 or int32 tensors.");
                        }
                        multiplier = $img.dtype === 'float32' ? 255 : 1;
                        bytes = new Uint8ClampedArray(width * height * 4);
                        for (i = 0; i < height * width; ++i) {
                            r = void 0, g = void 0, b = void 0, a = void 0;
                            if (depth === 1) {
                                r = data[i] * multiplier;
                                g = data[i] * multiplier;
                                b = data[i] * multiplier;
                                a = 255;
                            }
                            else if (depth === 3) {
                                r = data[i * 3] * multiplier;
                                g = data[i * 3 + 1] * multiplier;
                                b = data[i * 3 + 2] * multiplier;
                                a = 255;
                            }
                            else if (depth === 4) {
                                r = data[i * 4] * multiplier;
                                g = data[i * 4 + 1] * multiplier;
                                b = data[i * 4 + 2] * multiplier;
                                a = data[i * 4 + 3] * multiplier;
                            }
                            j = i * 4;
                            bytes[j + 0] = Math.round(r);
                            bytes[j + 1] = Math.round(g);
                            bytes[j + 2] = Math.round(b);
                            bytes[j + 3] = Math.round(a);
                        }
                        if (canvas != null) {
                            canvas.width = width;
                            canvas.height = height;
                            ctx = canvas.getContext('2d');
                            imageData = new ImageData(bytes, width, height);
                            ctx.putImageData(imageData, 0, 0);
                        }
                        if ($img !== img) {
                            $img.dispose();
                        }
                        return [2 /*return*/, bytes];
                }
            });
        });
    }
    var fromPixels$1 = op({ fromPixels_: fromPixels_ });

    var browser = /*#__PURE__*/Object.freeze({
        toPixels: toPixels,
        fromPixels: fromPixels$1
    });

    /**
     * @license
     * Copyright 2018 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.
     * =============================================================================
     */
    /**
     * 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 = /*#__PURE__*/Object.freeze({
        Serializable: Serializable,
        SerializationMap: SerializationMap,
        registerClass: registerClass
    });

    /**
     * @license
     * Copyright 2017 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.
     * =============================================================================
     */
    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));
    }

    var test_util = /*#__PURE__*/Object.freeze({
        TEST_EPSILON_FLOAT16: TEST_EPSILON_FLOAT16,
        expectArraysClose: expectArraysClose,
        testEpsilon: testEpsilon,
        expectPromiseToFail: expectPromiseToFail,
        expectArraysEqual: expectArraysEqual,
        expectNumbersClose: expectNumbersClose,
        expectValuesInRange: expectValuesInRange,
        expectArrayBuffersEqual: expectArrayBuffersEqual
    });

    /** @license See the LICENSE file. */
    // This code is auto-generated, do not modify this file!
    var version = '1.7.0';

    /**
     * @license
     * Copyright 2019 Google LLC. All Rights Reserved.
     * Licensed under the Apache License, Version 2.0 (the "License");
     * you may not use this file except in compliance with the License.
     * You may obtain a copy of the License at
     *
     * http://www.apache.org/licenses/LICENSE-2.0
     *
     * Unless required by applicable law or agreed to in writing, software
     * distributed under the License is distributed on an "AS IS" BASIS,
     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
     * See the License for the specific language governing permissions and
     * limitations under the License.
     * =============================================================================
     */
    /**
     * Enforce use of half precision textures if available on the platform.
     */
    /** @doc {heading: 'Environment', namespace: 'webgl'} */
    function forceHalfFloat() {
        env().set('WEBGL_FORCE_F16_TEXTURES', true);
    }

    var webgl = /*#__PURE__*/Object.freeze({
        gpgpu_util: gpgpu_util,
        webgl_util: webgl_util,
        forceHalfFloat: forceHalfFloat,
        MathBackendWebGL: MathBackendWebGL,
        setWebGLContext: setWebGLContext,
        GPGPUContext: GPGPUContext
    });

