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More information: https://www.jsdelivr.com/using-sri-with-dynamic-files\n */\n","(function (global, factory) {\n            typeof exports === 'object' && typeof module !== 'undefined' ? factory(exports) :\n            typeof define === 'function' && define.amd ? define(['exports'], factory) :\n            (factory((global.tensorscript = {})));\n}(this, (function (exports) { 'use strict';\n\n            var global$1 = (typeof global !== \"undefined\" ? global :\n                        typeof self !== \"undefined\" ? self :\n                        typeof window !== \"undefined\" ? window : {});\n\n            // shim for using process in browser\n            // based off https://github.com/defunctzombie/node-process/blob/master/browser.js\n\n            function defaultSetTimout() {\n                throw new Error('setTimeout has not been defined');\n            }\n            function defaultClearTimeout () {\n                throw new Error('clearTimeout has not been defined');\n   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                 return cachedClearTimeout.call(this, marker);\n                    }\n                }\n\n\n\n            }\n            var queue = [];\n            var draining = false;\n            var currentQueue;\n            var queueIndex = -1;\n\n            function cleanUpNextTick() {\n                if (!draining || !currentQueue) {\n                    return;\n                }\n                draining = false;\n                if (currentQueue.length) {\n                    queue = currentQueue.concat(queue);\n                } else {\n                    queueIndex = -1;\n                }\n                if (queue.length) {\n                    drainQueue();\n                }\n            }\n\n            function drainQueue() {\n                if (draining) {\n                    return;\n                }\n                var timeout = runTimeout(cleanUpNextTick);\n                draining = true;\n\n                var len = queue.length;\n                while(len) {\n                    currentQueue = queue;\n                    queue = [];\n                    while (++queueIndex < len) {\n                        if (currentQueue) {\n                            currentQueue[queueIndex].run();\n                        }\n                    }\n                    queueIndex = -1;\n                    len = queue.length;\n                }\n                currentQueue = null;\n                draining = false;\n                runClearTimeout(timeout);\n            }\n            function nextTick(fun) {\n                var args = new Array(arguments.length - 1);\n                if (arguments.length > 1) {\n                    for (var i = 1; i < arguments.length; i++) {\n                        args[i - 1] = arguments[i];\n                    }\n                }\n                queue.push(new Item(fun, args));\n                if (queue.length === 1 && !draining) {\n                    runTimeout(drainQueue);\n                }\n 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supported');\n            }\n\n            function cwd () { return '/' }\n            function chdir (dir) {\n                throw new Error('process.chdir is not supported');\n            }function umask() { return 0; }\n\n            // from https://github.com/kumavis/browser-process-hrtime/blob/master/index.js\n            var performance$1 = global$1.performance || {};\n            var performanceNow =\n              performance$1.now        ||\n              performance$1.mozNow     ||\n              performance$1.msNow      ||\n              performance$1.oNow       ||\n              performance$1.webkitNow  ||\n              function(){ return (new Date()).getTime() };\n\n            // generate timestamp or delta\n            // see http://nodejs.org/api/process.html#process_process_hrtime\n            function hrtime(previousTimestamp){\n              var clocktime = performanceNow.call(performance$1)*1e-3;\n              var seconds = Math.floor(clocktime);\n              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off,\n              removeListener: removeListener,\n              removeAllListeners: removeAllListeners,\n              emit: emit,\n              binding: binding,\n              cwd: cwd,\n              chdir: chdir,\n              umask: umask,\n              hrtime: hrtime,\n              platform: platform,\n              release: release,\n              config: config,\n              uptime: uptime\n            };\n\n            var lookup = [];\n            var revLookup = [];\n            var Arr = typeof Uint8Array !== 'undefined' ? Uint8Array : Array;\n            var inited = false;\n            function init () {\n              inited = true;\n              var code = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/';\n              for (var i = 0, len = code.length; i < len; ++i) {\n                lookup[i] = code[i];\n                revLookup[code.charCodeAt(i)] = i;\n              }\n\n              revLookup['-'.charCodeAt(0)] = 62;\n              revLookup['_'.charCodeAt(0)] = 63;\n            }\n\n            function toByteArray (b64) {\n              if (!inited) {\n                init();\n              }\n              var i, j, l, tmp, placeHolders, arr;\n              var len = b64.length;\n\n              if (len % 4 > 0) {\n                throw new Error('Invalid string. Length must be a multiple of 4')\n              }\n\n              // the number of equal signs (place holders)\n              // if there are two placeholders, than the two characters before it\n              // represent one byte\n              // if there is only one, then the three characters before it represent 2 bytes\n              // this is just a cheap hack to not do indexOf twice\n              placeHolders = b64[len - 2] === '=' ? 2 : b64[len - 1] === '=' ? 1 : 0;\n\n              // base64 is 4/3 + up to two characters of the original data\n              arr = new Arr(len * 3 / 4 - placeHolders);\n\n              // if there are placeholders, only get up to the last complete 4 chars\n              l = placeHolders > 0 ? len - 4 : len;\n\n              var L = 0;\n\n              for (i = 0, j = 0; i < l; i += 4, j += 3) {\n                tmp = (revLookup[b64.charCodeAt(i)] << 18) | (revLookup[b64.charCodeAt(i + 1)] << 12) | (revLookup[b64.charCodeAt(i + 2)] << 6) | revLookup[b64.charCodeAt(i + 3)];\n                arr[L++] = (tmp >> 16) & 0xFF;\n                arr[L++] = (tmp >> 8) & 0xFF;\n                arr[L++] = tmp & 0xFF;\n              }\n\n              if (placeHolders === 2) {\n                tmp = (revLookup[b64.charCodeAt(i)] << 2) | (revLookup[b64.charCodeAt(i + 1)] >> 4);\n                arr[L++] = tmp & 0xFF;\n              } else if (placeHolders === 1) {\n                tmp = (revLookup[b64.charCodeAt(i)] << 10) | (revLookup[b64.charCodeAt(i + 1)] << 4) | (revLookup[b64.charCodeAt(i + 2)] >> 2);\n                arr[L++] = (tmp >> 8) & 0xFF;\n                arr[L++] = tmp & 0xFF;\n              }\n\n              return arr\n            }\n\n            function tripletToBase64 (num) {\n              return lookup[num >> 18 & 0x3F] + lookup[num >> 12 & 0x3F] + lookup[num >> 6 & 0x3F] + lookup[num & 0x3F]\n            }\n\n            function encodeChunk (uint8, start, end) {\n              var tmp;\n              var output = [];\n              for (var i = start; i < end; i += 3) {\n                tmp = (uint8[i] << 16) + (uint8[i + 1] << 8) + (uint8[i + 2]);\n                output.push(tripletToBase64(tmp));\n              }\n              return output.join('')\n            }\n\n            function fromByteArray (uint8) {\n              if (!inited) {\n                init();\n              }\n              var tmp;\n              var len = uint8.length;\n              var extraBytes = len % 3; // if we have 1 byte left, pad 2 bytes\n              var output = '';\n              var parts = [];\n              var maxChunkLength = 16383; // must be multiple of 3\n\n              // go through the array every three bytes, we'll deal with trailing stuff later\n              for (var i = 0, len2 = len - extraBytes; i < len2; i += maxChunkLength) {\n                parts.push(encodeChunk(uint8, i, (i + maxChunkLength) > len2 ? len2 : (i + maxChunkLength)));\n              }\n\n              // pad the end with zeros, but make sure to not forget the extra bytes\n              if (extraBytes === 1) {\n                tmp = uint8[len - 1];\n                output += lookup[tmp >> 2];\n                output += lookup[(tmp << 4) & 0x3F];\n                output += '==';\n              } else if (extraBytes === 2) {\n                tmp = (uint8[len - 2] << 8) + (uint8[len - 1]);\n                output += lookup[tmp >> 10];\n                output += lookup[(tmp >> 4) & 0x3F];\n                output += lookup[(tmp << 2) & 0x3F];\n                output += '=';\n              }\n\n              parts.push(output);\n\n              return parts.join('')\n            }\n\n            function read (buffer, offset, isLE, mLen, nBytes) {\n              var e, m;\n              var eLen = nBytes * 8 - mLen - 1;\n              var eMax = (1 << eLen) - 1;\n              var eBias = eMax >> 1;\n              var nBits = -7;\n              var i = isLE ? (nBytes - 1) : 0;\n              var d = isLE ? -1 : 1;\n              var s = buffer[offset + i];\n\n              i += d;\n\n              e = s & ((1 << (-nBits)) - 1);\n              s >>= (-nBits);\n              nBits += eLen;\n              for (; nBits > 0; e = e * 256 + buffer[offset + i], i += d, nBits -= 8) {}\n\n              m = e & ((1 << (-nBits)) - 1);\n              e >>= (-nBits);\n              nBits += mLen;\n              for (; nBits > 0; m = m * 256 + buffer[offset + i], i += d, nBits -= 8) {}\n\n              if (e === 0) {\n                e = 1 - eBias;\n              } else if (e === eMax) {\n                return m ? NaN : ((s ? -1 : 1) * Infinity)\n              } else {\n                m = m + Math.pow(2, mLen);\n                e = e - eBias;\n              }\n              return (s ? -1 : 1) * m * Math.pow(2, e - mLen)\n            }\n\n            function write (buffer, value, offset, isLE, mLen, nBytes) {\n              var e, m, c;\n              var eLen = nBytes * 8 - mLen - 1;\n              var eMax = (1 << eLen) - 1;\n              var eBias = eMax >> 1;\n              var rt = (mLen === 23 ? Math.pow(2, -24) - Math.pow(2, -77) : 0);\n              var i = isLE ? 0 : (nBytes - 1);\n              var d = isLE ? 1 : -1;\n              var s = value < 0 || (value === 0 && 1 / value < 0) ? 1 : 0;\n\n              value = Math.abs(value);\n\n              if (isNaN(value) || value === Infinity) {\n                m = isNaN(value) ? 1 : 0;\n                e = eMax;\n              } else {\n                e = Math.floor(Math.log(value) / Math.LN2);\n                if (value * (c = Math.pow(2, -e)) < 1) {\n                  e--;\n                  c *= 2;\n                }\n                if (e + eBias >= 1) {\n                  value += rt / c;\n                } else {\n                  value += rt * Math.pow(2, 1 - eBias);\n                }\n                if (value * c >= 2) {\n                  e++;\n                  c /= 2;\n                }\n\n                if (e + eBias >= eMax) {\n                  m = 0;\n                  e = eMax;\n                } else if (e + eBias >= 1) {\n                  m = (value * c - 1) * Math.pow(2, mLen);\n                  e = e + eBias;\n                } else {\n                  m = value * Math.pow(2, eBias - 1) * Math.pow(2, mLen);\n                  e = 0;\n                }\n              }\n\n              for (; mLen >= 8; buffer[offset + i] = m & 0xff, i += d, m /= 256, mLen -= 8) {}\n\n              e = (e << mLen) | m;\n              eLen += mLen;\n              for (; eLen > 0; buffer[offset + i] = e & 0xff, i += d, e /= 256, eLen -= 8) {}\n\n              buffer[offset + i - d] |= s * 128;\n            }\n\n            var toString = {}.toString;\n\n            var isArray = Array.isArray || function (arr) {\n              return toString.call(arr) == '[object Array]';\n            };\n\n            var INSPECT_MAX_BYTES = 50;\n\n            /**\n             * If `Buffer.TYPED_ARRAY_SUPPORT`:\n             *   === true    Use Uint8Array implementation (fastest)\n             *   === false   Use Object implementation (most compatible, even IE6)\n             *\n             * Browsers that support typed arrays are IE 10+, Firefox 4+, Chrome 7+, Safari 5.1+,\n             * Opera 11.6+, iOS 4.2+.\n             *\n             * Due to various browser bugs, sometimes the Object implementation will be used even\n             * when the browser supports typed arrays.\n             *\n             * Note:\n             *\n             *   - Firefox 4-29 lacks support for adding new properties to `Uint8Array` instances,\n             *     See: https://bugzilla.mozilla.org/show_bug.cgi?id=695438.\n             *\n             *   - Chrome 9-10 is missing the `TypedArray.prototype.subarray` function.\n             *\n             *   - IE10 has a broken `TypedArray.prototype.subarray` function which returns arrays of\n             *     incorrect length in some situations.\n\n             * We detect these buggy browsers and set `Buffer.TYPED_ARRAY_SUPPORT` to `false` so they\n             * get the Object implementation, which is slower but behaves correctly.\n             */\n            Buffer.TYPED_ARRAY_SUPPORT = global$1.TYPED_ARRAY_SUPPORT !== undefined\n              ? global$1.TYPED_ARRAY_SUPPORT\n              : true;\n\n            function kMaxLength () {\n              return Buffer.TYPED_ARRAY_SUPPORT\n                ? 0x7fffffff\n                : 0x3fffffff\n            }\n\n            function createBuffer (that, length) {\n              if (kMaxLength() < length) {\n                throw new RangeError('Invalid typed array length')\n              }\n              if (Buffer.TYPED_ARRAY_SUPPORT) {\n                // Return an augmented `Uint8Array` instance, for best performance\n                that = new Uint8Array(length);\n                that.__proto__ = Buffer.prototype;\n              } else {\n                // Fallback: Return an object instance of the Buffer class\n                if (that === null) {\n                  that = new Buffer(length);\n                }\n                that.length = length;\n              }\n\n              return that\n            }\n\n            /**\n             * The Buffer constructor returns instances of `Uint8Array` that have their\n             * prototype changed to `Buffer.prototype`. Furthermore, `Buffer` is a subclass of\n             * `Uint8Array`, so the returned instances will have all the node `Buffer` methods\n             * and the `Uint8Array` methods. Square bracket notation works as expected -- it\n             * returns a single octet.\n             *\n             * The `Uint8Array` prototype remains unmodified.\n             */\n\n            function Buffer (arg, encodingOrOffset, length) {\n              if (!Buffer.TYPED_ARRAY_SUPPORT && !(this instanceof Buffer)) {\n                return new Buffer(arg, encodingOrOffset, length)\n              }\n\n              // Common case.\n              if (typeof arg === 'number') {\n                if (typeof encodingOrOffset === 'string') {\n                  throw new Error(\n                    'If encoding is specified then the first argument must be a string'\n                  )\n                }\n                return allocUnsafe(this, arg)\n              }\n              return from(this, arg, encodingOrOffset, length)\n            }\n\n            Buffer.poolSize = 8192; // not used by this implementation\n\n            // TODO: Legacy, not needed anymore. Remove in next major version.\n            Buffer._augment = function (arr) {\n              arr.__proto__ = Buffer.prototype;\n              return arr\n            };\n\n            function from (that, value, encodingOrOffset, length) {\n              if (typeof value === 'number') {\n                throw new TypeError('\"value\" argument must not be a number')\n              }\n\n              if (typeof ArrayBuffer !== 'undefined' && value instanceof ArrayBuffer) {\n                return fromArrayBuffer(that, value, encodingOrOffset, length)\n              }\n\n              if (typeof value === 'string') {\n                return fromString(that, value, encodingOrOffset)\n              }\n\n              return fromObject(that, value)\n            }\n\n            /**\n             * Functionally equivalent to Buffer(arg, encoding) but throws a TypeError\n             * if value is a number.\n             * Buffer.from(str[, encoding])\n             * Buffer.from(array)\n             * Buffer.from(buffer)\n             * Buffer.from(arrayBuffer[, byteOffset[, length]])\n             **/\n            Buffer.from = function (value, encodingOrOffset, length) {\n              return from(null, value, encodingOrOffset, length)\n            };\n\n            if (Buffer.TYPED_ARRAY_SUPPORT) {\n              Buffer.prototype.__proto__ = Uint8Array.prototype;\n              Buffer.__proto__ = Uint8Array;\n            }\n\n            function assertSize (size) {\n              if (typeof size !== 'number') {\n                throw new TypeError('\"size\" argument must be a number')\n              } else if (size < 0) {\n                throw new RangeError('\"size\" argument must not be negative')\n              }\n            }\n\n            function alloc (that, size, fill, encoding) {\n              assertSize(size);\n              if (size <= 0) {\n                return createBuffer(that, size)\n              }\n              if (fill !== undefined) {\n                // Only pay attention to encoding if it's a string. This\n                // prevents accidentally sending in a number that would\n                // be interpretted as a start offset.\n                return typeof encoding === 'string'\n                  ? createBuffer(that, size).fill(fill, encoding)\n                  : createBuffer(that, size).fill(fill)\n              }\n              return createBuffer(that, size)\n            }\n\n            /**\n             * Creates a new filled Buffer instance.\n             * alloc(size[, fill[, encoding]])\n             **/\n            Buffer.alloc = function (size, fill, encoding) {\n              return alloc(null, size, fill, encoding)\n            };\n\n            function allocUnsafe (that, size) {\n              assertSize(size);\n              that = createBuffer(that, size < 0 ? 0 : checked(size) | 0);\n              if (!Buffer.TYPED_ARRAY_SUPPORT) {\n                for (var i = 0; i < size; ++i) {\n                  that[i] = 0;\n                }\n              }\n              return that\n            }\n\n            /**\n             * Equivalent to Buffer(num), by default creates a non-zero-filled Buffer instance.\n             * */\n            Buffer.allocUnsafe = function (size) {\n              return allocUnsafe(null, size)\n            };\n            /**\n             * Equivalent to SlowBuffer(num), by default creates a non-zero-filled Buffer instance.\n             */\n            Buffer.allocUnsafeSlow = function (size) {\n              return allocUnsafe(null, size)\n            };\n\n            function fromString (that, string, encoding) {\n              if (typeof encoding !== 'string' || encoding === '') {\n                encoding = 'utf8';\n              }\n\n              if (!Buffer.isEncoding(encoding)) {\n                throw new TypeError('\"encoding\" must be a valid string encoding')\n              }\n\n              var length = byteLength(string, encoding) | 0;\n              that = createBuffer(that, length);\n\n              var actual = that.write(string, encoding);\n\n              if (actual !== length) {\n                // Writing a hex string, for example, that contains invalid characters will\n                // cause everything after the first invalid character to be ignored. (e.g.\n                // 'abxxcd' will be treated as 'ab')\n                that = that.slice(0, actual);\n              }\n\n              return that\n            }\n\n            function fromArrayLike (that, array) {\n              var length = array.length < 0 ? 0 : checked(array.length) | 0;\n              that = createBuffer(that, length);\n              for (var i = 0; i < length; i += 1) {\n                that[i] = array[i] & 255;\n              }\n              return that\n            }\n\n            function fromArrayBuffer (that, array, byteOffset, length) {\n              array.byteLength; // this throws if `array` is not a valid ArrayBuffer\n\n              if (byteOffset < 0 || array.byteLength < byteOffset) {\n                throw new RangeError('\\'offset\\' is out of bounds')\n              }\n\n              if (array.byteLength < byteOffset + (length || 0)) {\n                throw new RangeError('\\'length\\' is out of bounds')\n              }\n\n              if (byteOffset === undefined && length === undefined) {\n                array = new Uint8Array(array);\n              } else if (length === undefined) {\n                array = new Uint8Array(array, byteOffset);\n              } else {\n                array = new Uint8Array(array, byteOffset, length);\n              }\n\n              if (Buffer.TYPED_ARRAY_SUPPORT) {\n                // Return an augmented `Uint8Array` instance, for best performance\n                that = array;\n                that.__proto__ = Buffer.prototype;\n              } else {\n                // Fallback: Return an object instance of the Buffer class\n                that = fromArrayLike(that, array);\n              }\n              return that\n            }\n\n            function fromObject (that, obj) {\n              if (internalIsBuffer(obj)) {\n                var len = checked(obj.length) | 0;\n                that = createBuffer(that, len);\n\n                if (that.length === 0) {\n                  return that\n                }\n\n                obj.copy(that, 0, 0, len);\n                return that\n              }\n\n              if (obj) {\n                if ((typeof ArrayBuffer !== 'undefined' &&\n                    obj.buffer instanceof ArrayBuffer) || 'length' in obj) {\n                  if (typeof obj.length !== 'number' || isnan(obj.length)) {\n                    return createBuffer(that, 0)\n                  }\n                  return fromArrayLike(that, obj)\n                }\n\n                if (obj.type === 'Buffer' && isArray(obj.data)) {\n                  return fromArrayLike(that, obj.data)\n                }\n              }\n\n              throw new TypeError('First argument must be a string, Buffer, ArrayBuffer, Array, or array-like object.')\n            }\n\n            function checked (length) {\n              // Note: cannot use `length < kMaxLength()` here because that fails when\n              // length is NaN (which is otherwise coerced to zero.)\n              if (length >= kMaxLength()) {\n                throw new RangeError('Attempt to allocate Buffer larger than maximum ' +\n                                     'size: 0x' + kMaxLength().toString(16) + ' bytes')\n              }\n              return length | 0\n            }\n            Buffer.isBuffer = isBuffer;\n            function internalIsBuffer (b) {\n              return !!(b != null && b._isBuffer)\n            }\n\n            Buffer.compare = function compare (a, b) {\n              if (!internalIsBuffer(a) || !internalIsBuffer(b)) {\n                throw new TypeError('Arguments must be Buffers')\n              }\n\n              if (a === b) return 0\n\n              var x = a.length;\n              var y = b.length;\n\n              for (var i = 0, len = Math.min(x, y); i < len; ++i) {\n                if (a[i] !== b[i]) {\n                  x = a[i];\n                  y = b[i];\n                  break\n                }\n              }\n\n              if (x < y) return -1\n              if (y < x) return 1\n              return 0\n            };\n\n            Buffer.isEncoding = function isEncoding (encoding) {\n              switch (String(encoding).toLowerCase()) {\n                case 'hex':\n                case 'utf8':\n                case 'utf-8':\n                case 'ascii':\n                case 'latin1':\n                case 'binary':\n                case 'base64':\n                case 'ucs2':\n                case 'ucs-2':\n                case 'utf16le':\n                case 'utf-16le':\n                  return true\n                default:\n                  return false\n              }\n            };\n\n            Buffer.concat = function concat (list, length) {\n              if (!isArray(list)) {\n                throw new TypeError('\"list\" argument must be an Array of Buffers')\n              }\n\n              if (list.length === 0) {\n                return Buffer.alloc(0)\n              }\n\n              var i;\n              if (length === undefined) {\n                length = 0;\n                for (i = 0; i < list.length; ++i) {\n                  length += list[i].length;\n                }\n              }\n\n              var buffer = Buffer.allocUnsafe(length);\n              var pos = 0;\n              for (i = 0; i < list.length; ++i) {\n                var buf = list[i];\n                if (!internalIsBuffer(buf)) {\n                  throw new TypeError('\"list\" argument must be an Array of Buffers')\n                }\n                buf.copy(buffer, pos);\n                pos += buf.length;\n              }\n              return buffer\n            };\n\n            function byteLength (string, encoding) {\n              if (internalIsBuffer(string)) {\n                return string.length\n              }\n              if (typeof ArrayBuffer !== 'undefined' && typeof ArrayBuffer.isView === 'function' &&\n                  (ArrayBuffer.isView(string) || string instanceof ArrayBuffer)) {\n                return string.byteLength\n              }\n              if (typeof string !== 'string') {\n                string = '' + string;\n              }\n\n              var len = string.length;\n              if (len === 0) return 0\n\n              // Use a for loop to avoid recursion\n              var loweredCase = false;\n              for (;;) {\n                switch (encoding) {\n                  case 'ascii':\n                  case 'latin1':\n                  case 'binary':\n                    return len\n                  case 'utf8':\n                  case 'utf-8':\n                  case undefined:\n                    return utf8ToBytes(string).length\n                  case 'ucs2':\n                  case 'ucs-2':\n                  case 'utf16le':\n                  case 'utf-16le':\n                    return len * 2\n                  case 'hex':\n                    return len >>> 1\n                  case 'base64':\n                    return base64ToBytes(string).length\n                  default:\n                    if (loweredCase) return utf8ToBytes(string).length // assume utf8\n                    encoding = ('' + encoding).toLowerCase();\n                    loweredCase = true;\n                }\n              }\n            }\n            Buffer.byteLength = byteLength;\n\n            function slowToString (encoding, start, end) {\n              var loweredCase = false;\n\n              // No need to verify that \"this.length <= MAX_UINT32\" since it's a read-only\n              // property of a typed array.\n\n              // This behaves neither like String nor Uint8Array in that we set start/end\n              // to their upper/lower bounds if the value passed is out of range.\n              // undefined is handled specially as per ECMA-262 6th Edition,\n              // Section 13.3.3.7 Runtime Semantics: KeyedBindingInitialization.\n              if (start === undefined || start < 0) {\n                start = 0;\n              }\n              // Return early if start > this.length. Done here to prevent potential uint32\n              // coercion fail below.\n              if (start > this.length) {\n                return ''\n              }\n\n              if (end === undefined || end > this.length) {\n                end = this.length;\n              }\n\n              if (end <= 0) {\n                return ''\n              }\n\n              // Force coersion to uint32. This will also coerce falsey/NaN values to 0.\n              end >>>= 0;\n              start >>>= 0;\n\n              if (end <= start) {\n                return ''\n              }\n\n              if (!encoding) encoding = 'utf8';\n\n              while (true) {\n                switch (encoding) {\n                  case 'hex':\n                    return hexSlice(this, start, end)\n\n                  case 'utf8':\n                  case 'utf-8':\n                    return utf8Slice(this, start, end)\n\n                  case 'ascii':\n                    return asciiSlice(this, start, end)\n\n                  case 'latin1':\n                  case 'binary':\n                    return latin1Slice(this, start, end)\n\n                  case 'base64':\n                    return base64Slice(this, start, end)\n\n                  case 'ucs2':\n                  case 'ucs-2':\n                  case 'utf16le':\n                  case 'utf-16le':\n                    return utf16leSlice(this, start, end)\n\n                  default:\n                    if (loweredCase) throw new TypeError('Unknown encoding: ' + encoding)\n                    encoding = (encoding + '').toLowerCase();\n                    loweredCase = true;\n                }\n              }\n            }\n\n            // The property is used by `Buffer.isBuffer` and `is-buffer` (in Safari 5-7) to detect\n            // Buffer instances.\n            Buffer.prototype._isBuffer = true;\n\n            function swap (b, n, m) {\n              var i = b[n];\n              b[n] = b[m];\n              b[m] = i;\n            }\n\n            Buffer.prototype.swap16 = function swap16 () {\n              var len = this.length;\n              if (len % 2 !== 0) {\n                throw new RangeError('Buffer size must be a multiple of 16-bits')\n              }\n              for (var i = 0; i < len; i += 2) {\n                swap(this, i, i + 1);\n              }\n              return this\n            };\n\n            Buffer.prototype.swap32 = function swap32 () {\n              var len = this.length;\n              if (len % 4 !== 0) {\n                throw new RangeError('Buffer size must be a multiple of 32-bits')\n              }\n              for (var i = 0; i < len; i += 4) {\n                swap(this, i, i + 3);\n                swap(this, i + 1, i + 2);\n              }\n              return this\n            };\n\n            Buffer.prototype.swap64 = function swap64 () {\n              var len = this.length;\n              if (len % 8 !== 0) {\n                throw new RangeError('Buffer size must be a multiple of 64-bits')\n              }\n              for (var i = 0; i < len; i += 8) {\n                swap(this, i, i + 7);\n                swap(this, i + 1, i + 6);\n                swap(this, i + 2, i + 5);\n                swap(this, i + 3, i + 4);\n              }\n              return this\n            };\n\n            Buffer.prototype.toString = function toString () {\n              var length = this.length | 0;\n              if (length === 0) return ''\n              if (arguments.length === 0) return utf8Slice(this, 0, length)\n              return slowToString.apply(this, arguments)\n            };\n\n            Buffer.prototype.equals = function equals (b) {\n              if (!internalIsBuffer(b)) throw new TypeError('Argument must be a Buffer')\n              if (this === b) return true\n              return Buffer.compare(this, b) === 0\n            };\n\n            Buffer.prototype.inspect = function inspect () {\n              var str = '';\n              var max = INSPECT_MAX_BYTES;\n              if (this.length > 0) {\n                str = this.toString('hex', 0, max).match(/.{2}/g).join(' ');\n                if (this.length > max) str += ' ... ';\n              }\n              return '<Buffer ' + str + '>'\n            };\n\n            Buffer.prototype.compare = function compare (target, start, end, thisStart, thisEnd) {\n              if (!internalIsBuffer(target)) {\n                throw new TypeError('Argument must be a Buffer')\n              }\n\n              if (start === undefined) {\n                start = 0;\n              }\n              if (end === undefined) {\n                end = target ? target.length : 0;\n              }\n              if (thisStart === undefined) {\n                thisStart = 0;\n              }\n              if (thisEnd === undefined) {\n                thisEnd = this.length;\n              }\n\n              if (start < 0 || end > target.length || thisStart < 0 || thisEnd > this.length) {\n                throw new RangeError('out of range index')\n              }\n\n              if (thisStart >= thisEnd && start >= end) {\n                return 0\n              }\n              if (thisStart >= thisEnd) {\n                return -1\n              }\n              if (start >= end) {\n                return 1\n              }\n\n              start >>>= 0;\n              end >>>= 0;\n              thisStart >>>= 0;\n              thisEnd >>>= 0;\n\n              if (this === target) return 0\n\n              var x = thisEnd - thisStart;\n              var y = end - start;\n              var len = Math.min(x, y);\n\n              var thisCopy = this.slice(thisStart, thisEnd);\n              var targetCopy = target.slice(start, end);\n\n              for (var i = 0; i < len; ++i) {\n                if (thisCopy[i] !== targetCopy[i]) {\n                  x = thisCopy[i];\n                  y = targetCopy[i];\n                  break\n                }\n              }\n\n              if (x < y) return -1\n              if (y < x) return 1\n              return 0\n            };\n\n            // Finds either the first index of `val` in `buffer` at offset >= `byteOffset`,\n            // OR the last index of `val` in `buffer` at offset <= `byteOffset`.\n            //\n            // Arguments:\n            // - buffer - a Buffer to search\n            // - val - a string, Buffer, or number\n            // - byteOffset - an index into `buffer`; will be clamped to an int32\n            // - encoding - an optional encoding, relevant is val is a string\n            // - dir - true for indexOf, false for lastIndexOf\n            function bidirectionalIndexOf (buffer, val, byteOffset, encoding, dir) {\n              // Empty buffer means no match\n              if (buffer.length === 0) return -1\n\n              // Normalize byteOffset\n              if (typeof byteOffset === 'string') {\n                encoding = byteOffset;\n                byteOffset = 0;\n              } else if (byteOffset > 0x7fffffff) {\n                byteOffset = 0x7fffffff;\n              } else if (byteOffset < -0x80000000) {\n                byteOffset = -0x80000000;\n              }\n              byteOffset = +byteOffset;  // Coerce to Number.\n              if (isNaN(byteOffset)) {\n                // byteOffset: it it's undefined, null, NaN, \"foo\", etc, search whole buffer\n                byteOffset = dir ? 0 : (buffer.length - 1);\n              }\n\n              // Normalize byteOffset: negative offsets start from the end of the buffer\n              if (byteOffset < 0) byteOffset = buffer.length + byteOffset;\n              if (byteOffset >= buffer.length) {\n                if (dir) return -1\n                else byteOffset = buffer.length - 1;\n              } else if (byteOffset < 0) {\n                if (dir) byteOffset = 0;\n                else return -1\n              }\n\n              // Normalize val\n              if (typeof val === 'string') {\n                val = Buffer.from(val, encoding);\n              }\n\n              // Finally, search either indexOf (if dir is true) or lastIndexOf\n              if (internalIsBuffer(val)) {\n                // Special case: looking for empty string/buffer always fails\n                if (val.length === 0) {\n                  return -1\n                }\n                return arrayIndexOf(buffer, val, byteOffset, encoding, dir)\n              } else if (typeof val === 'number') {\n                val = val & 0xFF; // Search for a byte value [0-255]\n                if (Buffer.TYPED_ARRAY_SUPPORT &&\n                    typeof Uint8Array.prototype.indexOf === 'function') {\n                  if (dir) {\n                    return Uint8Array.prototype.indexOf.call(buffer, val, byteOffset)\n                  } else {\n                    return Uint8Array.prototype.lastIndexOf.call(buffer, val, byteOffset)\n                  }\n                }\n                return arrayIndexOf(buffer, [ val ], byteOffset, encoding, dir)\n              }\n\n              throw new TypeError('val must be string, number or Buffer')\n            }\n\n            function arrayIndexOf (arr, val, byteOffset, encoding, dir) {\n              var indexSize = 1;\n              var arrLength = arr.length;\n              var valLength = val.length;\n\n              if (encoding !== undefined) {\n                encoding = String(encoding).toLowerCase();\n                if (encoding === 'ucs2' || encoding === 'ucs-2' ||\n                    encoding === 'utf16le' || encoding === 'utf-16le') {\n                  if (arr.length < 2 || val.length < 2) {\n                    return -1\n                  }\n                  indexSize = 2;\n                  arrLength /= 2;\n                  valLength /= 2;\n                  byteOffset /= 2;\n                }\n              }\n\n              function read$$1 (buf, i) {\n                if (indexSize === 1) {\n                  return buf[i]\n                } else {\n                  return buf.readUInt16BE(i * indexSize)\n                }\n              }\n\n              var i;\n              if (dir) {\n                var foundIndex = -1;\n                for (i = byteOffset; i < arrLength; i++) {\n                  if (read$$1(arr, i) === read$$1(val, foundIndex === -1 ? 0 : i - foundIndex)) {\n                    if (foundIndex === -1) foundIndex = i;\n                    if (i - foundIndex + 1 === valLength) return foundIndex * indexSize\n                  } else {\n                    if (foundIndex !== -1) i -= i - foundIndex;\n                    foundIndex = -1;\n                  }\n                }\n              } else {\n                if (byteOffset + valLength > arrLength) byteOffset = arrLength - valLength;\n                for (i = byteOffset; i >= 0; i--) {\n                  var found = true;\n                  for (var j = 0; j < valLength; j++) {\n                    if (read$$1(arr, i + j) !== read$$1(val, j)) {\n                      found = false;\n                      break\n                    }\n                  }\n                  if (found) return i\n                }\n              }\n\n              return -1\n            }\n\n            Buffer.prototype.includes = function includes (val, byteOffset, encoding) {\n              return this.indexOf(val, byteOffset, encoding) !== -1\n            };\n\n            Buffer.prototype.indexOf = function indexOf (val, byteOffset, encoding) {\n              return bidirectionalIndexOf(this, val, byteOffset, encoding, true)\n            };\n\n            Buffer.prototype.lastIndexOf = function lastIndexOf (val, byteOffset, encoding) {\n              return bidirectionalIndexOf(this, val, byteOffset, encoding, false)\n            };\n\n            function hexWrite (buf, string, offset, length) {\n              offset = Number(offset) || 0;\n              var remaining = buf.length - offset;\n              if (!length) {\n                length = remaining;\n              } else {\n                length = Number(length);\n                if (length > remaining) {\n                  length = remaining;\n                }\n              }\n\n              // must be an even number of digits\n              var strLen = string.length;\n              if (strLen % 2 !