    /**
     * @license
     * Copyright 2018 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.
     * =============================================================================
     */
    /** @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.
         */
        Optimizer.prototype.computeGradients = function (f, varList) {
            return variableGrads(f, varList);
        };
        /**
         * Dispose the variables (if any) owned by this optimizer instance.
         */
        Optimizer.prototype.dispose = function () {
            if (this.iterations_ != null) {
                dispose(this.iterations_);
            }
        };
        Optimizer.prototype.saveIterations = function () {
            return __awaiter(this, void 0, void 0, function () {
                return __generator(this, function (_a) {
                    if (this.iterations_ == null) {
                        this.iterations_ = 0;
                    }
                    return [2 /*return*/, {
                            name: 'iter',
                            // TODO(cais): Use 'int64' type when available.
                            tensor: scalar(this.iterations_, 'int32')
                        }];
                });
            });
        };
        Optimizer.prototype.getWeights = function () {
            return __awaiter(this, void 0, void 0, function () {
                return __generator(this, function (_a) {
                    throw new Error('getWeights() is not implemented for this optimizer yet.');
                });
            });
        };
        Optimizer.prototype.setWeights = function (weightValues) {
            return __awaiter(this, void 0, void 0, function () {
                return __generator(this, function (_a) {
                    throw new Error("setWeights() is not implemented for this optimizer class " +
                        ("" + this.getClassName()));
                });
            });
        };
        /**
         * Extract the first element of the weight values and set it
         * as the iterations counter variable of this instance of optimizer.
         *
         * @param weightValues
         * @returns Weight values with the first element consumed and excluded.
         */
        Optimizer.prototype.extractIterations = function (weightValues) {
            return __awaiter(this, void 0, void 0, function () {
                var _a;
                return __generator(this, function (_b) {
                    switch (_b.label) {
                        case 0:
                            _a = this;
                            return [4 /*yield*/, weightValues[0].tensor.data()];
                        case 1:
                            _a.iterations_ = (_b.sent())[0];
                            return [2 /*return*/, weightValues.slice(1)];
                    }
                });
            });
        };
        return Optimizer;
    }(Serializable));
    Object.defineProperty(Optimizer, Symbol.hasInstance, {
        value: function (instance) {
            return instance.minimize != null && instance.computeGradients != null &&
                instance.applyGradients != null;
        }
    });

    /**
     * @license
     * Copyright 2018 Google 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.
     * =============================================================================
     */
    /** @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 = accumulatedGrad.mul(_this.rho).add(gradient.square().mul(1 - _this.rho));
                    var updates = accumulatedUpdate.add(_this.epsilon)
                        .sqrt()
                        .div(accumulatedGrad.add(_this.epsilon).sqrt())
                        .mul(gradient);
                    var newAccumulatedUpdate = accumulatedUpdate.mul(_this.rho).add(updates.square().mul(1 - _this.rho));
                    accumulatedGrad.assign(newAccumulatedGrad);
                    accumulatedUpdate.assign(newAccumulatedUpdate);
                    var newValue = updates.mul(-_this.learningRate).add(value);
                    value.assign(newValue);
                });
            });
            this.incrementIterations();
        };
        AdadeltaOptimizer.prototype.dispose = function () {
            if (this.accumulatedUpdates != null) {
                dispose(this.accumulatedGrads.map(function (v) { return v.variable; }));
                dispose(this.accumulatedUpdates.map(function (v) { return v.variable; }));
            }
        };
        AdadeltaOptimizer.prototype.getWeights = function () {
            return __awaiter(this, void 0, void 0, function () {
                var variables;
                return __generator(this, function (_a) {
                    switch (_a.label) {
                        case 0:
                            variables = this.accumulatedGrads.concat(this.accumulatedUpdates);
                            return [4 /*yield*/, this.saveIterations()];
                        case 1: return [2 /*return*/, [_a.sent()].concat(variables.map(function (v) { return ({ name: v.originalName, tensor: v.variable }); }))];
                    }
                });
            });
        };
        AdadeltaOptimizer.prototype.setWeights = function (weightValues) {
            return __awaiter(this, void 0, void 0, function () {
                var variableCount, trainable;
                return __generator(this, function (_a) {
                    switch (_a.label) {
                        case 0: return [4 /*yield*/, this.extractIterations(weightValues)];
                        case 1:
                            weightValues = _a.sent();
                            variableCount = weightValues.length / 2;
                            trainable = false;
                            this.accumulatedGrads =
                                weightValues.slice(0, variableCount).map(function (v) { return ({
                                    originalName: v.name,
                                    variable: v.tensor.variable(trainable)
                                }); });
                            this.accumulatedUpdates =
                                weightValues.slice(variableCount, variableCount * 2)
                                    .map(function (v) { return ({
                                    originalName: v.name,
                                    variable: v.tensor.variable(trainable)
                                }); });
                            return [2 /*return*/];
                    }
                });
            });
        };
        AdadeltaOptimizer.prototype.getConfig = function () {
            return {
                'learningRate': this.learningRate,
                'rho': this.rho,
                'epsilon': this.epsilon
            };
        };
        /** @nocollapse */
        AdadeltaOptimizer.fromConfig = function (cls, config) {
            return new cls(config['learningRate'], config['rho'], config['epsilon']);
        };
        /** @nocollapse */
        AdadeltaOptimizer.className = 'Adadelta'; // Name matters for Python compatibility.
        return AdadeltaOptimizer;
    }(Optimizer));
    registerClass(AdadeltaOptimizer);