== 0) throw new TypeError('Invalid hex string')\n\n              if (length > strLen / 2) {\n                length = strLen / 2;\n              }\n              for (var i = 0; i < length; ++i) {\n                var parsed = parseInt(string.substr(i * 2, 2), 16);\n                if (isNaN(parsed)) return i\n                buf[offset + i] = parsed;\n              }\n              return i\n            }\n\n            function utf8Write (buf, string, offset, length) {\n              return blitBuffer(utf8ToBytes(string, buf.length - offset), buf, offset, length)\n            }\n\n            function asciiWrite (buf, string, offset, length) {\n              return blitBuffer(asciiToBytes(string), buf, offset, length)\n            }\n\n            function latin1Write (buf, string, offset, length) {\n              return asciiWrite(buf, string, offset, length)\n            }\n\n            function base64Write (buf, string, offset, length) {\n              return blitBuffer(base64ToBytes(string), buf, offset, length)\n            }\n\n            function ucs2Write (buf, string, offset, length) {\n              return blitBuffer(utf16leToBytes(string, buf.length - offset), buf, offset, length)\n            }\n\n            Buffer.prototype.write = function write$$1 (string, offset, length, encoding) {\n              // Buffer#write(string)\n              if (offset === undefined) {\n                encoding = 'utf8';\n                length = this.length;\n                offset = 0;\n              // Buffer#write(string, encoding)\n              } else if (length === undefined && typeof offset === 'string') {\n                encoding = offset;\n                length = this.length;\n                offset = 0;\n              // Buffer#write(string, offset[, length][, encoding])\n              } else if (isFinite(offset)) {\n                offset = offset | 0;\n                if (isFinite(length)) {\n                  length = length | 0;\n                  if (encoding === undefined) encoding = 'utf8';\n                } else {\n                  encoding = length;\n                  length = undefined;\n                }\n              // legacy write(string, encoding, offset, length) - remove in v0.13\n              } else {\n                throw new Error(\n                  'Buffer.write(string, encoding, offset[, length]) is no longer supported'\n                )\n              }\n\n              var remaining = this.length - offset;\n              if (length === undefined || length > remaining) length = remaining;\n\n              if ((string.length > 0 && (length < 0 || offset < 0)) || offset > this.length) {\n                throw new RangeError('Attempt to write outside buffer bounds')\n              }\n\n              if (!encoding) encoding = 'utf8';\n\n              var loweredCase = false;\n              for (;;) {\n                switch (encoding) {\n                  case 'hex':\n                    return hexWrite(this, string, offset, length)\n\n                  case 'utf8':\n                  case 'utf-8':\n                    return utf8Write(this, string, offset, length)\n\n                  case 'ascii':\n                    return asciiWrite(this, string, offset, length)\n\n                  case 'latin1':\n                  case 'binary':\n                    return latin1Write(this, string, offset, length)\n\n                  case 'base64':\n                    // Warning: maxLength not taken into account in base64Write\n                    return base64Write(this, string, offset, length)\n\n                  case 'ucs2':\n                  case 'ucs-2':\n                  case 'utf16le':\n                  case 'utf-16le':\n                    return ucs2Write(this, string, offset, length)\n\n                  default:\n                    if (loweredCase) throw new TypeError('Unknown encoding: ' + encoding)\n                    encoding = ('' + encoding).toLowerCase();\n                    loweredCase = true;\n                }\n              }\n            };\n\n            Buffer.prototype.toJSON = function toJSON () {\n              return {\n                type: 'Buffer',\n                data: Array.prototype.slice.call(this._arr || this, 0)\n              }\n            };\n\n            function base64Slice (buf, start, end) {\n              if (start === 0 && end === buf.length) {\n                return fromByteArray(buf)\n              } else {\n                return fromByteArray(buf.slice(start, end))\n              }\n            }\n\n            function utf8Slice (buf, start, end) {\n              end = Math.min(buf.length, end);\n              var res = [];\n\n              var i = start;\n              while (i < end) {\n                var firstByte = buf[i];\n                var codePoint = null;\n                var bytesPerSequence = (firstByte > 0xEF) ? 4\n                  : (firstByte > 0xDF) ? 3\n                  : (firstByte > 0xBF) ? 2\n                  : 1;\n\n                if (i + bytesPerSequence <= end) {\n                  var secondByte, thirdByte, fourthByte, tempCodePoint;\n\n                  switch (bytesPerSequence) {\n                    case 1:\n                      if (firstByte < 0x80) {\n                        codePoint = firstByte;\n                      }\n                      break\n                    case 2:\n                      secondByte = buf[i + 1];\n                      if ((secondByte & 0xC0) === 0x80) {\n                        tempCodePoint = (firstByte & 0x1F) << 0x6 | (secondByte & 0x3F);\n                        if (tempCodePoint > 0x7F) {\n                          codePoint = tempCodePoint;\n                        }\n                      }\n                      break\n                    case 3:\n                      secondByte = buf[i + 1];\n                      thirdByte = buf[i + 2];\n                      if ((secondByte & 0xC0) === 0x80 && (thirdByte & 0xC0) === 0x80) {\n                        tempCodePoint = (firstByte & 0xF) << 0xC | (secondByte & 0x3F) << 0x6 | (thirdByte & 0x3F);\n                        if (tempCodePoint > 0x7FF && (tempCodePoint < 0xD800 || tempCodePoint > 0xDFFF)) {\n                          codePoint = tempCodePoint;\n                        }\n                      }\n                      break\n                    case 4:\n                      secondByte = buf[i + 1];\n                      thirdByte = buf[i + 2];\n                      fourthByte = buf[i + 3];\n                      if ((secondByte & 0xC0) === 0x80 && (thirdByte & 0xC0) === 0x80 && (fourthByte & 0xC0) === 0x80) {\n                        tempCodePoint = (firstByte & 0xF) << 0x12 | (secondByte & 0x3F) << 0xC | (thirdByte & 0x3F) << 0x6 | (fourthByte & 0x3F);\n                        if (tempCodePoint > 0xFFFF && tempCodePoint < 0x110000) {\n                          codePoint = tempCodePoint;\n                        }\n                      }\n                  }\n                }\n\n                if (codePoint === null) {\n                  // we did not generate a valid codePoint so insert a\n                  // replacement char (U+FFFD) and advance only 1 byte\n                  codePoint = 0xFFFD;\n                  bytesPerSequence = 1;\n                } else if (codePoint > 0xFFFF) {\n                  // encode to utf16 (surrogate pair dance)\n                  codePoint -= 0x10000;\n                  res.push(codePoint >>> 10 & 0x3FF | 0xD800);\n                  codePoint = 0xDC00 | codePoint & 0x3FF;\n                }\n\n                res.push(codePoint);\n                i += bytesPerSequence;\n              }\n\n              return decodeCodePointsArray(res)\n            }\n\n            // Based on http://stackoverflow.com/a/22747272/680742, the browser with\n            // the lowest limit is Chrome, with 0x10000 args.\n            // We go 1 magnitude less, for safety\n            var MAX_ARGUMENTS_LENGTH = 0x1000;\n\n            function decodeCodePointsArray (codePoints) {\n              var len = codePoints.length;\n              if (len <= MAX_ARGUMENTS_LENGTH) {\n                return String.fromCharCode.apply(String, codePoints) // avoid extra slice()\n              }\n\n              // Decode in chunks to avoid \"call stack size exceeded\".\n              var res = '';\n              var i = 0;\n              while (i < len) {\n                res += String.fromCharCode.apply(\n                  String,\n                  codePoints.slice(i, i += MAX_ARGUMENTS_LENGTH)\n                );\n              }\n              return res\n            }\n\n            function asciiSlice (buf, start, end) {\n              var ret = '';\n              end = Math.min(buf.length, end);\n\n              for (var i = start; i < end; ++i) {\n                ret += String.fromCharCode(buf[i] & 0x7F);\n              }\n              return ret\n            }\n\n            function latin1Slice (buf, start, end) {\n              var ret = '';\n              end = Math.min(buf.length, end);\n\n              for (var i = start; i < end; ++i) {\n                ret += String.fromCharCode(buf[i]);\n              }\n              return ret\n            }\n\n            function hexSlice (buf, start, end) {\n              var len = buf.length;\n\n              if (!start || start < 0) start = 0;\n              if (!end || end < 0 || end > len) end = len;\n\n              var out = '';\n              for (var i = start; i < end; ++i) {\n                out += toHex(buf[i]);\n              }\n              return out\n            }\n\n            function utf16leSlice (buf, start, end) {\n              var bytes = buf.slice(start, end);\n              var res = '';\n              for (var i = 0; i < bytes.length; i += 2) {\n                res += String.fromCharCode(bytes[i] + bytes[i + 1] * 256);\n              }\n              return res\n            }\n\n            Buffer.prototype.slice = function slice (start, end) {\n              var len = this.length;\n              start = ~~start;\n              end = end === undefined ? len : ~~end;\n\n              if (start < 0) {\n                start += len;\n                if (start < 0) start = 0;\n              } else if (start > len) {\n                start = len;\n              }\n\n              if (end < 0) {\n                end += len;\n                if (end < 0) end = 0;\n              } else if (end > len) {\n                end = len;\n              }\n\n              if (end < start) end = start;\n\n              var newBuf;\n              if (Buffer.TYPED_ARRAY_SUPPORT) {\n                newBuf = this.subarray(start, end);\n                newBuf.__proto__ = Buffer.prototype;\n              } else {\n                var sliceLen = end - start;\n                newBuf = new Buffer(sliceLen, undefined);\n                for (var i = 0; i < sliceLen; ++i) {\n                  newBuf[i] = this[i + start];\n                }\n              }\n\n              return newBuf\n            };\n\n            /*\n             * Need to make sure that buffer isn't trying to write out of bounds.\n             */\n            function checkOffset (offset, ext, length) {\n              if ((offset % 1) !== 0 || offset < 0) throw new RangeError('offset is not uint')\n              if (offset + ext > length) throw new RangeError('Trying to access beyond buffer length')\n            }\n\n            Buffer.prototype.readUIntLE = function readUIntLE (offset, byteLength, noAssert) {\n              offset = offset | 0;\n              byteLength = byteLength | 0;\n              if (!noAssert) checkOffset(offset, byteLength, this.length);\n\n              var val = this[offset];\n              var mul = 1;\n              var i = 0;\n              while (++i < byteLength && (mul *= 0x100)) {\n                val += this[offset + i] * mul;\n              }\n\n              return val\n            };\n\n            Buffer.prototype.readUIntBE = function readUIntBE (offset, byteLength, noAssert) {\n              offset = offset | 0;\n              byteLength = byteLength | 0;\n              if (!noAssert) {\n                checkOffset(offset, byteLength, this.length);\n              }\n\n              var val = this[offset + --byteLength];\n              var mul = 1;\n              while (byteLength > 0 && (mul *= 0x100)) {\n                val += this[offset + --byteLength] * mul;\n              }\n\n              return val\n            };\n\n            Buffer.prototype.readUInt8 = function readUInt8 (offset, noAssert) {\n              if (!noAssert) checkOffset(offset, 1, this.length);\n              return this[offset]\n            };\n\n            Buffer.prototype.readUInt16LE = function readUInt16LE (offset, noAssert) {\n              if (!noAssert) checkOffset(offset, 2, this.length);\n              return this[offset] | (this[offset + 1] << 8)\n            };\n\n            Buffer.prototype.readUInt16BE = function readUInt16BE (offset, noAssert) {\n              if (!noAssert) checkOffset(offset, 2, this.length);\n              return (this[offset] << 8) | this[offset + 1]\n            };\n\n            Buffer.prototype.readUInt32LE = function readUInt32LE (offset, noAssert) {\n              if (!noAssert) checkOffset(offset, 4, this.length);\n\n              return ((this[offset]) |\n                  (this[offset + 1] << 8) |\n                  (this[offset + 2] << 16)) +\n                  (this[offset + 3] * 0x1000000)\n            };\n\n            Buffer.prototype.readUInt32BE = function readUInt32BE (offset, noAssert) {\n              if (!noAssert) checkOffset(offset, 4, this.length);\n\n              return (this[offset] * 0x1000000) +\n                ((this[offset + 1] << 16) |\n                (this[offset + 2] << 8) |\n                this[offset + 3])\n            };\n\n            Buffer.prototype.readIntLE = function readIntLE (offset, byteLength, noAssert) {\n              offset = offset | 0;\n              byteLength = byteLength | 0;\n              if (!noAssert) checkOffset(offset, byteLength, this.length);\n\n              var val = this[offset];\n              var mul = 1;\n              var i = 0;\n              while (++i < byteLength && (mul *= 0x100)) {\n                val += this[offset + i] * mul;\n              }\n              mul *= 0x80;\n\n              if (val >= mul) val -= Math.pow(2, 8 * byteLength);\n\n              return val\n            };\n\n            Buffer.prototype.readIntBE = function readIntBE (offset, byteLength, noAssert) {\n              offset = offset | 0;\n              byteLength = byteLength | 0;\n              if (!noAssert) checkOffset(offset, byteLength, this.length);\n\n              var i = byteLength;\n              var mul = 1;\n              var val = this[offset + --i];\n              while (i > 0 && (mul *= 0x100)) {\n                val += this[offset + --i] * mul;\n              }\n              mul *= 0x80;\n\n              if (val >= mul) val -= Math.pow(2, 8 * byteLength);\n\n              return val\n            };\n\n            Buffer.prototype.readInt8 = function readInt8 (offset, noAssert) {\n              if (!noAssert) checkOffset(offset, 1, this.length);\n              if (!(this[offset] & 0x80)) return (this[offset])\n              return ((0xff - this[offset] + 1) * -1)\n            };\n\n            Buffer.prototype.readInt16LE = function readInt16LE (offset, noAssert) {\n              if (!noAssert) checkOffset(offset, 2, this.length);\n              var val = this[offset] | (this[offset + 1] << 8);\n              return (val & 0x8000) ? val | 0xFFFF0000 : val\n            };\n\n            Buffer.prototype.readInt16BE = function readInt16BE (offset, noAssert) {\n              if (!noAssert) checkOffset(offset, 2, this.length);\n              var val = this[offset + 1] | (this[offset] << 8);\n              return (val & 0x8000) ? val | 0xFFFF0000 : val\n            };\n\n            Buffer.prototype.readInt32LE = function readInt32LE (offset, noAssert) {\n              if (!noAssert) checkOffset(offset, 4, this.length);\n\n              return (this[offset]) |\n                (this[offset + 1] << 8) |\n                (this[offset + 2] << 16) |\n                (this[offset + 3] << 24)\n            };\n\n            Buffer.prototype.readInt32BE = function readInt32BE (offset, noAssert) {\n              if (!noAssert) checkOffset(offset, 4, this.length);\n\n              return (this[offset] << 24) |\n                (this[offset + 1] << 16) |\n                (this[offset + 2] << 8) |\n                (this[offset + 3])\n            };\n\n            Buffer.prototype.readFloatLE = function readFloatLE (offset, noAssert) {\n              if (!noAssert) checkOffset(offset, 4, this.length);\n              return read(this, offset, true, 23, 4)\n            };\n\n            Buffer.prototype.readFloatBE = function readFloatBE (offset, noAssert) {\n              if (!noAssert) checkOffset(offset, 4, this.length);\n              return read(this, offset, false, 23, 4)\n            };\n\n            Buffer.prototype.readDoubleLE = function readDoubleLE (offset, noAssert) {\n              if (!noAssert) checkOffset(offset, 8, this.length);\n              return read(this, offset, true, 52, 8)\n            };\n\n            Buffer.prototype.readDoubleBE = function readDoubleBE (offset, noAssert) {\n              if (!noAssert) checkOffset(offset, 8, this.length);\n              return read(this, offset, false, 52, 8)\n            };\n\n            function checkInt (buf, value, offset, ext, max, min) {\n              if (!internalIsBuffer(buf)) throw new TypeError('\"buffer\" argument must be a Buffer instance')\n              if (value > max || value < min) throw new RangeError('\"value\" argument is out of bounds')\n              if (offset + ext > buf.length) throw new RangeError('Index out of range')\n            }\n\n            Buffer.prototype.writeUIntLE = function writeUIntLE (value, offset, byteLength, noAssert) {\n              value = +value;\n              offset = offset | 0;\n              byteLength = byteLength | 0;\n              if (!noAssert) {\n                var maxBytes = Math.pow(2, 8 * byteLength) - 1;\n                checkInt(this, value, offset, byteLength, maxBytes, 0);\n              }\n\n              var mul = 1;\n              var i = 0;\n              this[offset] = value & 0xFF;\n              while (++i < byteLength && (mul *= 0x100)) {\n                this[offset + i] = (value / mul) & 0xFF;\n              }\n\n              return offset + byteLength\n            };\n\n            Buffer.prototype.writeUIntBE = function writeUIntBE (value, offset, byteLength, noAssert) {\n              value = +value;\n              offset = offset | 0;\n              byteLength = byteLength | 0;\n              if (!noAssert) {\n                var maxBytes = Math.pow(2, 8 * byteLength) - 1;\n                checkInt(this, value, offset, byteLength, maxBytes, 0);\n              }\n\n              var i = byteLength - 1;\n              var mul = 1;\n              this[offset + i] = value & 0xFF;\n              while (--i >= 0 && (mul *= 0x100)) {\n                this[offset + i] = (value / mul) & 0xFF;\n              }\n\n              return offset + byteLength\n            };\n\n            Buffer.prototype.writeUInt8 = function writeUInt8 (value, offset, noAssert) {\n              value = +value;\n              offset = offset | 0;\n              if (!noAssert) checkInt(this, value, offset, 1, 0xff, 0);\n              if (!Buffer.TYPED_ARRAY_SUPPORT) value = Math.floor(value);\n              this[offset] = (value & 0xff);\n              return offset + 1\n            };\n\n            function objectWriteUInt16 (buf, value, offset, littleEndian) {\n              if (value < 0) value = 0xffff + value + 1;\n              for (var i = 0, j = Math.min(buf.length - offset, 2); i < j; ++i) {\n                buf[offset + i] = (value & (0xff << (8 * (littleEndian ? i : 1 - i)))) >>>\n                  (littleEndian ? i : 1 - i) * 8;\n              }\n            }\n\n            Buffer.prototype.writeUInt16LE = function writeUInt16LE (value, offset, noAssert) {\n              value = +value;\n              offset = offset | 0;\n              if (!noAssert) checkInt(this, value, offset, 2, 0xffff, 0);\n              if (Buffer.TYPED_ARRAY_SUPPORT) {\n                this[offset] = (value & 0xff);\n                this[offset + 1] = (value >>> 8);\n              } else {\n                objectWriteUInt16(this, value, offset, true);\n              }\n              return offset + 2\n            };\n\n            Buffer.prototype.writeUInt16BE = function writeUInt16BE (value, offset, noAssert) {\n              value = +value;\n              offset = offset | 0;\n              if (!noAssert) checkInt(this, value, offset, 2, 0xffff, 0);\n              if (Buffer.TYPED_ARRAY_SUPPORT) {\n                this[offset] = (value >>> 8);\n                this[offset + 1] = (value & 0xff);\n              } else {\n                objectWriteUInt16(this, value, offset, false);\n              }\n              return offset + 2\n            };\n\n            function objectWriteUInt32 (buf, value, offset, littleEndian) {\n              if (value < 0) value = 0xffffffff + value + 1;\n              for (var i = 0, j = Math.min(buf.length - offset, 4); i < j; ++i) {\n                buf[offset + i] = (value >>> (littleEndian ? i : 3 - i) * 8) & 0xff;\n              }\n            }\n\n            Buffer.prototype.writeUInt32LE = function writeUInt32LE (value, offset, noAssert) {\n              value = +value;\n              offset = offset | 0;\n              if (!noAssert) checkInt(this, value, offset, 4, 0xffffffff, 0);\n              if (Buffer.TYPED_ARRAY_SUPPORT) {\n                this[offset + 3] = (value >>> 24);\n                this[offset + 2] = (value >>> 16);\n                this[offset + 1] = (value >>> 8);\n                this[offset] = (value & 0xff);\n              } else {\n                objectWriteUInt32(this, value, offset, true);\n              }\n              return offset + 4\n            };\n\n            Buffer.prototype.writeUInt32BE = function writeUInt32BE (value, offset, noAssert) {\n              value = +value;\n              offset = offset | 0;\n              if (!noAssert) checkInt(this, value, offset, 4, 0xffffffff, 0);\n              if (Buffer.TYPED_ARRAY_SUPPORT) {\n                this[offset] = (value >>> 24);\n                this[offset + 1] = (value >>> 16);\n                this[offset + 2] = (value >>> 8);\n                this[offset + 3] = (value & 0xff);\n              } else {\n                objectWriteUInt32(this, value, offset, false);\n              }\n              return offset + 4\n            };\n\n            Buffer.prototype.writeIntLE = function writeIntLE (value, offset, byteLength, noAssert) {\n              value = +value;\n              offset = offset | 0;\n              if (!noAssert) {\n                var limit = Math.pow(2, 8 * byteLength - 1);\n\n                checkInt(this, value, offset, byteLength, limit - 1, -limit);\n              }\n\n              var i = 0;\n              var mul = 1;\n              var sub = 0;\n              this[offset] = value & 0xFF;\n              while (++i < byteLength && (mul *= 0x100)) {\n                if (value < 0 && sub === 0 && this[offset + i - 1] !== 0) {\n                  sub = 1;\n                }\n                this[offset + i] = ((value / mul) >> 0) - sub & 0xFF;\n              }\n\n              return offset + byteLength\n            };\n\n            Buffer.prototype.writeIntBE = function writeIntBE (value, offset, byteLength, noAssert) {\n              value = +value;\n              offset = offset | 0;\n              if (!noAssert) {\n                var limit = Math.pow(2, 8 * byteLength - 1);\n\n                checkInt(this, value, offset, byteLength, limit - 1, -limit);\n              }\n\n              var i = byteLength - 1;\n              var mul = 1;\n              var sub = 0;\n              this[offset + i] = value & 0xFF;\n              while (--i >= 0 && (mul *= 0x100)) {\n                if (value < 0 && sub === 0 && this[offset + i + 1] !== 0) {\n                  sub = 1;\n                }\n                this[offset + i] = ((value / mul) >> 0) - sub & 0xFF;\n              }\n\n              return offset + byteLength\n            };\n\n            Buffer.prototype.writeInt8 = function writeInt8 (value, offset, noAssert) {\n              value = +value;\n              offset = offset | 0;\n              if (!noAssert) checkInt(this, value, offset, 1, 0x7f, -0x80);\n              if (!Buffer.TYPED_ARRAY_SUPPORT) value = Math.floor(value);\n              if (value < 0) value = 0xff + value + 1;\n              this[offset] = (value & 0xff);\n              return offset + 1\n            };\n\n            Buffer.prototype.writeInt16LE = function writeInt16LE (value, offset, noAssert) {\n              value = +value;\n              offset = offset | 0;\n              if (!noAssert) checkInt(this, value, offset, 2, 0x7fff, -0x8000);\n              if (Buffer.TYPED_ARRAY_SUPPORT) {\n                this[offset] = (value & 0xff);\n                this[offset + 1] = (value >>> 8);\n              } else {\n                objectWriteUInt16(this, value, offset, true);\n              }\n              return offset + 2\n            };\n\n            Buffer.prototype.writeInt16BE = function writeInt16BE (value, offset, noAssert) {\n              value = +value;\n              offset = offset | 0;\n              if (!noAssert) checkInt(this, value, offset, 2, 0x7fff, -0x8000);\n              if (Buffer.TYPED_ARRAY_SUPPORT) {\n                this[offset] = (value >>> 8);\n                this[offset + 1] = (value & 0xff);\n              } else {\n                objectWriteUInt16(this, value, offset, false);\n              }\n              return offset + 2\n            };\n\n            Buffer.prototype.writeInt32LE = function writeInt32LE (value, offset, noAssert) {\n              value = +value;\n              offset = offset | 0;\n              if (!noAssert) checkInt(this, value, offset, 4, 0x7fffffff, -0x80000000);\n              if (Buffer.TYPED_ARRAY_SUPPORT) {\n                this[offset] = (value & 0xff);\n                this[offset + 1] = (value >>> 8);\n                this[offset + 2] = (value >>> 16);\n                this[offset + 3] = (value >>> 24);\n              } else {\n                objectWriteUInt32(this, value, offset, true);\n              }\n              return offset + 4\n            };\n\n            Buffer.prototype.writeInt32BE = function writeInt32BE (value, offset, noAssert) {\n              value = +value;\n              offset = offset | 0;\n              if (!noAssert) checkInt(this, value, offset, 4, 0x7fffffff, -0x80000000);\n              if (value < 0) value = 0xffffffff + value + 1;\n              if (Buffer.TYPED_ARRAY_SUPPORT) {\n                this[offset] = (value >>> 24);\n                this[offset + 1] = (value >>> 16);\n                this[offset + 2] = (value >>> 8);\n                this[offset + 3] = (value & 0xff);\n              } else {\n                objectWriteUInt32(this, value, offset, false);\n              }\n              return offset + 4\n            };\n\n            function checkIEEE754 (buf, value, offset, ext, max, min) {\n              if (offset + ext > buf.length) throw new RangeError('Index out of range')\n              if (offset < 0) throw new RangeError('Index out of range')\n            }\n\n            function writeFloat (buf, value, offset, littleEndian, noAssert) {\n              if (!noAssert) {\n                checkIEEE754(buf, value, offset, 4, 3.4028234663852886e+38, -3.4028234663852886e+38);\n              }\n              write(buf, value, offset, littleEndian, 23, 4);\n              return offset + 4\n            }\n\n            Buffer.prototype.writeFloatLE = function writeFloatLE (value, offset, noAssert) {\n              return writeFloat(this, value, offset, true, noAssert)\n            };\n\n            Buffer.prototype.writeFloatBE = function writeFloatBE (value, offset, noAssert) {\n              return writeFloat(this, value, offset, false, noAssert)\n            };\n\n            function writeDouble (buf, value, offset, littleEndian, noAssert) {\n              if (!noAssert) {\n                checkIEEE754(buf, value, offset, 8, 1.7976931348623157E+308, -1.7976931348623157E+308);\n              }\n              write(buf, value, offset, littleEndian, 52, 8);\n              return offset + 8\n            }\n\n            Buffer.prototype.writeDoubleLE = function writeDoubleLE (value, offset, noAssert) {\n              return writeDouble(this, value, offset, true, noAssert)\n            };\n\n            Buffer.prototype.writeDoubleBE = function writeDoubleBE (value, offset, noAssert) {\n              return writeDouble(this, value, offset, false, noAssert)\n            };\n\n            // copy(targetBuffer, targetStart=0, sourceStart=0, sourceEnd=buffer.length)\n            Buffer.prototype.copy = function copy (target, targetStart, start, end) {\n              if (!start) start = 0;\n              if (!end && end !== 0) end = this.length;\n              if (targetStart >= target.length) targetStart = target.length;\n              if (!targetStart) targetStart = 0;\n              if (end > 0 && end < start) end = start;\n\n              // Copy 0 bytes; we're done\n              if (end === start) return 0\n              if (target.length === 0 || this.length === 0) return 0\n\n              // Fatal error conditions\n              if (targetStart < 0) {\n                throw new RangeError('targetStart out of bounds')\n              }\n              if (start < 0 || start >= this.length) throw new RangeError('sourceStart out of bounds')\n              if (end < 0) throw new RangeError('sourceEnd out of bounds')\n\n              // Are we oob?\n              if (end > this.length) end = this.length;\n              if (target.length - targetStart < end - start) {\n                end = target.length - targetStart + start;\n              }\n\n              var len = end - start;\n              var i;\n\n              if (this === target && start < targetStart && targetStart < end) {\n                // descending copy from end\n                for (i = len - 1; i >= 0; --i) {\n                  target[i + targetStart] = this[i + start];\n                }\n              } else if (len < 1000 || !Buffer.TYPED_ARRAY_SUPPORT) {\n                // ascending copy from start\n                for (i = 0; i < len; ++i) {\n                  target[i + targetStart] = this[i + start];\n                }\n              } else {\n                Uint8Array.prototype.set.call(\n                  target,\n                  this.subarray(start, start + len),\n                  targetStart\n                );\n              }\n\n              return len\n            };\n\n            // Usage:\n            //    buffer.fill(number[, offset[, end]])\n            //    buffer.fill(buffer[, offset[, end]])\n            //    buffer.fill(string[, offset[, end]][, encoding])\n            Buffer.prototype.fill = function fill (val, start, end, encoding) {\n              // Handle string cases:\n              if (typeof val === 'string') {\n                if (typeof start === 'string') {\n                  encoding = start;\n                  start = 0;\n                  end = this.length;\n                } else if (typeof end === 'string') {\n                  encoding = end;\n                  end = this.length;\n                }\n                if (val.length === 1) {\n                  var code = val.charCodeAt(0);\n                  if (code < 256) {\n                    val = code;\n                  }\n                }\n                if (encoding !== undefined && typeof encoding !== 'string') {\n                  throw new TypeError('encoding must be a string')\n                }\n                if (typeof encoding === 'string' && !Buffer.isEncoding(encoding)) {\n                  throw new TypeError('Unknown encoding: ' + encoding)\n                }\n              } else if (typeof val === 'number') {\n                val = val & 255;\n              }\n\n              // Invalid ranges are not set to a default, so can range check early.\n              if (start < 0 || this.length < start || this.length < end) {\n                throw new RangeError('Out of range index')\n              }\n\n              if (end <= start) {\n                return this\n              }\n\n              start = start >>> 0;\n              end = end === undefined ? this.length : end >>> 0;\n\n              if (!val) val = 0;\n\n              var i;\n              if (typeof val === 'number') {\n                for (i = start; i < end; ++i) {\n                  this[i] = val;\n                }\n              } else {\n                var bytes = internalIsBuffer(val)\n                  ? val\n                  : utf8ToBytes(new Buffer(val, encoding).toString());\n                var len = bytes.length;\n                for (i = 0; i < end - start; ++i) {\n                  this[i + start] = bytes[i % len];\n                }\n              }\n\n              return this\n            };\n\n            // HELPER FUNCTIONS\n            // ================\n\n            var INVALID_BASE64_RE = /[^+\\/0-9A-Za-z-_]/g;\n\n            function base64clean (str) {\n              // Node strips out invalid characters like \\n and \\t from the string, base64-js does not\n              str = stringtrim(str).replace(INVALID_BASE64_RE, '');\n              // Node converts strings with length < 2 to ''\n              if (str.length < 2) return ''\n              // Node allows for non-padded base64 strings (missing trailing ===), base64-js does not\n              while (str.length % 4 !== 0) {\n                str = str + '=';\n              }\n              return str\n            }\n\n            function stringtrim (str) {\n              if (str.trim) return str.trim()\n              return str.replace(/^\\s+|\\s+$/g, '')\n            }\n\n            function toHex (n) {\n              if (n < 16) return '0' + n.toString(16)\n              return n.toString(16)\n            }\n\n            function utf8ToBytes (string, units) {\n              units = units || Infinity;\n              var codePoint;\n              var length = string.length;\n              var leadSurrogate = null;\n              var bytes = [];\n\n              for (var i = 0; i < length; ++i) {\n                codePoint = string.charCodeAt(i);\n\n                // is surrogate component\n                if (codePoint > 0xD7FF && codePoint < 0xE000) {\n                  // last char was a lead\n                  if (!leadSurrogate) {\n                    // no lead yet\n                    if (codePoint > 0xDBFF) {\n                      // unexpected trail\n                      if ((units -= 3) > -1) bytes.push(0xEF, 0xBF, 0xBD);\n                      continue\n                    } else if (i + 1 === length) {\n                      // unpaired lead\n                      if ((units -= 3) > -1) bytes.push(0xEF, 0xBF, 0xBD);\n                      continue\n                    }\n\n                    // valid lead\n                    leadSurrogate = codePoint;\n\n                    continue\n                  }\n\n                  // 2 leads in a row\n                  if (codePoint < 0xDC00) {\n                    if ((units -= 3) > -1) bytes.push(0xEF, 0xBF, 0xBD);\n                    leadSurrogate = codePoint;\n                    continue\n                  }\n\n                  // valid surrogate pair\n                  codePoint = (leadSurrogate - 0xD800 << 10 | codePoint - 0xDC00) + 0x10000;\n                } else if (leadSurrogate) {\n                  // valid bmp char, but last char was a lead\n                  if ((units -= 3) > -1) bytes.push(0xEF, 0xBF, 0xBD);\n                }\n\n                leadSurrogate = null;\n\n                // encode utf8\n                if (codePoint < 0x80) {\n                  if ((units -= 1) < 0) break\n                  bytes.push(codePoint);\n                } else if (codePoint < 0x800) {\n                  if ((units -= 2) < 0) break\n                  bytes.push(\n                    codePoint >> 0x6 | 0xC0,\n                    codePoint & 0x3F | 0x80\n                  );\n                } else if (codePoint < 0x10000) {\n                  if ((units -= 3) < 0) break\n                  bytes.push(\n                    codePoint >> 0xC | 0xE0,\n                    codePoint >> 0x6 & 0x3F | 0x80,\n                    codePoint & 0x3F | 0x80\n                  );\n                } else if (codePoint < 0x110000) {\n                  if ((units -= 4) < 0) break\n                  bytes.push(\n                    codePoint >> 0x12 | 0xF0,\n                    codePoint >> 0xC & 0x3F | 0x80,\n                    codePoint >> 0x6 & 0x3F | 0x80,\n                    codePoint & 0x3F | 0x80\n                  );\n                } else {\n                  throw new Error('Invalid code point')\n                }\n              }\n\n              return bytes\n            }\n\n            function asciiToBytes (str) {\n              var byteArray = [];\n              for (var i = 0; i < str.length; ++i) {\n                // Node's code seems to be doing this and not & 0x7F..\n                byteArray.push(str.charCodeAt(i) & 0xFF);\n              }\n              return byteArray\n            }\n\n            function utf16leToBytes (str, units) {\n              var c, hi, lo;\n              var byteArray = [];\n              for (var i = 0; i < str.length; ++i) {\n                if ((units -= 2) < 0) break\n\n                c = str.charCodeAt(i);\n                hi = c >> 8;\n                lo = c % 256;\n                byteArray.push(lo);\n                byteArray.push(hi);\n              }\n\n              return byteArray\n            }\n\n\n            function base64ToBytes (str) {\n              return toByteArray(base64clean(str))\n            }\n\n            function blitBuffer (src, dst, offset, length) {\n              for (var i = 0; i < length; ++i) {\n                if ((i + offset >= dst.length) || (i >= src.length)) break\n                dst[i + offset] = src[i];\n              }\n              return i\n            }\n\n            function isnan (val) {\n              return val !== val // eslint-disable-line no-self-compare\n            }\n\n\n            // the following is from is-buffer, also by Feross Aboukhadijeh and with same lisence\n            // The _isBuffer check is for Safari 5-7 support, because it's missing\n            // Object.prototype.constructor. Remove this eventually\n            function isBuffer(obj) {\n              return obj != null && (!!obj._isBuffer || isFastBuffer(obj) || isSlowBuffer(obj))\n            }\n\n            function isFastBuffer (obj) {\n              return !!obj.constructor && typeof obj.constructor.isBuffer === 'function' && obj.constructor.isBuffer(obj)\n            }\n\n            // For Node v0.10 support. Remove this eventually.\n            function isSlowBuffer (obj) {\n              return typeof obj.readFloatLE === 'function' && typeof obj.slice === 'function' && isFastBuffer(obj.slice(0, 0))\n            }\n\n            /**\n             * @license\n             * Copyright 2018 Google LLC. All Rights Reserved.\n             * Licensed under the Apache License, Version 2.0 (the \"License\");\n             * you may not use this file except in compliance with the License.\n             * You may obtain a copy of the License at\n             *\n             * http://www.apache.org/licenses/LICENSE-2.0\n             *\n             * Unless required by applicable law or agreed to in writing, software\n             * distributed under the License is distributed on an \"AS IS\" BASIS,\n             * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n             * See the License for the specific language governing permissions and\n             * limitations under the License.\n             * =============================================================================\n             */\n            var extendStatics=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t;}||function(e,t){for(var r in t)t.hasOwnProperty(r)&&(e[r]=t[r]);};function __extends(e,t){function r(){this.constructor=e;}extendStatics(e,t),e.prototype=null===t?Object.create(t):(r.prototype=t.prototype,new r);}var __assign=Object.assign||function(e){for(var t,r=1,n=arguments.length;r<n;r++)for(var o in t=arguments[r])Object.prototype.hasOwnProperty.call(t,o)&&(e[o]=t[o]);return e};function __awaiter(e,t,r,n){return new(r||(r=Promise))(function(o,a){function i(e){try{u(n.next(e));}catch(e){a(e);}}function s(e){try{u(n.throw(e));}catch(e){a(e);}}function u(e){e.done?o(e.value):new r(function(t){t(e.value);}).then(i,s);}u((n=n.apply(e,t||[])).next());})}function __generator(e,t){var r,n,o,a,i={label:0,sent:function(){if(1&o[0])throw o[1];return o[1]},trys:[],ops:[]};return a={next:s(0),throw:s(1),return:s(2)},\"function\"==typeof Symbol&&(a[Symbol.iterator]=function(){return this}),a;function s(a){return function(s){return function(a){if(r)throw new TypeError(\"Generator is already 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)|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(e.substr(0,4))}function shuffle(e){for(var t=e.length,r=0,n=0;t>0;)n=Math.random()*t|0,r=e[--t],e[t]=e[n],e[n]=r;}function clamp(e,t,r){return Math.max(e,Math.min(t,r))}function nearestLargerEven(e){return e%2==0?e:e+1}function sum(e){for(var t=0,r=0;r<e.length;r++)t+=e[r];return t}function randUniform(e,t){var r=Math.random();return t*r+(1-r)*e}function distSquared(e,t){for(var r=0,n=0;n<e.length;n++){var o=Number(e[n])-Number(t[n]);r+=o*o;}return r}function assert(e,t){if(!e)throw new Error(\"string\"==typeof t?t:t())}function assertShapesMatch(e,t,r){void 0===r&&(r=\"\"),assert(arraysEqual(e,t),r+\" Shapes \"+e+\" and \"+t+\" must match\");}function assertNonNull(e){assert(null!=e,\"The input to the tensor constructor must be a non-null value.\");}function flatten(e,t){if(void 0===t&&(t=[]),Array.isArray(e))for(var r=0;r<e.length;++r)flatten(e[r],t);else t.push(e);return t}function sizeFromShape(e){if(0===e.length)return 1;for(var t=e[0],r=1;r<e.length;r++)t*=e[r];return t}function isScalarShape(e){return 0===e.length}function arraysEqual(e,t){if(e===t)return !0;if(null==e||null==t)return !1;if(e.length!==t.length)return !1;for(var r=0;r<e.length;r++)if(e[r]!==t[r])return !1;return !0}function isInt(e){return e%1==0}function tanh(e){if(null!=Math.tanh)return Math.tanh(e);if(e===1/0)return 1;if(e===-1/0)return -1;var t=Math.exp(2*e);return (t-1)/(t+1)}function sizeToSquarishShape(e){for(var t=Math.floor(Math.sqrt(e));t>1;--t)if(e%t==0)return [t,e/t];return [1,e]}function createShuffledIndices(e){for(var t=new Uint32Array(e),r=0;r<e;++r)t[r]=r;return shuffle(t),t}function rightPad(e,t){return t<=e.length?e:e+\" \".repeat(t-e.length)}function repeatedTry(e,t,r){return void 0===t&&(t=function(e){return 0}),new Promise(function(n,o){var a=0,i=function(){if(e())n();else{var s=t(++a);null!=r&&a>=r?o():setTimeout(i,s);}};i();})}function inferFromImplicitShape(e,t){for(var r=1,n=-1,o=0;o<e.length;++o)if(e[o]>=0)r*=e[o];else if(-1===e[o]){if(-1!==n)throw Error(\"Shapes can only have 1 implicit size. Found -1 at dim \"+n+\" and dim \"+o);n=o;}else if(e[o]<0)throw Error(\"Shapes can not be < 0. Found \"+e[o]+\" at dim \"+o);if(-1===n){if(t>0&&t!==r)throw Error(\"Size(\"+t+\") must match the product of shape \"+e);return e}if(0===r)throw Error(\"Cannot infer the missing size in [\"+e+\"] when there are 0 elements\");if(t%r!=0)throw Error(\"The implicit shape can't be a fractional number. Got \"+t+\" / \"+r);var a=e.slice();return a[n]=t/r,a}function squeezeShape(e,t){for(var r=[],n=[],o=0,a=0;a<e.length;++a){if(null!=t){if(t[o]===a&&1!==e[a])throw new Error(\"Can't squeeze axis \"+a+\" since its dim '\"+e[a]+\"' is not 1\");(null==t[o]||t[o]>a)&&1===e[a]&&(r.push(e[a]),n.push(a)),t[o]<=a&&o++;}1!==e[a]&&(r.push(e[a]),n.push(a));}return {newShape:r,keptDims:n}}function getTypedArrayFromDType(e,t){var r=null;if(null==e||\"float32\"===e)r=new Float32Array(t);else if(\"int32\"===e)r=new Int32Array(t);else{if(\"bool\"!