    /**
     * @license
     * Copyright 2018 Google 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.
     * =============================================================================
     */
    /** @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 = accumulatedGrad.add(gradient.square());
                    accumulatedGrad.assign(newAccumulatedGrad);
                    var newValue = gradient
                        .div(newAccumulatedGrad.add(ENGINE.backend.epsilon()).sqrt())
                        .mul(-_this.learningRate)
                        .add(value);
                    value.assign(newValue);
                });
            });
            this.incrementIterations();
        };
        AdagradOptimizer.prototype.dispose = function () {
            if (this.accumulatedGrads != null) {
                dispose(this.accumulatedGrads.map(function (v) { return v.variable; }));
            }
        };
        AdagradOptimizer.prototype.getWeights = function () {
            return __awaiter(this, void 0, void 0, function () {
                return __generator(this, function (_a) {
                    switch (_a.label) {
                        case 0: return [4 /*yield*/, this.saveIterations()];
                        case 1: 
                        // Order matters for Python compatibility.
                        return [2 /*return*/, [_a.sent()].concat(this.accumulatedGrads.map(function (v) { return ({ name: v.originalName, tensor: v.variable }); }))];
                    }
                });
            });
        };
        AdagradOptimizer.prototype.setWeights = function (weightValues) {
            return __awaiter(this, void 0, void 0, function () {
                var trainable;
                return __generator(this, function (_a) {
                    switch (_a.label) {
                        case 0: return [4 /*yield*/, this.extractIterations(weightValues)];
                        case 1:
                            weightValues = _a.sent();
                            trainable = false;
                            this.accumulatedGrads = weightValues.map(function (v) { return ({ originalName: v.name, variable: v.tensor.variable(trainable) }); });
                            return [2 /*return*/];
                    }
                });
            });
        };
        AdagradOptimizer.prototype.getConfig = function () {
            return {
                'learningRate': this.learningRate,
                'initialAccumulatorValue': this.initialAccumulatorValue,
            };
        };
        /** @nocollapse */
        AdagradOptimizer.fromConfig = function (cls, config) {
            return new cls(config['learningRate'], config['initialAccumulatorValue']);
        };
        /** @nocollapse */
        AdagradOptimizer.className = 'Adagrad'; // Note: Name matters for Python compatibility.
        return AdagradOptimizer;
    }(Optimizer));
    registerClass(AdagradOptimizer);