==e)throw new Error(\"Unknown data type \"+e);r=new Uint8Array(t);}return r}function checkComputationForNaN(e,t,r){if(\"float32\"===t)for(var n=0;n<e.length;n++)if(isNaN(e[n]))throw Error(\"The result of the '\"+r+\"' has NaNs.\")}function checkConversionForNaN(e,t){if(\"float32\"!==t)for(var r=0;r<e.length;r++)if(isNaN(e[r]))throw Error(\"NaN is not a valid value for dtype: '\"+t+\"'.\")}function hasEncodingLoss(e,t){return \"complex64\"!==t&&((\"float32\"!==t||\"complex64\"===e)&&((\"int32\"!==t||\"float32\"===e||\"complex64\"===e)&&(\"bool\"!==t||\"bool\"!==e)))}function copyTypedArray(e,t,r){if(null==t||\"float32\"===t||\"complex64\"===t)return new Float32Array(e);if(\"int32\"===t)return r&&checkConversionForNaN(e,t),new Int32Array(e);if(\"bool\"===t){for(var n=new Uint8Array(e.length),o=0;o<n.length;++o)0!==Math.round(e[o])&&(n[o]=1);return n}throw new Error(\"Unknown data type \"+t)}function isTypedArray(e){return e instanceof Float32Array||e instanceof Int32Array||e instanceof Uint8Array}function bytesPerElement(e){if(\"float32\"===e||\"int32\"===e)return 4;if(\"complex64\"===e)return 8;if(\"bool\"===e)return 1;throw new Error(\"Unknown dtype \"+e)}function isFunction(e){return !!(e&&e.constructor&&e.call&&e.apply)}function nearestDivisor(e,t){for(var r=t;r<e;++r)if(e%r==0)return r;return e}function computeStrides(e){var t=e.length;if(t<2)return [];var r=new Array(t-1);r[t-2]=e[t-1];for(var n=t-3;n>=0;--n)r[n]=r[n+1]*e[n+1];return r}function toTypedArray(e,t,r){return noConversionNeeded(e,t)?e:(Array.isArray(e)&&(e=flatten(e)),copyTypedArray(e,t,r))}function noConversionNeeded(e,t){return e instanceof Float32Array&&\"float32\"===t||e instanceof Int32Array&&\"int32\"===t||e instanceof Uint8Array&&\"bool\"===t}function makeOnesTypedArray(e,t){for(var r=makeZerosTypedArray(e,t),n=0;n<r.length;n++)r[n]=1;return r}function makeZerosTypedArray(e,t){if(null==t||\"float32\"===t||\"complex64\"===t)return new Float32Array(e);if(\"int32\"===t)return new Int32Array(e);if(\"bool\"===t)return new Uint8Array(e);throw new Error(\"Unknown data type \"+t)}function now(){if(\"undefined\"!=typeof performance)return performance.now();if(\"undefined\"!=typeof process){var e=process.hrtime();return 1e3*e[0]+e[1]/1e6}throw new Error(\"Cannot measure time in this environment. You should run tf.js in the browser or in Node.js\")}var util=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,squeezeShape:squeezeShape,getTypedArrayFromDType:getTypedArrayFromDType,checkComputationForNaN:checkComputationForNaN,checkConversionForNaN:checkConversionForNaN,hasEncodingLoss:hasEncodingLoss,isTypedArray:isTypedArray,bytesPerElement:bytesPerElement,isFunction:isFunction,nearestDivisor:nearestDivisor,computeStrides:computeStrides,toTypedArray:toTypedArray,makeOnesTypedArray:makeOnesTypedArray,makeZerosTypedArray:makeZerosTypedArray,now:now}),Profiler=function(){function e(e,t){this.backendTimer=e,this.logger=t,null==t&&(this.logger=new Logger);}return e.prototype.profileKernel=function(e,t){var r,n=this,o=this.backendTimer.time(function(){r=t();});return (Array.isArray(r)?r:[r]).forEach(function(t){var r=t.dataSync();checkComputationForNaN(r,t.dtype,e),o.then(function(o){var a=\"\";null!=o.getExtraProfileInfo&&(a=o.getExtraProfileInfo()),n.logger.logKernelProfile(e,t,r,o.kernelMs,a);});}),r},e}(),Logger=function(){function e(){}return e.prototype.logKernelProfile=function(e,t,r,n,o){var a=rightPad(n+\"ms\",9),i=rightPad(e,25),s=t.rank,u=t.size,l=rightPad(t.shape.toString(),14);console.log(\"%c\"+i+\"\\t%c\"+a+\"\\t%c\"+s+\"D \"+l+\"\\t%c\"+u+\"\\t%c\"+o,\"font-weight:bold\",\"color:red\",\"color:blue\",\"color: orange\",\"color: green\");},e}(),FORMAT_LIMIT_NUM_VALS=20,FORMAT_NUM_FIRST_LAST_VALS=3,FORMAT_NUM_SIG_DIGITS=7;function tensorToString(e,t,r,n){var o=computeStrides(t),a=computeMaxSizePerColumn(e,t,r,o),i=t.length,s=subTensorToString(e,t,r,o,a),u=[\"Tensor\"];return n&&(u.push(\"  dtype: \"+r),u.push(\"  rank: \"+i),u.push(\"  shape: [\"+t+\"]\"),u.push(\"  values:\")),u.push(s.map(function(e){return \"    \"+e}).join(\"\\n\")),u.join(\"\\n\")}function computeMaxSizePerColumn(e,t,r,n){var o=sizeFromShape(t),a=n[n.length-1],i=new Array(a).fill(0),s=t.length,u=\"complex64\"===r?createComplexTuples(e):e;if(s>1)for(var l=0;l<o/a;l++)for(var c=l*a,p=0;p<a;p++)i[p]=Math.max(i[p],valToString(u[c+p],0).length);return i}function valToString(e,t){return rightPad(Array.isArray(e)?parseFloat(e[0].toFixed(FORMAT_NUM_SIG_DIGITS))+\" + \"+parseFloat(e[1].toFixed(FORMAT_NUM_SIG_DIGITS))+\"j\":parseFloat(e.toFixed(FORMAT_NUM_SIG_DIGITS)).toString(),t)}function subTensorToString(e,t,r,n,o,a){void 0===a&&(a=!0);var i=\"complex64\"===r?2:1,s=t[0],u=t.length;if(0===u)return \"complex64\"===r?[valToString(createComplexTuples(e)[0],0)]:[e[0].toString()];if(1===u){if(s>FORMAT_LIMIT_NUM_VALS){var l=FORMAT_NUM_FIRST_LAST_VALS*i,c=Array.from(e.subarray(0,l)),p=Array.from(e.subarray(s-FORMAT_NUM_FIRST_LAST_VALS*i,s));return \"complex64\"===r&&(c=createComplexTuples(c),p=createComplexTuples(p)),[\"[\"+c.map(function(e,t){return valToString(e,o[t])}).join(\", \")+\", ..., \"+p.map(function(e,t){return valToString(e,o[s-FORMAT_NUM_FIRST_LAST_VALS+t])}).join(\", \")+\"]\"]}return [\"[\"+(\"complex64\"===r?createComplexTuples(e):Array.from(e)).map(function(e,t){return valToString(e,o[t])}).join(\", \")+\"]\"]}var d=t.slice(1),h=n.slice(1),f=n[0]*i,m=[];if(s>FORMAT_LIMIT_NUM_VALS){for(var g=0;g<FORMAT_NUM_FIRST_LAST_VALS;g++){var v=(y=g*f)+f;m.push.apply(m,subTensorToString(e.subarray(y,v),d,r,h,o,!1));}m.push(\"...\");for(g=s-FORMAT_NUM_FIRST_LAST_VALS;g<s;g++){v=(y=g*f)+f;m.push.apply(m,subTensorToString(e.subarray(y,v),d,r,h,o,g===s-1));}}else for(g=0;g<s;g++){var y;v=(y=g*f)+f;m.push.apply(m,subTensorToString(e.subarray(y,v),d,r,h,o,g===s-1));}var x=2===u?\",\":\"\";m[0]=\"[\"+m[0]+x;for(g=1;g<m.length-1;g++)m[g]=\" \"+m[g]+x;var T=\",\\n\";for(g=2;g<u;g++)T+=\"\\n\";return m[m.length-1]=\" \"+m[m.length-1]+\"]\"+(a?\"\":T),m}function createComplexTuples(e){for(var t=[],r=0;r<e.length;r+=2)t.push([e[r],e[r+1]]);return t}var TensorBuffer=function(){function e(e,t,r){if(this.dtype=t,this.shape=e.slice(),this.size=sizeFromShape(e),null!=r){var n=r.length;assert(n===this.size,\"Length of values '\"+n+\"' does not match the size inferred by the shape '\"+this.size+\"'.\");}if(\"complex64\"===t)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=r||getTypedArrayFromDType(t,sizeFromShape(this.shape)),this.strides=computeStrides(e);}return e.prototype.set=function(e){for(var t=[],r=1;r<arguments.length;r++)t[r-1]=arguments[r];0===t.length&&(t=[0]),assert(t.length===this.rank,\"The number of provided coordinates (\"+t.length+\") must match the rank (\"+this.rank+\")\");var n=this.locToIndex(t);this.values[n]=e;},e.prototype.get=function(){for(var e=[],t=0;t<arguments.length;t++)e[t]=arguments[t];0===e.length&&(e=[0]);for(var r=e[e.length-1],n=0;n<e.length-1;++n)r+=this.strides[n]*e[n];return this.values[r]},e.prototype.locToIndex=function(e){if(0===this.rank)return 0;if(1===this.rank)return e[0];for(var t=e[e.length-1],r=0;r<e.length-1;++r)t+=this.strides[r]*e[r];return t},e.prototype.indexToLoc=function(e){if(0===this.rank)return [];if(1===this.rank)return [e];for(var t=new Array(this.shape.length),r=0;r<t.length-1;++r)t[r]=Math.floor(e/this.strides[r]),e-=t[r]*this.strides[r];return t[t.length-1]=e,t},Object.defineProperty(e.prototype,\"rank\",{get:function(){return this.shape.length},enumerable:!0,configurable:!0}),e.prototype.toTensor=function(){return Tensor.make(this.shape,{values:this.values},this.dtype)},e}(),trackerFn=null,opHandler=null;function setTensorTracker(e){trackerFn=e;}function setOpHandler(e){opHandler=e;}var Tensor=function(){function e(e,t,r,n){this.isDisposedInternal=!1,this.shape=e.slice(),this.dtype=t||\"float32\",this.size=sizeFromShape(e),null!=r&&assert(this.size===r.length,\"Based on the provided shape, [\"+e+\"], and dtype \"+this.dtype+\", the tensor should have \"+this.size+\" values but has \"+r.length),this.strides=computeStrides(e),this.dataId=null!=n?n:{},this.id=trackerFn().nextTensorId(),this.rankType=this.rank<5?this.rank.toString():\"higher\",trackerFn().registerTensor(this),null!=r&&trackerFn().write(this.dataId,r);}return e.make=function(t,r,n){return new e(t,n,r.values,r.dataId)},e.prototype.flatten=function(){return this.throwIfDisposed(),this.as1D()},e.prototype.asScalar=function(){return this.throwIfDisposed(),assert(1===this.size,\"The array must have only 1 element.\"),this.reshape([])},e.prototype.as1D=function(){return this.throwIfDisposed(),this.reshape([this.size])},e.prototype.as2D=function(e,t){return this.throwIfDisposed(),this.reshape([e,t])},e.prototype.as3D=function(e,t,r){return this.throwIfDisposed(),this.reshape([e,t,r])},e.prototype.as4D=function(e,t,r,n){return this.throwIfDisposed(),this.reshape([e,t,r,n])},e.prototype.asType=function(e){return this.throwIfDisposed(),opHandler.cast(this,e)},Object.defineProperty(e.prototype,\"rank\",{get:function(){return this.shape.length},enumerable:!0,configurable:!0}),e.prototype.get=function(){for(var e=[],t=0;t<arguments.length;t++)e[t]=arguments[t];assert(e.length===this.rank,\"Number of coordinates in get() must match the rank of the tensor\"),assert(\"complex64\"!==this.dtype,\"Tensor.get() is not supported for complex64 tensors yet.\"),this.throwIfDisposed(),0===e.length&&(e=[0]);for(var r=e[e.length-1],n=0;n<e.length-1;++n)r+=this.strides[n]*e[n];return this.dataSync()[r]},e.prototype.buffer=function(){return opHandler.buffer(this.shape,this.dtype,this.dataSync())},e.prototype.data=function(){return __awaiter(this,void 0,void 0,function(){return __generator(this,function(e){return this.throwIfDisposed(),[2,trackerFn().read(this.dataId)]})})},e.prototype.dataSync=function(){return this.throwIfDisposed(),trackerFn().readSync(this.dataId)},e.prototype.dispose=function(){this.isDisposed||(trackerFn().disposeTensor(this),this.isDisposedInternal=!0);},Object.defineProperty(e.prototype,\"isDisposed\",{get:function(){return this.isDisposedInternal},enumerable:!0,configurable:!0}),e.prototype.throwIfDisposed=function(){if(this.isDisposed)throw new Error(\"Tensor is disposed.\")},e.prototype.toFloat=function(){return this.asType(\"float32\")},e.prototype.toInt=function(){return this.asType(\"int32\")},e.prototype.toBool=function(){return this.asType(\"bool\")},e.prototype.print=function(e){return void 0===e&&(e=!1),opHandler.print(this,e)},e.prototype.reshape=function(e){return this.throwIfDisposed(),opHandler.reshape(this,e)},e.prototype.reshapeAs=function(e){return this.throwIfDisposed(),this.reshape(e.shape)},e.prototype.expandDims=function(e){return void 0===e&&(e=0),opHandler.expandDims(this,e)},e.prototype.cumsum=function(e,t,r){return void 0===e&&(e=0),void 0===t&&(t=!1),void 0===r&&(r=!1),opHandler.cumsum(this,e,t,r)},e.prototype.squeeze=function(e){return this.throwIfDisposed(),opHandler.squeeze(this,e)},e.prototype.clone=function(){return this.throwIfDisposed(),opHandler.clone(this)},e.prototype.toString=function(e){return void 0===e&&(e=!1),tensorToString(this.dataSync(),this.shape,this.dtype,e)},e.prototype.tile=function(e){return this.throwIfDisposed(),opHandler.tile(this,e)},e.prototype.gather=function(e,t){return void 0===t&&(t=0),this.throwIfDisposed(),opHandler.gather(this,e,t)},e.prototype.matMul=function(e,t,r){return void 0===t&&(t=!1),void 0===r&&(r=!1),this.throwIfDisposed(),opHandler.matMul(this,e,t,r)},e.prototype.dot=function(e){return this.throwIfDisposed(),opHandler.dot(this,e)},e.prototype.norm=function(e,t,r){return void 0===e&&(e=\"euclidean\"),void 0===t&&(t=null),void 0===r&&(r=!1),this.throwIfDisposed(),opHandler.norm(this,e,t,r)},e.prototype.slice=function(e,t){return this.throwIfDisposed(),opHandler.slice(this,e,t)},e.prototype.reverse=function(e){return this.throwIfDisposed(),opHandler.reverse(this,e)},e.prototype.concat=function(e,t){return void 0===t&&(t=0),this.throwIfDisposed(),opHandler.concat([this,e],t)},e.prototype.split=function(e,t){return void 0===t&&(t=0),this.throwIfDisposed(),opHandler.split(this,e,t)},e.prototype.stack=function(e,t){return void 0===t&&(t=0),opHandler.stack([this,e],t)},e.prototype.unstack=function(e,t){return void 0===t&&(t=0),opHandler.unstack(this,t)},e.prototype.pad=function(e,t){return void 0===t&&(t=0),opHandler.pad(this,e,t)},e.prototype.batchNormalization=function(e,t,r,n,o){return void 0===r&&(r=.001),this.throwIfDisposed(),opHandler.batchNormalization(this,e,t,r,n,o)},e.prototype.all=function(e,t){return void 0===e&&(e=null),void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.all(this,e,t)},e.prototype.any=function(e,t){return void 0===e&&(e=null),void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.any(this,e,t)},e.prototype.logSumExp=function(e,t){return void 0===e&&(e=null),void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.logSumExp(this,e,t)},e.prototype.sum=function(e,t){return void 0===e&&(e=null),void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.sum(this,e,t)},e.prototype.prod=function(e,t){return void 0===e&&(e=null),void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.prod(this,e,t)},e.prototype.mean=function(e,t){return void 0===e&&(e=null),void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.mean(this,e,t)},e.prototype.min=function(e,t){return void 0===e&&(e=null),void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.min(this,e,t)},e.prototype.max=function(e,t){return void 0===e&&(e=null),void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.max(this,e,t)},e.prototype.argMin=function(e){return void 0===e&&(e=null),this.throwIfDisposed(),opHandler.argMin(this,e)},e.prototype.argMax=function(e){return void 0===e&&(e=null),this.throwIfDisposed(),opHandler.argMax(this,e)},e.prototype.cast=function(e){return this.throwIfDisposed(),opHandler.cast(this,e)},e.prototype.add=function(e){return this.throwIfDisposed(),opHandler.add(this,e)},e.prototype.addStrict=function(e){return this.throwIfDisposed(),opHandler.addStrict(this,e)},e.prototype.atan2=function(e){return this.throwIfDisposed(),opHandler.atan2(this,e)},e.prototype.sub=function(e){return this.throwIfDisposed(),opHandler.sub(this,e)},e.prototype.subStrict=function(e){return this.throwIfDisposed(),opHandler.subStrict(this,e)},e.prototype.pow=function(e){return this.throwIfDisposed(),opHandler.pow(this,e)},e.prototype.powStrict=function(e){return this.throwIfDisposed(),opHandler.powStrict(this,e)},e.prototype.mul=function(e){return this.throwIfDisposed(),opHandler.mul(this,e)},e.prototype.mulStrict=function(e){return this.throwIfDisposed(),opHandler.mulStrict(this,e)},e.prototype.div=function(e){return this.throwIfDisposed(),opHandler.div(this,e)},e.prototype.floorDiv=function(e){return this.throwIfDisposed(),opHandler.floorDiv(this,e)},e.prototype.divStrict=function(e){return this.throwIfDisposed(),opHandler.divStrict(this,e)},e.prototype.minimum=function(e){return this.throwIfDisposed(),opHandler.minimum(this,e)},e.prototype.minimumStrict=function(e){return this.throwIfDisposed(),opHandler.minimumStrict(this,e)},e.prototype.maximum=function(e){return this.throwIfDisposed(),opHandler.maximum(this,e)},e.prototype.maximumStrict=function(e){return this.throwIfDisposed(),opHandler.maximumStrict(this,e)},e.prototype.mod=function(e){return this.throwIfDisposed(),opHandler.mod(this,e)},e.prototype.modStrict=function(e){return this.throwIfDisposed(),opHandler.modStrict(this,e)},e.prototype.squaredDifference=function(e){return this.throwIfDisposed(),opHandler.squaredDifference(this,e)},e.prototype.squaredDifferenceStrict=function(e){return this.throwIfDisposed(),opHandler.squaredDifferenceStrict(this,e)},e.prototype.transpose=function(e){return this.throwIfDisposed(),opHandler.transpose(this,e)},e.prototype.notEqual=function(e){return this.throwIfDisposed(),opHandler.notEqual(this,e)},e.prototype.notEqualStrict=function(e){return this.throwIfDisposed(),opHandler.notEqualStrict(this,e)},e.prototype.less=function(e){return this.throwIfDisposed(),opHandler.less(this,e)},e.prototype.lessStrict=function(e){return this.throwIfDisposed(),opHandler.lessStrict(this,e)},e.prototype.equal=function(e){return this.throwIfDisposed(),opHandler.equal(this,e)},e.prototype.equalStrict=function(e){return this.throwIfDisposed(),opHandler.equalStrict(this,e)},e.prototype.lessEqual=function(e){return this.throwIfDisposed(),opHandler.lessEqual(this,e)},e.prototype.lessEqualStrict=function(e){return this.throwIfDisposed(),opHandler.lessEqualStrict(this,e)},e.prototype.greater=function(e){return this.throwIfDisposed(),opHandler.greater(this,e)},e.prototype.greaterStrict=function(e){return this.throwIfDisposed(),opHandler.greaterStrict(this,e)},e.prototype.greaterEqual=function(e){return this.throwIfDisposed(),opHandler.greaterEqual(this,e)},e.prototype.greaterEqualStrict=function(e){return this.throwIfDisposed(),opHandler.greaterEqualStrict(this,e)},e.prototype.logicalAnd=function(e){return this.throwIfDisposed(),opHandler.logicalAnd(this,e)},e.prototype.logicalOr=function(e){return this.throwIfDisposed(),opHandler.logicalOr(this,e)},e.prototype.logicalNot=function(){return this.throwIfDisposed(),opHandler.logicalNot(this)},e.prototype.logicalXor=function(e){return this.throwIfDisposed(),opHandler.logicalXor(this,e)},e.prototype.where=function(e,t){return this.throwIfDisposed(),opHandler.where(e,this,t)},e.prototype.neg=function(){return this.throwIfDisposed(),opHandler.neg(this)},e.prototype.ceil=function(){return this.throwIfDisposed(),opHandler.ceil(this)},e.prototype.floor=function(){return this.throwIfDisposed(),opHandler.floor(this)},e.prototype.sign=function(){return this.throwIfDisposed(),opHandler.sign(this)},e.prototype.exp=function(){return this.throwIfDisposed(),opHandler.exp(this)},e.prototype.expm1=function(){return this.throwIfDisposed(),opHandler.expm1(this)},e.prototype.log=function(){return this.throwIfDisposed(),opHandler.log(this)},e.prototype.log1p=function(){return this.throwIfDisposed(),opHandler.log1p(this)},e.prototype.sqrt=function(){return this.throwIfDisposed(),opHandler.sqrt(this)},e.prototype.rsqrt=function(){return this.throwIfDisposed(),opHandler.rsqrt(this)},e.prototype.square=function(){return this.throwIfDisposed(),opHandler.square(this)},e.prototype.reciprocal=function(){return this.throwIfDisposed(),opHandler.reciprocal(this)},e.prototype.abs=function(){return this.throwIfDisposed(),opHandler.abs(this)},e.prototype.clipByValue=function(e,t){return this.throwIfDisposed(),opHandler.clipByValue(this,e,t)},e.prototype.relu=function(){return this.throwIfDisposed(),opHandler.relu(this)},e.prototype.elu=function(){return this.throwIfDisposed(),opHandler.elu(this)},e.prototype.selu=function(){return this.throwIfDisposed(),opHandler.selu(this)},e.prototype.leakyRelu=function(e){return void 0===e&&(e=.2),this.throwIfDisposed(),opHandler.leakyRelu(this,e)},e.prototype.prelu=function(e){return this.throwIfDisposed(),opHandler.prelu(this,e)},e.prototype.sigmoid=function(){return this.throwIfDisposed(),opHandler.sigmoid(this)},e.prototype.logSigmoid=function(){return this.throwIfDisposed(),opHandler.logSigmoid(this)},e.prototype.softplus=function(){return this.throwIfDisposed(),opHandler.softplus(this)},e.prototype.zerosLike=function(){return this.throwIfDisposed(),opHandler.zerosLike(this)},e.prototype.onesLike=function(){return this.throwIfDisposed(),opHandler.onesLike(this)},e.prototype.sin=function(){return this.throwIfDisposed(),opHandler.sin(this)},e.prototype.cos=function(){return this.throwIfDisposed(),opHandler.cos(this)},e.prototype.tan=function(){return this.throwIfDisposed(),opHandler.tan(this)},e.prototype.asin=function(){return this.throwIfDisposed(),opHandler.asin(this)},e.prototype.acos=function(){return this.throwIfDisposed(),opHandler.acos(this)},e.prototype.atan=function(){return this.throwIfDisposed(),opHandler.atan(this)},e.prototype.sinh=function(){return this.throwIfDisposed(),opHandler.sinh(this)},e.prototype.cosh=function(){return this.throwIfDisposed(),opHandler.cosh(this)},e.prototype.tanh=function(){return this.throwIfDisposed(),opHandler.tanh(this)},e.prototype.asinh=function(){return this.throwIfDisposed(),opHandler.asinh(this)},e.prototype.acosh=function(){return this.throwIfDisposed(),opHandler.acosh(this)},e.prototype.atanh=function(){return this.throwIfDisposed(),opHandler.atanh(this)},e.prototype.erf=function(){return this.throwIfDisposed(),opHandler.erf(this)},e.prototype.round=function(){return this.throwIfDisposed(),opHandler.round(this)},e.prototype.step=function(e){return void 0===e&&(e=0),this.throwIfDisposed(),opHandler.step(this,e)},e.prototype.softmax=function(e){return void 0===e&&(e=-1),this.throwIfDisposed(),opHandler.softmax(this,e)},e.prototype.resizeBilinear=function(e,t){return void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.image.resizeBilinear(this,e,t)},e.prototype.resizeNearestNeighbor=function(e,t){return void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.image.resizeNearestNeighbor(this,e,t)},e.prototype.conv1d=function(e,t,r,n,o,a){return void 0===n&&(n=\"NWC\"),void 0===o&&(o=1),this.throwIfDisposed(),opHandler.conv1d(this,e,t,r,n,o,a)},e.prototype.conv2d=function(e,t,r,n,o,a){return void 0===n&&(n=\"NHWC\"),void 0===o&&(o=[1,1]),this.throwIfDisposed(),opHandler.conv2d(this,e,t,r,n,o,a)},e.prototype.conv2dTranspose=function(e,t,r,n,o){return this.throwIfDisposed(),opHandler.conv2dTranspose(this,e,t,r,n,o)},e.prototype.depthwiseConv2D=function(e,t,r,n,o,a){return void 0===n&&(n=\"NHWC\"),void 0===o&&(o=[1,1]),this.throwIfDisposed(),opHandler.depthwiseConv2d(this,e,t,r,n,o,a)},e.prototype.separableConv2d=function(e,t,r,n,o,a){return void 0===o&&(o=[1,1]),void 0===a&&(a=\"NHWC\"),this.throwIfDisposed(),opHandler.separableConv2d(this,e,t,r,n,o,a)},e.prototype.avgPool=function(e,t,r,n){return this.throwIfDisposed(),opHandler.avgPool(this,e,t,r,n)},e.prototype.maxPool=function(e,t,r,n){return this.throwIfDisposed(),opHandler.maxPool(this,e,t,r,n)},e.prototype.localResponseNormalization=function(e,t,r,n){return void 0===e&&(e=5),void 0===t&&(t=1),void 0===r&&(r=1),void 0===n&&(n=.5),opHandler.localResponseNormalization(this,e,t,r,n)},e.prototype.pool=function(e,t,r,n,o){return this.throwIfDisposed(),opHandler.pool(this,e,t,r,n,o)},e.prototype.variable=function(e,t,r){return void 0===e&&(e=!0),this.throwIfDisposed(),Variable.variable(this,e,t,r)},e.prototype.unsortedSegmentSum=function(e,t){return this.throwIfDisposed(),opHandler.unsortedSegmentSum(this,e,t)},e.prototype.batchToSpaceND=function(e,t){return this.throwIfDisposed(),opHandler.batchToSpaceND(this,e,t)},e.prototype.spaceToBatchND=function(e,t){return this.throwIfDisposed(),opHandler.spaceToBatchND(this,e,t)},e.prototype.topk=function(e,t){return void 0===e&&(e=1),void 0===t&&(t=!0),this.throwIfDisposed(),opHandler.topk(this,e,t)},e.prototype.stridedSlice=function(e,t,r,n,o){return void 0===n&&(n=0),void 0===o&&(o=0),this.throwIfDisposed(),opHandler.stridedSlice(this,e,t,r,n,o)},e.prototype.depthToSpace=function(e,t){return this.throwIfDisposed(),opHandler.depthToSpace(this,e,t)},e.prototype.fft=function(){return this.throwIfDisposed(),opHandler.spectral.fft(this)},e.prototype.ifft=function(){return this.throwIfDisposed(),opHandler.spectral.ifft(this)},e}();Object.defineProperty(Tensor,Symbol.hasInstance,{value:function(e){return !!e&&null!=e.shape&&null!=e.dtype}});var Variable=function(e){function t(t,r,n){void 0===r&&(r=!0);var o=e.call(this,t.shape,t.dtype,null,t.dataId)||this;o.trainable=r,o.name=n,null==o.name&&(o.name=trackerFn().nextVariableId().toString());try{trackerFn().registerVariable(o);}catch(e){throw trackerFn().disposeTensor(o),e}return o}return __extends(t,e),t.variable=function(e,r,n,o){return void 0===r&&(r=!0),null!=o&&o!==e.dtype&&(e=e.asType(o)),new t(e,r,n)},t.prototype.assign=function(e){if(e.dtype!==this.dtype)throw new Error(\"dtype of the new value (\"+e.dtype+\") and previous value (\"+this.dtype+\") must match\");if(!arraysEqual(e.shape,this.shape))throw new Error(\"shape of the new value (\"+e.shape+\") and previous value (\"+this.shape+\") must match\");trackerFn().disposeTensor(this),this.dataId=e.dataId,trackerFn().registerTensor(this);},t}(Tensor);Object.defineProperty(Variable,Symbol.hasInstance,{value:function(e){return e instanceof Tensor&&null!=e.assign&&e.assign instanceof Function}});var variable=Variable.variable;function getFilteredNodesXToY(e,t,r){for(var n={},o={},a=0;a<t.length;a++)n[t[a].id]=!0;for(a=0;a<e.length;a++){var i=(f=e[a]).inputs;for(var s in i){for(var u=i[s],l=!1,c=0;c<t.length;c++)if(n[u.id]){f.outputs.forEach(function(e){return n[e.id]=!0}),l=!0,o[f.id]=!0;break}if(l)break}}var p={};p[r.id]=!0;var d={};for(a=e.length-1;a>=0;a--)for(i=(f=e[a]).inputs,c=0;c<f.outputs.length;c++)if(p[f.outputs[c].id]){for(var s in i)p[i[s].id]=!0,d[f.id]=!0;break}var h=[];for(a=0;a<e.length;a++){var f;if(o[(f=e[a]).id]&&d[f.id]){var m={};for(var s in f.inputs){var g=f.inputs[s];n[g.id]&&(m[s]=g);}var v=Object.assign({},f);v.inputs=m,v.outputs=f.outputs,h.push(v);}}return h}function backpropagateGradients(e,t){for(var r=function(r){var n=t[r],o=[];if(n.outputs.forEach(function(t){var r=e[t.id];if(null!=r)o.push(r);else{var n=Tensor.make(t.shape,{values:makeZerosTypedArray(t.size,t.dtype)},t.dtype);o.push(n);}}),null==n.gradient)throw new Error(\"Cannot compute gradient: gradient function not found for \"+n.name+\".\");var a=n.gradient(1===n.outputs.length?o[0]:o);for(var i in n.inputs){if(!(i in a))throw new Error(\"Cannot backprop through input \"+i+\". Available gradients found: \"+Object.keys(a)+\".\");var s=a[i](),u=n.inputs[i];if(!arraysEqual(s.shape,u.shape))throw new Error(\"Error in gradient for op \"+n.name+\". The gradient of input '\"+i+\"' has shape '\"+s.shape+\"', which does not match the shape of the input '\"+u.shape+\"'\");if(null==e[u.id])e[u.id]=s;else{var l=e[u.id];e[u.id]=l.add(s),l.dispose();}}},n=t.length-1;n>=0;n--)r(n);}function assertTypesMatch(e,t){assert(e.dtype===t.dtype,\"The dtypes of the first(\"+e.dtype+\") and second(\"+t.dtype+\") input must match\");}function isTensorInList(e,t){for(var r=0;r<t.length;r++)if(t[r].id===e.id)return !0;return !1}function getTensorsInContainer(e){var t=[];return walkTensorContainer(e,t,new Set),t}function walkTensorContainer(e,t,r){if(null!=e)if(e instanceof Tensor)t.push(e);else if(isIterable(e)){var n=e;for(var o in n){var a=n[o];r.has(a)||(r.add(a),walkTensorContainer(a,t,r));}}}function isIterable(e){return Array.isArray(e)||\"object\"==typeof e}var Type,Engine=function(){function e(e,t,r){this.backend=e,this.safeMode=t,this.debugMode=r,this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numDataBuffers=0,this.profiling=!1,this.gradientScopeCount=0,this.customGradientDepth=0,this.keepTensors=new Set,this.tensorInfo=new WeakMap,this.activeScope={track:[],name:\"default scope\"},this.scopeStack=[this.activeScope],this.profiler=new Profiler(e),this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null};}return e.prototype.moveData=function(e){this.write(e,this.readSync(e));},e.prototype.tidy=function(e,t,r){var n=this;void 0===r&&(r=!1);var o,a=null;if(null==t){if(\"function\"!=typeof e)throw new Error(\"Please provide a function to tidy()\");t=e;}else{if(\"string\"!=typeof e&&!(e instanceof String))throw new Error(\"When calling with two arguments, the first argument to tidy() must be a string\");if(\"function\"!=typeof t)throw new Error(\"When calling with two arguments, the 2nd argument to tidy() must be a function\");a=e;}return this.scopedRun(function(){return n.startScope(a,r)},function(){return n.endScope(o,r)},function(){return (o=t())instanceof Promise&&console.error(\"Cannot return a Promise inside of tidy.\"),o})},e.prototype.scopedRun=function(e,t,r){e();try{var n=r();return t(),n}catch(e){throw t(),e}},e.prototype.nextTensorId=function(){return e.nextTensorId++},e.prototype.nextVariableId=function(){return e.nextVariableId++},e.prototype.runKernel=function(e,t,r){var n,o=this,a=[],i=function(e){return a.push(e),e},s=this.activeScope.name,u=this.numBytes,l=this.numTensors;if(this.scopedRun(function(){return o.customGradientDepth++},function(){return o.customGradientDepth--},function(){n=o.debugMode()?o.profiler.profileKernel(s,function(){return e(o.backend,i)}):e(o.backend,i);}),this.shouldRecord()){var c={id:this.nextTapeNodeId++,name:s,inputs:t,outputs:Array.isArray(n)?n:[n]};null!=r&&(c.gradient=function(e){return r(e,a)}),this.activeTape.push(c);}return this.profiling&&this.activeProfile.kernels.push({name:s,bytesAdded:this.numBytes-u,totalBytesSnapshot:this.numBytes,tensorsAdded:this.numTensors-l,totalTensorsSnapshot:this.numTensors,inputShapes:Object.keys(t).map(function(e){return t[e].shape}),outputShape:Array.isArray(n)?n.map(function(e){return e.shape}):n.shape}),n},e.prototype.registerTensor=function(e){var t=this.tensorInfo.has(e.dataId)?this.tensorInfo.get(e.dataId).refCount:0;this.numTensors++,0===t&&(this.numDataBuffers++,\"complex64\"!==e.dtype&&(this.numBytes+=sizeFromShape(e.shape)*bytesPerElement(e.dtype)),this.tensorInfo.set(e.dataId,{backend:this.backend,dtype:e.dtype,shape:e.shape,refCount:0}),this.backend.register(e.dataId,e.shape,e.dtype)),this.tensorInfo.get(e.dataId).refCount++,e instanceof Variable||this.track(e);},e.prototype.registerVariable=function(e){if(null!=this.registeredVariables[e.name])throw new Error(\"Variable with name \"+e.name+\" was already registered\");this.registeredVariables[e.name]=e;},e.prototype.disposeTensor=function(e){this.tensorInfo.has(e.dataId)&&(this.keepTensors.has(e.id)&&this.keepTensors.delete(e.id),this.numTensors--,this.tensorInfo.get(e.dataId).refCount<=1?(this.tensorInfo.get(e.dataId).backend.disposeData(e.dataId),this.numDataBuffers--,\"complex64\"!==e.dtype&&(this.numBytes-=sizeFromShape(e.shape)*bytesPerElement(e.dtype)),this.tensorInfo.delete(e.dataId)):this.tensorInfo.get(e.dataId).refCount--);},e.prototype.disposeVariables=function(){for(var e in this.registeredVariables){var t=this.registeredVariables[e];this.disposeTensor(t),delete this.registeredVariables[e];}},e.prototype.memory=function(){var e=this.backend.memory();return e.numTensors=this.numTensors,e.numDataBuffers=this.numDataBuffers,e.numBytes=this.numBytes,e},e.prototype.profile=function(e){return __awaiter(this,void 0,void 0,function(){var t,r;return __generator(this,function(n){return this.profiling=!0,t=this.numBytes,r=this.numTensors,this.activeProfile.kernels=[],this.activeProfile.result=e(),this.profiling=!1,this.activeProfile.peakBytes=Math.max.apply(Math,this.activeProfile.kernels.map(function(e){return e.totalBytesSnapshot})),this.activeProfile.newBytes=this.numBytes-t,this.activeProfile.newTensors=this.numTensors-r,[2,this.activeProfile]})})},e.prototype.shouldRecord=function(){return null!=this.activeTape&&0===this.customGradientDepth},e.prototype.addTapeNode=function(e,t,r){var n={};e.forEach(function(e,t){n[t]=e;});var o={id:this.nextTapeNodeId++,name:this.activeScope.name,inputs:n,outputs:[t],gradient:function(e){var t={};return r(e).forEach(function(e,r){t[r]=function(){return e};}),t}};this.activeTape.push(o);},e.prototype.keep=function(e){if(1===this.scopeStack.length&&this.safeMode)throw new Error(\"Safe mode is ON. Enclose all tensor operations inside tf.tidy(): tf.tidy(() => {...}) to avoid memory leaks.\");return this.keepTensors.add(e.id),e},e.prototype.startScope=function(e,t){void 0===t&&(t=!1),t&&0===this.gradientScopeCount&&(this.activeTape=[]),t&&this.gradientScopeCount++;var r={track:[],name:\"unnamed scope\"};e&&(r.name=e),this.scopeStack.push(r),this.activeScope=r;},e.prototype.endScope=function(e,t){var r=this;void 0===t&&(t=!1),t&&(this.gradientScopeCount--,0===this.gradientScopeCount&&(this.activeTape=null));var n=new Set(this.keepTensors),o=getTensorsInContainer(e);o.forEach(function(e){return n.add(e.id)});for(var a=0;a<this.activeScope.track.length;a++){var i=this.activeScope.track[a];n.has(i.id)||(null!=this.activeTape?o.push(i):i.dispose());}var s=this.scopeStack.pop();this.activeScope=0===this.scopeStack.length?{track:[],name:\"default scope\"}:this.scopeStack[this.scopeStack.length-1],o.forEach(function(e){!r.keepTensors.has(e.id)&&isTensorInList(e,s.track)&&r.track(e);});},e.prototype.gradients=function(e,t,r,n){var o=this;return void 0===n&&(n=!1),assert(t.length>0,\"gradients() received an empty list of xs.\"),this.tidy(\"gradients\",function(){var a=e();assert(a instanceof Tensor,\"The result y returned by f() must be a tensor.\");var i=getFilteredNodesXToY(o.activeTape,t,a);if(!n&&0===i.length&&t.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.\");var s={};return s[a.id]=null==r?ones(a.shape):r,backpropagateGradients(s,i),{value:a,grads:t.map(function(e){return s[e.id]})}},!0)},e.prototype.customGrad=function(e){var t=this;return assert(isFunction(e),\"The f passed in customGrad(f) must be a function.\"),function(){for(var r,n,o=[],a=0;a<arguments.length;a++)o[a]=arguments[a];if(assert(o.every(function(e){return e instanceof Tensor}),\"The args passed in customGrad(f)(x1, x2,...) must all be tensors\"),t.scopedRun(function(){return t.customGradientDepth++},function(){return t.customGradientDepth--},function(){n=t.tidy(e.name,function(){var t=e.apply(void 0,o),n=t.value,a=t.gradFunc;return assert(n instanceof Tensor,\"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor\"),assert(isFunction(a),\"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function.\"),r=a,n},!0);}),t.shouldRecord()){t.addTapeNode(o,n,function(e){var t=r(e),n=Array.isArray(t)?t:[t];return assert(n.length===o.length,\"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(n.every(function(e){return e instanceof Tensor}),\"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.\"),n});}return n}},e.prototype.write=function(e,t){var r=this.tensorInfo.get(e);this.backend!==r.backend&&(r.backend.disposeData(e),r.backend=this.backend,this.backend.register(e,r.shape,r.dtype)),this.backend.write(e,t);},e.prototype.readSync=function(e){return this.tensorInfo.get(e).backend.readSync(e)},e.prototype.read=function(e){return this.tensorInfo.get(e).backend.read(e)},e.prototype.fromPixels=function(e,t){return this.backend.fromPixels(e,t)},e.prototype.time=function(e){return __awaiter(this,void 0,void 0,function(){var t,r;return __generator(this,function(n){switch(n.label){case 0:return t=now(),[4,this.backend.time(e)];case 1:return (r=n.sent()).wallMs=now()-t,[2,r]}})})},e.prototype.track=function(e){if(1===this.scopeStack.length&&this.safeMode)throw new Error(\"Safe mode is ON. Enclose all tensor operations inside tf.tidy(): tf.tidy(() => {op();...}); to avoid memory leaks.\");return this.activeScope.track.push(e),e},e.nextTensorId=0,e.nextVariableId=0,e}();function ones(e){var t=makeOnesTypedArray(sizeFromShape(e),\"float32\");return Tensor.make(e,{values:t})}!function(e){e[e.NUMBER=0]=\"NUMBER\",e[e.BOOLEAN=1]=\"BOOLEAN\",e[e.STRING=2]=\"STRING\";}(Type||(Type={}));var MAX_TEXTURE_SIZE,URL_PROPERTIES=[{name:\"DEBUG\",type:Type.BOOLEAN},{name:\"IS_BROWSER\",type:Type.BOOLEAN},{name:\"WEBGL_CPU_FORWARD\",type:Type.BOOLEAN},{name:\"WEBGL_PACK_BATCHNORMALIZATION\",type:Type.BOOLEAN},{name:\"WEBGL_CONV_IM2COL\",type:Type.BOOLEAN},{name:\"WEBGL_MAX_TEXTURE_SIZE\",type:Type.NUMBER},{name:\"WEBGL_PAGING_ENABLED\",type:Type.BOOLEAN},{name:\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\",type:Type.NUMBER},{name:\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\",type:Type.BOOLEAN},{name:\"WEBGL_VERSION\",type:Type.NUMBER},{name:\"WEBGL_RENDER_FLOAT32_ENABLED\",type:Type.BOOLEAN},{name:\"WEBGL_DOWNLOAD_FLOAT_ENABLED\",type:Type.BOOLEAN},{name:\"WEBGL_FENCE_API_ENABLED\",type:Type.BOOLEAN},{name:\"WEBGL_SIZE_UPLOAD_UNIFORM\",type:Type.NUMBER},{name:\"BACKEND\",type:Type.STRING},{name:\"EPSILON\",type:Type.NUMBER},{name:\"PROD\",type:Type.BOOLEAN},{name:\"TENSORLIKE_CHECK_SHAPE_CONSISTENCY\",type:Type.BOOLEAN}];function isWebGLVersionEnabled(e){try{if(null!=getWebGLContext(e))return !0}catch(e){return !1}return !1}function getWebGLMaxTextureSize(e){if(null==MAX_TEXTURE_SIZE){var t=getWebGLContext(e);MAX_TEXTURE_SIZE=t.getParameter(t.MAX_TEXTURE_SIZE);}return MAX_TEXTURE_SIZE}function getWebGLDisjointQueryTimerVersion(e){if(0===e)return 0;var t=getWebGLContext(e);return hasExtension(t,\"EXT_disjoint_timer_query_webgl2\")&&2===e?2:hasExtension(t,\"EXT_disjoint_timer_query\")?1:0}function isRenderToFloatTextureEnabled(e){if(0===e)return !1;var t=getWebGLContext(e);if(1===e){if(!hasExtension(t,\"OES_texture_float\"))return !1}else if(!hasExtension(t,\"EXT_color_buffer_float\"))return !1;return createFloatTextureAndBindToFramebuffer(t,e)}function isDownloadFloatTextureEnabled(e){if(0===e)return !1;var t=getWebGLContext(e);if(1===e){if(!hasExtension(t,\"OES_texture_float\"))return !1;if(!hasExtension(t,\"WEBGL_color_buffer_float\"))return !1}else if(!hasExtension(t,\"EXT_color_buffer_float\"))return !1;return createFloatTextureAndBindToFramebuffer(t,e)}function isWebGLFenceEnabled(e){return 2===e&&null!=getWebGLContext(e).fenceSync}function isChrome(){return \"undefined\"!=typeof navigator&&null!=navigator&&null!=navigator.userAgent&&/Chrome/.test(navigator.userAgent)&&/Google Inc/.test(navigator.vendor)}var TENSORFLOWJS_FLAGS_PREFIX=\"tfjsflags\";function getFeaturesFromURL(){var e={};if(\"undefined\"==typeof window||void 0===window.location)return e;var t=getQueryParams(window.location.search);if(TENSORFLOWJS_FLAGS_PREFIX in t){var r={};t[TENSORFLOWJS_FLAGS_PREFIX].split(\",\").forEach(function(e){var t=e.split(\":\"),n=t[0],o=t[1];r[n]=o;}),URL_PROPERTIES.forEach(function(t){t.name in r&&(console.log(\"Setting feature override from URL \"+t.name+\": \"+r[t.name]),t.type===Type.NUMBER?e[t.name]=+r[t.name]:t.type===Type.BOOLEAN?e[t.name]=\"true\"===r[t.name]:t.type===Type.STRING?e[t.name]=r[t.name]:console.warn(\"Unknown URL param: \"+t.name+\".\"));});}return e}function hasExtension(e,t){return null!=e.getExtension(t)}function createFloatTextureAndBindToFramebuffer(e,t){var r=e.createFramebuffer(),n=e.createTexture();e.bindTexture(e.TEXTURE_2D,n);var o=2===t?e.RGBA32F:e.RGBA;e.texImage2D(e.TEXTURE_2D,0,o,1,1,0,e.RGBA,e.FLOAT,null),e.bindFramebuffer(e.FRAMEBUFFER,r),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,n,0);var a=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(n),e.deleteFramebuffer(r),a}function getQueryParams(e){var t={};return e.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g,function(e){for(var r=[],n=1;n<arguments.length;n++)r[n-1]=arguments[n];return decodeParam(t,r[0],r[1]),r.join(\"=\")}),t}function decodeParam(e,t,r){e[decodeURIComponent(t)]=decodeURIComponent(r||\"\");}var EPSILON_FLOAT16=.001,TEST_EPSILON_FLOAT16=.1,EPSILON_FLOAT32=1e-7,TEST_EPSILON_FLOAT32=.001,Environment=function(){function e(e){this.features={},this.registry={},null!=e&&(this.features=e),this.get(\"DEBUG\")&&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.\");}return e.setBackend=function(e,t){if(void 0===t&&(t=!1),!(e in ENV.registry))throw new Error(\"Backend name '\"+e+\"' not found in registry\");ENV.engine.backend=ENV.findBackend(e),ENV.backendName=e;},e.getBackend=function(){return ENV.initEngine(),ENV.backendName},e.disposeVariables=function(){ENV.engine.disposeVariables();},e.memory=function(){return ENV.engine.memory()},e.profile=function(e){return ENV.engine.profile(e)},e.tidy=function(e,t,r){return void 0===r&&(r=!1),ENV.engine.tidy(e,t,r)},e.dispose=function(e){getTensorsInContainer(e).forEach(function(e){return e.dispose()});},e.keep=function(e){return ENV.engine.keep(e)},e.time=function(e){return ENV.engine.time(e)},e.prototype.get=function(e){return e in this.features?this.features[e]:(this.features[e]=this.evaluateFeature(e),this.features[e])},e.prototype.getFeatures=function(){return this.features},e.prototype.set=function(e,t){this.features[e]=t;},e.prototype.getBestBackendName=function(){var e=this;if(0===Object.keys(this.registry).length)throw new Error(\"No backend found in registry.\");return Object.keys(this.registry).map(function(t){return {name:t,entry:e.registry[t]}}).sort(function(e,t){return t.entry.priority-e.entry.priority})[0].name},e.prototype.evaluateFeature=function(e){if(\"DEBUG\"===e)return !1;if(\"IS_BROWSER\"===e)return \"undefined\"!=typeof window;if(\"IS_NODE\"===e)return \"undefined\"!=typeof process&&void 0!==process.versions.node;if(\"IS_CHROME\"===e)return isChrome();if(\"WEBGL_CPU_FORWARD\"===e)return !0;if(\"WEBGL_PACK_BATCHNORMALIZATION\"===e)return !1;if(\"WEBGL_CONV_IM2COL\"===e)return !1;if(\"WEBGL_PAGING_ENABLED\"===e)return this.get(\"IS_BROWSER\")&&!this.get(\"PROD\");if(\"WEBGL_MAX_TEXTURE_SIZE\"===e)return getWebGLMaxTextureSize(this.get(\"WEBGL_VERSION\"));if(\"IS_TEST\"===e)return !1;if(\"BACKEND\"===e)return this.getBestBackendName();if(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"===e){var t=this.get(\"WEBGL_VERSION\");return 0===t?0:getWebGLDisjointQueryTimerVersion(t)}if(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\"===e)return this.get(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")>0&&!isMobile();if(\"HAS_WEBGL\"===e)return this.get(\"WEBGL_VERSION\")>0;if(\"WEBGL_VERSION\"===e)return isWebGLVersionEnabled(2)?2:isWebGLVersionEnabled(1)?1:0;if(\"WEBGL_RENDER_FLOAT32_ENABLED\"===e)return isRenderToFloatTextureEnabled(this.get(\"WEBGL_VERSION\"));if(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\"===e)return isDownloadFloatTextureEnabled(this.get(\"WEBGL_VERSION\"));if(\"WEBGL_FENCE_API_ENABLED\"===e)return isWebGLFenceEnabled(this.get(\"WEBGL_VERSION\"));if(\"WEBGL_SIZE_UPLOAD_UNIFORM\"===e)return this.get(\"WEBGL_RENDER_FLOAT32_ENABLED\")?4:0;if(\"TEST_EPSILON\"===e)return 32===this.backend.floatPrecision()?TEST_EPSILON_FLOAT32:TEST_EPSILON_FLOAT16;if(\"EPSILON\"===e)return 32===this.backend.floatPrecision()?EPSILON_FLOAT32:EPSILON_FLOAT16;if(\"PROD\"===e)return !1;if(\"TENSORLIKE_CHECK_SHAPE_CONSISTENCY\"===e)return !this.get(\"PROD\");throw new Error(\"Unknown feature \"+e+\".\")},e.prototype.setFeatures=function(e){this.features=Object.assign({},e);},e.prototype.reset=function(){this.features=getFeaturesFromURL(),null!=this.globalEngine&&(this.globalEngine=null);},Object.defineProperty(e.prototype,\"backend\",{get:function(){return this.engine.backend},enumerable:!0,configurable:!0}),e.prototype.findBackend=function(e){return e in this.registry?this.registry[e].backend:null},e.prototype.registerBackend=function(e,t,r,n){var o=this;if(void 0===r&&(r=1),e in this.registry)return console.warn(e+\" backend was already registered. Reusing existing backend\"),null!=n&&n(function(){return o.engine}),!1;try{var a=t();return a.setDataMover({moveData:function(e){return o.engine.moveData(e)}}),this.registry[e]={backend:a,priority:r},!0}catch(t){return console.warn(\"Registration of backend \"+e+\" failed\"),console.warn(t.stack||t.message),!1}},e.prototype.removeBackend=function(e){if(!