    /**
     * @license
     * Copyright 2018 Google 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.
     * =============================================================================
     */
    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 = firstMoment.mul(_this.beta1).add(gradient.mul(1 - _this.beta1));
                    var newSecondMoment = secondMoment.mul(_this.beta2)
                        .add(gradient.square().mul(1 - _this.beta2));
                    var biasCorrectedFirstMoment = newFirstMoment.div(oneMinusAccBeta1);
                    var biasCorrectedSecondMoment = newSecondMoment.div(oneMinusAccBeta2);
                    firstMoment.assign(newFirstMoment);
                    secondMoment.assign(newSecondMoment);
                    var newValue = biasCorrectedFirstMoment
                        .div(biasCorrectedSecondMoment.sqrt().add(_this.epsilon))
                        .mul(-_this.learningRate)
                        .add(value);
                    value.assign(newValue);
                });
                _this.accBeta1.assign(_this.accBeta1.mul(_this.beta1));
                _this.accBeta2.assign(_this.accBeta2.mul(_this.beta2));
            });
            this.incrementIterations();
        };
        AdamOptimizer.prototype.dispose = function () {
            this.accBeta1.dispose();
            this.accBeta2.dispose();
            if (this.accumulatedFirstMoment != null) {
                dispose(this.accumulatedFirstMoment.map(function (v) { return v.variable; }));
            }
            if (this.accumulatedSecondMoment != null) {
                dispose(this.accumulatedSecondMoment.map(function (v) { return v.variable; }));
            }
        };
        AdamOptimizer.prototype.getWeights = function () {
            return __awaiter(this, void 0, void 0, function () {
                var variables;
                return __generator(this, function (_a) {
                    switch (_a.label) {
                        case 0:
                            variables = this.accumulatedFirstMoment.concat(this.accumulatedSecondMoment);
                            return [4 /*yield*/, this.saveIterations()];
                        case 1: return [2 /*return*/, [_a.sent()].concat(variables.map(function (v) { return ({ name: v.originalName, tensor: v.variable }); }))];
                    }
                });
            });
        };
        AdamOptimizer.prototype.setWeights = function (weightValues) {
            return __awaiter(this, void 0, void 0, function () {
                var variableCount, trainable;
                var _this = this;
                return __generator(this, function (_a) {
                    switch (_a.label) {
                        case 0: return [4 /*yield*/, this.extractIterations(weightValues)];
                        case 1:
                            weightValues = _a.sent();
                            tidy(function () {
                                _this.accBeta1.assign(pow(_this.beta1, _this.iterations_ + 1));
                                _this.accBeta2.assign(pow(_this.beta2, _this.iterations_ + 1));
                            });
                            variableCount = weightValues.length / 2;
                            trainable = false;
                            this.accumulatedFirstMoment =
                                weightValues.slice(0, variableCount).map(function (v) { return ({
                                    originalName: v.name,
                                    variable: v.tensor.variable(trainable)
                                }); });
                            this.accumulatedSecondMoment =
                                weightValues.slice(variableCount, variableCount * 2)
                                    .map(function (v) { return ({
                                    originalName: v.name,
                                    variable: v.tensor.variable(trainable)
                                }); });
                            return [2 /*return*/];
                    }
                });
            });
        };
        AdamOptimizer.prototype.getConfig = function () {
            return {
                'learningRate': this.learningRate,
                'beta1': this.beta1,
                'beta2': this.beta2,
                'epsilon': this.epsilon,
            };
        };
        /** @nocollapse */
        AdamOptimizer.fromConfig = function (cls, config) {
            return new cls(config['learningRate'], config['beta1'], config['beta2'], config['epsilon']);
        };
        /** @nocollapse */
        AdamOptimizer.className = 'Adam'; // Note: Name matters for Python compatibility.
        return AdamOptimizer;
    }(Optimizer));
    registerClass(AdamOptimizer);

    /**
    * @license
    * Copyright 2018 Google 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.
    * =============================================================================
    */
    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, _this.iteration.mul(_this.decay).add(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 = firstMoment.mul(_this.beta1).add(gradient.mul(1 - _this.beta1));
                    var ut0 = weightedInfNorm.mul(_this.beta2);
                    var ut1 = gradient.abs();
                    var newWeightedInfNorm = ut0.maximum(ut1);
                    firstMoment.assign(newFirstMoment);
                    weightedInfNorm.assign(newWeightedInfNorm);
                    var newValue = lr.div(oneMinusAccBeta1)
                        .mul(newFirstMoment.div(newWeightedInfNorm.add(_this.epsilon)))
                        .add(value);
                    value.assign(newValue);
                });
                _this.iteration.assign(_this.iteration.add(1));
                _this.accBeta1.assign(_this.accBeta1.mul(_this.beta1));
            });
            this.incrementIterations();
        };
        AdamaxOptimizer.prototype.dispose = function () {
            this.accBeta1.dispose();
            this.iteration.dispose();
            if (this.accumulatedFirstMoment != null) {
                dispose(this.accumulatedFirstMoment.map(function (v) { return v.variable; }));
            }
            if (this.accumulatedWeightedInfNorm != null) {
                dispose(this.accumulatedWeightedInfNorm.map(function (v) { return v.variable; }));
            }
        };
        AdamaxOptimizer.prototype.getWeights = function () {
            return __awaiter(this, void 0, void 0, function () {
                return __generator(this, function (_a) {
                    throw new Error('getWeights() is not implemented for Adamax yet.');
                });
            });
        };
        AdamaxOptimizer.prototype.setWeights = function (weightValues) {
            return __awaiter(this, void 0, void 0, function () {
                return __generator(this, function (_a) {
                    throw new Error('setWeights() is not implemented for Adamax yet.');
                });
            });
        };
        AdamaxOptimizer.prototype.getConfig = function () {
            return {
                'learningRate': this.learningRate,
                'beta1': this.beta1,
                'beta2': this.beta2,
                'epsilon': this.epsilon,
                'decay': this.decay
            };
        };
        /** @nocollapse */
        AdamaxOptimizer.fromConfig = function (cls, config) {
            return new cls(config['learningRate'], config['beta1'], config['beta2'], config['epsilon'], config['decay']);
        };
        /** @nocollapse */
        AdamaxOptimizer.className = 'Adamax'; // Note: Name matters for Python compatbility.
        return AdamaxOptimizer;
    }(Optimizer));
    registerClass(AdamaxOptimizer);

    /**
     * @license
     * Copyright 2018 Google 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.
     * =============================================================================
     */
    /** @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 = _this.c.mul(gradient).add(value);
                    value.assign(newValue);
                });
            });
            this.incrementIterations();
        };
        /**
         * Sets the learning rate of the optimizer.
         */
        SGDOptimizer.prototype.setLearningRate = function (learningRate) {
            this.learningRate = learningRate;
            if (this.c != null) {
                this.c.dispose();
            }
            this.c = keep(scalar(-learningRate));
        };
        SGDOptimizer.prototype.dispose = function () {
            this.c.dispose();
        };
        SGDOptimizer.prototype.getWeights = function () {
            return __awaiter(this, void 0, void 0, function () {
                return __generator(this, function (_a) {
                    switch (_a.label) {
                        case 0: return [4 /*yield*/, this.saveIterations()];
                        case 1: return [2 /*return*/, [_a.sent()]];
                    }
                });
            });
        };
        SGDOptimizer.prototype.setWeights = function (weightValues) {
            return __awaiter(this, void 0, void 0, function () {
                return __generator(this, function (_a) {
                    switch (_a.label) {
                        case 0: return [4 /*yield*/, this.extractIterations(weightValues)];
                        case 1:
                            weightValues = _a.sent();
                            if (weightValues.length !== 0) {
                                throw new Error('SGD optimizer does not have settable weights.');
                            }
                            return [2 /*return*/];
                    }
                });
            });
        };
        SGDOptimizer.prototype.getConfig = function () {
            return { 'learningRate': this.learningRate };
        };
        /** @nocollapse */
        SGDOptimizer.fromConfig = function (cls, config) {
            return new cls(config['learningRate']);
        };
        /** @nocollapse */
        SGDOptimizer.className = 'SGD'; // Note: Name matters for Python compatibility.
        return SGDOptimizer;
    }(Optimizer));
    registerClass(SGDOptimizer);

    /**
     * @license
     * Copyright 2018 Google 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.
     * =============================================================================
     */
    /** @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 = _this.m.mul(accumulation).add(gradient);
                    if (_this.useNesterov) {
                        newValue =
                            _this.c.mul(gradient.add(newAccumulation.mul(_this.m))).add(value);
                    }
                    else {
                        newValue = _this.c.mul(newAccumulation).add(value);
                    }
                    accumulation.assign(newAccumulation);
                    value.assign(newValue);
                });
            });
            this.incrementIterations();
        };
        MomentumOptimizer.prototype.dispose = function () {
            this.m.dispose();
            if (this.accumulations != null) {
                dispose(this.accumulations.map(function (v) { return v.variable; }));
            }
        };
        /**
         * Sets the momentum of the optimizer.
         *
         * @param momentum
         */
        MomentumOptimizer.prototype.setMomentum = function (momentum) {
            this.momentum = momentum;
        };
        MomentumOptimizer.prototype.getWeights = function () {
            return __awaiter(this, void 0, void 0, function () {
                return __generator(this, function (_a) {
                    switch (_a.label) {
                        case 0: return [4 /*yield*/, this.saveIterations()];
                        case 1: 
                        // Order matters for Python compatibility.
                        return [2 /*return*/, [_a.sent()].concat(this.accumulations.map(function (v) { return ({ name: v.originalName, tensor: v.variable }); }))];
                    }
                });
            });
        };
        MomentumOptimizer.prototype.setWeights = function (weightValues) {
            return __awaiter(this, void 0, void 0, function () {
                var trainable;
                return __generator(this, function (_a) {
                    switch (_a.label) {
                        case 0: return [4 /*yield*/, this.extractIterations(weightValues)];
                        case 1:
                            weightValues = _a.sent();
                            trainable = false;
                            this.accumulations = weightValues.map(function (v) { return ({ originalName: v.name, variable: v.tensor.variable(trainable) }); });
                            return [2 /*return*/];
                    }
                });
            });
        };
        MomentumOptimizer.prototype.getConfig = function () {
            return {
                'learningRate': this.learningRate,
                'momentum': this.momentum,
                'useNesterov': this.useNesterov
            };
        };
        /** @nocollapse */
        MomentumOptimizer.fromConfig = function (cls, config) {
            return new cls(config['learningRate'], config['momentum'], config['useNesterov']);
        };
        /** @nocollapse */
        MomentumOptimizer.className = 'Momentum'; // Name matters for Python compatibility.
        return MomentumOptimizer;
    }(SGDOptimizer));
    registerClass(MomentumOptimizer);