(e in this.registry))throw new Error(e+\" backend not found in registry\");this.registry[e].backend.dispose(),delete this.registry[e];},Object.defineProperty(e.prototype,\"engine\",{get:function(){return this.initEngine(),this.globalEngine},enumerable:!0,configurable:!0}),e.prototype.initEngine=function(){var e=this;if(null==this.globalEngine){this.backendName=this.get(\"BACKEND\");var t=this.findBackend(this.backendName);this.globalEngine=new Engine(t,!1,function(){return e.get(\"DEBUG\")});}},e}();function getGlobalNamespace(){var e;if(\"undefined\"!=typeof window)e=window;else{if(\"undefined\"==typeof process)throw new Error(\"Could not find a global object\");e=process;}return e}function getOrMakeEnvironment(){var e=getGlobalNamespace();return null==e.ENV&&(e.ENV=new Environment(getFeaturesFromURL()),setTensorTracker(function(){return e.ENV.engine})),e.ENV}var ENV=getOrMakeEnvironment(),environment=Object.freeze({Environment:Environment,ENV:ENV});function grad(e){return assert(isFunction(e),\"The f passed in grad(f) must be a function\"),function(t,r){return assert(t instanceof Tensor,\"The x passed in grad(f)(x) must be a tensor\"),assert(null==r||r instanceof Tensor,\"The dy passed in grad(f)(x, dy) must be a tensor\"),ENV.engine.tidy(function(){var n=ENV.engine.gradients(function(){return e(t)},[t],r),o=n.value,a=n.grads;return null!=r&&assertShapesMatch(o.shape,r.shape,\"The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)\"),checkGrads(a),a[0]})}}function grads(e){return assert(isFunction(e),\"The f passed in grads(f) must be a function\"),function(t,r){return assert(Array.isArray(t)&&t.every(function(e){return e instanceof Tensor}),\"The args passed in grads(f)(args) must be an array of tensors\"),assert(null==r||r instanceof Tensor,\"The dy passed in grads(f)(args, dy) must be a tensor\"),ENV.engine.tidy(function(){var n=ENV.engine.gradients(function(){return e.apply(void 0,t)},t,r),o=n.value,a=n.grads;return null!=r&&assertShapesMatch(o.shape,r.shape,\"The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])\"),checkGrads(a),a})}}function valueAndGrad(e){return assert(isFunction(e),\"The f passed in valueAndGrad(f) must be a function\"),function(t,r){assert(t instanceof Tensor,\"The x passed in valueAndGrad(f)(x) must be a tensor\"),assert(null==r||r instanceof Tensor,\"The dy passed in valueAndGrad(f)(x, dy) must be a tensor\");var n=ENV.engine.gradients(function(){return e(t)},[t],r),o=n.grads,a=n.value;return checkGrads(o),{grad:o[0],value:a}}}function valueAndGrads(e){return assert(isFunction(e),\"The f passed in valueAndGrads(f) must be a function\"),function(t,r){assert(Array.isArray(t)&&t.every(function(e){return e instanceof Tensor}),\"The args passed in valueAndGrads(f)(args) must be array of tensors\"),assert(null==r||r instanceof Tensor,\"The dy passed in valueAndGrads(f)(args, dy) must be a tensor\");var n=ENV.engine.gradients(function(){return e.apply(void 0,t)},t,r);return null!=r&&assertShapesMatch(n.value.shape,r.shape,\"The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])\"),checkGrads(n.grads),n}}function variableGrads(e,t){if(assert(isFunction(e),\"The f passed in variableGrads(f) must be a function\"),assert(null==t||Array.isArray(t)&&t.every(function(e){return e instanceof Variable}),\"The varList passed in variableGrads(f, varList) must be an array of variables\"),null==t)for(var r in t=[],ENV.engine.registeredVariables)t.push(ENV.engine.registeredVariables[r]);var n=t.length;assert((t=t.filter(function(e){return e.trainable})).length>0,\"variableGrads() expects at least one of the input variables to be trainable, but none of the \"+n+\" variables is trainable.\");var o=ENV.engine.gradients(e,t,null,!0),a=o.value,i=o.grads;assert(i.some(function(e){return null!=e}),\"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(0===a.rank,\"The f passed in variableGrads(f) must return a scalar, but it returned a rank-\"+a.rank+\" tensor\");var s={};return t.forEach(function(e,t){null!=i[t]&&(s[e.name]=i[t]);}),{value:a,grads:s}}function customGrad(e){return ENV.engine.customGrad(e)}function checkGrads(e){if(e.filter(function(e){return null==e}).length>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.\")}var tidy=Environment.tidy,keep=Environment.keep,dispose=Environment.dispose,time=Environment.time,profile=Environment.profile;function warn(){for(var e=[],t=0;t<arguments.length;t++)e[t]=arguments[t];ENV.get(\"IS_TEST\")||console.warn.apply(console,e);}function getReshaped(e,t,r,n){void 0===n&&(n=!0);var o=[];if(n)(o=o.concat(t.slice(0))).push(e[0]/r),o=o.concat(e.slice(1));else{o=o.concat(e[0]);for(var a=t.length,i=0;i<a;++i)o=o.concat([e[i+1]/t[i],t[i]]);o=o.concat(e.slice(a+1));}return o}function getPermuted(e,t,r){void 0===r&&(r=!0);var n=[];if(r){n.push(t);for(var o=t+1;o<e;++o)o<=2*t?(n.push(o),n.push(o-(t+1))):n.push(o);}else{var a=[],i=[];for(o=1;o<e;++o)o>=2*t+1||o%2==1?i.push(o):a.push(o);n.push.apply(n,a),n.push(0),n.push.apply(n,i);}return n}function getReshapedPermuted(e,t,r,n){void 0===n&&(n=!0);var o=[];n?o.push(e[0]/r):o.push(e[0]*r);for(var a=1;a<e.length;++a)a<=t.length?n?o.push(t[a-1]*e[a]):o.push(e[a]/t[a-1]):o.push(e[a]);return o}function getSliceBeginCoords(e,t){for(var r=[0],n=0;n<t;++n)r.push(e[n][0]);return r}function getSliceSize(e,t,r){for(var n=e.slice(0,1),o=0;o<r;++o)n.push(e[o+1]-t[o][0]-t[o][1]);return n}function axesAreInnerMostDims(e,t){for(var r=0;r<e.length;++r)if(e[e.length-r-1]!==t-1-r)return !1;return !0}function combineLocations(e,t,r){for(var n=e.length+t.length,o=[],a=0,i=0,s=0;s<n;s++)-1===r.indexOf(s)?o.push(e[a++]):o.push(t[i++]);return o}function computeOutAndReduceShapes(e,t){for(var r=[],n=e.length,o=0;o<n;o++)-1===t.indexOf(o)&&r.push(e[o]);return [r,t.map(function(t){return e[t]})]}function expandShapeToKeepDim(e,t){return combineLocations(e,t.map(function(e){return 1}),t)}function parseAxisParam(e,t){var r=t.length;return assert((e=null==e?t.map(function(e,t){return t}):[].concat(e)).every(function(e){return e>=-r&&e<r}),\"All values in axis param must be in range [-\"+r+\", \"+r+\") but got axis \"+e),assert(e.every(function(e){return isInt(e)}),\"All values in axis param must be integers but got axis \"+e),e.map(function(e){return e<0?r+e:e})}function assertAxesAreInnerMostDims(e,t,r){assert(axesAreInnerMostDims(t,r),e+\" supports only inner-most axes for now. Got axes \"+t+\" and rank-\"+r+\" input.\");}function getAxesPermutation(e,t){if(axesAreInnerMostDims(e,t))return null;for(var r=[],n=0;n<t;++n)-1===e.indexOf(n)&&r.push(n);return e.forEach(function(e){return r.push(e)}),r}function getUndoAxesPermutation(e){return e.map(function(e,t){return [t,e]}).sort(function(e,t){return e[1]-t[1]}).map(function(e){return e[0]})}function getInnerMostAxes(e,t){for(var r=[],n=t-e;n<t;++n)r.push(n);return r}function assertParamsConsistent(e,t){var r=e[0].length;e.forEach(function(e,t){assert(e.length===r,\"Error in concat\"+r+\"D: rank of tensors[\"+t+\"] must be the same as the rank of the rest (\"+r+\")\");}),assert(t>=0&&t<r,\"Error in concat\"+r+\"D: axis must be between 0 and \"+(r-1)+\".\");var n=e[0];e.forEach(function(e,o){for(var a=0;a<r;a++)assert(a===t||e[a]===n[a],\"Error in concat\"+r+\"D: Shape of tensors[\"+o+\"] (\"+e+\") does not match the shape of the rest (\"+n+\") along the non-concatenated axis \"+o+\".\");});}function computeOutShape(e,t){for(var r=e[0].slice(),n=1;n<e.length;n++)r[t]+=e[n][t];return r}function prepareAndValidate(e,t){if(e.rank<1)throw new Error(\"tf.gatherND() expects the input to be rank 1 or higher, but the rank was \"+e.rank+\".\");if(t.rank<1)throw new Error(\"tf.gatherND() expects the indices to be rank 1 or higher, but the rank was \"+t.rank+\".\");if(\"int32\"!==t.dtype)throw new Error(\"tf.gatherND() expects the indices to be int32 type, but the dtype was \"+t.dtype+\".\");if(t.shape[t.rank-1]>e.rank)throw new Error(\"index innermost dimension length must be <= tensor rank; saw: \"+t.shape[t.rank-1]+\" vs. \"+e.rank);if(0===e.size)throw new Error(\"Requested more than 0 entries, but input is empty. Input shape: \"+e.shape+\".\");for(var r=t.shape,n=r[r.length-1],o=1,a=0;a<r.length-1;++a)o*=r[a];var i=e.shape,s=r.slice();s.pop();var u=1;for(a=n;a<e.rank;++a)u*=i[a],s.push(i[a]);var l=computeStrides(e.shape).map(function(e){return e/u}).concat([1]).slice(0,n);return [s,o,u,l]}var PARALLELIZE_THRESHOLD=30;function computeOptimalWindowSize(e){return e<=PARALLELIZE_THRESHOLD?e:nearestDivisor(e,Math.floor(Math.sqrt(e)))}function validateUpdateShape(e,t,r){var n=t.rank>1?t.shape[t.rank-1]:1,o=t.rank>1?t.rank-1:1,a=\"Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: \"+r.shape+\", indices.shape: \"+t.shape+\", shape: \"+e+\", sliceDim: \"+n+\", and batchDim: \"+o+\".\";if(r.rank<o)throw new Error(a+\" update.rank < \"+o+\". \");if(e.length<n+(r.rank-o))throw new Error(a+\" Output shape length < \"+(n+(r.rank-o)));if(r.rank!==o+e.length-n)throw new Error(a+\" update.rank != \"+(o+e.length-n));for(var i=0;i<o;++i)if(r.shape[i]!==t.shape[i])throw new Error(a+\" updates.shape[\"+i+\"] (\"+r.shape[i]+\") != indices.shape[\"+i+\"] (\"+t.shape[i]+\").\");for(i=0;i<r.rank-o;++i)if(r.shape[i+o]!==e[i+n])throw new Error(a+\" updates.shape[\"+(i+o)+\"] (\"+r.shape[i+o]+\") != shape[\"+(i+o)+\"] (\"+e[i+o]+\")\")}function validateInput(e,t,r){if(t.rank<1)throw new Error(\"tf.scatterND() expects the indices to be rank 1 or higher, but the rank was \"+t.rank+\".\");if(e.rank<1)throw new Error(\"tf.scatterND() expects the updates to be rank 1 or higher, but the rank was \"+e.rank+\".\");if(\"int32\"!==t.dtype)throw new Error(\"The dtype of 'indices' should be int32, but got dtype: \"+t.dtype);if(r.length<1)throw new Error(\"Output rank must be greater or equal to 1, but got shape: \"+r);if(0===r.length){if(0===t.size)throw new Error(\"Indices specified for empty output. indices shape: \"+t.shape);if(0===e.size)throw new Error(\"Updates specified for empty output. updates shape: \"+e.shape)}validateUpdateShape(r,t,e);}function calculateShapes(e,t,r){for(var n=t.rank>1?t.shape[t.rank-1]:1,o=r.length,a=1,i=n;i<o;++i)a*=r[i];var s=n<1?1:n,u=t.size/s,l=computeStrides(r).concat([1]);return {sliceRank:n,numUpdates:u,sliceSize:a,strides:l.slice(l.length-n,l.length),outputSize:sizeFromShape(r)}}function segOpComputeOptimalWindowSize(e,t){var r,n=!1;for(e<=PARALLELIZE_THRESHOLD?(r=e,n=!0):r=nearestDivisor(e,Math.floor(Math.sqrt(e)));!n;){if(r>t||r===e){n=!0;break}r=nearestDivisor(e,r+1);}return r}function computeOutShape$1(e,t,r){for(var n=[],o=e.length,a=0;a<o;a++)a!==t?n.push(e[a]):n.push(r);return n}function assertParamsValid(e,t,r){assert(e.rank===t.length,\"Error in slice\"+e.rank+\"D: Length of begin \"+t+\" must match the rank of the array (\"+e.rank+\").\"),assert(e.rank===r.length,\"Error in slice\"+e.rank+\"D: Length of size \"+r+\" must match the rank of the array (\"+e.rank+\").\");for(var n=0;n<e.rank;++n)assert(t[n]+r[n]<=e.shape[n],\"Error in slice\"+e.rank+\"D: begin[\"+n+\"] + size[\"+n+\"] (\"+(t[n]+r[n])+\") would overflow input.shape[\"+n+\"] (\"+e.shape[n]+\")\");}function getStridedSlicedInfo(e,t,r,n,o,a,i,s,u){if(void 0===o&&(o=0),void 0===a&&(a=0),void 0===i&&(i=0),void 0===s&&(s=0),void 0===u&&(u=0),0!==i)throw new Error(\"ellipsis mask is not yet supported\");if(0!==s)throw new Error(\"new axis mask is not yet supported\");for(var l=[],c=[],p=[],d=0;d<e.length;d++)l[d]=startForAxis(o,t,n,e,d),c[d]=stopForAxis(a,r,n,e,d),u&1<<d&&(c[d]=l[d]+1,p.push(d));var h=new Array(e.length).fill(0);return h=h.map(function(e,t){for(var r=0,o=l[t];!(n[t]>0?o>=c[t]:o<=c[t]);o+=n[t])r+=1;return r}),[l,h,p]}function startForAxis(e,t,r,n,o){var a=t[o];e&1<<o&&(a=r[o]>0?Number.MIN_SAFE_INTEGER:Number.MAX_SAFE_INTEGER);var i=n[o];return a<0&&(a+=i),a=clamp(0,a,i-1)}function stopForAxis(e,t,r,n,o){var a=t[o];e&1<<o&&(a=r[o]>0?Number.MAX_SAFE_INTEGER:Number.MIN_SAFE_INTEGER);var i=n[o];return a<0&&(a+=i),a=r[o]>0?clamp(0,a,i):clamp(-1,a,i-1)}function inferShape(e){var t=e;if(isTypedArray(e))return [e.length];if(!Array.isArray(e))return [];for(var r=[];t instanceof Array;)r.push(t.length),t=t[0];return e instanceof Array&&ENV.get(\"TENSORLIKE_CHECK_SHAPE_CONSISTENCY\")&&deepAssertShapeConsistency(e,r,[]),r}function deepAssertShapeConsistency(e,t,r){if(r=r||[],e instanceof Array){assert(t.length>0,function(){return \"Element arr[\"+r.join(\"][\")+\"] should be a primitive, but is an array of \"+e.length+\" elements\"}),assert(e.length===t[0],function(){return \"Element arr[\"+r.join(\"][\")+\"] should have \"+t[0]+\" elements, but has \"+e.length+\" elements\"});for(var n=t.slice(1),o=0;o<e.length;++o)deepAssertShapeConsistency(e[o],n,r.concat(o));}else assert(0===t.length,function(){return \"Element arr[\"+r.join(\"][\")+\"] is a primitive, but should be an array of \"+t[0]+\" elements\"});}function convertToTensor(e,t,r,n){if(void 0===n&&(n=\"float32\"),n=n||\"float32\",e instanceof Tensor)return e;if(!isTypedArray(e)&&!Array.isArray(e)&&\"number\"!=typeof e&&\"boolean\"!=typeof e)throw new Error(\"Argument '\"+t+\"' passed to '\"+r+\"' must be a Tensor or TensorLike, but got \"+e.constructor.name);var o=inferShape(e);return isTypedArray(e)||Array.isArray(e)||(e=[e]),Tensor.make(o,{values:toTypedArray(e,n,ENV.get(\"DEBUG\"))},n)}function convertToTensorArray(e,t,r){if(!Array.isArray(e))throw new Error(\"Argument \"+t+\" passed to \"+r+\" must be a `Tensor[]` or `TensorLike[]`\");return e.map(function(e,n){return convertToTensor(e,t+\"[\"+n+\"]\",r)})}function op(e){var t=Object.keys(e);if(1!==t.length)throw new Error(\"Please provide an object with a single key (operation name) mapping to a function. Got an object with \"+t.length+\" keys.\");var r=t[0],n=e[r];r.endsWith(\"_\")&&(r=r.substring(0,r.length-1));var o=function(){for(var e=[],t=0;t<arguments.length;t++)e[t]=arguments[t];ENV.engine.startScope(r);try{var o=n.apply(void 0,e);return o instanceof Promise&&console.error(\"Cannot return a Promise inside of tidy.\"),ENV.engine.endScope(o),o}catch(e){throw ENV.engine.endScope(null),e}};return Object.defineProperty(o,\"name\",{value:r,configurable:!0}),o}function softmax_(e,t){void 0===t&&(t=-1);var r=convertToTensor(e,\"logits\",\"softmax\");if(-1===t&&(t=r.rank-1),t!==r.rank-1)throw Error(\"Softmax along a non-last dimension is not yet supported. Logits was rank \"+r.rank+\" and dim was \"+t);return customGrad(function(e){var r=e.logSumExp([t],!0),n=e.toFloat().sub(r).exp();return {value:n,gradFunc:function(e){var r=e.mul(n);return r.sub(r.sum([t],!0).mul(n))}}})(r)}var softmax=op({softmax_:softmax_});function complex_(e,t){var r=convertToTensor(e,\"real\",\"complex\"),n=convertToTensor(t,\"imag\",\"complex\");return assertShapesMatch(r.shape,n.shape,\"real and imag shapes, \"+r.shape+\" and \"+n.shape+\", must match in call to tf.complex().\"),ENV.engine.runKernel(function(e){return e.complex(r,n)},{$real:r,$imag:n})}function real_(e){var t=convertToTensor(e,\"input\",\"real\");return ENV.engine.runKernel(function(e){return e.real(t)},{$input:t})}function imag_(e){var t=convertToTensor(e,\"input\",\"imag\");return ENV.engine.runKernel(function(e){return e.imag(t)},{$input:t})}var complex=op({complex_:complex_}),real=op({real_:real_}),imag=op({imag_:imag_});function tensor(e,t,r){if(void 0===r&&(r=\"float32\"),\"complex64\"===r)throw new Error(\"Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).\");if(!isTypedArray(e)&&!Array.isArray(e)&&\"number\"!=typeof e&&\"boolean\"!=typeof e)throw new Error(\"values passed to tensor(values) must be an array of numbers or booleans, or a TypedArray\");var n=inferShape(e);return null!=t&&1!==n.length&&assertShapesMatch(t,n,\"Error creating a new Tensor. Inferred shape (\"+n+\") does not match the provided shape (\"+t+\"). \"),isTypedArray(e)||Array.isArray(e)||(e=[e]),t=t||n,Tensor.make(t,{values:toTypedArray(e,r,ENV.get(\"DEBUG\"))},r)}function scalar(e,t){if(void 0===t&&(t=\"float32\"),(isTypedArray(e)||Array.isArray(e))&&\"complex64\"!==t)throw new Error(\"Error creating a new Scalar: value must be a primitive (number|boolean)\");return tensor(e,[],t)}function tensor1d(e,t){void 0===t&&(t=\"float32\"),assertNonNull(e);var r=inferShape(e);if(1!==r.length)throw new Error(\"tensor1d() requires values to be a flat/TypedArray\");return tensor(e,r,t)}function tensor2d(e,t,r){if(void 0===r&&(r=\"float32\"),assertNonNull(e),null!=t&&2!==t.length)throw new Error(\"tensor2d() requires shape to have two numbers\");var n=inferShape(e);if(2!==n.length&&1!==n.length)throw new Error(\"tensor2d() requires values to be number[][] or flat/TypedArray\");if(1===n.length&&null==t)throw new Error(\"tensor2d() requires shape to be provided when `values` are a flat/TypedArray\");return tensor(e,t=t||n,r)}function tensor3d(e,t,r){if(void 0===r&&(r=\"float32\"),assertNonNull(e),null!=t&&3!==t.length)throw new Error(\"tensor3d() requires shape to have three numbers\");var n=inferShape(e);if(3!==n.length&&1!==n.length)throw new Error(\"tensor3d() requires values to be number[][][] or flat/TypedArray\");if(1===n.length&&null==t)throw new Error(\"tensor3d() requires shape to be provided when `values` are a flat array\");return tensor(e,t=t||n,r)}function tensor4d(e,t,r){if(void 0===r&&(r=\"float32\"),assertNonNull(e),null!=t&&4!==t.length)throw new Error(\"tensor4d() requires shape to have four numbers\");var n=inferShape(e);if(4!==n.length&&1!==n.length)throw new Error(\"tensor4d() requires values to be number[][][][] or flat/TypedArray\");if(1===n.length&&null==t)throw new Error(\"tensor4d() requires shape to be provided when `values` are a flat array\");return tensor(e,t=t||n,r)}function tensor5d(e,t,r){if(void 0===r&&(r=\"float32\"),assertNonNull(e),null!=t&&5!==t.length)throw new Error(\"tensor5d() requires shape to have five numbers\");var n=inferShape(e);if(5!==n.length&&1!==n.length)throw new Error(\"tensor5d() requires values to be number[][][][][] or flat/TypedArray\");if(1===n.length&&null==t)throw new Error(\"tensor5d() requires shape to be provided when `values` are a flat array\");return tensor(e,t=t||n,r)}function tensor6d(e,t,r){if(void 0===r&&(r=\"float32\"),assertNonNull(e),null!=t&&6!==t.length)throw new Error(\"tensor6d() requires shape to have six numbers\");var n=inferShape(e);if(6!==n.length&&1!==n.length)throw new Error(\"tensor6d() requires values to be number[][][][] or flat/TypedArray\");if(1===n.length&&null==t)throw new Error(\"tensor6d() requires shape to be provided when `values` are a flat array\");return tensor(e,t=t||n,r)}function ones$1(e,t){if(void 0===t&&(t=\"float32\"),\"complex64\"===t){var r=ones$1(e,\"float32\"),n=ones$1(e,\"float32\");return complex(r,n)}var o=makeOnesTypedArray(sizeFromShape(e),t);return Tensor.make(e,{values:o},t)}function zeros(e,t){if(void 0===t&&(t=\"float32\"),\"complex64\"===t){var r=zeros(e,\"float32\"),n=zeros(e,\"float32\");return complex(r,n)}var o=makeZerosTypedArray(sizeFromShape(e),t);return Tensor.make(e,{values:o},t)}function fill(e,t,r){void 0===r&&(r=\"float32\");var n=getTypedArrayFromDType(r,sizeFromShape(e));return n.fill(t),Tensor.make(e,{values:n},r)}function onesLike_(e){var t=convertToTensor(e,\"x\",\"onesLike\");return ones$1(t.shape,t.dtype)}function zerosLike_(e){var t=convertToTensor(e,\"x\",\"zerosLike\");return zeros(t.shape,t.dtype)}function linspace(e,t,r){if(0===r)throw new Error(\"Cannot request zero samples\");var n=(t-e)/(r-1),o=makeZerosTypedArray(r,\"float32\");o[0]=e;for(var a=1;a<o.length;a++)o[a]=o[a-1]+n;return tensor1d(o,\"float32\")}function range(e,t,r,n){if(void 0===r&&(r=1),void 0===n&&(n=\"float32\"),0===r)throw new Error(\"Cannot have a step of zero\");if(e===t||e<t&&r<0||t<e&&r>1)return zeros([0],n);var o=makeZerosTypedArray(Math.abs(Math.ceil((t-e)/r)),n);t<e&&1===r&&(r=-1),o[0]=e;for(var a=1;a<o.length;a++)o[a]=o[a-1]+r;return tensor1d(o,n)}var DType,Rank,UpcastInt32AndMap,UpcastBoolAndMap,UpcastFloat32AndMap,UpcastComplex64AndMap,onesLike=op({onesLike_:onesLike_}),zerosLike=op({zerosLike_:zerosLike_});!function(e){e.float32=\"float32\",e.int32=\"int32\",e.bool=\"bool\";}(DType||(DType={})),function(e){e.R0=\"R0\",e.R1=\"R1\",e.R2=\"R2\",e.R3=\"R3\",e.R4=\"R4\",e.R5=\"R5\",e.R6=\"R6\";}(Rank||(Rank={})),function(e){e.float32=\"float32\",e.int32=\"int32\",e.bool=\"int32\",e.complex64=\"complex64\";}(UpcastInt32AndMap||(UpcastInt32AndMap={})),function(e){e.float32=\"float32\",e.int32=\"int32\",e.bool=\"bool\",e.complex64=\"complex64\";}(UpcastBoolAndMap||(UpcastBoolAndMap={})),function(e){e.float32=\"float32\",e.int32=\"float32\",e.bool=\"float32\",e.complex64=\"complex64\";}(UpcastFloat32AndMap||(UpcastFloat32AndMap={})),function(e){e.float32=\"complex64\",e.int32=\"complex64\",e.bool=\"complex64\",e.complex64=\"complex64\";}(UpcastComplex64AndMap||(UpcastComplex64AndMap={}));var upcastTypeMap={float32:UpcastFloat32AndMap,int32:UpcastInt32AndMap,bool:UpcastBoolAndMap,complex64:UpcastComplex64AndMap};function upcastType(e,t){return upcastTypeMap[e][t]}function sumOutType(e){return upcastType(e,\"int32\")}var DataStorage=function(){function e(e){this.dataMover=e,this.data=new WeakMap;}return e.prototype.get=function(e){return this.data.has(e)||this.dataMover.moveData(e),this.data.get(e)},e.prototype.set=function(e,t){this.data.set(e,t);},e.prototype.has=function(e){return this.data.has(e)},e.prototype.delete=function(e){return this.data.delete(e)},e}(),KernelBackend=function(){function e(){}return e.prototype.time=function(e){throw new Error(\"Not yet implemented.\")},e.prototype.read=function(e){throw new Error(\"Not yet implemented.\")},e.prototype.readSync=function(e){throw new Error(\"Not yet implemented.\")},e.prototype.disposeData=function(e){throw new Error(\"Not yet implemented.\")},e.prototype.write=function(e,t){throw new Error(\"Not yet implemented.\")},e.prototype.fromPixels=function(e,t){throw new Error(\"Not yet implemented.\")},e.prototype.register=function(e,t,r){throw new Error(\"Not yet implemented.\")},e.prototype.memory=function(){throw new Error(\"Not yet implemented.\")},e.prototype.floatPrecision=function(){throw new Error(\"Not yet implemented\")},e.prototype.batchMatMul=function(e,t,r,n){throw new Error(\"Not yet implemented\")},e.prototype.slice=function(e,t,r){throw new Error(\"Not yet implemented\")},e.prototype.stridedSlice=function(e,t,r,n,o,a,i,s,u){throw new Error(\"Not yet implemented\")},e.prototype.reverse=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.concat=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.neg=function(e){throw new Error(\"Not yet implemented\")},e.prototype.add=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.addN=function(e){throw new Error(\"Not yet implemented\")},e.prototype.subtract=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.multiply=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.realDivide=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.floorDiv=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.sum=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.prod=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.unsortedSegmentSum=function(e,t,r){throw new Error(\"Not yet implemented\")},e.prototype.argMin=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.argMax=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.equal=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.notEqual=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.less=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.lessEqual=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.greater=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.greaterEqual=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.logicalNot=function(e){throw new Error(\"Not yet implemented\")},e.prototype.logicalAnd=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.logicalOr=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.where=function(e){throw new Error(\"Not yet implemented\")},e.prototype.select=function(e,t,r){throw new Error(\"Not yet implemented\")},e.prototype.topk=function(e,t,r){throw new Error(\"Not yet implemented\")},e.prototype.min=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.minimum=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.mod=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.max=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.maximum=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.all=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.any=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.squaredDifference=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.ceil=function(e){throw new Error(\"Not yet implemented\")},e.prototype.floor=function(e){throw new Error(\"Not yet implemented\")},e.prototype.round=function(e){throw new Error(\"Not yet implemented\")},e.prototype.sign=function(e){throw new Error(\"Not yet implemented\")},e.prototype.pow=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.exp=function(e){throw new Error(\"Not yet implemented\")},e.prototype.expm1=function(e){throw new Error(\"Not yet implemented\")},e.prototype.log=function(e){throw new Error(\"Not yet implemented\")},e.prototype.log1p=function(e){throw new Error(\"Not yet implemented\")},e.prototype.sqrt=function(e){throw new Error(\"Not yet implemented\")},e.prototype.rsqrt=function(e){throw new Error(\"Not yet implemented\")},e.prototype.square=function(e){throw new Error(\"Not yet implemented\")},e.prototype.reciprocal=function(e){throw new Error(\"Not yet implemented\")},e.prototype.relu=function(e){throw new Error(\"Not yet implemented\")},e.prototype.elu=function(e){throw new Error(\"Not yet implemented\")},e.prototype.eluDer=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.selu=function(e){throw new Error(\"Not yet implemented\")},e.prototype.int=function(e){throw new Error(\"Not yet implemented\")},e.prototype.clip=function(e,t,r){throw new Error(\"Not yet implemented\")},e.prototype.abs=function(e){throw new Error(\"Not yet implemented\")},e.prototype.complexAbs=function(e){throw new Error(\"Not yet implemented\")},e.prototype.sigmoid=function(e){throw new Error(\"Not yet implemented\")},e.prototype.softplus=function(e){throw new Error(\"Not yet implemented\")},e.prototype.sin=function(e){throw new Error(\"Not yet implemented\")},e.prototype.cos=function(e){throw new Error(\"Not yet implemented\")},e.prototype.tan=function(e){throw new Error(\"Not yet implemented\")},e.prototype.asin=function(e){throw new Error(\"Not yet implemented\")},e.prototype.acos=function(e){throw new Error(\"Not yet implemented\")},e.prototype.atan=function(e){throw new Error(\"Not yet implemented\")},e.prototype.atan2=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.sinh=function(e){throw new Error(\"Not yet implemented\")},e.prototype.cosh=function(e){throw new Error(\"Not yet implemented\")},e.prototype.tanh=function(e){throw new Error(\"Not yet implemented\")},e.prototype.asinh=function(e){throw new Error(\"Not yet implemented\")},e.prototype.acosh=function(e){throw new Error(\"Not yet implemented\")},e.prototype.atanh=function(e){throw new Error(\"Not yet implemented\")},e.prototype.erf=function(e){throw new Error(\"Not yet implemented\")},e.prototype.step=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.conv2d=function(e,t,r){throw new Error(\"Not yet implemented\")},e.prototype.conv2dDerInput=function(e,t,r){throw new Error(\"Not yet implemented\")},e.prototype.conv2dDerFilter=function(e,t,r){throw new Error(\"Not yet implemented\")},e.prototype.depthwiseConv2D=function(e,t,r){throw new Error(\"Not yet implemented\")},e.prototype.depthwiseConv2DDerInput=function(e,t,r){throw new Error(\"Not yet implemented\")},e.prototype.depthwiseConv2DDerFilter=function(e,t,r){throw new Error(\"Not yet implemented\")},e.prototype.maxPool=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.maxPoolBackprop=function(e,t,r,n){throw new Error(\"Not yet implemented\")},e.prototype.avgPool=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.avgPoolBackprop=function(e,t,r){throw new Error(\"Not yet implemented\")},e.prototype.reshape=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.cast=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.tile=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.pad=function(e,t,r){throw new Error(\"Not yet implemented\")},e.prototype.transpose=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.gather=function(e,t,r){throw new Error(\"Not yet implemented\")},e.prototype.gatherND=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.scatterND=function(e,t,r){throw new Error(\"Not yet implemented\")},e.prototype.batchToSpaceND=function(e,t,r){throw new Error(\"Not yet implemented\")},e.prototype.spaceToBatchND=function(e,t,r){throw new Error(\"Not yet implemented\")},e.prototype.resizeBilinear=function(e,t,r,n){throw new Error(\"Not yet implemented\")},e.prototype.resizeBilinearBackprop=function(e,t,r){throw new Error(\"Not yet implemented\")},e.prototype.resizeNearestNeighbor=function(e,t,r,n){throw new Error(\"Not yet implemented\")},e.prototype.resizeNearestNeighborBackprop=function(e,t,r){throw new Error(\"Not yet implemented\")},e.prototype.batchNormalization=function(e,t,r,n,o,a){throw new Error(\"Not yet implemented\")},e.prototype.localResponseNormalization4D=function(e,t,r,n,o){throw new Error(\"Not yet implemented\")},e.prototype.LRNGrad=function(e,t,r,n,o,a,i){throw new Error(\"Not yet implemented\")},e.prototype.multinomial=function(e,t,r,n){throw new Error(\"Not yet implemented\")},e.prototype.oneHot=function(e,t,r,n){throw new Error(\"Not yet implemented\")},e.prototype.cumsum=function(e,t,r,n){throw new Error(\"Not yet implemented\")},e.prototype.nonMaxSuppression=function(e,t,r,n,o){throw new Error(\"Not yet implemented\")},e.prototype.fft=function(e){throw new Error(\"Not yet implemented\")},e.prototype.ifft=function(e){throw new Error(\"Not yet implemented\")},e.prototype.complex=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.real=function(e){throw new Error(\"Not yet implemented\")},e.prototype.imag=function(e){throw new Error(\"Not yet implemented\")},e.prototype.cropAndResize=function(e,t,r,n,o,a){throw new Error(\"Not yet implemented\")},e.prototype.depthToSpace=function(e,t,r){throw new Error(\"Not yet implemented\")},e.prototype.split=function(e,t,r){throw new Error(\"Not yet implemented\")},e.prototype.sparseToDense=function(e,t,r,n){throw new Error(\"Not yet implemented\")},e.prototype.setDataMover=function(e){throw new Error(\"Not yet implemented\")},e.prototype.dispose=function(){throw new Error(\"Not yet implemented\")},e}();function castTensor(e,t,r){if(\"complex64\"===t){if(\"complex64\"===e.dtype)return e.clone();var n=zeros(e.shape),o=e.toFloat(),a=r.complex(o,n);return n.dispose(),o.dispose(),a}if(!hasEncodingLoss(e.dtype,t))return Tensor.make(e.shape,{dataId:e.dataId},t);if(\"complex64\"===e.dtype){var i=r.real(e);a=i.cast(t);return i.dispose(),a}if(\"int32\"===t)return r.int(e);if(\"bool\"===t){var s=scalar(0,e.dtype);a=r.notEqual(e,s);return s.dispose(),a}throw new Error(\"Error in Cast: unknown dtype argument (\"+t+\")\")}function reshapeTensor(e,t){return Tensor.make(t,{dataId:e.dataId},e.dtype)}function mergeRealAndImagArrays(e,t){if(e.length!==t.length)throw new Error(\"Cannot merge real and imag arrays of different lengths. real:\"+e.length+\", imag: \"+t.length+\".\");for(var r=new Float32Array(2*e.length),n=0;n<r.length;n+=2)r[n]=e[n/2],r[n+1]=t[n/2];return r}function splitRealAndImagArrays(e){for(var t=new Float32Array(e.length/2),r=new Float32Array(e.length/2),n=0;n<e.length;n+=2)t[n/2]=e[n],r[n/2]=e[n+1];return {real:t,imag:r}}function complexWithEvenIndex(e){for(var t=Math.ceil(e.length/4),r=new Float32Array(t),n=new Float32Array(t),o=0;o<e.length;o+=4)r[Math.floor(o/4)]=e[o],n[Math.floor(o/4)]=e[o+1];return {real:r,imag:n}}function complexWithOddIndex(e){for(var t=Math.floor(e.length/4),r=new Float32Array(t),n=new Float32Array(t),o=2;o<e.length;o+=4)r[Math.floor(o/4)]=e[o],n[Math.floor(o/4)]=e[o+1];return {real:r,imag:n}}function getComplexWithIndex(e,t){return {real:e[2*t],imag:e[2*t+1]}}function assignToTypedArray(e,t,r,n){e[2*n]=t,e[2*n+1]=r;}function exponents(e,t){for(var r=new Float32Array(e/2),n=new Float32Array(e/2),o=0;o<Math.ceil(e/2);o++){var a=(t?2:-2)*Math.PI*(o/e);r[o]=Math.cos(a),n[o]=Math.sin(a);}return {real:r,imag:n}}function exponent(e,t,r){var n=(r?2:-2)*Math.PI*(e/t);return {real:Math.cos(n),imag:Math.sin(n)}}function nonMaxSuppressionImpl(e,t,r,n,o){for(var a=Array.from(t).map(function(e,t){return {score:e,boxIndex:t}}).filter(function(e){return e.score>o}).sort(function(e,t){return t.score-e.score}),i=[],s=0;s<a.length;s++){var u=a[s],l=u.score,c=u.boxIndex;if(l<o)break;for(var p=!1,d=i.length-1;d>=0;--d){if(intersectionOverUnion(e,c,i[d])>=n){p=!0;break}}if(!p&&(i.push(c),i.length>=r))break}return tensor1d(i,\"int32\")}function intersectionOverUnion(e,t,r){var n=e.subarray(4*t,4*t+4),o=e.subarray(4*r,4*r+4),a=Math.min(n[0],n[2]),i=Math.min(n[1],n[3]),s=Math.max(n[0],n[2]),u=Math.max(n[1],n[3]),l=Math.min(o[0],o[2]),c=Math.min(o[1],o[3]),p=Math.max(o[0],o[2]),d=Math.max(o[1],o[3]),h=(s-a)*(u-i),f=(p-l)*(d-c);if(h<=0||f<=0)return 0;var m=Math.max(a,l),g=Math.max(i,c),v=Math.min(s,p),y=Math.min(u,d),x=Math.max(v-m,0)*Math.max(y-g,0);return x/(h+f-x)}function split(e,t,r){var n=Array(e.rank).fill(0),o=e.shape.slice();return t.map(function(t){o[r]=t;var a=e.slice(n,o);return n[r]+=t,a})}function topkImpl(e,t,r,n,o){for(var a=t[t.length-1],i=[e.length/a,a],s=i[0],u=i[1],l=getTypedArrayFromDType(r,s*n),c=getTypedArrayFromDType(\"int32\",s*n),p=0;p<s;p++){for(var d=p*u,h=e.subarray(d,d+u),f=[],m=0;m<h.length;m++)f.push({value:h[m],index:m});f.sort(function(e,t){return t.value-e.value});var g=p*n,v=l.subarray(g,g+n),y=c.subarray(g,g+n);for(m=0;m<n;m++)v[m]=f[m].value,y[m]=f[m].index;}var x=t.slice();return x[x.length-1]=n,[tensor(l,x,r),tensor(c,x,\"int32\")]}var ArgMinMaxProgram=function(){return function(e,t,r){this.variableNames=[\"A\"];var n=e.windowSize,o=e.batchSize,a=e.inSize,i=Math.ceil(a/n);r||this.variableNames.push(\"bestIndicesA\"),this.outputShape=[o,i];var s=\"max\"===t?\">\":\"<\",u=r?\"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 * \"+n+\";\\n\\n        int bestIndex = inOffset;\\n        float bestValue = getA(batch, bestIndex);\\n\\n        for (int i = 0; i < \"+n+\"; i++) {\\n          int inIdx = \"+u+\";\\n          float candidate = getA(batch, inIdx);\\n          if (candidate \"+s+\" bestValue) {\\n            bestValue = candidate;\\n            bestIndex = inIdx;\\n          }\\n        }\\n        setOutput(float(bestIndex));\\n      }\\n    \";}}(),AvgPool2DBackpropProgram=function(){return function(e){this.variableNames=[\"dy\"],this.outputShape=e.inShape;var t=e.filterHeight,r=e.filterWidth,n=e.strideHeight,o=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,s=e.effectiveFilterHeight,u=e.effectiveFilterWidth,l=s-1-e.padInfo.top,c=u-1-e.padInfo.left,p=1/(t*r);this.userCode=\"\\n      const ivec2 pads = ivec2(\"+l+\", \"+c+\");\\n      const float avgMultiplier = float(\"+p+\");\\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 < \"+s+\";\\n            wR += \"+a+\") {\\n          float dyR = float(dyRCorner + wR) / \"+n+\".0;\\n\\n          if (dyR < 0.0 || dyR >= \"+e.outHeight+\".0 || fract(dyR) > 0.0) {\\n            continue;\\n          }\\n          int idyR = int(dyR);\\n\\n          for (int wC = 0; wC < \"+u+\";\\n            wC+= \"+i+\") {\\n            float dyC = float(dyCCorner + wC) / \"+o+\".0;\\n\\n            if (dyC < 0.0 || dyC >= \"+e.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    \";}}();function getBroadcastDims(e,t){for(var r=e.length,n=[],o=0;o<r;o++){var a=r-1-o,i=e[a]||1;(t[t.length-1-o]||1)>1&&1===i&&n.unshift(a);}return n}function getReductionAxes(e,t){for(var r=[],n=0;n<t.length;n++){var o=e[e.length-n-1],a=t.length-n-1,i=t[a];(null==o||1===o&&i>1)&&r.unshift(a);}return r}function broadcastDimsAreOuter(e){for(var t=0;t<e.length;t++)if(e[t]!==t)return !1;return !0}function assertAndGetBroadcastShape(e,t){for(var r=[],n=Math.max(e.length,t.length),o=0;o<n;o++){var a=e[e.length-o-1];null==a&&(a=1);var i=t[t.length-o-1];if(null==i&&(i=1),1===a)r.unshift(i);else if(1===i)r.unshift(a);else{if(a!==i)throw Error(\"Operands could not be broadcast together with shapes \"+e+\" and \"+t+\".\");r.unshift(a);}}return r}var BatchNormProgram=function(){return function(e,t,r,n,o,a){this.outputShape=[],this.supportsBroadcasting=!0,this.variableNames=[\"x\",\"mean\",\"variance\"],assertAndGetBroadcastShape(e,t),assertAndGetBroadcastShape(e,r);var i=\"0.0\";null!=n&&(assertAndGetBroadcastShape(e,n),this.variableNames.push(\"offset\"),i=\"getOffsetAtOutCoords()\");var s=\"1.0\";null!=o&&(assertAndGetBroadcastShape(e,o),this.variableNames.push(\"scale\"),s=\"getScaleAtOutCoords()\"),this.outputShape=e,this.userCode=\"\\n      void main() {\\n        float x = getXAtOutCoords();\\n        float mean = getMeanAtOutCoords();\\n        float variance = getVarianceAtOutCoords();\\n        float offset = \"+i+\";\\n        float scale = \"+s+\";\\n        float inv = scale * inversesqrt(variance + float(\"+a+\"));\\n        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\\n      }\\n    \";}}(),BatchNormPackedProgram=function(){return function(e,t,r,n,o,a){this.supportsBroadcasting=!0,this.usesPackedTextures=!0,this.variableNames=[\"x\",\"mean\",\"variance\"],assertAndGetBroadcastShape(e,t),assertAndGetBroadcastShape(e,r);var i=broadcastSample(\"mean\",t.length),s=broadcastSample(\"variance\",r.length),u=\"vec4 offset = vec4(0.0)\";null!=n&&(assertAndGetBroadcastShape(e,n),this.variableNames.push(\"offset\"),u=broadcastSample(\"offset\",n.length));var l=\"vec4 scale = vec4(1.0)\";null!=o&&(assertAndGetBroadcastShape(e,o),this.variableNames.push(\"scale\"),l=broadcastSample(\"scale\",o.length)),this.outputShape=e,this.userCode=\"\\n      void main() {\\n        ivec4 rc = getOutputCoords();\\n\\n        \"+u+\";\\n        \"+l+\";\\n\\n        vec4 x = getX(rc.x, rc.y, rc.z, rc.w);\\n        \"+i+\";\\n        \"+s+\";\\n\\n        vec4 inv = scale * inversesqrt(variance + vec4(\"+a+\"));\\n\\n        setOutput((x - mean) * inv + offset);\\n      }\\n    \";}}();function broadcastSample(e,t){var r=\"get\"+e.charAt(0).toUpperCase()+e.slice(1);return 1===t?\"\\n      vec4 \"+e+\"Sample = \"+r+\"(rc.w);\\n      vec4 \"+e+\" = vec4(\"+e+\"Sample.xy, \"+e+\"Sample.xy);\\n    \":\"vec4 \"+e+\" = \"+r+\"(rc.x, rc.y, rc.z, rc.w)\"}var COMPLEX_MULTIPLY={REAL:\"return areal * breal - aimag * bimag;\",IMAG:\"return areal * bimag + aimag * breal;\"},BinaryOpComplexProgram=function(){return function(e,t,r){this.variableNames=[\"AReal\",\"AImag\",\"BReal\",\"BImag\"],this.supportsBroadcasting=!0,this.outputShape=assertAndGetBroadcastShape(t,r),this.userCode=\"\\n      float binaryOpComplex(\\n          float areal, float aimag, float breal, float bimag) {\\n        \"+e+\"\\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    \";}}(),CHECK_NAN_SNIPPET=\"\\n  if (isNaN(a)) return a;\\n  if (isNaN(b)) return b;\\n\",ADD=\"return a + b;\",SUB=\"return a - b;\",MUL=\"return a * b;\",DIV=\"if (a == b) return 1.0;\\n  return a / b;\",INT_DIV=\"\\n  float resultSign = sign(a) * sign(b);\\n  int ia = round(a);\\n  int ib = round(b);\\n  int result = ia / ib;\\n  int amodb = ia - ib * result;\\n\\n  if (resultSign < 0.0 && amodb != 0) {\\n    result -= 1;\\n  }\\n  return float(result);\\n\",POW=\"\\nif(a < 0.0 && floor(b) < b){\\n  return NAN;\\n}\\nreturn (round(mod(b, 2.0)) == 0 || round(mod(b, 2.0)) == 2) ?\\n    pow(abs(a), b) : sign(a) * pow(abs(a), b);\\n\",SQUARED_DIFFERENCE=\"return (a - b) * (a - b);\",EQUAL=\"return float(a == b);\",NOT_EQUAL=\"return float(a != b);\",LESS=\"return float(a < b);\",LESS_EQUAL=\"return float(a <= b);\",GREATER=\"return float(a > b);\",GREATER_EQUAL=\"return float(a >= b);\",LOGICAL_AND=\"return float(a >= 1.0 && b >= 1.0);\",LOGICAL_OR=\"return float(a >= 1.0 || b >= 1.0);\",MAX=CHECK_NAN_SNIPPET+\"\\n  return max(a, b);\\n\",MIN=CHECK_NAN_SNIPPET+\"\\n  return min(a, b);\\n\",MOD=\"if (b == 0.0) return NAN;\\n  return mod(a, b);\",ATAN2=CHECK_NAN_SNIPPET+\"\\n  return atan(a, b);\\n\",ELU_DER=\"return (b >= 1.0) ? a : a * (b + 1.0);\",BinaryOpProgram=function(){function e(e,t,r){this.variableNames=[\"A\",\"B\"],this.supportsBroadcasting=!0,this.outputShape=assertAndGetBroadcastShape(t,r),this.userCode=\"\\n      uniform float NAN;\\n      float binaryOperation(float a, float b) {\\n        \"+e+\"\\n      }\\n\\n      void main() {\\n        float a = getAAtOutCoords();\\n        float b = getBAtOutCoords();\\n        setOutput(binaryOperation(a, b));\\n      }\\n    \";}return e.prototype.getCustomSetupFunc=function(){var e=this;return function(t,r){null==e.startLoc&&(e.startLoc=t.getUniformLocationNoThrow(r,\"NAN\"),null==e.startLoc)||t.gl.uniform1f(e.startLoc,NaN);}},e}(),ClipProgram=function(){return function(e,t,r){this.variableNames=[\"A\"],this.outputShape=e,this.userCode=\"\\n      void main() {\\n        float value = getAAtOutCoords();\\n        if (isNaN(value)) {\\n          setOutput(value);\\n          return;\\n        }\\n\\n        setOutput(clamp(value, float(\"+t+\"), float(\"+r+\")));\\n      }\\n    \";}}(),ComplexAbsProgram=function(){return function(e){this.variableNames=[\"real\",\"imag\"],this.outputShape=e,this.userCode=\"\\n      void main() {\\n        float real = getRealAtOutCoords();\\n        float imag = getImagAtOutCoords();\\n        vec2 v = vec2(real, imag);\\n\\n        setOutput(sqrt(dot(v, v)));\\n      }\\n    \";}}(),ConcatProgram=function(){return function(e,t){this.variableNames=[\"A\",\"B\"],this.outputShape=[],this.outputShape=computeOutShape([e,t],1),this.userCode=\"\\n      void main() {\\n        ivec2 coords = getOutputCoords();\\n        int yR = coords.x;\\n        int yC = coords.y;\\n\\n        float value = 0.0;\\n        if (yC < \"+e[1]+\") {\\n          value = getA(yR, yC);\\n        } else {\\n          yC -= \"+e[1]+\";\\n          value = getB(yR, yC);\\n        }\\n\\n        setOutput(value);\\n      }\\n    \";}}(),Conv2DDerFilterProgram=function(){return function(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;var t=e.strideHeight,r=e.strideWidth,n=e.padInfo.top,o=e.padInfo.left;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 < \"+e.batchSize+\"; b++) {\\n          for (int yR = 0; yR < \"+e.outHeight+\"; yR++) {\\n            int xR = wR + yR * \"+t+\" - \"+n+\";\\n\\n            if (xR < 0 || xR >= \"+e.inHeight+\") {\\n              continue;\\n            }\\n\\n            for (int yC = 0; yC < \"+e.outWidth+\"; yC++) {\\n              int xC = wC + yC * \"+r+\" - \"+o+\";\\n\\n              if (xC < 0 || xC >= \"+e.