    /**
     * @license
     * Copyright 2018 Google 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.
     * =============================================================================
     */
    /** @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 = accumulatedMeanSquare.mul(_this.decay)
                        .add(gradient.square().mul(1 - _this.decay));
                    if (_this.centered) {
                        var accumulatedMeanGrad = _this.accumulatedMeanGrads[i].variable;
                        // Centered gradient
                        var newAccumulatedMeanGrad = accumulatedMeanGrad.mul(_this.decay)
                            .add(gradient.mul(1 - _this.decay));
                        var newAccumulatedMoments = accumulatedMoments.mul(_this.momentum)
                            .add(gradient.mul(_this.learningRate)
                            .div(newAccumulatedMeanSquare
                            .sub(newAccumulatedMeanGrad.square().add(_this.epsilon))
                            .sqrt()));
                        accumulatedMeanSquare.assign(newAccumulatedMeanSquare);
                        accumulatedMeanGrad.assign(newAccumulatedMeanGrad);
                        accumulatedMoments.assign(newAccumulatedMoments);
                        var newValue = value.sub(newAccumulatedMoments);
                        value.assign(newValue);
                    }
                    else {
                        // Plain gradient
                        var newAccumulatedMeanSquare_1 = accumulatedMeanSquare.mul(_this.decay)
                            .add(gradient.square().mul(1 - _this.decay));
                        var newAccumulatedMoments = accumulatedMoments.mul(_this.momentum)
                            .add(gradient.mul(_this.learningRate)
                            .div(newAccumulatedMeanSquare_1.add(_this.epsilon)
                            .sqrt()));
                        accumulatedMeanSquare.assign(newAccumulatedMeanSquare_1);
                        accumulatedMoments.assign(newAccumulatedMoments);
                        var newValue = value.sub(newAccumulatedMoments);
                        value.assign(newValue);
                    }
                });
            });
            this.incrementIterations();
        };
        RMSPropOptimizer.prototype.dispose = function () {
            if (this.accumulatedMeanSquares != null) {
                dispose(this.accumulatedMeanSquares.map(function (v) { return v.variable; }));
            }
            if (this.accumulatedMeanGrads != null && this.centered) {
                dispose(this.accumulatedMeanGrads.map(function (v) { return v.variable; }));
            }
            if (this.accumulatedMoments != null) {
                dispose(this.accumulatedMoments.map(function (v) { return v.variable; }));
            }
        };
        RMSPropOptimizer.prototype.getWeights = function () {
            return __awaiter(this, void 0, void 0, function () {
                var variables;
                return __generator(this, function (_a) {
                    switch (_a.label) {
                        case 0:
                            variables = this.accumulatedMeanSquares.concat(this.accumulatedMoments);
                            if (this.centered) {
                                variables.push.apply(variables, this.accumulatedMeanGrads);
                            }
                            return [4 /*yield*/, this.saveIterations()];
                        case 1: return [2 /*return*/, [_a.sent()].concat(variables.map(function (v) { return ({ name: v.originalName, tensor: v.variable }); }))];
                    }
                });
            });
        };
        RMSPropOptimizer.prototype.setWeights = function (weightValues) {
            return __awaiter(this, void 0, void 0, function () {
                var variableCount, trainable;
                return __generator(this, function (_a) {
                    switch (_a.label) {
                        case 0: return [4 /*yield*/, this.extractIterations(weightValues)];
                        case 1:
                            weightValues = _a.sent();
                            variableCount = this.centered ? weightValues.length / 3 : weightValues.length / 2;
                            trainable = false;
                            this.accumulatedMeanSquares =
                                weightValues.slice(0, variableCount).map(function (v) { return ({
                                    originalName: v.name,
                                    variable: v.tensor.variable(trainable)
                                }); });
                            this.accumulatedMoments =
                                weightValues.slice(variableCount, variableCount * 2)
                                    .map(function (v) { return ({
                                    originalName: v.name,
                                    variable: v.tensor.variable(trainable)
                                }); });
                            if (this.centered) {
                                this.accumulatedMeanGrads =
                                    weightValues.slice(variableCount * 2, variableCount * 3)
                                        .map(function (v) { return ({
                                        originalName: v.name,
                                        variable: v.tensor.variable(trainable)
                                    }); });
                            }
                            return [2 /*return*/];
                    }
                });
            });
        };
        RMSPropOptimizer.prototype.getConfig = function () {
            return {
                'learningRate': this.learningRate,
                'decay': this.decay,
                'momentum': this.momentum,
                'epsilon': this.epsilon,
                'centered': this.centered
            };
        };
        /** @nocollapse */
        RMSPropOptimizer.fromConfig = function (cls, config) {
            return new cls(config['learningRate'], config['decay'], config['momentum'], config['epsilon'], config['centered']);
        };
        /** @nocollapse */
        RMSPropOptimizer.className = 'RMSProp'; // Note: Name matters for Python compatibility.
        return RMSPropOptimizer;
    }(Optimizer));
    registerClass(RMSPropOptimizer);