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    \";}}(),Conv2DDerInputProgram=function(){return function(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;var t=e.filterHeight,r=e.filterWidth,n=e.strideHeight,o=e.strideWidth,a=t-1-e.padInfo.top,i=r-1-e.padInfo.left;this.userCode=\"\\n      const ivec2 pads = ivec2(\"+a+\", \"+i+\");\\n\\n      void main() {\\n        ivec4 coords = getOutputCoords();\\n        int batch = coords[0];\\n        int d1 = coords[3];\\n\\n        ivec2 dyCorner = coords.yz - 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 < \"+t+\"; wR++) {\\n          float dyR = float(dyRCorner + wR) / \"+n+\".0;\\n\\n          if (dyR < 0.0 || dyR >= \"+e.outHeight+\".0 || fract(dyR) > 0.0) {\\n            continue;\\n          }\\n          int idyR = int(dyR);\\n\\n          int wRPerm = \"+t+\" - 1 - wR;\\n\\n          for (int wC = 0; wC < \"+r+\"; wC++) {\\n            float dyC = float(dyCCorner + wC) / \"+o+\".0;\\n\\n            if (dyC < 0.0 || dyC >= \"+e.outWidth+\".0 ||\\n                fract(dyC) > 0.0) {\\n              continue;\\n            }\\n            int idyC = int(dyC);\\n\\n            int wCPerm = \"+r+\" - 1 - wC;\\n\\n            for (int d2 = 0; d2 < \"+e.outChannels+\"; d2++) {\\n              float xValue = getDy(batch, idyR, idyC, d2);\\n              float wValue = getW(wRPerm, wCPerm, d1, d2);\\n              dotProd += xValue * wValue;\\n            }\\n          }\\n        }\\n        setOutput(dotProd);\\n      }\\n    \";}}(),DepthwiseConv2DDerFilterProgram=function(){return function(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;var t=e.strideHeight,r=e.strideWidth,n=e.padInfo.top,o=e.padInfo.left,a=e.outChannels/e.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 * \"+a+\" + dm;\\n\\n        float dotProd = 0.0;\\n\\n        // TODO: Vec4 over the batch size\\n        for (int b = 0; b < \"+e.batchSize+\"; b++) {\\n          for (int yR = 0; yR < \"+e.outHeight+\"; yR++) {\\n            int xR = wR + yR * \"+t+\" - \"+n+\";\\n\\n            if (xR < 0 || xR >= \"+e.inHeight+\") {\\n              continue;\\n            }\\n\\n            for (int yC = 0; yC < \"+e.outWidth+\"; yC++) {\\n              int xC = wC + yC * \"+r+\" - \"+o+\";\\n\\n              if (xC < 0 || xC >= \"+e.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    \";}}(),DepthwiseConv2DDerInputProgram=function(){return function(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;var t=e.filterHeight,r=e.filterWidth,n=e.strideHeight,o=e.strideWidth,a=t-1-e.padInfo.top,i=r-1-e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=\"\\n      const ivec2 pads = ivec2(\"+a+\", \"+i+\");\\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 < \"+t+\"; wR++) {\\n          float dyR = float(dyRCorner + wR) / \"+n+\".0;\\n\\n          if (dyR < 0.0 || dyR >= \"+e.outHeight+\".0 || fract(dyR) > 0.0) {\\n            continue;\\n          }\\n          int idyR = int(dyR);\\n\\n          int wRPerm = \"+t+\" - 1 - wR;\\n\\n          for (int wC = 0; wC < \"+r+\"; wC++) {\\n            float dyC = float(dyCCorner + wC) / \"+o+\".0;\\n\\n            if (dyC < 0.0 || dyC >= \"+e.outWidth+\".0 ||\\n                fract(dyC) > 0.0) {\\n              continue;\\n            }\\n            int idyC = int(dyC);\\n\\n            int wCPerm = \"+r+\" - 1 - wC;\\n\\n            // TODO: Vec4 over the channelMul\\n            for (int dm = 0; dm < \"+s+\"; dm++) {\\n              int d2 = d1 * \"+s+\" + 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    \";}}(),Conv2DProgram=function(){return function(e){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;var t=e.padInfo.top,r=e.padInfo.left,n=e.strideHeight,o=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,s=e.filterHeight,u=e.filterWidth,l=4*Math.floor(e.inChannels/4),c=e.inChannels%4;this.userCode=\"\\n      const ivec2 strides = ivec2(\"+n+\", \"+o+\");\\n      const ivec2 pads = ivec2(\"+t+\", \"+r+\");\\n\\n      void main() {\\n        ivec4 coords = getOutputCoords();\\n        int batch = coords[0];\\n        int d2 = coords[3];\\n\\n        ivec2 xRCCorner = coords.yz * 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 < \"+s+\"; wR++) {\\n          int xR = xRCorner + wR * \"+a+\";\\n\\n          if (xR < 0 || xR >= \"+e.inHeight+\") {\\n            continue;\\n          }\\n\\n          for (int wC = 0; wC < \"+u+\"; wC++) {\\n            int xC = xCCorner + wC * \"+i+\";\\n\\n            if (xC < 0 || xC >= \"+e.inWidth+\") {\\n              continue;\\n            }\\n\\n            for (int d1 = 0; d1 < \"+l+\"; d1 += 4) {\\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              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              dotProd += dot(xValues, wValues);\\n            }\\n\\n            if (\"+(1===c)+\") {\\n              dotProd +=\\n                getX(batch, xR, xC, \"+l+\") *\\n                getW(wR, wC, \"+l+\", d2);\\n            } else if (\"+(2===c)+\") {\\n              vec2 xValues = vec2(\\n                getX(batch, xR, xC, \"+l+\"),\\n                getX(batch, xR, xC, \"+l+\" + 1)\\n              );\\n              vec2 wValues = vec2(\\n                getW(wR, wC, \"+l+\", d2),\\n                getW(wR, wC, \"+l+\" + 1, d2)\\n              );\\n              dotProd += dot(xValues, wValues);\\n            } else if (\"+(3===c)+\") {\\n              vec3 xValues = vec3(\\n                getX(batch, xR, xC, \"+l+\"),\\n                getX(batch, xR, xC, \"+l+\" + 1),\\n                getX(batch, xR, xC, \"+l+\" + 2)\\n              );\\n              vec3 wValues = vec3(\\n                getW(wR, wC, \"+l+\", d2),\\n                getW(wR, wC, \"+l+\" + 1, d2),\\n                getW(wR, wC, \"+l+\" + 2, d2)\\n              );\\n              dotProd += dot(xValues, wValues);\\n            }\\n          }\\n        }\\n        setOutput(dotProd);\\n      }\\n    \";}}(),DepthwiseConv2DProgram=function(){return function(e){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;var t=e.inHeight,r=e.inWidth,n=e.padInfo.top,o=e.padInfo.left,a=e.strideHeight,i=e.strideWidth,s=e.dilationHeight,u=e.dilationWidth,l=e.filterHeight,c=e.filterWidth,p=e.outChannels/e.inChannels;this.userCode=\"\\n      const ivec2 strides = ivec2(\"+a+\", \"+i+\");\\n      const ivec2 pads = ivec2(\"+n+\", \"+o+\");\\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 / \"+p+\";\\n        int q = d2 - d1 * \"+p+\";\\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        // TODO(dsmilkov): Flatten the two for loops and vec4 the operations.\\n        for (int wR = 0; wR < \"+l+\"; wR++) {\\n          int xR = xRCorner + wR * \"+s+\";\\n\\n          if (xR < 0 || xR >= \"+t+\") {\\n            continue;\\n          }\\n\\n          for (int wC = 0; wC < \"+c+\"; wC++) {\\n            int xC = xCCorner + wC * \"+u+\";\\n\\n            if (xC < 0 || xC >= \"+r+\") {\\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        setOutput(dotProd);\\n      }\\n    \";}}(),CropAndResizeProgram=function(){return function(e,t,r,n,o){this.variableNames=[\"Image\",\"Boxes\",\"BoxInd\"],this.outputShape=[];var a=e[0],i=e[1],s=e[2],u=e[3],l=t[0],c=r[0],p=r[1];this.outputShape=[l,c,p,u];var d=\"bilinear\"===n?1:0,h=[i-1+\".0\",s-1+\".0\"],f=h[0],m=h[1],g=c>1?[\"\"+(i-1)/(c-1),\"(y2-y1) * height_ratio\",\"y1*\"+f+\" + float(y)*(height_scale)\"]:[\"0.0\",\"0.0\",\"0.5 * (y1+y2) * \"+f],v=g[0],y=g[1],x=g[2],T=p>1?[\"\"+(s-1)/(p-1),\"(x2-x1) * width_ratio\",\"x1*\"+m+\" + float(x)*(width_scale)\"]:[\"0.0\",\"0.0\",\"0.5 * (x1+x2) * \"+m],E=T[0],w=T[1],S=T[2];this.userCode=\"\\n      const float height_ratio = float(\"+v+\");\\n      const float width_ratio = float(\"+E+\");\\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 >= \"+a+\") {\\n          return;\\n        }\\n\\n        float height_scale = \"+y+\";\\n        float width_scale = \"+w+\";\\n\\n        float in_y = \"+x+\";\\n        if( in_y < 0.0 || in_y > \"+f+\" ) {\\n          setOutput(float(\"+o+\"));\\n          return;\\n        }\\n        float in_x = \"+S+\";\\n        if( in_x < 0.0 || in_x > \"+m+\" ) {\\n          setOutput(float(\"+o+\"));\\n          return;\\n        }\\n\\n        vec2 sourceFracIndexRC = vec2(in_y,in_x);\\n        if(\"+d+\" == 1) {\\n          // Compute the four integer indices.\\n          ivec2 sourceFloorRC = ivec2(sourceFracIndexRC);\\n          ivec2 sourceCeilRC = ivec2(ceil(sourceFracIndexRC));\\n\\n          float topLeft = getImage(b, sourceFloorRC.x, sourceFloorRC.y, d);\\n          float bottomLeft = getImage(b, sourceCeilRC.x, sourceFloorRC.y, d);\\n          float topRight = getImage(b, sourceFloorRC.x, sourceCeilRC.y, d);\\n          float bottomRight = getImage(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          setOutput(newValue);\\n        } else {\\n          // Compute the coordinators of nearest neighbor point.\\n          ivec2 sourceNearestRC = ivec2(floor(\\n            sourceFracIndexRC + vec2(0.5,0.5)));\\n          float newValue = getImage(b, sourceNearestRC.x, sourceNearestRC.y, d);\\n          setOutput(newValue);\\n        }\\n      }\\n    \";}}();function makeShader(e,t,r,n){var o=e.map(function(e){var t=sizeFromShape(e.shapeInfo.logicalShape);return e.shapeInfo.isUniform?\"uniform float \"+e.name+(t>1?\"[\"+t+\"]\":\"\")+\";\":\"uniform sampler2D \"+e.name+\";\"});o=o.join(\"\\n\");var a,i,s=e.map(function(e){return getInputSamplingSnippet(e,t,n)}).join(\"\\n\"),u=t.texShape;return t.isPacked?(a=getPackedOutputSamplingSnippet(t.logicalShape,u),i=FLOAT_TEXTURE_SET_RGBA_SNIPPET):(a=getOutputSamplingSnippet(t.logicalShape,u),i=FLOAT_TEXTURE_SET_R_SNIPPET),[SHADER_PREFIX,FLOAT_TEXTURE_SAMPLE_SNIPPET,i,o,a,s,r].join(\"\\n\")}function getSamplerFromInInfo(e){var t=e.shapeInfo.logicalShape;switch(t.length){case 0:return getSamplerScalar(e);case 1:return getSampler1D(e);case 2:return getSampler2D(e);case 3:return getSampler3D(e);case 4:return getSampler4D(e);case 5:return getSampler5D(e);case 6:return getSampler6D(e);default:throw new Error(t.length+\"-D input sampling is not yet supported\")}}function getPackedSamplerFromInInfo(e){var t=e.shapeInfo.logicalShape;switch(t.length){case 1:return getPackedSampler1D(e);case 2:return getPackedSampler2D(e);case 4:return getPackedSampler4D(e);default:throw new Error(\"Packed \"+t.length+\"-D input sampling is not yet supported\")}}function getInputSamplingSnippet(e,t,r){var n=getSamplerFlat(e);return e.shapeInfo.isPacked?n+=getPackedSamplerFromInInfo(e):n+=getSamplerFromInInfo(e),(r||arraysEqual(e.shapeInfo.logicalShape,t.logicalShape))&&(n+=getSamplerAtOutputCoords(e,t,r)),n}function getPackedOutputSamplingSnippet(e,t){switch(e.length){case 0:return getOutputScalarCoords();case 1:return getOutputPacked1DCoords(e,t);case 2:return getOutputPacked2DCoords(e,t);case 4:return getOutputPacked4DCoords(e,t);default:throw new Error(e.length+\"-D output packed sampling is not yet supported\")}}function getOutputSamplingSnippet(e,t){switch(e.length){case 0:return getOutputScalarCoords();case 1:return getOutput1DCoords(e,t);case 2:return getOutput2DCoords(e,t);case 3:return getOutput3DCoords(e,t);case 4:return getOutput4DCoords(e,t);case 5:return getOutput5DCoords(e,t);case 6:return getOutput6DCoords(e,t);default:throw new Error(e.length+\"-D output sampling is not yet supported\")}}var SAMPLE_1D_SNIPPET=\"\\nvec2 UVfrom1D(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\",SAMPLE_2D_SNIPPET=\"\\nvec2 UVfrom2D(int texNumR, int texNumC, int numC, int row, int col) {\\n  int index = row * numC + col;\\n  int texR = index / texNumC;\\n  int texC = index - texR * texNumC;\\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\\n}\\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\",SAMPLE_3D_SNIPPET=\"\\nvec2 UVfrom3D(int texNumR, int texNumC, int stride0,\\n    int stride1, 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;\\n  int texR = index / texNumC;\\n  int texC = index - texR * texNumC;\\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\\n}\\n\",SAMPLE_4D_SNIPPET=\"\\nvec2 UVfrom4D(int texNumR, int texNumC, int stride0,\\n    int stride1, int stride2, int row, int col, int depth,\\n    int depth2) {\\n  // Explicitly use integer operations as dot() only works on floats.\\n  int index = row * stride0 + col * stride1 + depth * stride2 + depth2;\\n  int texR = index / texNumC;\\n  int texC = index - texR * texNumC;\\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\\n}\\nvec2 packedUVfrom4D(int texNumR, int texNumC, int texelsInBatch2,\\n    int texelsInBatch, int texelsInLogicalRow, int b2, int b,\\n    int row, int col) {\\n  int index = b2 * texelsInBatch2 + b * texelsInBatch +\\n    (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\",SAMPLE_5D_SNIPPET=\"\\nvec2 UVfrom5D(int texNumR, int texNumC, int stride0,\\n    int stride1, int stride2, int stride3, int row, int col, int depth,\\n    int depth2, int depth3) {\\n  // Explicitly use integer operations as dot() only works on floats.\\n  int index = row * stride0 + col * stride1 +\\n              depth * stride2 + depth2 * stride3 + depth3;\\n  int texR = index / texNumC;\\n  int texC = index - texR * texNumC;\\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\\n}\\n\",SAMPLE_6D_SNIPPET=\"\\nvec2 UVfrom6D(int texNumR, int texNumC, int stride0,\\n    int stride1, int stride2, int stride3, int stride4,\\n    int row, int col, int depth, 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 + depth2 *\\n    stride3 + depth3 * stride4 + depth4;\\n  int texR = index / texNumC;\\n  int texC = index - texR * texNumC;\\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\\n}\\n\",FLOAT_TEXTURE_SAMPLE_SNIPPET=\"\\n  float sampleTexture(sampler2D textureSampler, vec2 uv) {\\n    return texture2D(textureSampler, uv).r;\\n  }\\n\",FLOAT_TEXTURE_SET_R_SNIPPET=\"\\n  void setOutput(float val) {\\n    gl_FragColor = vec4(val, 0, 0, 0);\\n  }\\n\",FLOAT_TEXTURE_SET_RGBA_SNIPPET=\"\\n  void setOutput(vec4 val) {\\n    gl_FragColor = val;\\n  }\\n\",SHADER_PREFIX=\"\\n  precision highp float;\\n  precision highp int;\\n  varying vec2 resultUV;\\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  bool isNaN(float val) {\\n    return (val < 1.0 || 0.0 < val || val == 0.0) ? false : true;\\n  }\\n\\n  bool hasNaN(vec4 values) {\\n    vec4 v1 = values * values;\\n    vec4 v2 = values * values;\\n    return any(notEqual(v1, v2));\\n  }\\n\\n  float getNaN(vec4 values) {\\n    return dot(vec4(1), values);\\n  }\\n\\n  int round(float value) {\\n    return int(floor(value + 0.5));\\n  }\\n\\n  int imod(int x, int y) {\\n    return x - y * (x / y);\\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  \"+SAMPLE_4D_SNIPPET+\"\\n  \"+SAMPLE_5D_SNIPPET+\"\\n  \"+SAMPLE_6D_SNIPPET+\"\\n\";function getOutputScalarCoords(){return \"\\n    int getOutputCoords() {\\n      return 0;\\n    }\\n  \"}function getOutputPacked1DCoords(e,t){var r=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return 1===t[0]?\"\\n      int getOutputCoords() {\\n        return 2 * int(resultUV.x * \"+r[1]+\".0);\\n      }\\n    \":1===t[1]?\"\\n      int getOutputCoords() {\\n        return 2 * int(resultUV.y * \"+r[0]+\".0);\\n      }\\n    \":\"\\n    int getOutputCoords() {\\n      ivec2 resTexRC = ivec2(resultUV.yx *\\n                             vec2(\"+r[0]+\", \"+r[1]+\"));\\n      return resTexRC.x * \"+r[1]+\" + resTexRC.y;\\n    }\\n  \"}function getOutput1DCoords(e,t){return 1===t[0]?\"\\n      int getOutputCoords() {\\n        return int(resultUV.x * \"+t[1]+\".0);\\n      }\\n    \":1===t[1]?\"\\n      int getOutputCoords() {\\n        return int(resultUV.y * \"+t[0]+\".0);\\n      }\\n    \":\"\\n    int getOutputCoords() {\\n      ivec2 resTexRC = ivec2(resultUV.yx *\\n                             vec2(\"+t[0]+\", \"+t[1]+\"));\\n      return resTexRC.x * \"+t[1]+\" + resTexRC.y;\\n    }\\n  \"}function getOutput3DCoords(e,t){var r=e[1]*e[2],n=e[2];return \"\\n    ivec3 getOutputCoords() {\\n      ivec2 resTexRC = ivec2(resultUV.yx *\\n                             vec2(\"+t[0]+\", \"+t[1]+\"));\\n      int index = resTexRC.x * \"+t[1]+\" + resTexRC.y;\\n      int r = index / \"+r+\";\\n      index -= r * \"+r+\";\\n      int c = index / \"+n+\";\\n      int d = index - c * \"+n+\";\\n      return ivec3(r, c, d);\\n    }\\n  \"}function getOutputPacked4DCoords(e,t){var r=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],n=Math.ceil(e[3]/2),o=n*Math.ceil(e[2]/2),a=o*e[1];return \"\\n    ivec4 getOutputCoords() {\\n      ivec2 resTexRC = ivec2(resultUV.yx *\\n                             vec2(\"+r[0]+\", \"+r[1]+\"));\\n      int index = resTexRC.x * \"+r[1]+\" + resTexRC.y;\\n\\n      int b2 = index / \"+a+\";\\n      index -= b2 * \"+a+\";\\n\\n      int b = index / \"+o+\";\\n      index -= b * \"+o+\";\\n\\n      int r = 2 * (index / \"+n+\");\\n      int c = int(mod(float(index), \"+n+\".)) * 2;\\n\\n      return ivec4(b2, b, r, c);\\n    }\\n  \"}function getOutput4DCoords(e,t){var r=e[3],n=e[2]*r,o=e[1]*n;return \"\\n    ivec4 getOutputCoords() {\\n      ivec2 resTexRC = ivec2(resultUV.yx *\\n        vec2(\"+t[0]+\", \"+t[1]+\"));\\n      int index = resTexRC.x * \"+t[1]+\" + resTexRC.y;\\n\\n      int r = index / \"+o+\";\\n      index -= r * \"+o+\";\\n\\n      int c = index / \"+n+\";\\n      index -= c * \"+n+\";\\n\\n      int d = index / \"+r+\";\\n      int d2 = index - d * \"+r+\";\\n\\n      return ivec4(r, c, d, d2);\\n    }\\n  \"}function getOutput5DCoords(e,t){var r=e[4],n=e[3]*r,o=e[2]*n,a=e[1]*o;return \"\\n    ivec5 getOutputCoords() {\\n      ivec2 resTexRC = ivec2(resultUV.yx * vec2(\"+t[0]+\",\\n                             \"+t[1]+\"));\\n\\n      int index = resTexRC.x * \"+t[1]+\" + resTexRC.y;\\n\\n      int r = index / \"+a+\";\\n      index -= r * \"+a+\";\\n\\n      int c = index / \"+o+\";\\n      index -= c * \"+o+\";\\n\\n      int d = index / \"+n+\";\\n      index -= d * \"+n+\";\\n\\n      int d2 = index  / \"+r+\";\\n      int d3 = index - d2 * \"+r+\";\\n\\n      ivec5 outShape = ivec5(r, c, d, d2, d3);\\n      return outShape;\\n    }\\n  \"}function getOutput6DCoords(e,t){var r=e[5],n=e[4]*r,o=e[3]*n,a=e[2]*o,i=e[1]*a;return \"\\n    ivec6 getOutputCoords() {\\n      ivec2 resTexRC = ivec2(resultUV.yx *\\n        vec2(\"+t[0]+\", \"+t[1]+\"));\\n      int index = resTexRC.x * \"+t[1]+\" + resTexRC.y;\\n\\n      int r = index / \"+i+\";\\n      index -= r * \"+i+\";\\n\\n      int c = index / \"+a+\";\\n      index -= c * \"+a+\";\\n\\n      int d = index / \"+o+\";\\n      index -= d * \"+o+\";\\n\\n      int d2 = index / \"+n+\";\\n      index -= d2 * \"+n+\";\\n\\n      int d3 = index / \"+r+\";\\n      int d4 = index - d3 * \"+r+\";\\n\\n      ivec6 result = ivec6(r, c, d, d2, d3, d4);\\n      return result;\\n    }\\n  \"}function getOutputPacked2DCoords(e,t){var r=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(arraysEqual(e,t))return \"\\n      ivec2 getOutputCoords() {\\n        return 2 * ivec2(resultUV.yx * vec2(\"+r[0]+\", \"+r[1]+\"));\\n      }\\n    \";var n=Math.ceil(e[1]/2);return \"\\n    ivec2 getOutputCoords() {\\n      ivec2 resTexRC = ivec2(resultUV.yx *\\n                             vec2(\"+r[0]+\", \"+r[1]+\"));\\n\\n      int index = resTexRC.x * \"+r[1]+\" + resTexRC.y;\\n      int r = 2 * (index / \"+n+\");\\n      int c = imod(index, \"+n+\") * 2;\\n\\n      return ivec2(r, c);\\n    }\\n  \"}function getOutput2DCoords(e,t){return arraysEqual(e,t)?\"\\n      ivec2 getOutputCoords() {\\n        return ivec2(resultUV.yx * vec2(\"+t[0]+\", \"+t[1]+\"));\\n      }\\n    \":1===e[1]?\"\\n      ivec2 getOutputCoords() {\\n        ivec2 resTexRC = ivec2(resultUV.yx *\\n                               vec2(\"+t[0]+\", \"+t[1]+\"));\\n        int index = resTexRC.x * \"+t[1]+\" + resTexRC.y;\\n        return ivec2(index, 0);\\n      }\\n    \":1===e[0]?\"\\n      ivec2 getOutputCoords() {\\n        ivec2 resTexRC = ivec2(resultUV.yx *\\n                               vec2(\"+t[0]+\", \"+t[1]+\"));\\n        int index = resTexRC.x * \"+t[1]+\" + resTexRC.y;\\n        return ivec2(0, index);\\n      }\\n    \":\"\\n    ivec2 getOutputCoords() {\\n      ivec2 resTexRC = ivec2(resultUV.yx *\\n                             vec2(\"+t[0]+\", \"+t[1]+\"));\\n      int index = resTexRC.x * \"+t[1]+\" + resTexRC.y;\\n      int r = index / \"+e[1]+\";\\n      int c = index - r * \"+e[1]+\";\\n      return ivec2(r, c);\\n    }\\n  \"}function getSamplerScalar(e){var t=e.name,r=\"get\"+t.charAt(0).toUpperCase()+t.slice(1);return e.shapeInfo.isUniform?\"float \"+r+\"() {return \"+t+\";}\":\"\\n    float \"+r+\"() {\\n      return sampleTexture(\"+t+\", halfCR);\\n    }\\n  \"}function getPackedSampler1D(e){var t=e.name,r=\"get\"+t.charAt(0).toUpperCase()+t.slice(1),n=e.shapeInfo.texShape,o=[Math.ceil(n[0]/2),Math.ceil(n[1]/2)];return \"\\n    vec4 \"+r+\"(int index) {\\n      vec2 uv = packedUVfrom1D(\\n        \"+o[0]+\", \"+o[1]+\", index);\\n      return texture2D(\"+t+\", uv);\\n    }\\n  \"}function getSampler1D(e){var t=e.name,r=\"get\"+t.charAt(0).toUpperCase()+t.slice(1);return \"\\n    float \"+r+\"(int index) {\\n      return \"+r+\"Flat(index);\\n    }\\n  \"}function getPackedSampler2D(e){var t=e.shapeInfo.logicalShape,r=e.name,n=\"get\"+r.charAt(0).toUpperCase()+r.slice(1),o=e.shapeInfo.texShape,a=o[0],i=o[1];if(null!=o&&arraysEqual(t,o))return \"\\n      vec4 \"+n+\"(int row, int col) {\\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(\"+i+\".0, \"+a+\".0);\\n\\n        return texture2D(\"+r+\", uv);\\n      }\\n    \";var s=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)];return \"\\n    vec4 \"+n+\"(int row, int col) {\\n      vec2 uv = packedUVfrom2D(\"+Math.ceil(t[1]/2)+\", \"+s[0]+\", \"+s[1]+\", row, col);\\n      return texture2D(\"+r+\", uv);\\n    }\\n  \"}function getSampler2D(e){var t=e.shapeInfo.logicalShape,r=e.name,n=\"get\"+r.charAt(0).toUpperCase()+r.slice(1),o=e.shapeInfo.texShape;if(null!=o&&arraysEqual(t,o)){var a=o[0];return \"\\n    float \"+n+\"(int row, int col) {\\n      vec2 uv = (vec2(col, row) + halfCR) / vec2(\"+o[1]+\".0, \"+a+\".0);\\n      return sampleTexture(\"+r+\", uv);\\n    }\\n  \"}var i=squeezeShape(t),s=i.newShape,u=i.keptDims,l=s;if(l.length<t.length){return \"\\n      \"+getSamplerFromInInfo(squeezeInputInfo(e,l))+\"\\n      float \"+n+\"(int row, int col) {\\n        return \"+n+\"(\"+getSqueezedParams([\"row\",\"col\"],u)+\");\\n      }\\n    \"}if(e.shapeInfo.isUniform)return \"\\n      float \"+n+\"(int row, int col) {\\n        float index = dot(vec2(row, col), vec2(\"+t[1]+\", 1));\\n        return \"+n+\"Flat(round(index));\\n      }\\n    \";var c=o[0],p=o[1];return 1===p?\"\\n    float \"+n+\"(int row, int col) {\\n      float index = dot(vec2(row, col), vec2(\"+t[1]+\", 1));\\n      vec2 uv = vec2(0.5, (index + 0.5) / \"+c+\".0);\\n      return sampleTexture(\"+r+\", uv);\\n    }\\n  \":1===c?\"\\n    float \"+n+\"(int row, int col) {\\n      float index = dot(vec2(row, col), vec2(\"+t[1]+\", 1));\\n      vec2 uv = vec2((index + 0.5) / \"+p+\".0, 0.5);\\n      return sampleTexture(\"+r+\", uv);\\n    }\\n  \":\"\\n  float \"+n+\"(int row, int col) {\\n    vec2 uv = UVfrom2D(\"+c+\", \"+p+\", \"+t[1]+\", row, col);\\n    return sampleTexture(\"+r+\", uv);\\n  }\\n\"}function getSampler3D(e){var t=e.shapeInfo.logicalShape,r=e.name,n=\"get\"+r.charAt(0).toUpperCase()+r.slice(1),o=t[1]*t[2],a=t[2],i=squeezeShape(t),s=i.newShape,u=i.keptDims,l=s;if(l.length<t.length){return \"\\n        \"+getSamplerFromInInfo(squeezeInputInfo(e,l))+\"\\n        float \"+n+\"(int row, int col, int depth) {\\n          return \"+n+\"(\"+getSqueezedParams([\"row\",\"col\",\"depth\"],u)+\");\\n        }\\n      \"}if(e.shapeInfo.isUniform)return \"\\n      float \"+n+\"(int row, int col, int depth) {\\n        float index = dot(vec3(row, col, depth),\\n                          vec3(\"+o+\", \"+a+\", 1));\\n        return \"+n+\"Flat(round(index));\\n      }\\n    \";var c=e.shapeInfo.texShape,p=c[0],d=c[1];return d===o?\"\\n        float \"+n+\"(int row, int col, int depth) {\\n          float texR = float(row);\\n          float texC = dot(vec2(col, depth), vec2(\"+a+\", 1));\\n          vec2 uv = (vec2(texC, texR) + halfCR) /\\n                     vec2(\"+d+\".0, \"+p+\".0);\\n          return sampleTexture(\"+r+\", uv);\\n        }\\n      \":d===a?\"\\n    float \"+n+\"(int row, int col, int depth) {\\n      float texR = dot(vec2(row, col), vec2(\"+t[1]+\", 1));\\n      float texC = float(depth);\\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(\"+d+\".0, \"+p+\".0);\\n      return sampleTexture(\"+r+\", uv);\\n    }\\n  \":\"\\n      float \"+n+\"(int row, int col, int depth) {\\n        vec2 uv = UVfrom3D(\\n            \"+p+\", \"+d+\", \"+o+\", \"+a+\", row, col, depth);\\n        return sampleTexture(\"+r+\", uv);\\n      }\\n  \"}function getPackedSampler4D(e){var t=e.shapeInfo.logicalShape,r=e.name,n=\"get\"+r.charAt(0).toUpperCase()+r.slice(1),o=e.shapeInfo.texShape,a=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)],i=a[0],s=a[1],u=Math.ceil(t[3]/2),l=u*Math.ceil(t[2]/2);return \"\\n    vec4 \"+n+\"(int b2, int b, int row, int col) {\\n      vec2 uv = packedUVfrom4D(\\n        \"+i+\", \"+s+\", \"+l*t[1]+\",\\n        \"+l+\", \"+u+\", b2, b, row, col);\\n      return texture2D(\"+r+\", uv);\\n    }\\n  \"}function getSampler4D(e){var t=e.shapeInfo.logicalShape,r=e.name,n=\"get\"+r.charAt(0).toUpperCase()+r.slice(1),o=t[3],a=t[2]*o,i=t[1]*a,s=squeezeShape(t),u=s.newShape,l=s.keptDims;if(u.length<t.length){return \"\\n      \"+getSamplerFromInInfo(squeezeInputInfo(e,u))+\"\\n      float \"+n+\"(int row, int col, int depth, int depth2) {\\n        return \"+n+\"(\"+getSqueezedParams([\"row\",\"col\",\"depth\",\"depth2\"],l)+\");\\n      }\\n    \"}if(e.shapeInfo.isUniform)return \"\\n      float \"+n+\"(int row, int col, int depth, int depth2) {\\n        float index = dot(vec4(row, col, depth, depth2),\\n                          vec4(\"+i+\", \"+a+\", \"+o+\", 1));\\n        return \"+n+\"Flat(round(index));\\n      }\\n    \";var c=e.shapeInfo.texShape,p=c[0],d=c[1];return d===i?\"\\n      float \"+n+\"(int row, int col, int depth, int depth2) {\\n        float texR = float(row);\\n        float texC =\\n            dot(vec3(col, depth, depth2), vec3(\"+a+\", \"+o+\", 1));\\n        vec2 uv = (vec2(texC, texR) + halfCR) /\\n                   vec2(\"+d+\".0, \"+p+\".0);\\n        return sampleTexture(\"+r+\", uv);\\n      }\\n    \":d===o?\"\\n      float \"+n+\"(int row, int col, int depth, int depth2) {\\n        float texR = dot(vec3(row, col, depth),\\n                         vec3(\"+t[1]*t[2]+\", \"+t[2]+\", 1));\\n        float texC = float(depth2);\\n        vec2 uv = (vec2(texC, texR) + halfCR) /\\n                  vec2(\"+d+\".0, \"+p+\".0);\\n        return sampleTexture(\"+r+\", uv);\\n      }\\n    \":\"\\n    float \"+n+\"(int row, int col, int depth, int depth2) {\\n      vec2 uv = UVfrom4D(\"+p+\", \"+d+\", \"+i+\", \"+a+\",\\n          \"+o+\", row, col, depth, depth2);\\n      return sampleTexture(\"+r+\", uv);\\n    }\\n  \"}function getSampler5D(e){var t=e.shapeInfo.logicalShape,r=e.name,n=\"get\"+r.charAt(0).toUpperCase()+r.slice(1),o=t[4],a=t[3]*o,i=t[2]*a,s=t[1]*i,u=squeezeShape(t),l=u.newShape,c=u.keptDims;if(l.length<t.length){return \"\\n      \"+getSamplerFromInInfo(squeezeInputInfo(e,l))+\"\\n      float \"+n+\"(int row, int col, int depth, int depth2, int depth3) {\\n        return \"+n+\"(\"+getSqueezedParams([\"row\",\"col\",\"depth\",\"depth2\",\"depth3\"],c)+\");\\n      }\\n    \"}if(e.shapeInfo.isUniform)return \"\\n      float \"+n+\"(int row, int col, int depth, int depth2, int depth3) {\\n        float index = dot(\\n          vec4(row, col, depth, depth2),\\n          vec4(\"+s+\", \"+i+\", \"+a+\", \"+o+\")) +\\n          depth3;\\n        return \"+n+\"Flat(index);\\n      }\\n    \";var p=e.shapeInfo.texShape,d=p[0],h=p[1];return h===s?\"\\n      float \"+n+\"(int row, int col, int depth, int depth2, int depth3) {\\n        int texR = row;\\n        float texC = dot(\\n          vec4(col, depth, depth2, depth3),\\n          vec4(\"+i+\", \"+a+\", \"+o+\", 1));\\n        vec2 uv = (vec2(texC, texR) + halfCR) /\\n                   vec2(\"+h+\".0, \"+d+\".0);\\n        return sampleTexture(\"+r+\", uv);\\n      }\\n    \":h===o?\"\\n      float \"+n+\"(int row, int col, int depth, int depth2, int depth3) {\\n        float texR = dot(\\n          vec4(row, col, depth, depth2),\\n          vec4(\"+t[1]*t[2]*t[3]+\", \"+t[2]*t[3]+\",\\n            \"+t[3]+\", 1));\\n        int texC = depth3;\\n        vec2 uv = (vec2(texC, texR) + halfCR) /\\n                  vec2(\"+h+\".0, \"+d+\".0);\\n        return sampleTexture(\"+r+\", uv);\\n      }\\n    \":\"\\n    float \"+n+\"(int row, int col, int depth, int depth2, int depth3) {\\n      vec2 uv = UVfrom5D(\"+d+\", \"+h+\", \"+s+\", \"+i+\",\\n          \"+a+\", \"+o+\", row, col, depth, depth2, depth3);\\n      return sampleTexture(\"+r+\", uv);\\n    }\\n  \"}function getSampler6D(e){var t=e.shapeInfo.logicalShape,r=e.name,n=\"get\"+r.charAt(0).toUpperCase()+r.slice(1),o=t[5],a=t[4]*o,i=t[3]*a,s=t[2]*i,u=t[1]*s,l=squeezeShape(t),c=l.newShape,p=l.keptDims;if(c.length<t.length){return \"\\n      \"+getSamplerFromInInfo(squeezeInputInfo(e,c))+\"\\n      float \"+n+\"(int row, int col, int depth,\\n                    int depth2, int depth3, int depth4) {\\n        return \"+n+\"(\"+getSqueezedParams([\"row\",\"col\",\"depth\",\"depth2\",\"depth3\",\"depth4\"],p)+\");\\n      }\\n    \"}if(e.shapeInfo.isUniform)return \"\\n      float \"+n+\"(int row, int col, int depth,\\n                  int depth2, int depth3, int depth4) {\\n        float index = dot(\\n          vec4(row, col, depth, depth2),\\n          vec4(\"+u+\", \"+s+\", \"+i+\", \"+a+\")) +\\n          dot(\\n            vec2(depth3, depth4),\\n            vec2(\"+o+\", 1));\\n        return \"+n+\"Flat(index);\\n      }\\n    \";var d=e.shapeInfo.texShape,h=d[0],f=d[1];return f===u?\"\\n      float \"+n+\"(int row, int col, int depth,\\n                    int depth2, int depth3, int depth4) {\\n        int texR = row;\\n        float texC = dot(\\n          vec4(col, depth, depth2, depth3),\\n          vec4(\"+s+\", \"+i+\", \"+a+\", \"+o+\")) + depth4;\\n        vec2 uv = (vec2(texC, texR) + halfCR) /\\n                   vec2(\"+f+\".0, \"+h+\".0);\\n        return sampleTexture(\"+r+\", uv);\\n      }\\n    \":f===o?\"\\n      float \"+n+\"(int row, int col, int depth,\\n                    int depth2, int depth3, int depth4) {\\n        float texR = dot(\\n          vec4(row, col, depth, depth2),\\n          vec4(\"+t[1]*t[2]*t[3]*t[4]+\",\\n               \"+t[2]*t[3]*t[4]+\",\\n               \"+t[3]*t[4]+\",\\n               \"+t[4]+\")) + depth3;\\n        int texC = depth4;\\n        vec2 uv = (vec2(texC, texR) + halfCR) /\\n                  vec2(\"+f+\".0, \"+h+\".0);\\n        return sampleTexture(\"+r+\", uv);\\n      }\\n    \":\"\\n    float \"+n+\"(int row, int col, int depth,\\n                  int depth2, int depth3, int depth4) {\\n      vec2 uv = UVfrom6D(\"+h+\", \"+f+\", \"+u+\", \"+s+\",\\n          \"+i+\", \"+a+\", \"+o+\"\\n          ,row, col, depth, depth2, depth3, depth4);\\n      return sampleTexture(\"+r+\", uv);\\n    }\\n  \"}function getSamplerFlat(e){var t=e.name,r=\"get\"+t.charAt(0).toUpperCase()+t.slice(1)+\"Flat\",n=sizeFromShape(e.shapeInfo.logicalShape);if(e.shapeInfo.isUniform)return 1===n?\"float \"+r+\"(int index) {return \"+t+\";}\":\"\\n      float \"+r+\"(int index) {\\n        for (int i = 0; i < \"+n+\"; i++) {\\n          if (i == index) {\\n            return \"+t+\"[i];\\n          }\\n        }\\n      }\\n    \";var o=e.shapeInfo.texShape,a=o[0],i=o[1];return 1===i&&1===a?\"\\n      float \"+r+\"(int index) {\\n        return sampleTexture(\"+t+\", halfCR);\\n      }\\n    \":1===i?\"\\n      float \"+r+\"(int index) {\\n        vec2 uv = vec2(0.5, (float(index) + 0.5) / \"+a+\".0);\\n        return sampleTexture(\"+t+\", uv);\\n      }\\n    \":1===a?\"\\n      float \"+r+\"(int index) {\\n        vec2 uv = vec2((float(index) + 0.5) / \"+i+\".0, 0.5);\\n        return sampleTexture(\"+t+\", uv);\\n      }\\n    \":\"\\n    float \"+r+\"(int index) {\\n      vec2 uv = UVfrom1D(\"+a+\", \"+i+\", index);\\n      return sampleTexture(\"+t+\", uv);\\n    }\\n  \"}function getBroadcastOutputCoordsSampler(e,t,r,n){var o=e.shapeInfo.logicalShape.length,a=t.logicalShape.length,i=\"int\";2===a?i=\"ivec2\":3===a?i=\"ivec3\":4===a&&(i=\"ivec4\");var s=getBroadcastDims(e.shapeInfo.logicalShape,t.logicalShape),u=a-o;return \"\\n    float \"+n+\"() {\\n      \"+i+\" coords = getOutputCoords();\\n      \"+(0===o?\"\":a<2&&s.length>=1?\"coords = 0;\":s.map(function(e){return \"coords[\"+(e+u)+\"] = 0;\"}).join(\"\\n\"))+\"\\n      return get\"+r+\"(\"+(a<2&&o>0?\"coords\":e.shapeInfo.logicalShape.map(function(e,t){return \"coords[\"+(t+u)+\"]\"}).join(\", \"))+\");\\n    }\\n  \"}function getSamplerAtOutputCoords(e,t,r){var n=e.name,o=n.charAt(0).toUpperCase()+n.slice(1),a=\"get\"+o+\"AtOutCoords\",i=getBroadcastDims(e.shapeInfo.logicalShape,t.logicalShape),s=e.shapeInfo.logicalShape.length,u=t.logicalShape.length,l=r&&(u>s||i.length>0),c=broadcastDimsAreOuter(i),p=e.shapeInfo.isUniform;if(l&&!c)return getBroadcastOutputCoordsSampler(e,t,o,a);var d=sizeFromShape(e.shapeInfo.logicalShape),h=\"\";l&&c&&(h=\"\\n        int mainPart = index / \"+d+\";\\n        index -= mainPart * \"+d+\";\\n      \");var f=t.texShape;if(p)return 1===d?\"float \"+a+\"() {return \"+n+\";}\":\"\\n      float \"+a+\"() {\\n        ivec2 resTexRC = ivec2(resultUV.yx *\\n                              vec2(\"+f[0]+\", \"+f[1]+\"));\\n        int index = resTexRC.x * \"+f[1]+\" + resTexRC.y;\\n        \"+h+\"\\n        return get\"+o+\"Flat(index);\\n      }\\n    \";var m=e.shapeInfo.texShape;return arraysEqual(m,f)?\"\\n      float \"+a+\"() {\\n        return sampleTexture(\"+n+\", resultUV);\\n      }\\n    \":\"\\n    float \"+a+\"() {\\n      ivec2 resTexRC = ivec2(resultUV.yx *\\n                             vec2(\"+f[0]+\", \"+f[1]+\"));\\n      int index = resTexRC.x * \"+f[1]+\" + resTexRC.y;\\n      \"+h+\"\\n      int texR = index / \"+m[1]+\";\\n      int texC = index - texR * \"+m[1]+\";\\n      vec2 uv = (vec2(texC, texR) + halfCR) /\\n                 vec2(\"+m[1]+\".0, \"+m[0]+\".0);\\n\\n      return sampleTexture(\"+n+\", uv);\\n    }\\n  \"}function getCoordsDataType(e){if(e<=1)return \"int\";if(2===e)return \"ivec2\";if(3===e)return \"ivec3\";if(4===e)return \"ivec4\";if(5===e)return \"ivec5\";if(6===e)return \"ivec6\";throw Error(\"GPU for rank \"+e+\" is not yet supported\")}function squeezeInputInfo(e,t){var r=JSON.parse(JSON.stringify(e));return r.shapeInfo.logicalShape=t,r}function getSqueezedParams(e,t){return t.map(function(t){return e[t]}).join(\", \")}var CumSumProgram=function(){return function(e,t,r){this.variableNames=[\"x\"],this.outputShape=e;var n=e.length,o=e[e.length-1],a=r?\"<\":\">\";this.userCode=\"\\n      int getIndex(int i) {\\n        \"+(r?\"return \"+o+\" -i - 1;\":\"return i;\")+\"\\n      }\\n\\n      void main() {\\n        \"+getCoordsDataType(n)+\" coords = getOutputCoords();\\n        int end = \"+getFinalCoord(n,\"coords\")+\";\\n        float val = 0.0;\\n        for (int i = \"+o+\" - 1; i >= 0; i -= 1) {\\n          int idx = getIndex(i);\\n          if (idx \"+a+\" end) {\\n            continue;\\n          }\\n          if (idx == end && \"+t+\") {\\n            continue;\\n          }\\n          \"+getFinalCoord(n,\"coords\")+\" = idx;\\n          val += getX(\"+getCoords(n,\"coords\")+\");\\n        }\\n        setOutput(val);\\n      }\\n    \";}}();function getCoords(e,t){if(1===e)return \"\"+t;if(2===e)return t+\".x, \"+t+\".y\";if(3===e)return t+\".x, \"+t+\".y, \"+t+\".z\";if(4===e)return t+\".x, \"+t+\".y, \"+t+\".z, \"+t+\".w\";throw Error(\"Cumulative sum for rank \"+e+\" is not yet supported\")}function getFinalCoord(e,t){if(1===e)return \"\"+t;if(2===e)return t+\".y\";if(3===e)return t+\".z\";if(4===e)return t+\".w\";throw Error(\"Cumulative sum for rank \"+e+\" is not yet supported\")}var DepthToSpaceProgram=function(){function e(e,t,r){this.variableNames=[\"x\"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=r,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 / \"+t+\";\\n      int offset_h = imod(h, \"+t+\");\\n      int in_w = w / \"+t+\";\\n      int offset_w = imod(w, \"+t+\");\\n      int offset_d = (offset_h * \"+t+\" + offset_w) *\\n        \"+this.getOutputDepthSize()+\";\\n      int in_d = d + offset_d;\\n\\n      float result = \"+this.getInputSamplingString()+\";\\n      setOutput(result);\\n    }\\n  \";}return e.prototype.getHeightCoordString=function(){return \"NHWC\"===this.dataFormat?\"coords[1]\":\"coords[2]\"},e.prototype.getWidthCoordString=function(){return \"NHWC\"===this.dataFormat?\"coords[2]\":\"coords[3]\"},e.prototype.getDepthCoordString=function(){return \"NHWC\"===this.dataFormat?\"coords[3]\":\"coords[1]\"},e.prototype.getOutputDepthSize=function(){return \"NHWC\"===this.dataFormat?this.outputShape[3]:this.outputShape[1]},e.prototype.getInputSamplingString=function(){return \"NHWC\"===this.dataFormat?\"getX(b, in_h, in_w, in_d)\":\"getX(b, in_d, in_h, in_w)\"},e}(),EncodeFloatProgram=function(){return function(e){this.variableNames=[\"A\"],this.outputShape=e,this.userCode=\"\\n      const float FLOAT_MAX = 1.70141184e38;\\n      const float FLOAT_MIN = 1.17549435e-38;\\n\\n      lowp vec4 encode_float(highp float v) {\\n        if (isNaN(v)) {\\n          return vec4(255, 255, 255, 255);\\n        }\\n\\n        highp float av = abs(v);\\n\\n        if(av < FLOAT_MIN) {\\n          return vec4(0.0, 0.0, 0.0, 0.0);\\n        } else if(v > FLOAT_MAX) {\\n          return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;\\n        } else if(v < -FLOAT_MAX) {\\n          return vec4(0.0, 0.0,  128.0, 255.0) / 255.0;\\n        }\\n\\n        highp vec4 c = vec4(0,0,0,0);\\n\\n        highp float e = floor(log2(av));\\n        highp float m = exp2(fract(log2(av))) - 1.0;\\n\\n        c[2] = floor(128.0 * m);\\n        m -= c[2] / 128.0;\\n        c[1] = floor(32768.0 * m);\\n        m -= c[1] / 32768.0;\\n        c[0] = floor(8388608.0 * m);\\n\\n        highp float ebias = e + 127.0;\\n        c[3] = floor(ebias / 2.0);\\n        ebias -= c[3] * 2.0;\\n        c[2] += floor(ebias) * 128.0;\\n\\n        c[3] += 128.0 * step(0.0, -v);\\n\\n        return c / 255.0;\\n      }\\n\\n      void main() {\\n        float x = getAAtOutCoords();\\n        gl_FragColor = encode_float(x);\\n      }\\n    \";}}(),COMPLEX_FFT={REAL:\"return real * expR - imag * expI;\",IMAG:\"return real * expI + imag * expR;\"},FFTProgram=function(){return function(e,t,r){this.variableNames=[\"real\",\"imag\"];var n=t[1];this.outputShape=t;var o=r?\"2.0 * \"+Math.PI:\"-2.0 * \"+Math.PI,a=r?n+\".0\":\"1.0\";this.userCode=\"\\n      const float exponentMultiplier = \"+o+\";\\n\\n      float unaryOpComplex(float real, float expR, float imag, float expI) {\\n        \"+e+\"\\n      }\\n\\n      float mulMatDFT(int batch, int index) {\\n        float indexRatio = float(index) / float(\"+n+\");\\n        float exponentMultiplierTimesIndexRatio =\\n            exponentMultiplier * indexRatio;\\n\\n        float result = 0.0;\\n\\n        for (int i = 0; i < \"+n+\"; 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) / \"+a+\";\\n        }\\n\\n        return result;\\n      }\\n\\n      void main() {\\n        ivec2 coords = getOutputCoords();\\n        setOutput(mulMatDFT(coords[0], coords[1]));\\n      }\\n    \";}}(),FromPixelsProgram=function(){return function(e){this.variableNames=[\"A\"];var t=e[0],r=e[1];this.outputShape=e,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(\"+r+\".0, \"+t+\".0);\\n\\n        vec4 values = 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    \";}}(),GatherProgram=function(){return function(e,t,r){this.variableNames=[\"A\",\"indices\"];var n=e.slice();n[r]=t,this.outputShape=n,this.rank=n.length;var o=getCoordsDataType(this.rank),a=getSourceCoords(e,r);this.userCode=\"\\n      void main() {\\n        \"+o+\" resRC = getOutputCoords();\\n        setOutput(getA(\"+a+\"));\\n      }\\n    \";}}();function getSourceCoords(e,t){var r=e.length;if(r>4)throw Error(\"Gather for rank \"+r+\" is not yet supported\");if(1===r)return \"int(getIndices(resRC))\";for(var n=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],o=[],a=0;a<e.length;a++)a===t?o.push(\"int(getIndices(\"+n[a]+\"))\"):o.push(\"\"+n[a]);return o.join()}var TextureUsage,PhysicalTextureType,GatherNDProgram=function(){return function(e,t,r){this.sliceDim=e,this.strides=t,this.variableNames=[\"x\",\"indices\"],this.outputShape=r;var n=getCoordsDataType(t.length),o=getCoordsDataType(r.length),a=this.sliceDim>1?\"strides[j]\":\"strides\";this.userCode=\"\\n        \"+n+\" strides = \"+n+\"(\"+this.strides+\");\\n         void main() {\\n          \"+o+\" 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 * \"+a+\";\\n          }\\n          setOutput(getX(flattenIndex, coords[1]));\\n        }\\n      \";}}();function getUnpackedMatrixTextureShapeWidthHeight(e,t){return [t,e]}function getUnpackedArraySizeFromMatrixSize(e,t){return e*t}function getMatrixSizeFromUnpackedArraySize(e,t){if(e%t!=0)throw new Error(\"unpackedSize (\"+e+\") must be a multiple of \"+t);return e/t}function encodeMatrixToUnpackedArray(e,t,r){var n=getUnpackedArraySizeFromMatrixSize(e.length,r);if(t.length<n)throw new Error(\"unpackedArray length (\"+t.length+\") must be >= \"+n);for(var o=0,a=0;a<e.length;++a)t[o]=e[a],o+=r;}function decodeMatrixFromUnpackedArray(e,t,r){var n=getMatrixSizeFromUnpackedArraySize(e.length,r);if(t.length<n)throw new Error(\"matrix length (\"+t.length+\") must be >= \"+n);for(var o=0,a=0;a<e.length;a+=r)t[o++]=e[a];}function getPackedMatrixTextureShapeWidthHeight(e,t){return [Math.ceil(t/2),Math.ceil(e/2)]}function getPackedRGBAArraySizeFromMatrixShape(e,t){var r=getPackedMatrixTextureShapeWidthHeight(e,t);return r[0]*r[1]*4}function encodeMatrixToPackedRGBA(e,t,r,n,o){var a=getPackedRGBAArraySizeFromMatrixShape(r,n);if(o.length<a)throw new Error(\"packedRGBA length (\"+o.length+\") must be >=\\n        \"+a);for(var i=n%2==1,s=r%2==1,u=Math.floor(n/2),l=Math.floor(r/2),c=Math.ceil(n/2),p=c*Math.ceil(r/2),d=nearestLargerEven(r)*nearestLargerEven(n),h=0;h<t;h++){for(var f=h*r*n,m=h*d,g=i?4:0,v=n,y=m,x=0;x<l;++x){for(var T=2*x*n,E=0;E<u;++E){var w=f+T+2*E;o[y]=e[w],o[y+1]=e[w+1],o[y+2]=e[w+v],o[y+3]=e[w+v+1],y+=4;}y+=g;}if(i){w=f+n-1,y=m+4*(c-1);var S=2*n;for(g=4*c,x=0;x<l;++x)o[y]=e[w],o[y+2]=e[w+n],w+=S,y+=g;}if(s){for(w=f+(r-1)*n,y=m+4*(p-c),E=0;E<u;++E)o[y++]=e[w++],o[y++]=e[w++],y+=2;i&&s&&(o[m+d-4]=e[w]);}}return o}function decodeMatrixFromPackedRGBA(e,t,r,n,o){var a=r*n;if(o.length<a)throw new Error(\"matrix length (\"+o.length+\") must be >= \"+a);for(var i=n%2==1,s=r%2==1,u=Math.floor(n/2),l=Math.floor(r/2),c=Math.ceil(n/2),p=c*Math.ceil(r/2),d=nearestLargerEven(r)*nearestLargerEven(n),h=0;h<t;h++){for(var f=h*r*n,m=h*d,g=i?4:0,v=n+(i?1:0),y=m,x=f,T=f+n,E=0;E<l;++E){for(var w=0;w<u;++w)o[x++]=e[y++],o[x++]=e[y++],o[T++]=e[y++],o[T++]=e[y++];y+=g,x+=v,T+=v;}if(i){y=m+4*(c-1);var S=f+n-1;for(g=4*c,v=2*n,E=0;E<l;++E)o[S]=e[y],o[S+n]=e[y+2],y+=g,S+=v;}if(s){for(y=m+4*(p-c),S=f+(r-1)*n,w=0;w<u;++w)o[S++]=e[y++],o[S++]=e[y++],y+=2;i&&(o[f+r*n-1]=e[y]);}}return o}function callAndCheck(e,t){var r=t();return checkWebGLError(e),r}!function(e){e[e.RENDER=0]=\"RENDER\",e[e.UPLOAD=1]=\"UPLOAD\",e[e.PIXELS=2]=\"PIXELS\",e[e.DOWNLOAD=3]=\"DOWNLOAD\";}(TextureUsage||(TextureUsage={})),function(e){e[e.UNPACKED_FLOAT16=0]=\"UNPACKED_FLOAT16\",e[e.UNPACKED_FLOAT32=1]=\"UNPACKED_FLOAT32\",e[e.PACKED_4X1_UNSIGNED_BYTE=2]=\"PACKED_4X1_UNSIGNED_BYTE\",e[e.PACKED_2X2_FLOAT32=3]=\"PACKED_2X2_FLOAT32\",e[e.PACKED_2X2_FLOAT16=4]=\"PACKED_2X2_FLOAT16\";}(PhysicalTextureType||(PhysicalTextureType={}));var webGLDebugErrorCheckingEnabled=!1;function enableDebugWebGLErrorChecking(e){webGLDebugErrorCheckingEnabled=e;}function checkWebGLError(e){if(webGLDebugErrorCheckingEnabled){var t=e.getError();if(t!==e.NO_ERROR)throw new Error(\"WebGL Error: \"+getWebGLErrorMessage(e,t))}}function getWebGLErrorMessage(e,t){switch(t){case e.NO_ERROR:return \"NO_ERROR\";case e.INVALID_ENUM:return \"INVALID_ENUM\";case e.INVALID_VALUE:return \"INVALID_VALUE\";case e.INVALID_OPERATION:return \"INVALID_OPERATION\";case e.INVALID_FRAMEBUFFER_OPERATION:return \"INVALID_FRAMEBUFFER_OPERATION\";case e.OUT_OF_MEMORY:return \"OUT_OF_MEMORY\";case e.CONTEXT_LOST_WEBGL:return \"CONTEXT_LOST_WEBGL\";default:return \"Unknown error code \"+t}}function getExtensionOrThrow(e,t){return throwIfNull(e,function(){return e.getExtension(t)},'Extension \"'+t+'\" not supported on this browser.')}function createVertexShader(e,t){var r=throwIfNull(e,function(){return e.createShader(e.VERTEX_SHADER)},\"Unable to create vertex WebGLShader.\");if(callAndCheck(e,function(){return e.shaderSource(r,t)}),callAndCheck(e,function(){return e.compileShader(r)}),!1===e.getShaderParameter(r,e.COMPILE_STATUS))throw console.log(e.getShaderInfoLog(r)),new Error(\"Failed to compile vertex shader.\");return r}function createFragmentShader(e,t){var r=throwIfNull(e,function(){return e.createShader(e.FRAGMENT_SHADER)},\"Unable to create fragment WebGLShader.\");if(callAndCheck(e,function(){return e.shaderSource(r,t)}),callAndCheck(e,function(){return e.compileShader(r)}),!1===e.getShaderParameter(r,e.COMPILE_STATUS))throw logShaderSourceAndInfoLog(t,e.getShaderInfoLog(r)),new Error(\"Failed to compile fragment shader.\");return r}var lineNumberRegex=/ERROR: [0-9]+:([0-9]+):/g;function logShaderSourceAndInfoLog(e,t){var r=lineNumberRegex.exec(t);if(null==r)return console.log(\"Couldn't parse line number in error: \"+t),void console.log(e);for(var n=+r[1],o=e.split(\"\\n\"),a=o.length.toString().length+2,i=o.map(function(e,t){return rightPad((t+1).toString(),a)+e}),s=0,u=0;u<i.length;u++)s=Math.max(i[u].length,s);var l=i.slice(0,n-1),c=i.slice(n-1,n),p=i.slice(n);console.log(l.join(\"\\n\")),console.log(t.split(\"\\n\")[0]),console.log(\"%c \"+rightPad(c[0],s),\"border:1px solid red; background-color:#e3d2d2; color:#a61717\"),console.log(p.join(\"\\n\"));}function createProgram(e){return throwIfNull(e,function(){return e.createProgram()},\"Unable to create WebGLProgram.\")}function linkProgram(e,t){if(callAndCheck(e,function(){return e.linkProgram(t)}),!1===e.getProgramParameter(t,e.LINK_STATUS))throw console.log(e.getProgramInfoLog(t)),new Error(\"Failed to link vertex and fragment shaders.\")}function validateProgram(e,t){if(callAndCheck(e,function(){return e.validateProgram(t)}),!1===e.getProgramParameter(t,e.VALIDATE_STATUS))throw console.log(e.getProgramInfoLog(t)),new Error(\"Shader program validation failed.\")}function createStaticVertexBuffer(e,t){var r=throwIfNull(e,function(){return e.createBuffer()},\"Unable to create WebGLBuffer\");return callAndCheck(e,function(){return e.bindBuffer(e.ARRAY_BUFFER,r)}),callAndCheck(e,function(){return e.bufferData(e.ARRAY_BUFFER,t,e.STATIC_DRAW)}),r}function createStaticIndexBuffer(e,t){var r=throwIfNull(e,function(){return e.createBuffer()},\"Unable to create WebGLBuffer\");return callAndCheck(e,function(){return e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,r)}),callAndCheck(e,function(){return e.bufferData(e.ELEMENT_ARRAY_BUFFER,t,e.STATIC_DRAW)}),r}function getNumChannels(){return 2===ENV.get(\"WEBGL_VERSION\")?1:4}function createTexture(e){return throwIfNull(e,function(){return e.createTexture()},\"Unable to create WebGLTexture.\")}function validateTextureSize(e,t){var r=ENV.get(\"WEBGL_MAX_TEXTURE_SIZE\");if(e<=0||t<=0){var n=\"[\"+e+\"x\"+t+\"]\";throw new Error(\"Requested texture size \"+n+\" is invalid.\")}if(e>r||t>r){n=\"[\"+e+\"x\"+t+\"]\";throw new Error(\"Requested texture size \"+n+\" greater than WebGL maximum on this browser / GPU \"+(\"[\"+r+\"x\"+r+\"]\")+\".\")}}function createFramebuffer(e){return throwIfNull(e,function(){return e.createFramebuffer()},\"Unable to create WebGLFramebuffer.\")}function bindVertexBufferToProgramAttribute(e,t,r,n,o,a,i){var s=e.getAttribLocation(t,r);return -1!==s&&(callAndCheck(e,function(){return e.bindBuffer(e.ARRAY_BUFFER,n)}),callAndCheck(e,function(){return e.vertexAttribPointer(s,o,e.FLOAT,!1,a,i)}),callAndCheck(e,function(){return e.enableVertexAttribArray(s)}),!0)}function bindTextureUnit(e,t,r){validateTextureUnit(e,r),callAndCheck(e,function(){return e.activeTexture(e.TEXTURE0+r)}),callAndCheck(e,function(){return e.bindTexture(e.TEXTURE_2D,t)});}function unbindTextureUnit(e,t){validateTextureUnit(e,t),callAndCheck(e,function(){return e.activeTexture(e.TEXTURE0+t)}),callAndCheck(e,function(){return e.bindTexture(e.TEXTURE_2D,null)});}function getProgramUniformLocationOrThrow(e,t,r){return throwIfNull(e,function(){return e.getUniformLocation(t,r)},'uniform \"'+r+'\" not present in program.')}function getProgramUniformLocation(e,t,r){return e.getUniformLocation(t,r)}function bindTextureToProgramUniformSampler(e,t,r,n,o){callAndCheck(e,function(){return bindTextureUnit(e,r,o)}),callAndCheck(e,function(){return e.uniform1i(n,o)});}function bindCanvasToFramebuffer(e){callAndCheck(e,function(){return e.bindFramebuffer(e.FRAMEBUFFER,null)}),callAndCheck(e,function(){return e.viewport(0,0,e.canvas.width,e.canvas.height)}),callAndCheck(e,function(){return e.scissor(0,0,e.canvas.width,e.canvas.height)});}function bindColorTextureToFramebuffer(e,t,r){callAndCheck(e,function(){return e.bindFramebuffer(e.FRAMEBUFFER,r)}),callAndCheck(e,function(){return e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,t,0)});}function unbindColorTextureFromFramebuffer(e,t){callAndCheck(e,function(){return e.bindFramebuffer(e.FRAMEBUFFER,t)}),callAndCheck(e,function(){return e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,null,0)});}function validateFramebuffer(e){var t=e.checkFramebufferStatus(e.FRAMEBUFFER);if(t!==e.FRAMEBUFFER_COMPLETE)throw new Error(\"Error binding framebuffer: \"+getFramebufferErrorMessage(e,t))}function getFramebufferErrorMessage(e,t){switch(t){case e.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:return \"FRAMEBUFFER_INCOMPLETE_ATTACHMENT\";case e.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:return \"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT\";case e.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:return \"FRAMEBUFFER_INCOMPLETE_DIMENSIONS\";case e.FRAMEBUFFER_UNSUPPORTED:return \"FRAMEBUFFER_UNSUPPORTED\";default:return \"unknown error \"+t}}function throwIfNull(e,t,r){var n=callAndCheck(e,function(){return t()});if(null==n)throw new Error(r);return n}function validateTextureUnit(e,t){var r=e.MAX_COMBINED_TEXTURE_IMAGE_UNITS-1,n=t+e.TEXTURE0;if(n<e.TEXTURE0||n>r)throw new Error(\"textureUnit must be in \"+(\"[gl.TEXTURE0, gl.TEXTURE\"+r+\"]\")+\".\")}function getTextureShapeFromLogicalShape(e,t){void 0===t&&(t=!1);var r=ENV.get(\"WEBGL_MAX_TEXTURE_SIZE\");if(t&&(r*=2,e=e.map(function(t,r){return r>=e.length-2?nearestLargerEven(e[r]):e[r]})),2!==e.length){var n=squeezeShape(e);e=n.newShape;}var o=sizeFromShape(e);return e.length<=1&&o<=r?[o,1]:2===e.length&&e[0]<=r&&e[1]<=r?e:3===e.length&&e[0]*e[1]<=r&&e[2]<=r?[e[0]*e[1],e[2]]:3===e.length&&e[0]<=r&&e[1]*e[2]<=r?[e[0],e[1]*e[2]]:4===e.length&&e[0]*e[1]*e[2]<=r&&e[3]<=r?[e[0]*e[1]*e[2],e[3]]:4===e.length&&e[0]<=r&&e[1]*e[2]*e[3]<=r?[e[0],e[1]*e[2]*e[3]]:sizeToSquarishShape(o)}var webgl_util=Object.freeze({callAndCheck:callAndCheck,enableDebugWebGLErrorChecking:enableDebugWebGLErrorChecking,checkWebGLError:checkWebGLError,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,getTextureShapeFromLogicalShape:getTextureShapeFromLogicalShape});function createVertexShader$1(e){return createVertexShader(e,\"\\n    precision highp float;\\n    attribute vec3 clipSpacePos;\\n    attribute vec2 uv;\\n    varying vec2 resultUV;\\n\\n    void main() {\\n      gl_Position = vec4(clipSpacePos, 1);\\n      resultUV = uv;\\n    }\")}function createVertexBuffer(e){return createStaticVertexBuffer(e,new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]))}function createIndexBuffer(e){return createStaticIndexBuffer(e,new Uint16Array([0,1,2,2,1,3]))}function getTextureConfig(e,t){var r,n,o,a,i,s,u,l=e;return 2===ENV.get(\"WEBGL_VERSION\")?(r=l.R32F,n=l.R16F,o=l.RGBA32F,a=l.RED,i=4,s=1,u=l.HALF_FLOAT):(r=e.RGBA,n=e.RGBA,o=l.RGBA,a=e.RGBA,i=4,s=4,u=null!=t?t.HALF_FLOAT_OES:null),{internalFormatFloat:r,internalFormatHalfFloat:n,internalFormatPackedFloat:o,textureFormatFloat:a,downloadTextureFormat:e.RGBA,downloadUnpackNumChannels:i,defaultNumChannels:s,textureTypeHalfFloat:u}}function createAndConfigureTexture(e,t,r,n,o,a){validateTextureSize(t,r);var i=createTexture(e),s=e.TEXTURE_2D;return callAndCheck(e,function(){return e.bindTexture(s,i)}),callAndCheck(e,function(){return e.texParameteri(s,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)}),callAndCheck(e,function(){return e.texParameteri(s,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)}),callAndCheck(e,function(){return e.texParameteri(s,e.TEXTURE_MIN_FILTER,e.NEAREST)}),callAndCheck(e,function(){return e.texParameteri(s,e.TEXTURE_MAG_FILTER,e.NEAREST)}),callAndCheck(e,function(){return e.texImage2D(s,0,n,t,r,0,o,a,null)}),callAndCheck(e,function(){return e.bindTexture(e.TEXTURE_2D,null)}),i}function createFloat32MatrixTexture(e,t,r,n){var o=getUnpackedMatrixTextureShapeWidthHeight(t,r);return createAndConfigureTexture(e,o[0],o[1],n.internalFormatFloat,n.textureFormatFloat,e.FLOAT)}function createFloat16MatrixTexture(e,t,r,n){var o=getUnpackedMatrixTextureShapeWidthHeight(t,r);return createAndConfigureTexture(e,o[0],o[1],n.internalFormatFloat,n.textureFormatFloat,n.textureTypeHalfFloat)}function createUnsignedBytesMatrixTexture(e,t,r,n){var o=getUnpackedMatrixTextureShapeWidthHeight(t,r);return createAndConfigureTexture(e,o[0],o[1],e.RGBA,e.RGBA,e.UNSIGNED_BYTE)}function createPackedMatrixTexture(e,t,r,n){var o=getPackedMatrixTextureShapeWidthHeight(t,r);return createAndConfigureTexture(e,o[0],o[1],n.internalFormatPackedFloat,e.RGBA,e.FLOAT)}function createFloat16PackedMatrixTexture(e,t,r,n){var o=getPackedMatrixTextureShapeWidthHeight(t,r);return createAndConfigureTexture(e,o[0],o[1],n.internalFormatHalfFloat,e.RGBA,n.textureTypeHalfFloat)}function bindVertexProgramAttributeStreams(e,t,r){return callAndCheck(e,function(){return e.bindBuffer(e.ARRAY_BUFFER,r)}),bindVertexBufferToProgramAttribute(e,t,\"clipSpacePos\",r,3,20,0)&&bindVertexBufferToProgramAttribute(e,t,\"uv\",r,2,20,12)}function uploadPixelDataToTexture(e,t,r){callAndCheck(e,function(){return e.bindTexture(e.TEXTURE_2D,t)}),callAndCheck(e,function(){return e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,r)}),callAndCheck(e,function(){return e.bindTexture(e.TEXTURE_2D,null)});}function uploadDataToTexture(e,t,r,n,o,a){validateTextureSize(r,n),callAndCheck(e,function(){return e.bindTexture(e.TEXTURE_2D,t)}),callAndCheck(e,function(){return e.texSubImage2D(e.TEXTURE_2D,0,0,0,r,n,a,e.FLOAT,o)}),callAndCheck(e,function(){return e.bindTexture(e.TEXTURE_2D,null)});}function uploadMatrixToTexture(e,t,r,n,o,a,i){var s,u=getUnpackedMatrixTextureShapeWidthHeight(r,n),l=u[0],c=u[1];1===i.defaultNumChannels?s=o:encodeMatrixToUnpackedArray(o,s=new Float32Array(getUnpackedArraySizeFromMatrixSize(o.length,a)),a),uploadDataToTexture(e,t,l,c,s,i.textureFormatFloat);}function uploadMatrixToPackedTexture(e,t,r,n,o,a,i){var s=getPackedMatrixTextureShapeWidthHeight(n,o),u=s[0],l=s[1],c=new Float32Array(getPackedRGBAArraySizeFromMatrixShape(n,o));encodeMatrixToPackedRGBA(a,r,n,o,c),uploadDataToTexture(e,t,u,l,c,e.RGBA);}function maybeCreateBufferFromOutputTexture(e,t,r,n,o){var a=t;if(2===ENV.get(\"WEBGL_VERSION\")){var i=e,s=i.createBuffer();callAndCheck(e,function(){return e.bindBuffer(i.PIXEL_PACK_BUFFER,s)});var u=4*getUnpackedArraySizeFromMatrixSize(r*n,o.downloadUnpackNumChannels);callAndCheck(e,function(){return e.bufferData(i.PIXEL_PACK_BUFFER,u,e.STATIC_DRAW)}),callAndCheck(e,function(){return i.readPixels(0,0,n,r,e.RGBA,e.FLOAT,0)}),callAndCheck(e,function(){return e.bindBuffer(i.PIXEL_PACK_BUFFER,null)}),a=s;}return a}function downloadFloat32MatrixFromBuffer(e,t,r,n,o){var a=e,i=new Float32Array(getUnpackedArraySizeFromMatrixSize(r*n,o.downloadUnpackNumChannels));a.bindBuffer(e.ARRAY_BUFFER,t),a.getBufferSubData(e.ARRAY_BUFFER,0,i),a.bindBuffer(e.ARRAY_BUFFER,null);var s=new Float32Array(r*n);return decodeMatrixFromUnpackedArray(i,s,o.downloadUnpackNumChannels),s}function downloadFloat32MatrixFromOutputTexture(e,t,r,n){var o=getUnpackedMatrixTextureShapeWidthHeight(t,r),a=o[0],i=o[1],s=new Float32Array(getUnpackedArraySizeFromMatrixSize(t*r,n.downloadUnpackNumChannels));callAndCheck(e,function(){return e.readPixels(0,0,a,i,n.downloadTextureFormat,e.FLOAT,s)});var u=new Float32Array(t*r);return decodeMatrixFromUnpackedArray(s,u,n.downloadUnpackNumChannels),u}function downloadByteEncodedFloatMatrixFromOutputTexture(e,t,r,n){var o=getUnpackedMatrixTextureShapeWidthHeight(t,r),a=o[0],i=o[1],s=new Uint8Array(getUnpackedArraySizeFromMatrixSize(t*r,4));return callAndCheck(e,function(){return e.readPixels(0,0,a,i,n.downloadTextureFormat,e.UNSIGNED_BYTE,s)}),new Float32Array(s.buffer)}function downloadMatrixFromPackedOutputTexture(e,t,r,n,o,a,i){var s=getPackedMatrixTextureShapeWidthHeight(o,a),u=s[0],l=s[1],c=new Float32Array(getPackedRGBAArraySizeFromMatrixShape(o,a));callAndCheck(e,function(){return e.readPixels(0,0,u,l,e.RGBA,e.FLOAT,c)});var p=new Float32Array(sizeFromShape([t,r,n]));return decodeMatrixFromPackedRGBA(c,t,r,n,p)}var gpgpu_util=Object.freeze({createVertexShader:createVertexShader$1,createVertexBuffer:createVertexBuffer,createIndexBuffer:createIndexBuffer,getTextureConfig:getTextureConfig,createFloat32MatrixTexture:createFloat32MatrixTexture,createFloat16MatrixTexture:createFloat16MatrixTexture,createUnsignedBytesMatrixTexture:createUnsignedBytesMatrixTexture,createPackedMatrixTexture:createPackedMatrixTexture,createFloat16PackedMatrixTexture:createFloat16PackedMatrixTexture,bindVertexProgramAttributeStreams:bindVertexProgramAttributeStreams,uploadPixelDataToTexture:uploadPixelDataToTexture,uploadMatrixToTexture:uploadMatrixToTexture,uploadMatrixToPackedTexture:uploadMatrixToPackedTexture,maybeCreateBufferFromOutputTexture:maybeCreateBufferFromOutputTexture,downloadFloat32MatrixFromBuffer:downloadFloat32MatrixFromBuffer,downloadFloat32MatrixFromOutputTexture:downloadFloat32MatrixFromOutputTexture,downloadByteEncodedFloatMatrixFromOutputTexture:downloadByteEncodedFloatMatrixFromOutputTexture,downloadMatrixFromPackedOutputTexture:downloadMatrixFromPackedOutputTexture}),GPGPUContext=function(){function e(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.autoDebugValidate=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[],this.gl=null!=e?e:getWebGLContext(ENV.get(\"WEBGL_VERSION\")),1===ENV.get(\"WEBGL_VERSION\")?(this.textureFloatExtension=getExtensionOrThrow(this.gl,\"OES_texture_float\"),this.colorBufferFloatExtension=this.gl.getExtension(\"WEBGL_color_buffer_float\"),ENV.get(\"WEBGL_RENDER_FLOAT32_ENABLED\")||(this.textureHalfFloatExtension=getExtensionOrThrow(this.gl,\"OES_texture_half_float\"),this.colorBufferHalfFloatExtension=this.gl.getExtension(\"EXT_color_buffer_half_float\"))):this.colorBufferFloatExtension=getExtensionOrThrow(this.gl,\"EXT_color_buffer_float\"),this.vertexBuffer=createVertexBuffer(this.gl),this.indexBuffer=createIndexBuffer(this.gl),this.framebuffer=createFramebuffer(this.gl),this.textureConfig=getTextureConfig(this.gl,this.textureHalfFloatExtension);}return e.prototype.dispose=function(){var e=this;if(!this.disposed){null!=this.program&&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.\"),null!=this.outputTexture&&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 t=this.gl;callAndCheck(t,function(){return t.finish()}),callAndCheck(t,function(){return t.bindFramebuffer(t.FRAMEBUFFER,null)}),callAndCheck(t,function(){return t.deleteFramebuffer(e.framebuffer)}),callAndCheck(t,function(){return t.bindBuffer(t.ARRAY_BUFFER,null)}),callAndCheck(t,function(){return t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,null)}),callAndCheck(t,function(){return t.deleteBuffer(e.indexBuffer)}),this.disposed=!0;}},e.prototype.enableAutomaticDebugValidation=function(e){this.autoDebugValidate=e,enableDebugWebGLErrorChecking(e);},e.prototype.createFloat32MatrixTexture=function(e,t){return this.throwIfDisposed(),createFloat32MatrixTexture(this.gl,e,t,this.textureConfig)},e.prototype.createFloat16MatrixTexture=function(e,t){return this.throwIfDisposed(),createFloat16MatrixTexture(this.gl,e,t,this.textureConfig)},e.prototype.createUnsignedBytesMatrixTexture=function(e,t){return this.throwIfDisposed(),createUnsignedBytesMatrixTexture(this.gl,e,t,this.textureConfig)},e.prototype.uploadPixelDataToTexture=function(e,t){this.throwIfDisposed(),uploadPixelDataToTexture(this.gl,e,t);},e.prototype.createFloat16PackedMatrixTexture=function(e,t){return this.throwIfDisposed(),createFloat16PackedMatrixTexture(this.gl,e,t,this.textureConfig)},e.prototype.createPackedMatrixTexture=function(e,t){return this.throwIfDisposed(),createPackedMatrixTexture(this.gl,e,t,this.textureConfig)},e.prototype.deleteMatrixTexture=function(e){var t=this;this.throwIfDisposed(),this.outputTexture===e&&(unbindColorTextureFromFramebuffer(this.gl,this.framebuffer),this.outputTexture=null),callAndCheck(this.gl,function(){return t.gl.deleteTexture(e)});},e.prototype.uploadMatrixToTexture=function(e,t,r,n){this.throwIfDisposed();var o=getNumChannels();return uploadMatrixToTexture(this.gl,e,t,r,n,o,this.textureConfig)},e.prototype.uploadMatrixToPackedTexture=function(e,t,r,n,o){return this.throwIfDisposed(),uploadMatrixToPackedTexture(this.gl,e,t,r,n,o,this.textureConfig)},e.prototype.downloadFloat32MatrixFromOutputTexture=function(e,t,r){var n=this;return this.downloadMatrixDriver(e,function(){return downloadFloat32MatrixFromOutputTexture(n.gl,t,r,n.textureConfig)})},e.prototype.downloadByteEncodedFloatMatrixFromOutputTexture=function(e,t,r){var n=this;return this.downloadMatrixDriver(e,function(){return downloadByteEncodedFloatMatrixFromOutputTexture(n.gl,t,r,n.textureConfig)})},e.prototype.downloadFloat32MatrixFromBuffer=function(e,t,r){return downloadFloat32MatrixFromBuffer(this.gl,e,t,r,this.textureConfig)},e.prototype.maybeCreateBufferFromTexture=function(e,t,r){this.bindTextureToFrameBuffer(e);var n=maybeCreateBufferFromOutputTexture(this.gl,e,t,r,this.textureConfig);return this.unbindTextureToFrameBuffer(),n},e.prototype.createAndWaitForFence=function(){var e=this.createFence(this.gl);return this.pollFence(e)},e.prototype.createFence=function(e){var t,r,n=this;if(ENV.get(\"WEBGL_FENCE_API_ENABLED\")){var o=e,a=o.fenceSync(o.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),r=function(){var e=o.clientWaitSync(a,0,0);return e===o.ALREADY_SIGNALED||e===o.CONDITION_SATISFIED},t=a;}else ENV.get(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")>0?(t=this.beginQuery(),this.endQuery(),r=function(){return n.isQueryAvailable(t,ENV.get(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))}):r=function(){return !0};return {query:t,isFencePassed:r}},e.prototype.downloadMatrixFromPackedTexture=function(e,t,r,n,o,a){var i=this;return this.downloadMatrixDriver(e,function(){return downloadMatrixFromPackedOutputTexture(i.gl,t,r,n,o,a,i.textureConfig)})},e.prototype.createProgram=function(e){this.throwIfDisposed();var t=this.gl,r=createFragmentShader(t,e),n=createVertexShader$1(t),o=createProgram(t);return callAndCheck(t,function(){return t.attachShader(o,n)}),callAndCheck(t,function(){return t.attachShader(o,r)}),linkProgram(t,o),this.autoDebugValidate&&validateProgram(t,o),this.vertexAttrsAreBound||(this.setProgram(o),this.vertexAttrsAreBound=bindVertexProgramAttributeStreams(t,this.program,this.vertexBuffer)),o},e.prototype.deleteProgram=function(e){var t=this;this.throwIfDisposed(),e===this.program&&(this.program=null),null!=e&&callAndCheck(this.gl,function(){return t.gl.deleteProgram(e)});},e.prototype.setProgram=function(e){var t=this;this.throwIfDisposed(),this.program=e,null!=this.program&&this.autoDebugValidate&&validateProgram(this.gl,this.program),callAndCheck(this.gl,function(){return t.gl.useProgram(e)});},e.prototype.getUniformLocation=function(e,t,r){return void 0===r&&(r=!0),this.throwIfDisposed(),r?getProgramUniformLocationOrThrow(this.gl,e,t):getProgramUniformLocation(this.gl,e,t)},e.prototype.getAttributeLocation=function(e,t){var r=this;return this.throwIfDisposed(),callAndCheck(this.gl,function(){return r.gl.getAttribLocation(e,t)})},e.prototype.getUniformLocationNoThrow=function(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)},e.prototype.setInputMatrixTexture=function(e,t,r){this.throwIfDisposed(),this.throwIfNoProgram(),bindTextureToProgramUniformSampler(this.gl,this.program,e,t,r);},e.prototype.setOutputMatrixTexture=function(e,t,r){this.setOutputMatrixTextureDriver(e,r,t);},e.prototype.setOutputPackedMatrixTexture=function(e,t,r){this.throwIfDisposed();var n=getPackedMatrixTextureShapeWidthHeight(t,r),o=n[0],a=n[1];this.setOutputMatrixTextureDriver(e,o,a);},e.prototype.setOutputMatrixWriteRegion=function(e,t,r,n){this.setOutputMatrixWriteRegionDriver(r,e,n,t);},e.prototype.setOutputPackedMatrixWriteRegion=function(e,t,r,n){throw new Error(\"setOutputPackedMatrixWriteRegion not implemented.\")},e.prototype.debugValidate=function(){null!=this.program&&validateProgram(this.gl,this.program),validateFramebuffer(this.gl);},e.prototype.executeProgram=function(){this.throwIfDisposed(),this.throwIfNoProgram();var e=this.gl;this.autoDebugValidate&&this.debugValidate(),callAndCheck(e,function(){return e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0)});},e.prototype.blockUntilAllProgramsCompleted=function(){var e=this;this.throwIfDisposed(),callAndCheck(this.gl,function(){return e.gl.finish()});},e.prototype.getQueryTimerExtension=function(){return null==this.disjointQueryTimerExtension&&(this.disjointQueryTimerExtension=getExtensionOrThrow(this.gl,2===ENV.get(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")?\"EXT_disjoint_timer_query_webgl2\":\"EXT_disjoint_timer_query\")),this.disjointQueryTimerExtension},e.prototype.getQueryTimerExtensionWebGL2=function(){return this.getQueryTimerExtension()},e.prototype.getQueryTimerExtensionWebGL1=function(){return this.getQueryTimerExtension()},e.prototype.beginQuery=function(){if(2===ENV.get(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")){var e=this.gl,t=this.getQueryTimerExtensionWebGL2(),r=e.createQuery();return e.beginQuery(t.TIME_ELAPSED_EXT,r),r}var n=this.getQueryTimerExtensionWebGL1(),o=n.createQueryEXT();return n.beginQueryEXT(n.TIME_ELAPSED_EXT,o),o},e.prototype.endQuery=function(){if(2!==ENV.get(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")){var e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT);}else{var t=this.gl,r=this.getQueryTimerExtensionWebGL2();t.endQuery(r.TIME_ELAPSED_EXT);}},e.prototype.waitForQueryAndGetTime=function(e){return __awaiter(this,void 0,void 0,function(){var t=this;return __generator(this,function(r){switch(r.label){case 0:return [4,repeatedTry(function(){return t.disposed||t.isQueryAvailable(e,ENV.get(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))})];case 1:return r.sent(),[2,this.getQueryTime(e,ENV.get(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))]}})})},e.prototype.getQueryTime=function(e,t){if(0===t)return null;if(2===t){var r=this.gl;return r.getQueryParameter(e,r.QUERY_RESULT)/1e6}var n=this.getQueryTimerExtensionWebGL1();return n.getQueryObjectEXT(e,n.QUERY_RESULT_EXT)/1e6},e.prototype.isQueryAvailable=function(e,t){if(0===t)return !0;if(2===t){var r=this.gl,n=this.getQueryTimerExtensionWebGL2(),o=r.getQueryParameter(e,r.QUERY_RESULT_AVAILABLE);return null==this.disjoint&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),o&&!this.disjoint}o=(n=this.getQueryTimerExtensionWebGL1()).getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return null==this.disjoint&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),o&&!this.disjoint},e.prototype.pollFence=function(e){var t=this;return new Promise(function(r){t.addItemToPoll(function(){return e.isFencePassed()},function(){return r()});})},e.prototype.pollItems=function(){for(var e=binSearchLastTrue(this.itemsToPoll.map(function(e){return e.isDoneFn})),t=0;t<=e;++t){(0, this.itemsToPoll[t].resolveFn)();}this.itemsToPoll=this.itemsToPoll.slice(e+1);},e.prototype.addItemToPoll=function(e,t){var r=this;this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),this.itemsToPoll.length>1||repeatedTry(function(){return r.pollItems(),0===r.itemsToPoll.length});},e.prototype.bindTextureToFrameBuffer=function(e){this.throwIfDisposed(),bindColorTextureToFramebuffer(this.gl,e,this.framebuffer),this.autoDebugValidate&&validateFramebuffer(this.gl);},e.prototype.unbindTextureToFrameBuffer=function(){null!=this.outputTexture?(bindColorTextureToFramebuffer(this.gl,this.outputTexture,this.framebuffer),this.autoDebugValidate&&validateFramebuffer(this.gl)):unbindColorTextureFromFramebuffer(this.gl,this.framebuffer);},e.prototype.downloadMatrixDriver=function(e,t){this.bindTextureToFrameBuffer(e);var r=t();return this.unbindTextureToFrameBuffer(),r},e.prototype.setOutputMatrixTextureDriver=function(e,t,r){this.throwIfDisposed();var n=this.gl;bindColorTextureToFramebuffer(n,e,this.framebuffer),this.autoDebugValidate&&validateFramebuffer(n),this.outputTexture=e,callAndCheck(n,function(){return n.viewport(0,0,t,r)}),callAndCheck(n,function(){return n.scissor(0,0,t,r)});},e.prototype.setOutputMatrixWriteRegionDriver=function(e,t,r,n){var o=this;this.throwIfDisposed(),callAndCheck(this.gl,function(){return o.gl.scissor(e,t,r,n)});},e.prototype.throwIfDisposed=function(){if(this.disposed)throw new Error(\"Attempted to use disposed GPGPUContext.\")},e.prototype.throwIfNoProgram=function(){if(null==this.program)throw new Error(\"No GPU program is currently set.\")},e}();function binSearchLastTrue(e){for(var t=0,r=e.length-1,n=-1;t<=r;){var o=t+r>>1;e[o]()?(n=o,t=o+1):r=o-1;}return n}function compileProgram(e,t,r,n){for(var o=t.userCode,a=r.map(function(e,r){var n={logicalShape:e.shape,texShape:e.isUniform?null:e.texData.texShape,isUniform:e.isUniform,isPacked:!e.isUniform&&e.texData.isPacked};return {name:t.variableNames[r],shapeInfo:n}}),i=a.map(function(e){return e.shapeInfo}),s={logicalShape:n.shape,texShape:n.texData.texShape,isUniform:!1,isPacked:n.texData.isPacked},u=makeShader(a,s,o,!0===t.supportsBroadcasting),l=e.createProgram(u),c={},p=0;p<t.variableNames.length;p++){var d=t.variableNames[p];c[d]=e.getUniformLocation(l,d,!1);}return {program:t,source:u,webGLProgram:l,uniformLocations:c,gpgpu:e,inShapeInfos:i,outShapeInfo:s}}function validateBinaryAndProgram(e,t){if(e.length!==t.length)throw Error(\"Binary was compiled with \"+e.length+\" inputs, but was executed with \"+t.length+\" inputs\");e.forEach(function(e,r){var n=e.logicalShape,o=t[r],a=o.shape;if(!arraysEqual(n,a))throw Error(\"Binary was compiled with different shapes than the current args. Shapes \"+n+\" and \"+a+\" must match\");if(!e.isUniform||!o.isUniform){var i=e.texShape,s=o.isUniform?null:o.texData.texShape;if(!arraysEqual(i,s))throw Error(\"Binary was compiled with different texture shapes than the current args. Shape \"+i+\" and \"+s+\" must match\")}});}function runProgram(e,t,r,n){validateBinaryAndProgram(e.inShapeInfos,t),validateBinaryAndProgram([e.outShapeInfo],[r]);var o=r.texData.texture,a=r.texData.texShape,i=e.gpgpu;r.texData.isPacked?i.setOutputPackedMatrixTexture(o,a[0],a[1]):i.setOutputMatrixTexture(o,a[0],a[1]),i.setProgram(e.webGLProgram),t.forEach(function(t,r){var n=e.program.variableNames[r],o=e.uniformLocations[n];if(null!=o){if(t.isUniform){if(1===sizeFromShape(t.shape))i.gl.uniform1f(o,t.uniformValues[0]);else{var a=t.uniformValues;a instanceof Float32Array||(a=new Float32Array(a)),i.gl.uniform1fv(o,a);}return}var s=t.texData.texture;i.setInputMatrixTexture(s,o,r);}}),null!=n&&n(i,e.webGLProgram),i.executeProgram();}function makeShaderKey(e,t,r){var n=\"\";t.concat(r).forEach(function(e){n+=e.shape+\"_\"+(e.isUniform?\"uniform\":e.texData.texShape);});var o=e.userCode,a=(!0===e.supportsBroadcasting).toString(),i=e.constructor.name;return i+=\"_\"+a+\"_\"+n+\"_\"+o}var Im2ColProgram=function(){return function(e,t,r){this.variableNames=[\"A\"],this.outputShape=e;var n=r.filterWidth,o=r.inChannels,a=r.strideWidth,i=r.strideHeight,s=r.padInfo,u=r.outWidth,l=r.dilationWidth,c=r.dilationHeight,p=s.left,d=s.top,h=o*n;this.userCode=\"\\n      void main() {\\n        ivec2 rc = getOutputCoords();\\n\\n        vec4 result = vec4(0);\\n\\n        for(int row=0; row<=1; row++) {\\n          for(int col=0; col<=1; col++) {\\n            int blockIndex = rc.y + col;\\n            int pos = rc.x + row;\\n\\n            if(blockIndex >= \"+e[1]+\" || pos >= \"+e[0]+\") continue;\\n\\n            int offsetY = int(blockIndex / (\"+u+\")) * \"+i+\" - \"+d+\";\\n            int d0 = offsetY + \"+c+\" * (pos / \"+h+\");\\n\\n            if(d0 >= \"+t[0]+\" || d0 < 0) continue;\\n\\n            int offsetX = int(mod(float(blockIndex), \"+u+\".) * \"+a+\". - \"+p+\".);\\n            int d1 = offsetX + \"+l+\" * (int(mod(float(pos), \"+h+\".) / \"+o+\".));\\n\\n            if(d1 >= \"+t[1]+\" || d1 < 0) continue;\\n\\n            result[row * 2 + col] = getA(d0, d1, int(mod(float(pos), \"+o+\".)));\\n          }\\n        }\\n\\n        gl_FragColor = result;\\n      }\\n    \";}}(),LRNProgram=function(){return function(e,t,r,n,o){this.variableNames=[\"x\"],this.outputShape=[];var a,i=t,s=e[3]-1;this.outputShape=e;var u=\"float(\"+r+\") + float(\"+n+\") * sum\";a=.5===o?\"inversesqrt(\"+u+\")\":1===o?\"1.0/(\"+u+\")\":\"exp(log(\"+u+\") * float(-\"+o+\"));\",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 = -\"+i+\"; j <= \"+i+\"; j++) {\\n          int idx = d + j;\\n          if (idx >= 0 && idx <=  \"+s+\") {\\n            float z = getX(b, r, c, idx);\\n            sum += z * z;\\n          }\\n        }\\n        float val = x * \"+a+\";\\n        setOutput(val);\\n      }\\n    \";}}(),LRNGradProgram=function(){return function(e,t,r,n,o){this.variableNames=[\"inputImage\",\"outputImage\",\"dy\"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=r,this.alpha=n,this.beta=o,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 - \"+t+\")));\\n          int depthEnd = int(min(float(\"+this.depth+\"),\\n              float(d + \"+t+\" + 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(\"+n+\") * norm + float(\"+r+\");\\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(\"+n+\")\\n                * float(\"+o+\")\\n                * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)\\n                / norm;\\n              if (k == d) {\\n                dyi += pow(norm, -1.0 * \"+o+\");\\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    \";}}(),MaxPool2DBackpropProgram=function(){return function(e){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=e.inShape;var t=e.strideHeight,r=e.strideWidth,n=e.dilationHeight,o=e.effectiveFilterHeight,a=e.effectiveFilterWidth,i=o-1-e.padInfo.top,s=a-1-e.padInfo.left,u=o*a-1;this.userCode=\"\\n      const ivec2 pads = ivec2(\"+i+\", \"+s+\");\\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 < \"+o+\";\\n          wR += \"+n+\") {\\n          float dyR = float(dyRCorner + wR) / \"+t+\".0;\\n\\n          if (dyR < 0.0 || dyR >= \"+e.outHeight+\".0 || fract(dyR) > 0.0) {\\n            continue;\\n          }\\n          int idyR = int(dyR);\\n\\n          for (int wC = 0; wC < \"+a+\"; wC++) {\\n            float dyC = float(dyCCorner + wC) / \"+r+\".0;\\n\\n            if (dyC < 0.0 || dyC >= \"+e.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 = \"+u+\" - 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 * \"+a+\" + wC;\\n            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\\n\\n            dotProd += dyValue * mask;\\n          }\\n        }\\n        setOutput(dotProd);\\n      }\\n    \";}}(),MatMulProgram=function(){return function(e,t,r,n){void 0===r&&(r=!1),void 0===n&&(n=!1),this.variableNames=[\"matrixA\",\"matrixB\"];var o=e[0],a=r?e[2]:e[1],i=n?t[1]:t[2],s=r?e[1]:e[2];this.outputShape=[o,a,i];var u=function(e,t){return r?\"batch, \"+t+\" + \"+e+\", aRow\":\"batch, aRow, \"+t+\" + \"+e},l=function(e,t){return n?\"batch, bCol, \"+t+\" + \"+e:\"batch, \"+t+\" + \"+e+\", bCol\"},c=4*Math.floor(s/4),p=s%4;this.userCode=\" float dotARowBCol(int batch, int aRow, int bCol) {\\n      float result = 0.0;\\n      for (int i = 0; i < \"+c+\"; i += 4) {\\n        vec4 a = vec4(\\n          getMatrixA(\"+u(0,\"i\")+\"),\\n          getMatrixA(\"+u(1,\"i\")+\"),\\n          getMatrixA(\"+u(2,\"i\")+\"),\\n          getMatrixA(\"+u(3,\"i\")+\")\\n        );\\n        vec4 b = vec4(\\n          getMatrixB(\"+l(0,\"i\")+\"),\\n          getMatrixB(\"+l(1,\"i\")+\"),\\n          getMatrixB(\"+l(2,\"i\")+\"),\\n          getMatrixB(\"+l(3,\"i\")+\")\\n        );\\n\\n        result += dot(a, b);\\n      }\\n\\n      if (\"+(1===p)+\") {\\n        result += getMatrixA(\"+u(0,c)+\") *\\n          getMatrixB(\"+l(0,c)+\");\\n      } else if (\"+(2===p)+\") {\\n        vec2 a = vec2(\\n          getMatrixA(\"+u(0,c)+\"),\\n          getMatrixA(\"+u(1,c)+\")\\n        );\\n        vec2 b = vec2(\\n          getMatrixB(\"+l(0,c)+\"),\\n          getMatrixB(\"+l(1,c)+\")\\n        );\\n        result += dot(a, b);\\n      } else if (\"+(3===p)+\") {\\n        vec3 a = vec3(\\n          getMatrixA(\"+u(0,c)+\"),\\n          getMatrixA(\"+u(1,c)+\"),\\n          getMatrixA(\"+u(2,c)+\")\\n        );\\n        vec3 b = vec3(\\n          getMatrixB(\"+l(0,c)+\"),\\n          getMatrixB(\"+l(1,c)+\"),\\n          getMatrixB(\"+l(2,c)+\")\\n        );\\n        result += dot(a, b);\\n      }\\n\\n      return result;\\n    }\\n\\n    void main() {\\n      ivec3 resBRC = getOutputCoords();\\n      setOutput(dotARowBCol(resBRC.x, resBRC.y, resBRC.z));\\n    }\\n    \";}}(),MatMulPackedProgram=function(){return function(e,t,r,n,o){void 0===n&&(n=!1),void 0===o&&(o=!1),this.variableNames=[\"matrixA\",\"matrixB\"],this.usesPackedTextures=!0,this.outputShape=r;var a=n?e[0]:e[1],i=Math.ceil(a/2),s=n?\"i * 2, rc.x\":\"rc.x, i * 2\",u=o?\"rc.y, i * 2\":\"i * 2, rc.y\",l=n?[\"a.xxyy\",\"a.zzww\"]:[\"a.xxzz\",\"a.yyww\"],c=o?[\"b.xzxz\",\"b.ywyw\"]:[\"b.xyxy\",\"b.zwzw\"];this.userCode=\"\\n      const float sharedDimension = \"+i+\".0;\\n\\n      vec4 dot2x2ARowBCol(ivec2 rc) {\\n        vec4 result = vec4(0);\\n        for (int i = 0; i < \"+i+\"; i++) {\\n          vec4 a = getMatrixA(\"+s+\");\\n          vec4 b = getMatrixB(\"+u+\");\\n\\n          result += (\"+l[0]+\" * \"+c[0]+\") + (\"+l[1]+\" * \"+c[1]+\");\\n        }\\n        return result;\\n      }\\n\\n      void main() {\\n        ivec2 rc = getOutputCoords();\\n        setOutput(dot2x2ARowBCol(rc));\\n      }\\n    \";}}(),MultinomialProgram=function(){function e(e,t,r){this.variableNames=[\"probs\"],this.outputShape=[e,r],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 < \"+(t-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(\"+(t-1)+\"));\\n      }\\n    \";}return e.prototype.getCustomSetupFunc=function(e){var t=this;return function(r,n){null==t.seedLoc&&(t.seedLoc=r.getUniformLocation(n,\"seed\")),r.gl.uniform1f(t.seedLoc,e);}},e}(),OneHotProgram=function(){return function(e,t,r,n){this.variableNames=[\"indices\"],this.outputShape=[e,t],this.userCode=\"\\n      void main() {\\n        ivec2 coords = getOutputCoords();\\n        int index = round(getIndices(coords.x));\\n        setOutput(mix(float(\"+n+\"), float(\"+r+\"),\\n                      float(index == coords.y)));\\n      }\\n    \";}}();function getChannels(e){return [\"x\",\"y\",\"z\",\"w\"].map(function(t){return e+\".\"+t})}function getInnerDims(e,t){return t.slice(0,e).slice(-2)}function getSourceCoords$1(e,t){if(1===e)return \"rc\";for(var r=\"\",n=0;n<e;n++)r+=t[n],n<e-1&&(r+=\",\");return r}var PackProgram=function(){return function(e){this.variableNames=[\"A\"],this.outputShape=e;var t=e.length,r=getChannels(\"rc\"),n=getCoordsDataType(t),o=getOutOfBoundsCondition(t,e,r),a=getSetup(t,e[e.length-1],e[e.length-2],r),i=getOutput(e,r);this.userCode=\"\\n      void main() {\\n        \"+n+\" rc = getOutputCoords();\\n\\n        if(\"+o+\") {\\n          gl_FragColor = vec4(0);\\n        } else {\\n          \"+a+\"\\n\\n          setOutput(vec4(\"+i+\"));\\n        }\\n      }\\n    \";}}();function getSourceCoordsArr(e,t){for(var r=[],n=0;n<=1;n++)for(var o=0;o<=1;o++){for(var a=(0===n?\"r\":\"rp1\")+\", \"+(0===o?\"c\":\"cp1\"),i=2;i<e;i++)a=t[t.length-1-i]+\",\"+a;r.push(a);}return r}function getOutOfBoundsCondition(e,t,r){if(1===e)return \"rc > \"+t[0];for(var n=\"\",o=0;o<e;o++)n+=r[o]+\" >= \"+t[o],o<e-1&&(n+=\"||\");return n}function getSetup(e,t,r,n){if(1===e)return \"\";var o=getInnerDims(e,n);return \"\\n    int r = \"+o[0]+\";\\n    int c = \"+o[1]+\";\\n    int rp1 = r + 1;\\n    int cp1 = c + 1;\\n\\n    bool cEdge = cp1 >= \"+t+\";\\n    bool rEdge = rp1 >= \"+r+\";\\n  \"}function getOutput(e,t){var r=e.length,n=getSourceCoordsArr(r,t);return 1===r?\"getA(rc),\\n            rc + 1 >= \"+e[0]+\" ? 0. : getA(rc + 1),\\n            0, 0\":\"getA(\"+n[0]+\"),\\n          cEdge ? 0. : getA(\"+n[1]+\"),\\n          rEdge ? 0. : getA(\"+n[2]+\"),\\n          rEdge || cEdge ? 0. : getA(\"+n[3]+\")\"}var PadProgram=function(){return function(e,t,r){this.variableNames=[\"x\"],this.outputShape=t.map(function(t,r){return t[0]+e[r]+t[1]});var n=e.length,o=getCoordsDataType(n),a=t.map(function(e){return e[0]}).join(\",\"),i=t.map(function(t,r){return t[0]+e[r]}).join(\",\"),s=[\"coords[0]\",\"coords[1]\",\"coords[2]\",\"coords[3]\"].slice(0,n);this.userCode=1!==n?\"\\n      \"+o+\" start = \"+o+\"(\"+a+\");\\n      \"+o+\" end = \"+o+\"(\"+i+\");\\n\\n      void main() {\\n        \"+o+\" outC = getOutputCoords();\\n        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {\\n          setOutput(float(\"+r+\"));\\n        } else {\\n          \"+o+\" coords = outC - start;\\n          setOutput(getX(\"+s+\"));\\n        }\\n      }\\n    \":\"\\n        int start = \"+a+\";\\n        int end = \"+i+\";\\n\\n        void main() {\\n          int outC = getOutputCoords();\\n          if (outC < start || outC >= end) {\\n            setOutput(float(\"+r+\"));\\n          } else {\\n            setOutput(getX(outC - start));\\n          }\\n        }\\n      \";}}(),Pool2DProgram=function(){return function(e,t,r){if(this.variableNames=[\"x\"],\"avg\"===t&&r)throw new Error(\"Cannot compute positions for average pool.\");var n=e.filterWidth,o=e.strideHeight,a=e.strideWidth,i=e.dilationHeight,s=e.dilationWidth,u=e.effectiveFilterHeight,l=e.effectiveFilterWidth,c=e.padInfo.top,p=e.padInfo.left;this.outputShape=e.outShape;var d=\"avg\"===t,h=\"0.0\";if(d||(h=\"-1.0 / 0.0\"),r)this.userCode=\"\\n        const ivec2 strides = ivec2(\"+o+\", \"+a+\");\\n        const ivec2 pads = ivec2(\"+c+\", \"+p+\");\\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 < \"+u+\";\\n              wR += \"+i+\") {\\n            int xR = xRCorner + wR;\\n\\n            if (xR < 0 || xR >= \"+e.inHeight+\") {\\n              continue;\\n            }\\n\\n            for (int wC = 0; wC < \"+l+\";\\n                wC += \"+s+\") {\\n              int xC = xCCorner + wC;\\n\\n              if (xC < 0 || xC >= \"+e.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 >= currMinMaxValue) {\\n                minMaxValue = value;\\n                minMaxValueFound = 1.0;\\n                minMaxPosition = wR * \"+l+\" + wC;\\n              }\\n            }\\n          }\\n          setOutput(float(minMaxPosition));\\n        }\\n      \";else{var f=t+\"(\"+t+\"(\"+t+\"(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])\";\"avg\"===t&&(f=\"avgValue / count\");var m=4*Math.floor(n/4),g=n%4,v=\"\\n      if (\"+d+\") {\\n        avgValue += dot(values, ones);\\n      } else {\\n        minMaxValue = max(values, minMaxValue);\\n      }\\n    \";this.userCode=\"\\n      const ivec2 strides = ivec2(\"+o+\", \"+a+\");\\n      const ivec2 pads = ivec2(\"+c+\", \"+p+\");\\n      const float initializationValue = \"+h+\";\\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 >= \"+e.