    /**
     * @license
     * Copyright 2018 Google 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.
     * =============================================================================
     */
    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 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.
     * =============================================================================
     */
    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 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.
     * =============================================================================
     */
    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 2020 Google LLC. All Rights Reserved.
     * Licensed under the Apache License, Version 2.0 (the "License");
     * you may not use this file except in compliance with the License.
     * You may obtain a copy of the License at
     *
     * http://www.apache.org/licenses/LICENSE-2.0
     *
     * Unless required by applicable law or agreed to in writing, software
     * distributed under the License is distributed on an "AS IS" BASIS,
     * WITHOUT 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.prototype.squaredDifference = function (b) {
        return squaredDifference(this, b);
    };

    /**
     * @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.
     * =============================================================================
     */

    /**
     * @license
     * Copyright 2017 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.
     * =============================================================================
     */
    setOpHandler(ops);

    exports.AdadeltaOptimizer = AdadeltaOptimizer;
    exports.AdagradOptimizer = AdagradOptimizer;
    exports.AdamOptimizer = AdamOptimizer;
    exports.AdamaxOptimizer = AdamaxOptimizer;
    exports.DataStorage = DataStorage;
    exports.Environment = Environment;
    exports.KernelBackend = KernelBackend;
    exports.MomentumOptimizer = MomentumOptimizer;
    exports.Optimizer = Optimizer;
    exports.RMSPropOptimizer = RMSPropOptimizer;
    exports.SGDOptimizer = SGDOptimizer;
    exports.Tensor = Tensor;
    exports.TensorBuffer = TensorBuffer;
    exports.Variable = Variable;
    exports.abs = abs;
    exports.acos = acos;
    exports.acosh = acosh;
    exports.add = add;
    exports.addN = addN;
    exports.addStrict = addStrict;
    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.batchNormalization = batchNormalization;
    exports.batchNormalization2d = batchNormalization2d;
    exports.batchNormalization3d = batchNormalization3d;
    exports.batchNormalization4d = batchNormalization4d;
    exports.batchToSpaceND = batchToSpaceND;
    exports.booleanMaskAsync = booleanMaskAsync;
    exports.broadcastTo = broadcastTo;
    exports.browser = browser;
    exports.buffer = buffer;
    exports.cast = cast;
    exports.ceil = ceil;
    exports.clipByValue = clipByValue;
    exports.clone = clone;
    exports.complex = complex;
    exports.concat = concat;
    exports.concat1d = concat1d;
    exports.concat2d = concat2d;
    exports.concat3d = concat3d;
    exports.concat4d = concat4d;
    exports.conv1d = conv1d;
    exports.conv2d = conv2d;
    exports.conv2dTranspose = conv2dTranspose;
    exports.conv3d = conv3d;
    exports.conv3dTranspose = conv3dTranspose;
    exports.cos = cos;
    exports.cosh = cosh;
    exports.cumsum = cumsum;
    exports.customGrad = customGrad;
    exports.deprecationWarn = deprecationWarn;
    exports.depthToSpace = depthToSpace;
    exports.depthwiseConv2d = depthwiseConv2d;
    exports.diag = diag;
    exports.disableDeprecationWarnings = disableDeprecationWarnings;
    exports.dispose = dispose;
    exports.disposeVariables = disposeVariables;
    exports.div = div;
    exports.divNoNan = divNoNan;
    exports.divStrict = divStrict;
    exports.dot = dot;
    exports.dropout = dropout;
    exports.elu = elu;
    exports.enableDebugMode = enableDebugMode;
    exports.enableProdMode = enableProdMode;