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(\"+h+\");\\n        float avgValue = 0.0;\\n        count = 0.0;\\n\\n        for (int wR = 0; wR < \"+u+\";\\n            wR += \"+i+\") {\\n          int xR = xRCorner + wR;\\n\\n          if (xR < 0 || xR >= \"+e.inHeight+\") {\\n            continue;\\n          }\\n\\n          for (int wC = 0; wC < \"+m+\"; wC += 4) {\\n            int xC = xCCorner + wC * \"+s+\";\\n\\n            vec4 values = vec4(\\n              getValue(batch, xR, xC, d),\\n              getValue(batch, xR, xC + \"+s+\", d),\\n              getValue(batch, xR, xC + 2 * \"+s+\", d),\\n              getValue(batch, xR, xC + 3 * \"+s+\", d)\\n            );\\n\\n            \"+v+\"\\n          }\\n\\n          int xC = xCCorner + \"+m+\";\\n          if (\"+(1===g)+\") {\\n            vec4 values = vec4(\\n              getValue(batch, xR, xC, d),\\n              initializationValue,\\n              initializationValue,\\n              initializationValue\\n            );\\n\\n            \"+v+\"\\n          } else if (\"+(2===g)+\") {\\n            vec4 values = vec4(\\n              getValue(batch, xR, xC, d),\\n              getValue(batch, xR, xC + \"+s+\", d),\\n              initializationValue,\\n              initializationValue\\n            );\\n\\n            \"+v+\"\\n          } else if (\"+(3===g)+\") {\\n            vec4 values = vec4(\\n              getValue(batch, xR, xC, d),\\n              getValue(batch, xR, xC + \"+s+\", d),\\n              getValue(batch, xR, xC + 2 * \"+s+\", d),\\n              initializationValue\\n            );\\n\\n            \"+v+\"\\n          }\\n        }\\n        setOutput(\"+f+\");\\n      }\\n    \";}}}(),ReduceProgram=function(){return function(e,t){this.variableNames=[\"x\"];var r=e.windowSize,n=e.batchSize,o=e.inSize,a=Math.ceil(o/r);this.outputShape=[n,a];var i=\"0.0\",s=\"\";\"prod\"===t?i=\"1.0\":\"min\"===t?(i=\"1.0 / 0.0\",s=\"min\"):\"max\"===t&&(i=\"-1.0 / 0.0\",s=\"max\");var u=t+\"(\"+t+\"(\"+t+\"(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])\";\"sum\"===t?u=\"sumValue\":\"prod\"===t?u=\"prodValue\":\"all\"===t?u=\"allValue\":\"any\"===t&&(u=\"anyValue\");var l=4*Math.floor(r/4),c=r%4,p=\"\\n      if (\"+(\"sum\"===t)+\") {\\n        sumValue += dot(values, ones);\\n      } else if (\"+(\"prod\"===t)+\") {\\n        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\\n        prodValue *= tmp[0] * tmp[1];\\n      } else {\\n        minMaxValue = \"+s+\"(values, minMaxValue);\\n      }\\n    \",d=\"vec4\";\"all\"===t?(i=\"1.0\",p=\"\\n        bool reducedAllValue = all(values);\\n        float floatedReducedAllValue = float(reducedAllValue);\\n        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\\n      \",d=\"bvec4\"):\"any\"===t&&(i=\"0.0\",p=\"\\n        bool reducedAnyValue = any(values);\\n        float floatedReducedAnyValue = float(reducedAnyValue);\\n        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\\n      \",d=\"bvec4\");var h=\"\";o%r>0&&(h=\"\\n        if (inIdx < 0 || inIdx >= \"+o+\") {\\n          return initializationValue;\\n        }\\n      \"),this.userCode=\"\\n      const float initializationValue = \"+i+\";\\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\\n\\n      float getValue(int batch, int inIdx) {\\n        \"+h+\"\\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 * \"+r+\";\\n\\n        vec4 minMaxValue = vec4(\"+i+\");\\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 < \"+l+\"; i += 4) {\\n          int inIdx = inOffset + i;\\n          \"+d+\" values = \"+d+\"(\\n            getValue(batch, inIdx),\\n            getValue(batch, inIdx + 1),\\n            getValue(batch, inIdx + 2),\\n            getValue(batch, inIdx + 3)\\n          );\\n\\n          \"+p+\"\\n        }\\n\\n        int inIdx = inOffset + \"+l+\";\\n        if (\"+(1===c)+\") {\\n          \"+d+\" values = \"+d+\"(\\n            getValue(batch, inIdx),\\n            initializationValue,\\n            initializationValue,\\n            initializationValue\\n          );\\n\\n          \"+p+\"\\n        } else if (\"+(2===c)+\") {\\n          \"+d+\" values = \"+d+\"(\\n            getValue(batch, inIdx),\\n            getValue(batch, inIdx + 1),\\n            initializationValue,\\n            initializationValue\\n          );\\n\\n          \"+p+\"\\n        } else if (\"+(3===c)+\") {\\n          \"+d+\" values = \"+d+\"(\\n            getValue(batch, inIdx),\\n            getValue(batch, inIdx + 1),\\n            getValue(batch, inIdx + 2),\\n            initializationValue\\n          );\\n\\n          \"+p+\"\\n        }\\n        setOutput(\"+u+\");\\n      }\\n    \";}}(),ResizeBilinearBackpropProgram=function(){return function(e,t,r){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=t.shape;var n=t.shape,o=n[1],a=n[2],i=e.shape,s=i[1],u=i[2],l=[r&&s>1?o-1:o,r&&u>1?a-1:a],c=[r&&s>1?s-1:s,r&&u>1?u-1:u],p=l[0]/c[0],d=l[1]/c[1],h=1/p,f=1/d,m=2*Math.ceil(h)+2,g=2*Math.ceil(f)+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(\"+p+\");\\n        const float widthScale = float(\"+d+\");\\n\\n        const float invHeightScale = float(\"+h+\");\\n        const float invWidthScale = float(\"+f+\");\\n\\n        const int winHeight = int(\"+m+\");\\n        const int winWidth = int(\"+g+\");\\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 >= \"+s+\") {\\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 >= \"+u+\") {\\n              continue;\\n            }\\n\\n            float dxR = float(dyR) * heightScale;\\n            int topDxRIndex = int(floor(dxR));\\n            int bottomDxRIndex = int(min(ceil(dxR), \"+(o-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), \"+(a-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    \";}}(),ResizeBilinearProgram=function(){return function(e,t,r,n){this.variableNames=[\"A\"],this.outputShape=[];var o=e[0],a=e[1],i=e[2],s=e[3];this.outputShape=[o,t,r,s];var u=[n&&t>1?a-1:a,n&&r>1?i-1:i],l=[n&&t>1?t-1:t,n&&r>1?r-1:r];this.userCode=\"\\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\\n          \"+u[0]/l[0]+\",\\n          \"+u[1]/l[1]+\");\\n      const vec2 inputShapeRC = vec2(\"+a+\".0, \"+i+\".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    \";}}(),ResizeNearestNeigborBackpropProgram=function(){return function(e,t,r){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=t.shape;var n=t.shape,o=n[1],a=n[2],i=e.shape,s=i[1],u=i[2],l=[r&&s>1?o-1:o,r&&u>1?a-1:a],c=[r&&s>1?s-1:s,r&&u>1?u-1:u],p=l[0]/c[0],d=l[1]/c[1],h=1/p,f=1/d,m=2*Math.ceil(h)+2,g=2*Math.ceil(f)+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(\"+p+\");\\n        const float widthScale = float(\"+d+\");\\n\\n        const float invHeightScale = float(\"+h+\");\\n        const float invWidthScale = float(\"+f+\");\\n\\n        const int winHeight = int(\"+m+\");\\n        const int winWidth = int(\"+g+\");\\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 >= \"+s+\") {\\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 >= \"+u+\") {\\n              continue;\\n            }\\n\\n            float sourceFracRow =\\n              float(\"+l[0]+\") *\\n                (float(dyR) / float(\"+c[0]+\"));\\n\\n            float sourceFracCol =\\n                float(\"+l[1]+\") *\\n                  (float(dyC) / float(\"+c[1]+\"));\\n\\n            int sourceNearestRow = int(min(\\n                float(int(\"+o+\") - 1),\\n                \"+r+\" ? float(round(sourceFracRow)) :\\n                                  float(floor(sourceFracRow))));\\n\\n            int sourceNearestCol = int(min(\\n                float(int(\"+a+\") - 1),\\n                \"+r+\" ? 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    \";}}(),ResizeNearestNeighborProgram=function(){return function(e,t,r,n){this.variableNames=[\"A\"],this.outputShape=[];var o=e[0],a=e[1],i=e[2],s=e[3];this.outputShape=[o,t,r,s];var u=[n&&t>1?a-1:a,n&&r>1?i-1:i],l=[n&&t>1?t-1:t,n&&r>1?r-1:r],c=n?\"0.5\":\"0.0\";this.userCode=\"\\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\\n          \"+u[0]/l[0]+\",\\n          \"+u[1]/l[1]+\");\\n      const vec2 inputShapeRC = vec2(\"+a+\".0, \"+i+\".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 + \"+c+\")));\\n\\n        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\\n\\n        setOutput(newValue);\\n      }\\n    \";}}(),ReverseProgram=function(){return function(e,t){this.variableNames=[\"x\"];var r=e.length;if(r>4)throw new Error(\"WebGL backend: Reverse of rank-\"+r+\" tensor is not yet supported\");if(this.outputShape=e,1!==r){var n=e.map(function(r,n){return function(r){return -1!==t.indexOf(r)&&1!==e[r]?e[r]+\" - coords[\"+r+\"] - 1\":\"coords[\"+r+\"]\"}(n)}).join(\",\"),o=getCoordsDataType(r);this.userCode=\"\\n      void main() {\\n        \"+o+\" coords = getOutputCoords();\\n        setOutput(getX(\"+n+\"));\\n      }\\n    \";}else this.userCode=\"\\n        void main() {\\n          int coord = getOutputCoords();\\n          setOutput(getX(\"+e[0]+\" - coord - 1));\\n        }\\n      \";}}(),ScatterProgram=function(){return function(e,t,r,n,o,a,i){void 0===i&&(i=!0),this.variableNames=[\"updates\",\"indices\",\"defaultValue\"],this.outputShape=a;var s=getCoordsDataType(o.length),u=getCoordsDataType(a.length),l=\"\";1===r?l=\"i\":2===r&&(l=\"i, j\");var c=\"getIndices(\"+l+\")\",p=\"\";1===n?p=\"i\":2===n&&(p=\"i, coords[1]\");var d=\"getUpdates(\"+p+\")\",h=t>1?\"strides[j]\":\"strides\";this.userCode=\"\\n        \"+s+\" strides = \"+s+\"(\"+o+\");\\n\\n        void main() {\\n          \"+u+\" coords = getOutputCoords();\\n          float sum = 0.0;\\n          bool found = false;\\n          for (int i = 0; i < \"+e+\"; i++) {\\n            int flattenedIndex = 0;\\n            for (int j = 0; j < \"+t+\"; j++) {\\n              int index = round(\"+c+\");\\n              flattenedIndex += index * \"+h+\";\\n            }\\n            if (flattenedIndex == coords[0]) {\\n              sum += \"+d+\";\\n              found = true;\\n            }\\n          }\\n          setOutput(mix(getDefaultValue(), sum, float(found)));\\n        }\\n      \";}}(),SegmentOpProgram=function(){return function(e,t){this.variableNames=[\"x\",\"segmentIds\"];var r=e.windowSize,n=e.batchSize,o=e.inSize,a=e.numSegments,i=a*Math.ceil(o/r);this.outputShape=[n,i];var s=4*Math.floor(r/4),u=r%4,l=\"\\n        sumValue += dot(values, filter);\\n    \",c=\"\";o%r>0&&(c=\"\\n        if (inIdx < 0 || inIdx >= \"+o+\") {\\n          return initializationValue;\\n        }\\n      \");var p=\"\";o%r>0&&(p=\"\\n        if (inIdx < 0 || inIdx >= \"+o+\") {\\n          return -1.0;\\n        }\\n      \"),this.userCode=\"\\n      const float initializationValue = 0.0;\\n\\n      float getValue(int batch, int inIdx) {\\n        \"+c+\"\\n        return getX(batch, inIdx);\\n      }\\n\\n      float getSegmentIdAtIndex(int inIdx) {\\n        \"+p+\"\\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          \"+a+\")) * float(\"+r+\"));\\n        int currentSeg = int(mod(float(outIdx), float(\"+a+\")));\\n\\n        float sumValue = 0.0;\\n\\n        for (int i = 0; i < \"+s+\"; 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 filter = 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          \"+l+\"\\n        }\\n\\n        int inIdx = inOffset + \"+s+\";\\n        if (\"+(1===u)+\") {\\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 filter = vec4(\\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\\n            0,\\n            0,\\n            0\\n          );\\n\\n          \"+l+\"\\n        } else if (\"+(2===u)+\") {\\n          vec4 values = vec4(\\n            getValue(batch, inIdx),\\n            getValue(batch, inIdx + 1),\\n            initializationValue,\\n            initializationValue\\n          );\\n\\n          vec4 filter = vec4(\\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\\n              0,\\n              0\\n          );\\n\\n          \"+l+\"\\n        } else if (\"+(3===u)+\") {\\n          vec4 values = vec4(\\n            getValue(batch, inIdx),\\n            getValue(batch, inIdx + 1),\\n            getValue(batch, inIdx + 2),\\n            initializationValue\\n          );\\n\\n          vec4 filter = 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          \"+l+\"\\n        }\\n        setOutput(sumValue);\\n      }\\n    \";}}(),SelectProgram=function(){return function(e,t,r){var n,o;if(this.variableNames=[\"c\",\"a\",\"b\"],this.outputShape=t,r>4)throw Error(\"Where for rank \"+r+\" is not yet supported\");if(1===r)o=\"resRC\",n=\"resRC\";else{for(var a=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],i=[],s=[],u=0;u<t.length;u++)s.push(\"\"+a[u]),u<e&&i.push(\"\"+a[u]);n=i.join(),o=s.join();}var l=getCoordsDataType(r);this.userCode=\"\\n      void main() {\\n        \"+l+\" resRC = getOutputCoords();\\n        float cVal = getC(\"+n+\");\\n        if (cVal >= 1.0) {\\n          setOutput(getA(\"+o+\"));\\n        } else {\\n          setOutput(getB(\"+o+\"));\\n        }\\n      }\\n    \";}}(),SliceProgram=function(){function e(e){this.variableNames=[\"source\"],this.outputShape=e,this.rank=e.length;var t=getCoordsDataType(this.rank),r=getCoords$1(this.rank);this.userCode=\"\\n      uniform \"+t+\" start;\\n\\n      void main() {\\n        \"+t+\" sourceLoc = start + getOutputCoords();\\n        setOutput(getSource(\"+r+\"));\\n      }\\n    \";}return e.prototype.getCustomSetupFunc=function(e){var t=this;if(e.length!==this.rank)throw Error(\"The rank (\"+this.rank+\") of the program must match the length of start (\"+e.length+\")\");return function(r,n){if(null!=t.startLoc||(t.startLoc=r.getUniformLocationNoThrow(n,\"start\"),null!=t.startLoc))if(1===t.rank)r.gl.uniform1i(t.startLoc,e[0]);else if(2===t.rank)r.gl.uniform2i(t.startLoc,e[0],e[1]);else if(3===t.rank)r.gl.uniform3i(t.startLoc,e[0],e[1],e[2]);else{if(4!==t.rank)throw Error(\"Slicing for rank \"+t.rank+\" is not yet supported\");r.gl.uniform4i(t.startLoc,e[0],e[1],e[2],e[3]);}}},e}();function getCoords$1(e){if(1===e)return \"sourceLoc\";if(2===e)return \"sourceLoc.x, sourceLoc.y\";if(3===e)return \"sourceLoc.x, sourceLoc.y, sourceLoc.z\";if(4===e)return \"sourceLoc.x, sourceLoc.y, sourceLoc.z, sourceLoc.w\";throw Error(\"Slicing for rank \"+e+\" is not yet supported\")}var StridedSliceProgram=function(){return function(e,t,r,n){this.variableNames=[\"x\"];var o=r.filter(function(e,t){return -1===n.indexOf(t)});this.outputShape=o;var a=r.length,i=getCoordsDataType(r.length),s=getCoordsDataType(o.length),u=\"\";if(1===a)u=\"coords * strides + begin\";else{var l=0;u=r.map(function(e,t){return -1===n.indexOf(t)?(l++,1===o.length?\"coords * strides[\"+t+\"] + begin[\"+t+\"]\":\"coords[\"+(l-1)+\"] * strides[\"+t+\"] + begin[\"+t+\"]\"):\"begin[\"+t+\"]\"}).join(\",\");}this.userCode=\"\\n      \"+i+\" begin = \"+i+\"(\"+e+\");\\n      \"+i+\" strides = \"+i+\"(\"+t+\");\\n\\n      void main() {\\n        \"+s+\" coords = getOutputCoords();\\n        setOutput(getX(\"+u+\"));\\n      }\\n    \";}}(),TextureManager=function(){function e(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={};}return e.prototype.acquireTexture=function(e,t,r){var n,o=getPhysicalFromLogicalTextureType(t,r),a=getKeyFromTextureShape(e,o,r);if(a in this.freeTextures||(this.freeTextures[a]=[]),a in this.usedTextures||(this.usedTextures[a]=[]),this.freeTextures[a].length>0){this.numFreeTextures--,this.numUsedTextures++,this.log();var i=this.freeTextures[a].shift();return this.usedTextures[a].push(i),i}return this.numUsedTextures++,this.log(),o===PhysicalTextureType.PACKED_2X2_FLOAT32?n=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):o===PhysicalTextureType.PACKED_2X2_FLOAT16?n=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):o===PhysicalTextureType.UNPACKED_FLOAT32?n=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):o===PhysicalTextureType.UNPACKED_FLOAT16?n=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):o===PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE&&(n=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[a].push(n),n},e.prototype.releaseTexture=function(e,t,r,n){if(null!=this.freeTextures){var o=getKeyFromTextureShape(t,getPhysicalFromLogicalTextureType(r,n),n);o in this.freeTextures||(this.freeTextures[o]=[]),this.freeTextures[o].push(e),this.numFreeTextures++,this.numUsedTextures--;var a=this.usedTextures[o],i=a.indexOf(e);if(i<0)throw new Error(\"Cannot release a texture that was never provided by this texture manager\");a.splice(i,1),this.log();}},e.prototype.log=function(){if(this.logEnabled){var e=this.numFreeTextures+this.numUsedTextures;console.log(\"Free/Used\",this.numFreeTextures+\" / \"+this.numUsedTextures,\"(\"+e+\")\");}},e.prototype.getNumUsedTextures=function(){return this.numUsedTextures},e.prototype.getNumFreeTextures=function(){return this.numFreeTextures},e.prototype.dispose=function(){var e=this;if(null!=this.freeTextures){for(var t in this.freeTextures)this.freeTextures[t].forEach(function(t){e.gpgpu.deleteMatrixTexture(t);});for(var t in this.usedTextures)this.usedTextures[t].forEach(function(t){e.gpgpu.deleteMatrixTexture(t);});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0;}},e}();function getPhysicalFromLogicalTextureType(e,t){if(t)return ENV.get(\"WEBGL_RENDER_FLOAT32_ENABLED\")?PhysicalTextureType.PACKED_2X2_FLOAT32:PhysicalTextureType.PACKED_2X2_FLOAT16;if(e===TextureUsage.DOWNLOAD||e===TextureUsage.PIXELS)return PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE;if(e===TextureUsage.UPLOAD)return PhysicalTextureType.UNPACKED_FLOAT32;if(e===TextureUsage.RENDER)return ENV.get(\"WEBGL_RENDER_FLOAT32_ENABLED\")?PhysicalTextureType.UNPACKED_FLOAT32:PhysicalTextureType.UNPACKED_FLOAT16;throw new Error(\"Unknown logical texture type \"+e)}function getKeyFromTextureShape(e,t,r){return e[0]+\"_\"+e[1]+\"_\"+t+\"_\"+r}var TileProgram=function(){return function(e,t){this.variableNames=[\"A\"];for(var r=new Array(e.length),n=0;n<r.length;n++)r[n]=e[n]*t[n];this.outputShape=r,this.rank=r.length;var o=getCoordsDataType(this.rank),a=getSourceCoords$2(e);this.userCode=\"\\n      void main() {\\n        \"+o+\" resRC = getOutputCoords();\\n        setOutput(getA(\"+a+\"));\\n      }\\n    \";}}();function getSourceCoords$2(e){var t=e.length;if(t>5)throw Error(\"Tile for rank \"+t+\" is not yet supported\");if(1===t)return \"imod(resRC, \"+e[0]+\")\";for(var r=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\",\"resRC.u\"],n=[],o=0;o<e.length;o++)n.push(\"imod(\"+r[o]+\", \"+e[o]+\")\");return n.join()}var TransposeProgram=function(){return function(e,t){this.variableNames=[\"A\"];for(var r=new Array(e.length),n=0;n<r.length;n++)r[n]=e[t[n]];this.outputShape=r,this.rank=r.length;var o=getCoordsDataType(this.rank),a=getSwitchedCoords(t);this.userCode=\"\\n    void main() {\\n      \"+o+\" resRC = getOutputCoords();\\n      setOutput(getA(\"+a+\"));\\n    }\\n    \";}}();function getSwitchedCoords(e){var t=e.length;if(t>6)throw Error(\"Transpose for rank \"+t+\" is not yet supported\");for(var r=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\",\"resRC.u\",\"resRC.v\"],n=new Array(t),o=0;o<e.length;o++)n[e[o]]=r[o];return n.join()}var ERF_P=.3275911,ERF_A1=.254829592,ERF_A2=-.284496736,ERF_A3=1.421413741,ERF_A4=-1.453152027,ERF_A5=1.061405429,SELU_SCALEALPHA=1.7580993408473768,SELU_SCALE=1.0507009873554805,UnaryOpProgram=function(){function e(e,t){this.variableNames=[\"A\"],this.outputShape=e,this.userCode=\"\\n      uniform float NAN;\\n      float unaryOperation(float x) {\\n        \"+t+\"\\n      }\\n\\n      void main() {\\n        float x = getAAtOutCoords();\\n        float y = unaryOperation(x);\\n\\n        setOutput(y);\\n      }\\n    \";}return e.prototype.getCustomSetupFunc=function(){var e=this;return function(t,r){null==e.startLoc&&(e.startLoc=t.getUniformLocationNoThrow(r,\"NAN\"),null==e.startLoc)||t.gl.uniform1f(e.startLoc,NaN);}},e}(),CHECK_NAN_SNIPPET$1=\"if (isNaN(x)) return x;\",ABS=\"return abs(x);\",RELU=CHECK_NAN_SNIPPET$1+\"\\n  return (x < 0.0) ? 0.0 : x;\\n\",ELU=\"return (x >= 0.0) ? x : (exp(x) - 1.0);\",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(e){return void 0===e&&(e=0),CHECK_NAN_SNIPPET$1+\"\\n    return x > 0.0 ? 1.0 : float(\"+e+\");\\n  \"}var NEG=\"return -x;\",CEIL=\"return ceil(x);\",FLOOR=\"return floor(x);\",SIGN=\"\\n  if (isNaN(x)) { return 0.0; }\\n  return sign(x);\\n\",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\",EXP=\"return exp(x);\",EXPM1=\"return exp(x) - 1.0;\",LOG=\"if (x < 0.0) return NAN;\\n  return log(x);\",LOG1P=\"return log(1.0 + x);\",SQRT=\"return sqrt(x);\",RSQRT=\"return inversesqrt(x);\",SIGMOID=\"return 1.0 / (1.0 + exp(-1.0 * x));\",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\",SIN=CHECK_NAN_SNIPPET$1+\"\\n  return sin(x);\\n\",COS=CHECK_NAN_SNIPPET$1+\"\\n  return cos(x);\\n\",TAN=\"return tan(x);\",ASIN=\"return asin(x);\",ACOS=\"return acos(x);\",ATAN=CHECK_NAN_SNIPPET$1+\"\\n  return atan(x);\\n\",SINH=\"\\n  float e2x = exp(x);\\n  return (e2x - 1.0 / e2x) / 2.0;\\n\",COSH=\"\\n  float e2x = exp(-x);\\n  return (e2x + 1.0 / e2x) / 2.0;\\n\",TANH=\"\\n  float e2x = exp(-2.0 * abs(x));\\n  return sign(x) * (1.0 - e2x) / (1.0 + e2x);\\n\",ASINH=\"return log(x + sqrt(x * x + 1.0));\",ACOSH=CHECK_NAN_SNIPPET$1+\"\\n  if (x < 1.0) return NAN;\\n  return log(x + sqrt(x * x - 1.0));\",ATANH=CHECK_NAN_SNIPPET$1+\"\\n  if ((x < -1.0) || (x > 1.0)) return NAN;\\n  return (log(1.0 + x) - log(1.0 - x)) / 2.0;\",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 t = 1.0 / (1.0 + p * x);\\n  return 1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x);\\n\",SQUARE=\"return x * x;\",RECIPROCAL=\"return 1.0 / x;\",LOGICAL_NOT=\"return float(!(x >= 1.0));\",TO_INT=\"return float(int(x));\",UnpackProgram=function(){return function(e){this.variableNames=[\"A\"],this.usesPackedTextures=!0,this.outputShape=e;var t=e.length,r=getChannels(\"rc\"),n=getCoordsDataType(t),o=getSourceCoords$1(t,r),a=getInnerDims(t,r),i=1===t?\"rc\":a.join(\",\");this.userCode=\"\\n      void main() {\\n        \"+n+\" rc = getOutputCoords();\\n        vec2 modCoord = mod(vec2(\"+i+\"), 2.);\\n        vec4 packedInput = getA(\"+o+\");\\n\\n        setOutput(\\n          modCoord.x == 0. ?\\n            (modCoord.y == 0. ? packedInput.r : packedInput.g) :\\n            (modCoord.y == 0. ? packedInput.b : packedInput.a)\\n        );\\n      }\\n    \";}}();function concat1d_(e){return concat(e,0)}function concat2d_(e,t){return concat(e,t)}function concat3d_(e,t){return concat(e,t)}function concat4d_(e,t){return concat(e,t)}function concat_(e,t){void 0===t&&(t=0),assert(e.length>=1,\"Pass at least one tensor to concat\");var r=convertToTensorArray(e,\"tensors\",\"concat\");t=parseAxisParam(t,r[0].shape)[0];var n=computeOutShape(r.map(function(e){return e.shape}),t);if(0===sizeFromShape(n))return tensor([],n);if(1===(r=r.filter(function(e){return e.size>0})).length)return r[0];var o=r.map(function(e){return e.shape});assertParamsConsistent(o,t);var a=r;return ENV.engine.runKernel(function(e){return e.concat(r,t)},a,function(e){var r=o.map(function(e){return e[t]});return split$1(e,r,t).map(function(e){return function(){return e}})})}function split_(e,t,r){void 0===r&&(r=0);var n,o=convertToTensor(e,\"x\",\"split\");r=parseAxisParam(r,o.shape)[0],\"number\"==typeof t?(assert(o.shape[r]%t==0,\"Number of splits must evenly divide the axis.\"),n=Array(t).fill(o.shape[r]/t)):(assert(o.shape[r]===t.reduce(function(e,t){return e+t}),\"The sum of sizes must match the size of the axis dimension.\"),n=t);return ENV.engine.runKernel(function(e){return e.split(o,n,r)},{$x:o},function(e){return {$x:function(){return concat(e,r)}}})}var concat=op({concat_:concat_}),concat1d=op({concat1d_:concat1d_}),concat2d=op({concat2d_:concat2d_}),concat3d=op({concat3d_:concat3d_}),concat4d=op({concat4d_:concat4d_}),split$1=op({split_:split_});function createCommonjsModule(e,t){return e(t={exports:{}},t.exports),t.exports}var alea=createCommonjsModule(function(e){!function(e,t,r){function n(e,t){return t.c=e.c,t.s0=e.s0,t.s1=e.s1,t.s2=e.s2,t}function o(e,t){var r=new function(e){var t,r=this,n=(t=4022871197,function(e){e=e.toString();for(var r=0;r<e.length;r++){var n=.02519603282416938*(t+=e.charCodeAt(r));n-=t=n>>>0,t=(n*=t)>>>0,t+=4294967296*(n-=t);}return 2.3283064365386963e-10*(t>>>0)});r.next=function(){var e=2091639*r.s0+2.3283064365386963e-10*r.c;return r.s0=r.s1,r.s1=r.s2,r.s2=e-(r.c=0|e)},r.c=1,r.s0=n(\" \"),r.s1=n(\" \"),r.s2=n(\" \"),r.s0-=n(e),r.s0<0&&(r.s0+=1),r.s1-=n(e),r.s1<0&&(r.s1+=1),r.s2-=n(e),r.s2<0&&(r.s2+=1),n=null;}(e),o=t&&t.state,a=r.next;return a.int32=function(){return 4294967296*r.next()|0},a.double=function(){return a()+1.1102230246251565e-16*(2097152*a()|0)},a.quick=a,o&&(\"object\"==typeof o&&n(o,r),a.state=function(){return n(r,{})}),a}t&&t.exports?t.exports=o:r&&r.amd?r(function(){return o}):this.alea=o;}(0,e,!1);}),xor128=createCommonjsModule(function(e){!function(e,t,r){function n(e,t){return t.x=e.x,t.y=e.y,t.z=e.z,t.w=e.w,t}function o(e,t){var r=new function(e){var t=this,r=\"\";t.x=0,t.y=0,t.z=0,t.w=0,t.next=function(){var e=t.x^t.x<<11;return t.x=t.y,t.y=t.z,t.z=t.w,t.w^=t.w>>>19^e^e>>>8},e===(0|e)?t.x=e:r+=e;for(var n=0;n<r.length+64;n++)t.x^=0|r.charCodeAt(n),t.next();}(e),o=t&&t.state,a=function(){return (r.next()>>>0)/4294967296};return a.double=function(){do{var e=((r.next()>>>11)+(r.next()>>>0)/4294967296)/(1<<21);}while(0===e);return e},a.int32=r.next,a.quick=a,o&&(\"object\"==typeof o&&n(o,r),a.state=function(){return n(r,{})}),a}t&&t.exports?t.exports=o:r&&r.amd?r(function(){return o}):this.xor128=o;}(0,e,!1);}),xorwow=createCommonjsModule(function(e){!function(e,t,r){function n(e,t){return t.x=e.x,t.y=e.y,t.z=e.z,t.w=e.w,t.v=e.v,t.d=e.d,t}function o(e,t){var r=new function(e){var t=this,r=\"\";t.next=function(){var e=t.x^t.x>>>2;return t.x=t.y,t.y=t.z,t.z=t.w,t.w=t.v,(t.d=t.d+362437|0)+(t.v=t.v^t.v<<4^e^e<<1)|0},t.x=0,t.y=0,t.z=0,t.w=0,t.v=0,e===(0|e)?t.x=e:r+=e;for(var 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ENV.engine.runKernel(function(e){return e.spaceToBatchND(n,t,r)},{$x:n},function(e){return {$x:function(){return e.batchToSpaceND(t,r)}}})}function unstack_(e,t){void 0===t&&(t=0);for(var r,n=convertToTensor(e,\"x\",\"unstack\"),o=n.shape[t],a=Array(n.rank-1).fill(0),i=0,s=0;s<n.rank;s++)s!==t&&(a[i]=n.shape[s],i++);r=Array(o).fill(1);var u=Array(n.rank).fill(0),l=n.shape.slice();return r.map(function(e){l[t]=e;var r=n.slice(u,l);return u[t]+=e,r.reshape(a)})}function cumsum_(e,t,r,n){void 0===t&&(t=0),void 0===r&&(r=!1),void 0===n&&(n=!1);var o=convertToTensor(e,\"x\",\"cumsum\"),a=getAxesPermutation([t|=0],o.rank),i=o;null!=a&&(i=o.transpose(a));var s=getInnerMostAxes(1,o.rank)[0],u=ENV.engine.runKernel(function(e){return e.cumsum(i,s,r,n)},{permutedX:i},function(e){return {permutedX:function(){return e.cumsum(t,r,!n)}}});return null!=a&&(u=u.transpose(a)),u}function expandDims_(e,t){void 0===t&&(t=0);var r=convertToTensor(e,\"x\",\"expandDims\");assert(t<=r.rank,\"Axis must be <= rank of the tensor\");var n=r.shape.slice();return t<0&&(assert(-(r.rank+1)<=t,\"Axis must be in the interval [\"+-(r.rank+1)+\", \"+r.rank+\"]\"),t=r.rank+t+1),n.splice(t,0,1),reshape(r,n)}function depthToSpace_(e,t,r){void 0===r&&(r=\"NHWC\");var n=convertToTensor(e,\"x\",\"depthToSpace\"),o=\"NHWC\"===r?n.shape[1]:n.shape[2],a=\"NHWC\"===r?n.shape[2]:n.shape[3],i=\"NHWC\"===r?n.shape[3]:n.shape[1];return assert(o*t>=0,\"Negative dimension size caused by overflow when multiplying\\n      \"+o+\" and \"+t+\"  for depthToSpace with input shape\\n      \"+n.shape),assert(a*t>=0,\"Negative dimension size caused by overflow when multiplying\\n      \"+a+\" and \"+t+\" for depthToSpace with input shape\\n          \"+n.shape),assert(i%(t*t)==0,\"Dimension size must be evenly divisible by \"+t*t+\" but is \"+i+\" for depthToSpace with input shape \"+n.shape),ENV.engine.runKernel(function(e){return e.depthToSpace(n,t,r)},{$x:n})}function buffer(e,t,r){return void 0===t&&(t=\"float32\"),new TensorBuffer(e,t,r)}function print(e,t){void 0===t&&(t=!1),console.log(e.toString(t));}var batchToSpaceND=op({batchToSpaceND_:batchToSpaceND_}),cast=op({cast_:cast_}),clone=op({clone_:clone_}),cumsum=op({cumsum_:cumsum_}),depthToSpace=op({depthToSpace_:depthToSpace_}),expandDims=op({expandDims_:expandDims_}),eye=op({eye_:eye_}),fromPixels=op({fromPixels_:fromPixels_}),multinomial=op({multinomial_:multinomial_}),oneHot=op({oneHot_:oneHot_}),pad=op({pad_:pad_}),pad1d=op({pad1d_:pad1d_}),pad2d=op({pad2d_:pad2d_}),pad3d=op({pad3d_:pad3d_}),pad4d=op({pad4d_:pad4d_}),rand=op({rand_:rand_}),randomNormal=op({randomNormal_:randomNormal_}),randomUniform=op({randomUniform_:randomUniform_}),reshape=op({reshape_:reshape_}),spaceToBatchND=op({spaceToBatchND_:spaceToBatchND_}),squeeze=op({squeeze_:squeeze_}),stack=op({stack_:stack_}),tile=op({tile_:tile_}),truncatedNormal=op({truncatedNormal_:truncatedNormal_}),unstack=op({unstack_:unstack_});function whereImpl(e,t){for(var r=[],n=0;n<t.length;n++)t[n]&&r.push(n);var o=buffer(e,\"int32\"),a=buffer([r.length,e.length],\"int32\");for(n=0;n<r.length;n++){var i=o.indexToLoc(r[n]),s=n*e.length;a.values.set(i,s);}return a.toTensor()}var CPU_HANDOFF_SIZE_THRESHOLD=10,BEFORE_PAGING_CONSTANT=300,MathBackendWebGL=function(){function e(e,t){if(void 0===t&&(t=!0),this.gpgpu=e,this.delayedStorage=t,this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.lruDataGPU=[],this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.binaryCache={},this.disposed=!1,ENV.get(\"WEBGL_VERSION\")<1)throw new Error(\"WebGL is not supported on this device\");if(null==e){var r=getWebGLContext(ENV.get(\"WEBGL_VERSION\"));this.gpgpu=new GPGPUContext(r),this.canvas=r.canvas,this.gpgpuCreatedLocally=!0;}else this.gpgpuCreatedLocally=!1,this.canvas=e.gl.canvas;ENV.get(\"WEBGL_PAGING_ENABLED\")&&(this.NUM_BYTES_BEFORE_PAGING=window.screen.height*window.screen.width*window.devicePixelRatio*BEFORE_PAGING_CONSTANT),this.textureManager=new TextureManager(this.gpgpu);}return e.prototype.register=function(e,t,r){if(this.texData.has(e))throw new Error(\"Data buffer is already registered\");this.texData.set(e,{shape:t,dtype:r,values:null,texture:null,complexTensors:null,texShape:null,usage:TextureUsage.RENDER,isPacked:!1});},e.prototype.setDataMover=function(e){this.texData=new DataStorage(e);},e.prototype.fromPixels=function(e,t){if(null==e)throw new Error(\"pixels passed to tf.fromPixels() can not be null\");var r=[e.height,e.width],n=[e.height,e.width,t];if(!(e instanceof HTMLVideoElement||e instanceof HTMLImageElement||e instanceof HTMLCanvasElement||e instanceof ImageData))throw new Error(\"pixels passed to tf.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement or ImageData, but was \"+e.constructor.name);if(e instanceof HTMLVideoElement){if(null==this.fromPixels2DContext){if(!ENV.get(\"IS_BROWSER\"))throw new Error(\"Can't read pixels from HTMLImageElement outside the browser.\");if(\"complete\"!==document.readyState)throw new Error(\"The DOM is not ready yet. Please call tf.fromPixels() once the DOM is ready. One way to do that is to add an event listener for `DOMContentLoaded` on the document object\");this.fromPixels2DContext=document.createElement(\"canvas\").getContext(\"2d\");}this.fromPixels2DContext.canvas.width=e.width,this.fromPixels2DContext.canvas.height=e.height,this.fromPixels2DContext.drawImage(e,0,0,e.width,e.height),e=this.fromPixels2DContext.canvas;}var o=this.makeTensorHandle(r,\"int32\");this.texData.get(o.dataId).usage=TextureUsage.PIXELS,this.gpgpu.uploadPixelDataToTexture(this.getTexture(o.dataId),e);var a=new FromPixelsProgram(n),i=this.compileAndRun(a,[o]);return this.disposeData(o.dataId),i},e.prototype.makeTensorHandle=function(e,t){var r={};return this.register(r,e,t),{dataId:r,shape:e,dtype:t}},e.prototype.write=function(e,t){if(null==t)throw new Error(\"MathBackendWebGL.write(): values can not be null\");var r=this.texData.get(e),n=r.texture,o=r.texShape,a=r.usage,i=r.dtype,s=r.isPacked;if(\"complex64\"===i)throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");null!=n&&(this.releaseTexture(e,n,o,a,s),r.texture=null,r.texShape=null),r.usage=TextureUsage.UPLOAD,r.values=t,this.delayedStorage||this.uploadToGPU(e);},e.prototype.readSync=function(e){var t=this.texData.get(e),r=t.values,n=t.dtype,o=t.complexTensors;if(null!=r)return this.cacheOnCPU(e),r;var a,i,s=null!=this.activeTimers;(s&&(a=performance.now()),\"complex64\"===n)?i=mergeRealAndImagArrays(o.real.dataSync(),o.imag.dataSync()):i=this.getValuesFromTexture(e);return s&&(this.downloadWaitMs+=performance.now()-a),this.cacheOnCPU(e,i),t.values},e.prototype.read=function(e){return __awaiter(this,void 0,void 0,function(){var t,r,n,o,a,i,s,u;return __generator(this,function(l){switch(l.label){case 0:if(this.pendingRead.has(e))return t=this.pendingRead.get(e),[2,new Promise(function(e){return t.push(e)})];if(r=this.texData.get(e),n=r.texture,o=r.values,a=r.texShape,null!=o)return this.cacheOnCPU(e),[2,o];if(this.pendingRead.set(e,[]),!ENV.get(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\")&&2===ENV.get(\"WEBGL_VERSION\"))throw new Error(\"tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.\");return i=this.gpgpu.maybeCreateBufferFromTexture(n,a[0],a[1]),[4,this.gpgpu.createAndWaitForFence()];case 1:return l.sent(),s=i instanceof WebGLTexture?this.getValuesFromTexture(e):this.gpgpu.downloadFloat32MatrixFromBuffer(i,a[0],a[1]),this.cacheOnCPU(e,s),u=this.pendingRead.get(e),this.pendingRead.delete(e),u.forEach(function(e){return e(s)}),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)),[2,s]}})})},e.prototype.getValuesFromTexture=function(e){var t=this.texData.get(e),r=t.shape,n=t.dtype,o=t.texture,a=t.texShape;if(ENV.get(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\")){if(this.texData.get(e).isPacked){var i=sizeFromShape(r.slice(0,r.length-2)),s=r.length>1?r[r.length-2]:1,u=r[r.length-1];return this.gpgpu.downloadMatrixFromPackedTexture(o,i,s,u,a[0],a[1])}return this.gpgpu.downloadFloat32MatrixFromOutputTexture(o,a[0],a[1])}var l=this.makeTensorHandle(r,\"float32\");l.size=sizeFromShape(r),this.texData.get(l.dataId).usage=TextureUsage.DOWNLOAD;var c=new EncodeFloatProgram(r);this.compileAndRun(c,[{shape:r,dtype:n,dataId:e}],l,null,!1);var p=this.texData.get(l.dataId),d=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(p.texture,p.texShape[0],p.texShape[1]);return this.disposeData(l.dataId),d},e.prototype.time=function(e){return __awaiter(this,void 0,void 0,function(){var t,r,n,o,a,i,s;return __generator(this,function(u){switch(u.label){case 0:return t=this.activeTimers,r=[],n=!1,null==this.programTimersStack?(this.programTimersStack=r,n=!0):this.activeTimers.push(r),this.activeTimers=r,e(),o=flatten(this.activeTimers.map(function(e){return e.query})).filter(function(e){return null!=e}),a=flatten(this.activeTimers.map(function(e){return e.name})).filter(function(e){return null!=e}),this.activeTimers=t,n&&(this.programTimersStack=null),[4,Promise.all(o)];case 1:return i=u.sent(),s={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:sum(i),getExtraProfileInfo:function(){return i.map(function(e,t){return {name:a[t],ms:e}}).map(function(e){return e.name+\": \"+e.ms}).join(\", \")},wallMs:null},this.uploadWaitMs=0,this.downloadWaitMs=0,[2,s]}})})},e.prototype.memory=function(){return {unreliable:!1,numBytesInGPU:this.numBytesInGPU}},e.prototype.startTimer=function(){return ENV.get(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")>0?this.gpgpu.beginQuery():{startMs:performance.now(),endMs:null}},e.prototype.endTimer=function(e){return ENV.get(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")>0?(this.gpgpu.endQuery(),e):(e.endMs=performance.now(),e)},e.prototype.getQueryTime=function(e){return __awaiter(this,void 0,void 0,function(){var t;return __generator(this,function(r){return ENV.get(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")>0?[2,this.gpgpu.waitForQueryAndGetTime(e)]:[2,(t=e).endMs-t.startMs]})})},e.prototype.disposeData=function(e){if(!this.pendingDisposal.has(e))if(this.pendingRead.has(e))this.pendingDisposal.add(e);else if(this.texData.has(e)){var t=this.texData.get(e),r=t.texture,n=t.texShape,o=t.usage,a=t.complexTensors,i=t.isPacked;null!=r&&this.releaseTexture(e,r,n,o,i),null!=a&&(a.real.dispose(),a.imag.dispose()),this.texData.delete(e);}},e.prototype.getTexture=function(e){return this.uploadToGPU(e),this.texData.get(e).texture},e.prototype.getCPUBackend=function(){return ENV.get(\"WEBGL_CPU_FORWARD\")?(null==this.cpuBackend&&(this.cpuBackend=ENV.findBackend(\"cpu\")),this.cpuBackend):null},e.prototype.shouldExecuteOnCPU=function(e,t){var r=this;return void 0===t&&(t=CPU_HANDOFF_SIZE_THRESHOLD),null!=this.getCPUBackend()&&e.every(function(e){return null==r.texData.get(e.dataId).texture&&e.size<t})},e.prototype.getGPGPUContext=function(){return this.gpgpu},e.prototype.getCanvas=function(){return this.canvas},e.prototype.complex=function(e,t){var r=this.makeOutputArray(e.shape,\"complex64\");return this.texData.get(r.dataId).complexTensors={real:ENV.engine.keep(e.clone()),imag:ENV.engine.keep(t.clone())},r},e.prototype.real=function(e){return this.texData.get(e.dataId).complexTensors.real.clone()},e.prototype.imag=function(e){return this.texData.get(e.dataId).complexTensors.imag.clone()},e.prototype.slice=function(e,t,r){if(this.shouldExecuteOnCPU([e]))return this.cpuBackend.slice(e,t,r);var n=new SliceProgram(r),o=n.getCustomSetupFunc(t);return this.compileAndRun(n,[e],null,o)},e.prototype.stridedSlice=function(e,t,r,n,o,a,i,s,u){if(this.shouldExecuteOnCPU([e]))return this.cpuBackend.stridedSlice(e,t,r,n,o,a,i,s,u);var l=getStridedSlicedInfo(e.shape,t,r,n,o,a,i,s,u),c=l[0],p=l[1],d=l[2],h=p.filter(function(e,t){return -1===d.indexOf(t)});if(h.some(function(e){return 0===e}))return tensor([],h);var f=new StridedSliceProgram(c,n,p,d);return this.compileAndRun(f,[e])},e.prototype.reverse=function(e,t){var r=new ReverseProgram(e.shape,t);return this.compileAndRun(r,[e])},e.prototype.concat2Tensors=function(e,t,r){var n=computeOutShape([e.shape,t.shape],r),o=e.as2D(-1,sizeFromShape(e.shape.slice(r))),a=t.as2D(-1,sizeFromShape(t.shape.slice(r))),i=new ConcatProgram(o.shape,a.shape);return this.compileAndRun(i,[o,a]).reshape(n)},e.prototype.concat=function(e,t){if(this.shouldExecuteOnCPU(e))return this.cpuBackend.concat(e,t);if(1===e.length)return e[0];for(var r=e[0],n=1;n<e.length;++n)r=this.concat2Tensors(r,e[n],t);return r},e.prototype.neg=function(e){var t=new UnaryOpProgram(e.shape,NEG);return this.compileAndRun(t,[e])},e.prototype.batchMatMul=function(e,t,r,n){var o=r?e.shape[2]:e.shape[1],a=n?t.shape[1]:t.shape[2];if(1===e.shape[0]&&1===t.shape[0]){var i=e.as2D(e.shape[1],e.shape[2]),s=t.as2D(t.shape[1],t.shape[2]),u=new MatMulPackedProgram(i.shape,s.shape,[o,a],r,n),l=this.unpackTensor(this.compileAndRun(u,[i,s],this.makePackedTensor(u.outputShape)));return l.reshape([1,l.shape[0],l.shape[1]])}return this.compileAndRun(new MatMulProgram(e.shape,t.shape,r,n),[e,t])},e.prototype.multiply=function(e,t){if(\"complex64\"===e.dtype){var r=this.texData.get(e.dataId),n=this.texData.get(t.dataId),o=new BinaryOpComplexProgram(COMPLEX_MULTIPLY.REAL,e.shape,t.shape),a=new BinaryOpComplexProgram(COMPLEX_MULTIPLY.IMAG,e.shape,t.shape),i=[this.makeComplexComponentTensorHandle(e,r.complexTensors.real),this.makeComplexComponentTensorHandle(e,r.complexTensors.imag),this.makeComplexComponentTensorHandle(t,n.complexTensors.real),this.makeComplexComponentTensorHandle(t,n.complexTensors.imag)],s=this.compileAndRun(o,i),u=this.compileAndRun(a,i),l=this.complex(s,u);return s.dispose(),u.dispose(),l}if(this.shouldExecuteOnCPU([e,t]))return this.cpuBackend.multiply(e,t);var c=new BinaryOpProgram(MUL,e.shape,t.shape),p=this.makeOutputArray(c.outputShape,e.dtype);return this.compileAndRun(c,[e,t],p)},e.prototype.batchNormalization=function(e,t,r,n,o,a){var i=[e,t,r],s=null;null!=a&&(s=a.shape,i.push(a));var u=null;null!=o&&(u=o.shape,i.push(o));var l=null,c=BatchNormProgram;ENV.get(\"WEBGL_PACK_BATCHNORMALIZATION\")&&(l=this.makePackedTensor(e.shape),c=BatchNormPackedProgram);var p=new c(e.shape,t.shape,r.shape,s,u,n);return this.compileAndRun(p,i,l)},e.prototype.localResponseNormalization4D=function(e,t,r,n,o){var a=new LRNProgram(e.shape,t,r,n,o);return this.compileAndRun(a,[e])},e.prototype.LRNGrad=function(e,t,r,n,o,a,i){var s=new LRNGradProgram(t.shape,n,o,a,i);return this.compileAndRun(s,[t,r,e])},e.prototype.tile=function(e,t){var r=new TileProgram(e.shape,t);return this.compileAndRun(r,[e])},e.prototype.pad=function(e,t,r){var n=new PadProgram(e.shape,t,r);return this.compileAndRun(n,[e])},e.prototype.transpose=function(e,t){var r=new TransposeProgram(e.shape,t);return this.compileAndRun(r,[e])},e.prototype.gather=function(e,t,r){var n=new GatherProgram(e.shape,t.size,r);return this.compileAndRun(n,[e,t])},e.prototype.batchToSpaceND=function(e,t,r){assert(e.rank<=4,\"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet\");var n=t.reduce(function(e,t){return e*t}),o=getReshaped(e.shape,t,n),a=getPermuted(o.length,t.length),i=getReshapedPermuted(e.shape,t,n),s=getSliceBeginCoords(r,t.length),u=getSliceSize(i,r,t.length);return e.reshape(o).transpose(a).reshape(i).slice(s,u)},e.prototype.spaceToBatchND=function(e,t,r){assert(e.rank<=4,\"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet\");var n=t.reduce(function(e,t){return e*t}),o=[[0,0]];o.push.apply(o,r);for(var a=1+t.length;a<e.shape.length;++a)o.push([0,0]);var i=e.pad(o),s=getReshaped(i.shape,t,n,!1),u=getPermuted(s.length,t.length,!1),l=getReshapedPermuted(i.shape,t,n,!1);return i.reshape(s).transpose(u).reshape(l)},e.prototype.reduce=function(e,t,r){var n=e.shape[0],o=e.shape[1],a=computeOptimalWindowSize(o),i=new ReduceProgram({windowSize:a,inSize:o,batchSize:n},t),s=i.outputShape,u=s[0],l=s[1],c=this.makeOutputArray([u,l],r);return this.compileAndRun(i,[e],c),1===c.shape[1]?c:this.reduce(c,t,r)},e.prototype.argReduce=function(e,t,r){void 0===r&&(r=null);var n=e.shape[0],o=e.shape[1];null!=r&&(n=r.shape[0],o=r.shape[1]);var a=computeOptimalWindowSize(o),i=new ArgMinMaxProgram({windowSize:a,inSize:o,batchSize:n},t,null==r),s=i.outputShape,u=s[0],l=s[1],c=this.makeOutputArray([u,l],\"int32\"),p=[e];return null!=r&&p.push(r),this.compileAndRun(i,p,c),1===c.shape[1]?c:this.argReduce(e,t,c)},e.prototype.sum=function(e,t){assertAxesAreInnerMostDims(\"sum\",t,e.rank);var r=computeOutAndReduceShapes(e.shape,t),n=r[0],o=sizeFromShape(r[1]),a=e.as2D(-1,o),i=sumOutType(e.dtype);return this.reduce(a,\"sum\",i).reshape(n)},e.prototype.prod=function(e,t){var r=computeOutAndReduceShapes(e.shape,t),n=r[0],o=sizeFromShape(r[1]),a=e.as2D(-1,o),i=sumOutType(e.dtype);return this.reduce(a,\"prod\",i).reshape(n)},e.prototype.unsortedSegmentSum=function(e,t,r){var n=0,o=getAxesPermutation([n],e.rank),a=e;null!=o&&(a=e.transpose(o),n=getInnerMostAxes(1,e.rank)[0]);var i=computeOutShape$1(a.shape,n,r),s=sizeFromShape([a.shape[n]]),u=a.as2D(-1,s),l=sumOutType(e.dtype),c=this.segOpCompute(u,\"unsortedSegmentSum\",t,l,r).reshape(i);return null!