    exports.engine = engine;
    exports.env = env;
    exports.equal = equal;
    exports.equalStrict = equalStrict;
    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.frame = frame;
    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.greaterEqualStrict = greaterEqualStrict;
    exports.greaterStrict = greaterStrict;
    exports.hammingWindow = hammingWindow;
    exports.hannWindow = hannWindow;
    exports.ifft = ifft;
    exports.imag = imag;
    exports.image = image_ops;
    exports.inTopKAsync = inTopKAsync;
    exports.io = io;
    exports.irfft = irfft;
    exports.isFinite = isFinite$1;
    exports.isInf = isInf;
    exports.isNaN = isNaN$1;
    exports.keep = keep;
    exports.leakyRelu = leakyRelu;
    exports.less = less;
    exports.lessEqual = lessEqual;
    exports.lessEqualStrict = lessEqualStrict;
    exports.lessStrict = lessStrict;
    exports.linalg = linalg_ops;
    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 = loss_ops;
    exports.matMul = matMul;
    exports.math = math;
    exports.max = max;
    exports.maxPool = maxPool;
    exports.maxPool3d = maxPool3d;
    exports.maximum = maximum;
    exports.maximumStrict = maximumStrict;
    exports.mean = mean;
    exports.memory = memory;
    exports.min = min;
    exports.minimum = minimum;
    exports.minimumStrict = minimumStrict;
    exports.mod = mod;
    exports.modStrict = modStrict;
    exports.moments = moments;
    exports.movingAverage = movingAverage;
    exports.mul = mul;
    exports.mulStrict = mulStrict;
    exports.multiRNNCell = multiRNNCell;
    exports.multinomial = multinomial;
    exports.neg = neg;
    exports.nextFrame = nextFrame;
    exports.norm = norm;
    exports.notEqual = notEqual;
    exports.notEqualStrict = notEqualStrict;
    exports.oneHot = oneHot;
    exports.ones = ones$1;
    exports.onesLike = onesLike;
    exports.op = op;
    exports.outerProduct = outerProduct;
    exports.pad = pad;
    exports.pad1d = pad1d;
    exports.pad2d = pad2d;
    exports.pad3d = pad3d;
    exports.pad4d = pad4d;
    exports.pool = pool;
    exports.pow = pow;
    exports.powStrict = powStrict;
    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_ops;
    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.sparseToDense = sparseToDense;
    exports.spectral = spectral_ops;
    exports.split = split;
    exports.sqrt = sqrt;
    exports.square = square;
    exports.squaredDifference = squaredDifference;
    exports.squaredDifferenceStrict = squaredDifferenceStrict;
    exports.squeeze = squeeze;
    exports.stack = stack;
    exports.step = step;
    exports.stft = stft;
    exports.stridedSlice = stridedSlice;
    exports.sub = sub;
    exports.subStrict = subStrict;
    exports.sum = sum$1;
    exports.sumOutType = sumOutType;
    exports.tan = tan;
    exports.tanh = tanh$1;
    exports.tensor = tensor;
    exports.tensor1d = tensor1d;
    exports.tensor2d = tensor2d;
    exports.tensor3d = tensor3d;
    exports.tensor4d = tensor4d;
    exports.tensor5d = tensor5d;
    exports.tensor6d = tensor6d;
    exports.tensor_util = tensor_util;
    exports.test_util = test_util;
    exports.tidy = tidy;
    exports.tile = tile;
    exports.time = time;
    exports.topk = topk;
    exports.train = train;
    exports.transpose = transpose;
    exports.truncatedNormal = truncatedNormal;
    exports.unregisterGradient = unregisterGradient;
    exports.unregisterKernel = unregisterKernel;
    exports.unsortedSegmentSum = unsortedSegmentSum;
    exports.unstack = unstack;
    exports.util = util;
    exports.valueAndGrad = valueAndGrad;
    exports.valueAndGrads = valueAndGrads;
    exports.variable = variable;
    exports.variableGrads = variableGrads;
    exports.version_core = version;
    exports.webgl = webgl;
    exports.where = where;
    exports.whereAsync = whereAsync;
    exports.zeros = zeros;
    exports.zerosLike = zerosLike;

    Object.defineProperty(exports, '__esModule', { value: true });

}));
//# sourceMappingURL=tf-core.js.map