=o&&(c=c.transpose(getUndoAxesPermutation(o))),c},e.prototype.segOpCompute=function(e,t,r,n,o){var a=e.shape[0],i=e.shape[1],s=segOpComputeOptimalWindowSize(i,o),u=new SegmentOpProgram({windowSize:s,inSize:i,batchSize:a,numSegments:o},t),l=u.outputShape,c=l[0],p=l[1],d=this.makeOutputArray([c,p],n);return this.compileAndRun(u,[e,r],d),d.shape[1]===o?d:(r=range(0,o).tile([i/s]),this.segOpCompute(d,t,r,n,o))},e.prototype.argMin=function(e,t){var r=[t];assertAxesAreInnerMostDims(\"argMin\",r,e.rank);var n=computeOutAndReduceShapes(e.shape,r),o=n[0],a=sizeFromShape(n[1]),i=e.as2D(-1,a);return this.argReduce(i,\"min\").reshape(o)},e.prototype.argMax=function(e,t){var r=[t];assertAxesAreInnerMostDims(\"argMax\",r,e.rank);var n=computeOutAndReduceShapes(e.shape,r),o=n[0],a=sizeFromShape(n[1]),i=e.as2D(-1,a);return this.argReduce(i,\"max\").reshape(o)},e.prototype.cumsum=function(e,t,r,n){if(t!==e.rank-1)throw new Error(\"WebGL cumsum shader expects an inner-most axis=\"+(e.rank-1)+\" but got axis=\"+t);var o=new CumSumProgram(e.shape,r,n);return this.compileAndRun(o,[e])},e.prototype.equal=function(e,t){var r=new BinaryOpProgram(EQUAL,e.shape,t.shape),n=this.makeOutputArray(r.outputShape,\"bool\");return this.compileAndRun(r,[e,t],n)},e.prototype.notEqual=function(e,t){var r=new BinaryOpProgram(NOT_EQUAL,e.shape,t.shape),n=this.makeOutputArray(r.outputShape,\"bool\");return this.compileAndRun(r,[e,t],n)},e.prototype.less=function(e,t){if(this.shouldExecuteOnCPU([e,t]))return this.cpuBackend.less(e,t);var r=new BinaryOpProgram(LESS,e.shape,t.shape),n=this.makeOutputArray(r.outputShape,\"bool\");return this.compileAndRun(r,[e,t],n)},e.prototype.lessEqual=function(e,t){var r=new BinaryOpProgram(LESS_EQUAL,e.shape,t.shape),n=this.makeOutputArray(r.outputShape,\"bool\");return this.compileAndRun(r,[e,t],n)},e.prototype.greater=function(e,t){if(this.shouldExecuteOnCPU([e,t]))return this.cpuBackend.greater(e,t);var r=new BinaryOpProgram(GREATER,e.shape,t.shape),n=this.makeOutputArray(r.outputShape,\"bool\");return this.compileAndRun(r,[e,t],n)},e.prototype.greaterEqual=function(e,t){var r=new BinaryOpProgram(GREATER_EQUAL,e.shape,t.shape),n=this.makeOutputArray(r.outputShape,\"bool\");return this.compileAndRun(r,[e,t],n)},e.prototype.logicalNot=function(e){var t=new UnaryOpProgram(e.shape,LOGICAL_NOT);return this.compileAndRun(t,[e])},e.prototype.logicalAnd=function(e,t){var r=new BinaryOpProgram(LOGICAL_AND,e.shape,t.shape),n=this.makeOutputArray(r.outputShape,\"bool\");return this.compileAndRun(r,[e,t],n)},e.prototype.logicalOr=function(e,t){var r=new BinaryOpProgram(LOGICAL_OR,e.shape,t.shape),n=this.makeOutputArray(r.outputShape,\"bool\");return this.compileAndRun(r,[e,t],n)},e.prototype.select=function(e,t,r){var n=new SelectProgram(e.rank,t.shape,t.rank),o=this.makeOutputArray(n.outputShape,upcastType(t.dtype,r.dtype));return this.compileAndRun(n,[e,t,r],o)},e.prototype.where=function(e){warn(\"tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead\");var t=e.dataSync();return whereImpl(e.shape,t)},e.prototype.topk=function(e,t,r){return topkImpl(e.dataSync(),e.shape,e.dtype,t,r)},e.prototype.min=function(e,t){assertAxesAreInnerMostDims(\"min\",t,e.rank);var r=computeOutAndReduceShapes(e.shape,t),n=r[0],o=sizeFromShape(r[1]),a=e.as2D(-1,o);return this.reduce(a,\"min\",a.dtype).reshape(n)},e.prototype.minimum=function(e,t){if(this.shouldExecuteOnCPU([e,t]))return this.cpuBackend.minimum(e,t);var r=new BinaryOpProgram(MIN,e.shape,t.shape);return this.compileAndRun(r,[e,t])},e.prototype.mod=function(e,t){var r=new BinaryOpProgram(MOD,e.shape,t.shape),n=r.getCustomSetupFunc();return this.compileAndRun(r,[e,t],null,n)},e.prototype.max=function(e,t){assertAxesAreInnerMostDims(\"max\",t,e.rank);var r=computeOutAndReduceShapes(e.shape,t),n=r[0],o=sizeFromShape(r[1]),a=e.as2D(-1,o);return this.reduce(a,\"max\",a.dtype).reshape(n)},e.prototype.maximum=function(e,t){if(this.shouldExecuteOnCPU([e,t]))return this.cpuBackend.maximum(e,t);var r=new BinaryOpProgram(MAX,e.shape,t.shape);return this.compileAndRun(r,[e,t])},e.prototype.all=function(e,t){assertAxesAreInnerMostDims(\"all\",t,e.rank);var r=computeOutAndReduceShapes(e.shape,t),n=r[0],o=sizeFromShape(r[1]),a=e.as2D(-1,o);return this.reduce(a,\"all\",a.dtype).reshape(n)},e.prototype.any=function(e,t){assertAxesAreInnerMostDims(\"any\",t,e.rank);var r=computeOutAndReduceShapes(e.shape,t),n=r[0],o=sizeFromShape(r[1]),a=e.as2D(-1,o);return this.reduce(a,\"any\",a.dtype).reshape(n)},e.prototype.squaredDifference=function(e,t){var r=new BinaryOpProgram(SQUARED_DIFFERENCE,e.shape,t.shape);return this.compileAndRun(r,[e,t])},e.prototype.realDivide=function(e,t){var r=new BinaryOpProgram(DIV,e.shape,t.shape),n=this.makeOutputArray(r.outputShape,\"float32\");return this.compileAndRun(r,[e,t],n)},e.prototype.floorDiv=function(e,t){var r=new BinaryOpProgram(INT_DIV,e.shape,t.shape),n=this.makeOutputArray(r.outputShape,\"int32\");return this.compileAndRun(r,[e,t],n)},e.prototype.add=function(e,t){if(\"complex64\"===e.dtype&&\"complex64\"===t.dtype)return this.complexSeparableBinaryOp(e,t,ADD);var r=new BinaryOpProgram(ADD,e.shape,t.shape),n=this.makeOutputArray(r.outputShape,upcastType(e.dtype,t.dtype));return this.compileAndRun(r,[e,t],n)},e.prototype.complexSeparableBinaryOp=function(e,t,r){var n=this,o=this.texData.get(e.dataId),a=this.texData.get(t.dataId),i=[[o.complexTensors.real,a.complexTensors.real],[o.complexTensors.imag,a.complexTensors.imag]].map(function(o){var a=o[0],i=o[1],s=new BinaryOpProgram(r,e.shape,t.shape),u=n.makeOutputArray(s.outputShape,upcastType(a.dtype,i.dtype)),l=n.makeComplexComponentTensorHandle(e,a),c=n.makeComplexComponentTensorHandle(t,i);return n.compileAndRun(s,[l,c],u)}),s=i[0],u=i[1],l=this.complex(s,u);return s.dispose(),u.dispose(),l},e.prototype.makeComplexComponentTensorHandle=function(e,t){return {dataId:t.dataId,dtype:t.dtype,shape:e.shape}},e.prototype.addN=function(e){for(var t=e[0],r=1;r<e.length;r++)t=this.add(t,e[r]);return t},e.prototype.subtract=function(e,t){if(\"complex64\"===e.dtype&&\"complex64\"===t.dtype)return this.complexSeparableBinaryOp(e,t,SUB);if(this.shouldExecuteOnCPU([e,t]))return this.cpuBackend.subtract(e,t);var r=new BinaryOpProgram(SUB,e.shape,t.shape),n=this.makeOutputArray(r.outputShape,upcastType(e.dtype,t.dtype));return this.compileAndRun(r,[e,t],n)},e.prototype.pow=function(e,t){var r=new BinaryOpProgram(POW,e.shape,t.shape),n=r.getCustomSetupFunc(),o=this.makeOutputArray(r.outputShape,upcastType(e.dtype,t.dtype));return this.compileAndRun(r,[e,t],o,n)},e.prototype.ceil=function(e){var t=new UnaryOpProgram(e.shape,CEIL);return this.compileAndRun(t,[e])},e.prototype.floor=function(e){var t=new UnaryOpProgram(e.shape,FLOOR);return this.compileAndRun(t,[e])},e.prototype.sign=function(e){var t=new UnaryOpProgram(e.shape,SIGN);return this.compileAndRun(t,[e])},e.prototype.round=function(e){var t=new UnaryOpProgram(e.shape,ROUND);return this.compileAndRun(t,[e])},e.prototype.exp=function(e){var t=new UnaryOpProgram(e.shape,EXP);return this.compileAndRun(t,[e])},e.prototype.expm1=function(e){var t=new UnaryOpProgram(e.shape,EXPM1);return this.compileAndRun(t,[e])},e.prototype.log=function(e){var t=new UnaryOpProgram(e.shape,LOG),r=t.getCustomSetupFunc();return this.compileAndRun(t,[e],null,r)},e.prototype.log1p=function(e){var t=new UnaryOpProgram(e.shape,LOG1P);return this.compileAndRun(t,[e])},e.prototype.sqrt=function(e){var t=new UnaryOpProgram(e.shape,SQRT);return this.compileAndRun(t,[e])},e.prototype.rsqrt=function(e){var t=new UnaryOpProgram(e.shape,RSQRT);return this.compileAndRun(t,[e])},e.prototype.square=function(e){var t=new UnaryOpProgram(e.shape,SQUARE);return this.compileAndRun(t,[e])},e.prototype.reciprocal=function(e){var t=new UnaryOpProgram(e.shape,RECIPROCAL);return this.compileAndRun(t,[e])},e.prototype.relu=function(e){var t=new UnaryOpProgram(e.shape,RELU);return this.compileAndRun(t,[e])},e.prototype.elu=function(e){var t=new UnaryOpProgram(e.shape,ELU);return this.compileAndRun(t,[e])},e.prototype.eluDer=function(e,t){var r=new BinaryOpProgram(ELU_DER,e.shape,t.shape);return this.compileAndRun(r,[e,t])},e.prototype.selu=function(e){var t=new UnaryOpProgram(e.shape,SELU);return this.compileAndRun(t,[e])},e.prototype.int=function(e){var t=new UnaryOpProgram(e.shape,TO_INT),r=this.makeOutputArray(t.outputShape,\"int32\");return this.compileAndRun(t,[e],r)},e.prototype.clip=function(e,t,r){var n=new ClipProgram(e.shape,t,r);return this.compileAndRun(n,[e])},e.prototype.abs=function(e){var t=new UnaryOpProgram(e.shape,ABS);return this.compileAndRun(t,[e])},e.prototype.complexAbs=function(e){var t=this.texData.get(e.dataId),r=new ComplexAbsProgram(e.shape),n=[this.makeComplexComponentTensorHandle(e,t.complexTensors.real),this.makeComplexComponentTensorHandle(e,t.complexTensors.imag)];return this.compileAndRun(r,n)},e.prototype.sigmoid=function(e){var t=new UnaryOpProgram(e.shape,SIGMOID);return this.compileAndRun(t,[e])},e.prototype.softplus=function(e){var t=new UnaryOpProgram(e.shape,SOFTPLUS);return this.compileAndRun(t,[e])},e.prototype.sin=function(e){var t=new UnaryOpProgram(e.shape,SIN);return this.compileAndRun(t,[e])},e.prototype.cos=function(e){var t=new UnaryOpProgram(e.shape,COS);return this.compileAndRun(t,[e])},e.prototype.tan=function(e){var t=new UnaryOpProgram(e.shape,TAN);return this.compileAndRun(t,[e])},e.prototype.asin=function(e){var t=new UnaryOpProgram(e.shape,ASIN);return this.compileAndRun(t,[e])},e.prototype.acos=function(e){var t=new UnaryOpProgram(e.shape,ACOS);return this.compileAndRun(t,[e])},e.prototype.atan=function(e){var t=new UnaryOpProgram(e.shape,ATAN);return this.compileAndRun(t,[e])},e.prototype.atan2=function(e,t){var r=new BinaryOpProgram(ATAN2,e.shape,t.shape);return this.compileAndRun(r,[e,t])},e.prototype.sinh=function(e){var t=new UnaryOpProgram(e.shape,SINH);return this.compileAndRun(t,[e])},e.prototype.cosh=function(e){var t=new UnaryOpProgram(e.shape,COSH);return this.compileAndRun(t,[e])},e.prototype.tanh=function(e){var t=new UnaryOpProgram(e.shape,TANH);return this.compileAndRun(t,[e])},e.prototype.asinh=function(e){var t=new UnaryOpProgram(e.shape,ASINH);return this.compileAndRun(t,[e])},e.prototype.acosh=function(e){var t=new UnaryOpProgram(e.shape,ACOSH),r=t.getCustomSetupFunc();return this.compileAndRun(t,[e],null,r)},e.prototype.atanh=function(e){var t=new UnaryOpProgram(e.shape,ATANH),r=t.getCustomSetupFunc();return this.compileAndRun(t,[e],null,r)},e.prototype.erf=function(e){var t=new UnaryOpProgram(e.shape,ERF);return this.compileAndRun(t,[e])},e.prototype.step=function(e,t){var r=new UnaryOpProgram(e.shape,STEP(t));return this.compileAndRun(r,[e])},e.prototype.conv2dWithIm2Row=function(e,t,r){var n=r.filterWidth,o=r.filterHeight,a=r.inChannels,i=r.outWidth,s=r.outHeight,u=n*o*a,l=s*i,c=[u,l],p=e.squeeze([0]),d=t.reshape([u,-1]),h=new Im2ColProgram(c,p.shape,r),f=this.compileAndRun(h,[p],this.makePackedTensor(c)),m=new MatMulPackedProgram(f.shape,d.shape,[l,r.outChannels],!0,!1);return this.unpackTensor(this.compileAndRun(m,[f,d],this.makePackedTensor(m.outputShape))).reshape([1,s,i,r.outChannels])},e.prototype.conv2d=function(e,t,r){if(ENV.get(\"WEBGL_CONV_IM2COL\")&&1===e.shape[0])return this.conv2dWithIm2Row(e,t,r);var n=new Conv2DProgram(r);return this.compileAndRun(n,[e,t])},e.prototype.conv2dDerInput=function(e,t,r){var n=new Conv2DDerInputProgram(r);return this.compileAndRun(n,[e,t])},e.prototype.conv2dDerFilter=function(e,t,r){var n=new Conv2DDerFilterProgram(r);return this.compileAndRun(n,[e,t])},e.prototype.depthwiseConv2D=function(e,t,r){var n=new DepthwiseConv2DProgram(r);return this.compileAndRun(n,[e,t])},e.prototype.depthwiseConv2DDerInput=function(e,t,r){var n=new DepthwiseConv2DDerInputProgram(r);return this.compileAndRun(n,[e,t])},e.prototype.depthwiseConv2DDerFilter=function(e,t,r){var n=new DepthwiseConv2DDerFilterProgram(r);return this.compileAndRun(n,[e,t])},e.prototype.maxPool=function(e,t){var r=new Pool2DProgram(t,\"max\",!1),n=this.makeOutputArray(r.outputShape,e.dtype);return this.compileAndRun(r,[e],n)},e.prototype.avgPool=function(e,t){var r=new Pool2DProgram(t,\"avg\",!1),n=this.makeOutputArray(r.outputShape,\"float32\");return this.compileAndRun(r,[e],n)},e.prototype.maxPoolBackprop=function(e,t,r,n){var o=new Pool2DProgram(n,\"max\",!0),a=this.compileAndRun(o,[t]),i=new MaxPool2DBackpropProgram(n),s=this.makeOutputArray(i.outputShape,t.dtype),u=this.compileAndRun(i,[e,a],s);return a.dispose(),u},e.prototype.avgPoolBackprop=function(e,t,r){var n=new AvgPool2DBackpropProgram(r),o=this.makeOutputArray(n.outputShape,t.dtype);return this.compileAndRun(n,[e],o)},e.prototype.cast=function(e,t){return castTensor(e,t,this)},e.prototype.reshape=function(e,t){return reshapeTensor(e,t)},e.prototype.resizeBilinear=function(e,t,r,n){var o=new ResizeBilinearProgram(e.shape,t,r,n);return this.compileAndRun(o,[e])},e.prototype.resizeBilinearBackprop=function(e,t,r){var n=new ResizeBilinearBackpropProgram(e,t,r);return this.compileAndRun(n,[e])},e.prototype.resizeNearestNeighbor=function(e,t,r,n){var o=new ResizeNearestNeighborProgram(e.shape,t,r,n);return this.compileAndRun(o,[e])},e.prototype.resizeNearestNeighborBackprop=function(e,t,r){var n=new ResizeNearestNeigborBackpropProgram(e,t,r);return this.compileAndRun(n,[e])},e.prototype.multinomial=function(e,t,r,n){var o=t?e:softmax(e),a=o.shape[0],i=o.shape[1],s=new MultinomialProgram(a,i,r),u=this.makeOutputArray(s.outputShape,\"int32\"),l=s.getCustomSetupFunc(n);return this.compileAndRun(s,[o],u,l)},e.prototype.oneHot=function(e,t,r,n){var o=new OneHotProgram(e.size,t,r,n);return this.compileAndRun(o,[e])},e.prototype.nonMaxSuppression=function(e,t,r,n,o){return warn(\"tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\"),nonMaxSuppressionImpl(e.dataSync(),t.dataSync(),r,n,o)},e.prototype.cropAndResize=function(e,t,r,n,o,a){var i=new CropAndResizeProgram(e.shape,t.shape,n,o,a);return this.compileAndRun(i,[e,t,r])},e.prototype.depthToSpace=function(e,t,r){assert(t>1,\"blockSize should be > 1 for depthToSpace, but was: \"+t);var n=e.shape[0],o=(\"NHWC\"===r?e.shape[1]:e.shape[2])*t,a=(\"NHWC\"===r?e.shape[2]:e.shape[3])*t,i=(\"NHWC\"===r?e.shape[3]:e.shape[1])/(t*t),s=new DepthToSpaceProgram(\"NHWC\"===r?[n,o,a,i]:[n,i,o,a],t,r);return this.compileAndRun(s,[e])},e.prototype.split=function(e,t,r){return split(e,t,r)},e.prototype.scatterND=function(e,t,r){var n=calculateShapes(t,e,r),o=n.sliceRank,a=n.numUpdates,i=n.sliceSize,s=n.strides,u=n.outputSize,l=[u/i,i],c=e.reshape([a,o]),p=t.reshape([a,i]);if(0===u)return reshapeTensor(tensor([]),r);var d=scalar(0),h=new ScatterProgram(a,o,c.rank,p.rank,s,l);return this.compileAndRun(h,[p,c,d]).reshape(r)},e.prototype.sparseToDense=function(e,t,r,n){var o=calculateShapes(t,e,r),a=o.sliceRank,i=o.numUpdates,s=o.strides,u=o.outputSize,l=new ScatterProgram(i,a,e.rank,t.rank,s,[u,1],!1);return this.compileAndRun(l,[t,e,n]).reshape(r)},e.prototype.fft=function(e){return this.fftImpl(e,!1)},e.prototype.ifft=function(e){return this.fftImpl(e,!0)},e.prototype.fftImpl=function(e,t){var r=this.texData.get(e.dataId),n=new FFTProgram(COMPLEX_FFT.REAL,e.shape,t),o=new FFTProgram(COMPLEX_FFT.IMAG,e.shape,t),a=[this.makeComplexComponentTensorHandle(e,r.complexTensors.real),this.makeComplexComponentTensorHandle(e,r.complexTensors.imag)],i=this.compileAndRun(n,a),s=this.compileAndRun(o,a),u=this.complex(i,s).as2D(e.shape[0],e.shape[1]);return i.dispose(),s.dispose(),u},e.prototype.gatherND=function(e,t){var r=t.shape,n=r[r.length-1],o=prepareAndValidate(e,t),a=o[0],i=o[1],s=o[2],u=o[3],l=t.reshape([i,n]),c=e.reshape([e.size/s,s]),p=new GatherNDProgram(n,u,[i,s]);return this.compileAndRun(p,[c,l]).reshape(a)},e.prototype.makeOutputArray=function(e,t){return Tensor.make(e,{},t)},e.prototype.makePackedTensor=function(e){var t=Tensor.make(e,{});return this.texData.get(t.dataId).isPacked=!0,t},e.prototype.unpackTensor=function(e){var t=new UnpackProgram(e.shape);return this.compileAndRun(t,[e])},e.prototype.compileAndRun=function(e,t,r,n,o){var a=this;if(void 0===o&&(o=!0),null==r&&(r=this.makeOutputArray(e.outputShape,t[0].dtype)),0===r.size)return this.texData.get(r.dataId).values=getTypedArrayFromDType(r.dtype,0),r;var i=t.map(function(t){if(\"complex64\"===t.dtype)throw new Error(\"GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.\");var r=a.texData.get(t.dataId);if(null==r.texture&&(r.isPacked||!e.usesPackedTextures)&&sizeFromShape(t.shape)<=ENV.get(\"WEBGL_SIZE_UPLOAD_UNIFORM\"))return {shape:t.shape,texData:null,isUniform:!0,uniformValues:a.readSync(t.dataId)};if(r.isPacked!==!!e.usesPackedTextures){var n=void 0,o=void 0;r.isPacked?(n=new UnpackProgram(t.shape),o=a.compileAndRun(n,[t])):(n=new PackProgram(t.shape),o=a.compileAndRun(n,[t],a.makePackedTensor(t.shape))),r=a.texData.get(o.dataId),t=o;}return a.uploadToGPU(t.dataId),{shape:t.shape,texData:r,isUniform:!1}});this.uploadToGPU(r.dataId);var s,u={shape:r.shape,texData:this.texData.get(r.dataId),isUniform:!1},l=makeShaderKey(e,i,u),c=this.getAndSaveBinary(l,function(){return compileProgram(a.gpgpu,e,i,u)}),p=null!=this.activeTimers;if(p&&(s=this.startTimer()),runProgram(c,i,u,n),ENV.get(\"WEBGL_PAGING_ENABLED\")&&o&&this.numBytesInGPU>this.NUM_BYTES_BEFORE_PAGING)for(var d=this.numBytesInGPU-this.NUM_BYTES_BEFORE_PAGING;d>0&&this.lruDataGPU.length>0;){var h=this.lruDataGPU.shift(),f=this.texData.get(h),m=f.shape,g=f.dtype;d-=this.computeBytes(m,g),this.read(h);}return p&&(s=this.endTimer(s),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(s)})),r},e.prototype.getAndSaveBinary=function(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]},e.prototype.getTextureManager=function(){return this.textureManager},e.prototype.dispose=function(){if(!this.disposed){for(var e in this.binaryCache)this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram);this.textureManager.dispose(),this.canvas.remove(),null!=this.fromPixels2DContext&&this.fromPixels2DContext.canvas.remove(),this.gpgpuCreatedLocally&&this.gpgpu.dispose(),this.disposed=!0;}},e.prototype.floatPrecision=function(){var e=this;return tidy(function(){return e.abs(scalar(1e-8)).get()>0?32:16})},e.prototype.uploadToGPU=function(e){var t=this.texData.get(e),r=t.shape,n=t.values,o=t.texture,a=t.dtype,i=t.usage,s=t.isPacked;if(null==o){var u,l=null!=this.activeTimers;l&&(u=performance.now());var c=getTextureShapeFromLogicalShape(r,s);t.texShape=c;var p=this.acquireTexture(e,c,i,s);if(t.texture=p,null!=n){if(s){var d=sizeFromShape(r.slice(0,r.length-2)),h=r.length>1?r[r.length-2]:1,f=r[r.length-1];this.gpgpu.uploadMatrixToPackedTexture(p,d,h,f,typedArrayToFloat32(n,a));}else this.gpgpu.uploadMatrixToTexture(p,c[0],c[1],typedArrayToFloat32(n,a));t.values=null,l&&(this.uploadWaitMs+=performance.now()-u);}}else{ENV.get(\"WEBGL_PAGING_ENABLED\")&&this.lruDataGPU.indexOf(e)>=0&&(this.lruDataGPU.splice(this.lruDataGPU.indexOf(e),1),this.lruDataGPU.push(e));}},e.prototype.cacheOnCPU=function(e,t){var r=this.delayedStorage,n=this.texData.get(e),o=n.texture,a=n.texShape,i=n.dtype,s=n.usage,u=n.isPacked;r&&null!=o&&(this.releaseTexture(e,o,a,s,u),n.texture=null,n.texShape=null),n.usage=TextureUsage.UPLOAD,null!=t&&(n.values=float32ToTypedArray(t,i));},e.prototype.releaseTexture=function(e,t,r,n,o){var a=this.texData.get(e),i=a.shape,s=a.dtype;if(ENV.get(\"WEBGL_PAGING_ENABLED\")){var u=this.lruDataGPU.indexOf(e);u>=0&&this.lruDataGPU.splice(u,1);}this.numBytesInGPU-=this.computeBytes(i,s),this.textureManager.releaseTexture(t,r,n,o);},e.prototype.acquireTexture=function(e,t,r,n){var o=this.texData.get(e),a=o.shape,i=o.dtype;return ENV.get(\"WEBGL_PAGING_ENABLED\")&&this.lruDataGPU.push(e),this.numBytesInGPU+=this.computeBytes(a,i),this.textureManager.acquireTexture(t,r,n)},e.prototype.computeBytes=function(e,t){return sizeFromShape(e)*bytesPerElement(t)},e}();function float32ToTypedArray(e,t){if(\"float32\"===t||\"complex64\"===t)return e;if(\"int32\"===t||\"bool\"===t){for(var r=\"int32\"===t?new Int32Array(e.length):new Uint8Array(e.length),n=0;n<r.length;++n)r[n]=Math.round(e[n]);return r}throw new Error(\"Unknown dtype \"+t)}function typedArrayToFloat32(e,t){return e instanceof Float32Array?e:new Float32Array(e)}function neg_(e){var t=convertToTensor(e,\"x\",\"neg\");return ENV.engine.runKernel(function(e){return e.neg(t)},{$x:t},function(e){return {$x:function(){return e.neg()}}})}function ceil_(e){var t=convertToTensor(e,\"x\",\"ceil\");return ENV.engine.runKernel(function(e){return e.ceil(t)},{$x:t},function(e){return {$x:function(){return zerosLike(e)}}})}function floor_(e){var t=convertToTensor(e,\"x\",\"floor\");return ENV.engine.runKernel(function(e){return e.floor(t)},{$x:t},function(e){return {$x:function(){return zerosLike(e)}}})}function sign_(e){var t=convertToTensor(e,\"x\",\"sign\");return ENV.engine.runKernel(function(e){return e.sign(t)},{$x:t},function(e){return {$x:function(){return zerosLike(e)}}})}function round_(e){var t=convertToTensor(e,\"x\",\"round\");return ENV.engine.runKernel(function(e){return e.round(t)},{$x:t},function(e){return {$x:function(){return zerosLike(e)}}})}function exp_(e){var t=convertToTensor(e,\"x\",\"exp\");return ENV.engine.runKernel(function(e,r){return r(e.exp(t))},{$x:t},function(e,t){var r=t[0];return {$x:function(){return e.mulStrict(r)}}})}function expm1_(e){var t=convertToTensor(e,\"x\",\"expm1\");return ENV.engine.runKernel(function(e){return e.expm1(t)},{$x:t},function(e){return {$x:function(){return e.mulStrict(t.exp())}}})}function log_(e){var t=convertToTensor(e,\"x\",\"log\");return ENV.engine.runKernel(function(e){return e.log(t)},{$x:t},function(e){return {$x:function(){return e.divStrict(t.toFloat())}}})}function log1p_(e){var t=convertToTensor(e,\"x\",\"log1p\");return ENV.engine.runKernel(function(e){return e.log1p(t)},{$x:t},function(e){return {$x:function(){return e.divStrict(t.add(scalar(1)))}}})}function sqrt_(e){var t=convertToTensor(e,\"x\",\"sqrt\");return ENV.engine.runKernel(function(e){return e.sqrt(t)},{$x:t},function(e){return {$x:function(){return e.divStrict(t.toFloat().sqrt().mul(scalar(2)))}}})}function rsqrt_(e){var t=convertToTensor(e,\"x\",\"rsqrt\");return ENV.engine.runKernel(function(e){return e.rsqrt(t)},{$x:t},function(e){return {$x:function(){return e.divStrict(t.pow(scalar(1.5)).mul(scalar(2))).neg()}}})}function square_(e){var t=convertToTensor(e,\"x\",\"square\");return ENV.engine.runKernel(function(e){return e.square(t)},{$x:t},function(e){return {$x:function(){return e.mulStrict(t.toFloat().mul(scalar(2)))}}})}function reciprocal_(e){var t=convertToTensor(e,\"x\",\"reciprocal\");return ENV.engine.runKernel(function(e){return e.reciprocal(t)},{$x:t},function(e){return {$x:function(){return e.divStrict(t.square().neg())}}})}function abs_(e){var t=convertToTensor(e,\"x\",\"abs\");if(\"complex64\"===t.dtype)return ENV.engine.runKernel(function(e){return e.complexAbs(t)},{$x:t});return ENV.engine.runKernel(function(e){return e.abs(t)},{$x:t},function(e){return {$x:function(){return e.mulStrict(t.toFloat().step(-1))}}})}function clipByValue_(e,t,r){var n=convertToTensor(e,\"x\",\"clipByValue\");assert(t<=r,\"Error in clip: min (\"+t+\") must be less than or equal to max (\"+r+\").\");return ENV.engine.runKernel(function(e){return e.clip(n,t,r)},{$x:n},function(e){return {$x:function(){return e.where(n.greaterEqual(scalar(t)).logicalAnd(n.lessEqual(scalar(r))),zerosLike(e))}}})}function sigmoid_(e){var t=convertToTensor(e,\"x\",\"sigmoid\");return ENV.engine.runKernel(function(e,r){return r(e.sigmoid(t))},{$x:t},function(e,t){var r=t[0];return {$x:function(){return e.mulStrict(r.mul(scalar(1).sub(r)))}}})}function logSigmoid_(e){var t=convertToTensor(e,\"x\",\"logSigmoid\");return ENV.engine.runKernel(function(e){return e.softplus(t.neg()).neg()},{$x:t},function(e){return {$x:function(){return e.mulStrict(t.neg().sigmoid())}}})}function softplus_(e){var t=convertToTensor(e,\"x\",\"softplus\");return ENV.engine.runKernel(function(e){return e.softplus(t)},{$x:t},function(e){return {$x:function(){return e.mulStrict(t.sigmoid())}}})}function sin_(e){var t=convertToTensor(e,\"x\",\"sin\");return ENV.engine.runKernel(function(e){return e.sin(t)},{$x:t},function(e){return {$x:function(){return t.toFloat().cos().mulStrict(e)}}})}function cos_(e){var t=convertToTensor(e,\"x\",\"cos\");return ENV.engine.runKernel(function(e){return e.cos(t)},{$x:t},function(e){return {$x:function(){return t.toFloat().sin().neg().mulStrict(e)}}})}function tan_(e){var t=convertToTensor(e,\"x\",\"tan\");return ENV.engine.runKernel(function(e){return e.tan(t)},{$x:t},function(e){return {$x:function(){return e.divStrict(t.cos().square())}}})}function asin_(e){var t=convertToTensor(e,\"x\",\"asin\");return ENV.engine.runKernel(function(e){return e.asin(t)},{$x:t},function(e){return {$x:function(){return e.divStrict(scalar(1).sub(t.toFloat().square()).sqrt())}}})}function acos_(e){var t=convertToTensor(e,\"x\",\"acos\");return ENV.engine.runKernel(function(e){return e.acos(t)},{$x:t},function(e){return {$x:function(){return e.divStrict(scalar(1).sub(t.toFloat().square()).sqrt()).neg()}}})}function atan_(e){var t=convertToTensor(e,\"x\",\"atan\");return ENV.engine.runKernel(function(e){return e.atan(t)},{$x:t},function(e){return {$x:function(){return e.divStrict(scalar(1).add(t.toFloat().square()))}}})}function sinh_(e){var t=convertToTensor(e,\"x\",\"sinh\");return ENV.engine.runKernel(function(e){return e.sinh(t)},{$x:t},function(e){return {$x:function(){return t.toFloat().cosh().mulStrict(e)}}})}function cosh_(e){var t=convertToTensor(e,\"x\",\"cosh\");return ENV.engine.runKernel(function(e){return e.cosh(t)},{$x:t},function(e){return {$x:function(){return t.toFloat().sinh().mulStrict(e)}}})}function tanh_(e){var t=convertToTensor(e,\"x\",\"tanh\");return ENV.engine.runKernel(function(e,r){return r(e.tanh(t))},{$x:t},function(e,t){var r=t[0];return {$x:function(){return scalar(1).sub(r.square()).mulStrict(e)}}})}function asinh_(e){var t=convertToTensor(e,\"x\",\"asinh\");return ENV.engine.runKernel(function(e){return e.asinh(t)},{$x:t},function(e){return {$x:function(){return e.divStrict(scalar(1).add(t.toFloat().square()).sqrt())}}})}function acosh_(e){var t=convertToTensor(e,\"x\",\"acosh\");return ENV.engine.runKernel(function(e){return e.acosh(t)},{$x:t},function(e){return {$x:function(){return e.divStrict(t.toFloat().square().sub(scalar(1)).sqrt())}}})}function atanh_(e){var t=convertToTensor(e,\"x\",\"atanh\");return ENV.engine.runKernel(function(e){return e.atanh(t)},{$x:t},function(e){return {$x:function(){return e.divStrict(scalar(1).sub(t.toFloat().square()))}}})}function erf_(e){var t=convertToTensor(e,\"x\",\"erf\");assert(\"int32\"===t.dtype||\"float32\"===t.dtype,\"Input dtype must be `int32` or `float32`.\"),\"int32\"===t.dtype&&(t=t.toFloat());return ENV.engine.runKernel(function(e){return e.erf(t)},{$x:t},function(e){return {$x:function(){return e.mulStrict(scalar(2/Math.sqrt(Math.PI)).mul(t.square().neg().exp()))}}})}function step_(e,t){void 0===t&&(t=0);var r=convertToTensor(e,\"x\",\"step\");return ENV.engine.runKernel(function(e){return e.step(r,t)},{$x:r},function(e){return {$x:function(){return zerosLike(e)}}})}ENV.get(\"IS_BROWSER\")&&ENV.registerBackend(\"webgl\",function(){return new MathBackendWebGL},2,setTensorTracker);var abs=op({abs_:abs_}),acos=op({acos_:acos_}),acosh=op({acosh_:acosh_}),asin=op({asin_:asin_}),asinh=op({asinh_:asinh_}),atan=op({atan_:atan_}),atanh=op({atanh_:atanh_}),ceil=op({ceil_:ceil_}),clipByValue=op({clipByValue_:clipByValue_}),cos=op({cos_:cos_}),cosh=op({cosh_:cosh_}),erf=op({erf_:erf_}),exp=op({exp_:exp_}),expm1=op({expm1_:expm1_}),floor=op({floor_:floor_}),log$1=op({log_:log_}),log1p=op({log1p_:log1p_}),logSigmoid=op({logSigmoid_:logSigmoid_}),neg=op({neg_:neg_}),reciprocal=op({reciprocal_:reciprocal_}),round=op({round_:round_}),rsqrt=op({rsqrt_:rsqrt_}),sigmoid=op({sigmoid_:sigmoid_}),sign=op({sign_:sign_}),sin=op({sin_:sin_}),sinh=op({sinh_:sinh_}),softplus=op({softplus_:softplus_}),sqrt=op({sqrt_:sqrt_}),square=op({square_:square_}),step=op({step_:step_}),tan=op({tan_:tan_}),tanh$1=op({tanh_:tanh_});function batchNormalization2d_(e,t,r,n,o,a){void 0===n&&(n=.001);var i,s,u=convertToTensor(e,\"x\",\"batchNormalization\"),l=convertToTensor(t,\"mean\",\"batchNormalization\"),c=convertToTensor(r,\"variance\",\"batchNormalization\");return null!=o&&(i=convertToTensor(o,\"scale\",\"batchNormalization\")),null!=a&&(s=convertToTensor(a,\"offset\",\"batchNormalization\")),assert(2===u.rank,\"Error in batchNormalization3D: x must be rank 3 but got rank \"+u.rank+\".\"),assert(2===l.rank||1===l.rank,\"Error in batchNormalization2D: mean must be rank 2 or rank 1 but got rank \"+l.rank+\".\"),assert(2===c.rank||1===c.rank,\"Error in batchNormalization2D: variance must be rank 2 or rank 1 but got rank \"+c.rank+\".\"),null!=i&&assert(2===i.rank||1===i.rank,\"Error in batchNormalization2D: scale must be rank 2 or rank 1 but got rank \"+i.rank+\".\"),null!=s&&assert(2===s.rank||1===s.rank,\"Error in batchNormalization2D: offset must be rank 2 or rank 1 but got rank \"+s.rank+\".\"),batchNormalization(u,l,c,n,i,s)}function batchNormalization3d_(e,t,r,n,o,a){void 0===n&&(n=.001);var i,s,u=convertToTensor(e,\"x\",\"batchNormalization\"),l=convertToTensor(t,\"mean\",\"batchNormalization\"),c=convertToTensor(r,\"variance\",\"batchNormalization\");return null!=o&&(i=convertToTensor(o,\"scale\",\"batchNormalization\")),null!=a&&(s=convertToTensor(a,\"offset\",\"batchNormalization\")),assert(3===u.rank,\"Error in batchNormalization3D: x must be rank 3 but got rank \"+u.rank+\".\"),assert(3===l.rank||1===l.rank,\"Error in batchNormalization3D: mean must be rank 3 or rank 1 but got rank \"+l.rank+\".\"),assert(3===c.rank||1===c.rank,\"Error in batchNormalization3D: variance must be rank 3 or rank 1 but got rank \"+c.rank+\".\"),null!=i&&assert(3===i.rank||1===i.rank,\"Error in batchNormalization3D: scale must be rank 3 or rank 1 but got rank \"+i.rank+\".\"),null!=s&&assert(3===s.rank||1===s.rank,\"Error in batchNormalization3D: offset must be rank 3 or rank 1 but got rank \"+s.rank+\".\"),batchNormalization(u,l,c,n,i,s)}function batchNormalization4d_(e,t,r,n,o,a){void 0===n&&(n=.001);var i,s,u=convertToTensor(e,\"x\",\"batchNormalization\"),l=convertToTensor(t,\"mean\",\"batchNormalization\"),c=convertToTensor(r,\"variance\",\"batchNormalization\");return null!=o&&(i=convertToTensor(o,\"scale\",\"batchNormalization\")),null!=a&&(s=convertToTensor(a,\"offset\",\"batchNormalization\")),assert(4===u.rank,\"Error in batchNormalization4D: x must be rank 4 but got rank \"+u.rank+\".\"),assert(4===l.rank||1===l.rank,\"Error in batchNormalization4D: mean must be rank 4 or rank 1 but got rank \"+l.rank+\".\"),assert(4===c.rank||1===c.rank,\"Error in batchNormalization4D: variance must be rank 4 or rank 1 but got rank \"+c.rank+\".\"),null!=i&&assert(4===i.rank||1===i.rank,\"Error in batchNormalization4D: scale must be rank 4 or rank 1 but got rank \"+i.rank+\".\"),null!=s&&assert(4===s.rank||1===s.rank,\"Error in batchNormalization4D: offset must be rank 4 or rank 1 but got rank \"+s.rank+\".\"),batchNormalization(u,l,c,n,i,s)}function batchNormalization_(e,t,r,n,o,a){void 0===n&&(n=.001);var i,s,u,l=convertToTensor(e,\"x\",\"batchNormalization\"),c=convertToTensor(t,\"mean\",\"batchNormalization\"),p=convertToTensor(r,\"variance\",\"batchNormalization\");null!=o&&(i=convertToTensor(o,\"scale\",\"batchNormalization\")),null!=a&&(s=convertToTensor(a,\"offset\",\"batchNormalization\")),assert(c.rank===p.rank,\"Batch normalization gradient requires mean and variance to have equal ranks.\"),assert(null==s||c.rank===s.rank,\"Batch normalization gradient requires mean and offset to have equal ranks.\"),assert(null==i||c.rank===i.rank,\"Batch normalization gradient requires mean and scale to have equal ranks.\"),u=0===l.rank||1===l.rank?l.as4D(1,1,1,l.size):2===l.rank?l.as4D(1,1,l.shape[0],l.shape[1]):3===l.rank?l.as4D(1,l.shape[0],l.shape[1],l.shape[2]):l;return ENV.engine.runKernel(function(e){return e.batchNormalization(u,batchnormReshape4D(c),batchnormReshape4D(p),n,batchnormReshape4D(i),batchnormReshape4D(s))},{$x:l,$mean:c,$variance:p,$scale:i,$offset:s},function(e){var t=null==i?scalar(1):i,r=getReductionAxes(c.shape,u.shape),o=[];if(1===c.rank){for(var a=0;a<u.shape.length-1;++a)o.push(u.shape[a]);o.push(1);}var s=l.sub(c),d=e.mul(t),h=rsqrt(p.add(scalar(n))),f=h.mul(h).mul(h).mul(scalar(-.5));return {$x:function(){return 1===c.rank?e.mul(tile(h.as4D(1,1,1,c.shape[0]),o)).mul(t).reshape(l.shape):e.mul(h).mul(t).reshape(l.shape)},$mean:function(){var e=h.mul(scalar(-1)).mul(d);return 1===c.rank&&(e=e.sum(r)),e.reshape(c.shape)},$variance:function(){var e=f.mul(s).mul(d);return 1===c.rank&&(e=e.sum(r)),e.reshape(c.shape)},$scale:function(){var t=s.mul(h),n=e.mul(t);return 1===c.rank&&(n=n.sum(r)),n.reshape(c.shape)},$offset:function(){var t=e;return 1===c.rank&&(t=t.sum(r)),t.reshape(c.shape)}}}).reshape(l.shape)}function batchnormReshape4D(e){return null==e?null:0===e.rank?e.as1D():1===e.rank?e:2===e.rank?e.as4D(1,1,e.shape[0],e.shape[1]):3===e.rank?e.as4D(1,e.shape[0],e.shape[1],e.shape[2]):e}var batchNormalization2d=op({batchNormalization2d_:batchNormalization2d_}),batchNormalization3d=op({batchNormalization3d_:batchNormalization3d_}),batchNormalization4d=op({batchNormalization4d_:batchNormalization4d_}),batchNormalization=op({batchNormalization_:batchNormalization_});function computePool2DInfo(e,t,r,n,o,a,i){void 0===i&&(i=\"channelsLast\");var s,u=parseTupleParam(t),l=u[0],c=u[1];if(\"channelsLast\"===i)s=[l,c,e[3],e[3]];else{if(\"channelsFirst\"!==i)throw new Error(\"Unknown dataFormat \"+i);s=[l,c,e[1],e[1]];}return computeConv2DInfo(e,s,r,n,o,a,!1,i)}function computeConv2DInfo(e,t,r,n,o,a,i,s){void 0===i&&(i=!1),void 0===s&&(s=\"channelsLast\");var u=[-1,-1,-1,-1],l=u[0],c=u[1],p=u[2],d=u[3];if(\"channelsLast\"===s)l=e[0],c=e[1],p=e[2],d=e[3];else{if(\"channelsFirst\"!==s)throw new Error(\"Unknown dataFormat \"+s);l=e[0],d=e[1],c=e[2],p=e[3];}var h,f=t[0],m=t[1],g=t[3],v=parseTupleParam(r),y=v[0],x=v[1],T=parseTupleParam(n),E=T[0],w=T[1],S=getEffectiveFilterSize(f,E),b=getEffectiveFilterSize(m,w),C=getPadAndOutInfo(o,c,p,y,x,S,b,a),_=C.padInfo,A=C.outHeight,N=C.outWidth,R=i?g*d:g;return \"channelsFirst\"===s?h=[l,R,A,N]:\"channelsLast\"===s&&(h=[l,A,N,R]),{batchSize:l,dataFormat:s,inHeight:c,inWidth:p,inChannels:d,outHeight:A,outWidth:N,outChannels:R,padInfo:_,strideHeight:y,strideWidth:x,filterHeight:f,filterWidth:m,effectiveFilterHeight:S,effectiveFilterWidth:b,dilationHeight:E,dilationWidth:w,inShape:e,outShape:h,filterShape:t}}function computeOutputShape3D(e,t,r,n,o,a){null==o&&(o=computeDefaultPad(e,t,n));var i=e[0],s=e[1],u=conditionalRound((i-t+2*o)/n+1,a);assert(isInt(u),\"The output # of rows (\"+u+\") must be an integer. Change the stride and/or zero pad parameters\");var l=conditionalRound((s-t+2*o)/n+1,a);return assert(isInt(l),\"The output # of columns (\"+l+\") must be an integer. Change the stride and/or zero pad parameters\"),[u,l,r]}function computeDefaultPad(e,t,r,n){void 0===n&&(n=1);var o=getEffectiveFilterSize(t,n);return Math.floor((e[0]*(r-1)-r+o)/2)}function parseTupleParam(e){return \"number\"==typeof e?[e,e]:e}function getEffectiveFilterSize(e,t){return t<=1?e:e+(e-1)*(t-1)}function getPadAndOutInfo(e,t,r,n,o,a,i,s){var u,l,c;if(\"number\"==typeof e){u={top:e,bottom:e,left:e,right:e,type:0===e?\"VALID\":\"NUMBER\"};var p=computeOutputShape3D([t,r,1],a,1,n,e,s);l=p[0],c=p[1];}else if(\"same\"===e){var d=((l=Math.ceil(t/n))-1)*n+a-t,h=((c=Math.ceil(r/o))-1)*o+i-r,f=Math.floor(d/2),m=d-f,g=Math.floor(h/2);u={top:f,bottom:m,left:g,right:h-g,type:\"SAME\"};}else{if(\"valid\"!==e)throw Error(\"Unknown padding parameter: \"+e);u={top:0,bottom:0,left:0,right:0,type:\"VALID\"},l=Math.ceil((t-a+1)/n),c=Math.ceil((r-i+1)/o);}return {padInfo:u,outHeight:l,outWidth:c}}function conditionalRound(e,t){if(!t)return e;switch(t){case\"round\":return Math.round(e);case\"ceil\":return Math.ceil(e);case\"floor\":return Math.floor(e);default:throw new Error(\"Unknown roundingMode \"+t)}}function tupleValuesAreOne(e){var t=parseTupleParam(e),r=t[0],n=t[1];return 1===r&&1===n}function eitherStridesOrDilationsAreOne(e,t){return tupleValuesAreOne(e)||tupleValuesAreOne(t)}function matMul_(e,t,r,n){void 0===r&&(r=!1),void 0===n&&(n=!1);var o=convertToTensor(e,\"a\",\"matMul\"),a=convertToTensor(t,\"b\",\"matMul\"),i=r?o.shape[o.rank-2]:o.shape[o.rank-1],s=n?a.shape[a.rank-1]:a.shape[a.rank-2],u=r?o.shape[o.rank-1]:o.shape[o.rank-2],l=n?a.shape[a.rank-2]:a.shape[a.rank-1],c=o.shape.slice(0,-2),p=a.shape.slice(0,-2),d=sizeFromShape(c),h=sizeFromShape(p);assert(o.rank>=2&&a.rank>=2&&o.rank===a.rank,\"Error in matMul: inputs must have the same rank of at least 2, got ranks \"+o.rank+\" and \"+a.rank+\".\"),assert(arraysEqual(c,p),\"Error in matMul: outer dimensions (\"+c+\") and (\"+p+\") of Tensors with shapes \"+o.shape+\" and \"+a.shape+\" must match.\"),assert(i===s,\"Error in matMul: inner shapes (\"+i+\") and (\"+s+\") of Tensors with shapes \"+o.shape+\" and \"+a.shape+\" and transposeA=\"+r+\" and transposeB=\"+n+\" must match.\");var f=o.shape.slice(0,-2).concat([u,l]),m=r?o.as3D(d,i,u):o.as3D(d,u,i),g=n?a.as3D(h,l,s):a.as3D(h,s,l);return ENV.engine.runKernel(function(e){return e.batchMatMul(m,g,r,n)},{$a:m,$b:g},function(e){return r||n?!r&&n?{$a:function(){return e.matMul(g.toFloat(),!1,!1)},$b:function(){return e.matMul(m.toFloat(),!0,!1)}}:r&&!n?{$a:function(){return g.toFloat().matMul(e,!1,!0)},$b:function(){return m.toFloat().matMul(e,!1,!1)}}:{$a:function(){return g.toFloat().matMul(e,!0,!0)},$b:function(){return e.matMul(m.toFloat(),!0,!0)}}:{$a:function(){return e.matMul(g.toFloat(),!1,!0)},$b:function(){return m.toFloat().matMul(e,!0,!1)}}}).reshape(f)}function outerProduct_(e,t){var r=convertToTensor(e,\"v1\",\"outerProduct\"),n=convertToTensor(t,\"v2\",\"outerProduct\");return assert(1===r.rank&&1===n.rank,\"Error in outerProduct: inputs must be rank 1, but got ranks \"+r.rank+\" and \"+n.rank+\".\"),r.as2D(-1,1).matMul(n.as2D(1,-1))}function dot_(e,t){var r=convertToTensor(e,\"t1\",\"dot\"),n=convertToTensor(t,\"t2\",\"dot\");assert(!(1!==r.rank&&2!==r.rank||1!==n.rank&&2!==n.rank),\"Error in dot: inputs must all be rank 1 or 2, but got ranks \"+r.rank+\" and \"+n.rank+\".\");var o=1===r.rank?r.size:r.shape[1],a=1===n.rank?n.size:n.shape[0];return assert(o===a,\"Error in dot: inner dimensions of inputs must match, but got \"+o+\" and \"+a+\".\"),1===r.rank&&1===n.rank?r.as2D(1,-1).matMul(n.as2D(-1,1)).asScalar():1===r.rank&&2===n.rank?r.as2D(1,-1).matMul(n.as2D(n.shape[0],n.shape[1])).as1D():2===r.rank&&1===n.rank?r.matMul(n.as2D(-1,1)).as1D():r.matMul(n.as2D(n.shape[0],n.shape[1]))}var matMul=op({matMul_:matMul_}),dot=op({dot_:dot_}),outerProduct=op({outerProduct_:outerProduct_});function conv1d_(e,t,r,n,o,a,i){void 0===o&&(o=\"NWC\"),void 0===a&&(a=1);var s=convertToTensor(e,\"x\",\"conv1d\"),u=convertToTensor(t,\"filter\",\"conv1d\"),l=s,c=!1;2===s.rank&&(c=!0,l=s.as3D(1,s.shape[0],s.shape[1])),assert(3===l.rank,\"Error in conv1d: input must be rank 3, but got rank \"+l.rank+\".\"),assert(3===u.rank,\"Error in conv1d: filter must be rank 3, but got rank \"+u.rank+\".\"),null!=i&&assert(isInt(n),\"Error in conv1d: pad must be an integer when using, dimRoundingMode \"+i+\" but got pad \"+n+\".\"),assert(l.shape[2]===u.shape[1],\"Error in conv1d: depth of input (\"+l.shape[2]+\") must match input depth for filter \"+u.shape[1]+\".\"),assert(eitherStridesOrDilationsAreOne(r,a),\"Error in conv1D: Either stride or dilation must be 1. Got stride \"+r+\" and dilation '\"+a+\"'\"),assert(\"NWC\"===o,\"Error in conv1d: got dataFormat of \"+o+\" but only NWC is currently supported.\");var p=u.as4D(1,u.shape[0],u.shape[1],u.shape[2]),d=l.as4D(l.shape[0],1,l.shape[1],l.shape[2]),h=conv2d(d,p,[1,r],n,\"NHWC\",[1,a],i);return c?h.as2D(h.shape[2],h.shape[3]):h.as3D(h.shape[0],h.shape[2],h.shape[3])}function conv2d_(e,t,r,n,o,a,i){void 0===o&&(o=\"NHWC\"),void 0===a&&(a=[1,1]);var s=convertToTensor(e,\"x\",\"conv2d\"),u=convertToTensor(t,\"filter\",\"conv2d\"),l=s,c=!1;3===s.rank&&(c=!0,l=s.as4D(1,s.shape[0],s.shape[1],s.shape[2])),assert(4===l.rank,\"Error in conv2d: input must be rank 4, but got rank \"+l.rank+\".\"),assert(4===u.rank,\"Error in conv2d: filter must be rank 4, but got rank \"+u.rank+\".\"),null!=i&&assert(isInt(n),\"Error in conv2d: pad must be an integer when using, dimRoundingMode \"+i+\" but got pad \"+n+\".\"),assert(l.shape[3]===u.shape[2],\"Error in conv2d: depth of input (\"+l.shape[3]+\") must match input depth for filter \"+u.shape[2]+\".\"),assert(eitherStridesOrDilationsAreOne(r,a),\"Error in conv2D: Either strides or dilations must be 1. Got strides \"+r+\" and dilations '\"+a+\"'\"),assert(\"NHWC\"===o,\"Error in conv2d: got dataFormat of \"+o+\" but only NHWC is currently supported.\");var p,d=computeConv2DInfo(l.shape,u.shape,r,a,n,i);if(1!==d.filterHeight||1!==d.filterWidth||1!==d.dilationHeight||1!==d.dilationWidth||1!==d.strideHeight||1!==d.strideWidth||\"SAME\"!==d.padInfo.type&&\"VALID\"!==d.padInfo.type){p=ENV.engine.runKernel(function(e){return e.conv2d(l,u,d)},{x:l,$filter:u},function(e){return assert(tupleValuesAreOne(a),\"Error in gradient of conv2D: dilation rates greater than 1 are notyet supported in gradients. Got dilations '\"+a+\"'\"),{x:function(){return conv2dDerInput_(l.shape,e,u,r,n)},$filter:function(){return conv2dDerFilter_(l,e,u.shape,r,n)}}});}else{var h=l.reshape([-1,d.inChannels]),f=u.reshape([d.inChannels,d.outChannels]);p=matMul(h,f).reshape(d.outShape);}return c?p.as3D(p.shape[1],p.shape[2],p.shape[3]):p}function conv2dDerInput_(e,t,r,n,o,a){assert(e.length===t.rank,\"Length of inShape (\"+e.length+\") and rank of dy (\"+t.rank+\") must match\");var i=e,s=t,u=!1;3===t.rank&&(u=!0,s=t.as4D(1,t.shape[0],t.shape[1],t.shape[2]),i=[1,e[0],e[1],e[2]]);var l=i[3],c=s.shape[3];assert(4===i.length,\"Error in conv2dDerInput: inShape must be length 4, but got length \"+i.length+\".\"),assert(4===s.rank,\"Error in conv2dDerInput: dy must be rank 4, but got rank \"+s.rank),assert(4===r.rank,\"Error in conv2dDerInput: filter must be rank 4, but got rank \"+r.rank),assert(l===r.shape[2],\"Error in conv2dDerInput: depth of input (\"+l+\") must match input depth for filter \"+r.shape[2]+\".\"),assert(c===r.shape[3],\"Error in conv2dDerInput: depth of output (\"+c+\") must match output depth for filter \"+r.shape[3]+\".\"),null!=a&&assert(isInt(o),\"Error in conv2dDerInput: pad must be an integer when using, dimRoundingMode \"+a+\" but got pad \"+o+\".\");var p=computeConv2DInfo(i,r.shape,n,1,o,a),d=ENV.engine.runKernel(function(e){return e.conv2dDerInput(s,r,p)},{dy4D:s,filter:r},function(e){return {dy4D:function(){return conv2d(e,r,n,o,\"NHWC\",1,a)},filter:function(){return conv2dDerFilter(e,s,r.shape,n,o,a)}}});return u?d.as3D(d.shape[1],d.shape[2],d.shape[3]):d}function conv2dDerFilter_(e,t,r,n,o,a){var i=e;3===e.rank&&(i=e.as4D(1,e.shape[0],e.shape[1],e.shape[2]));var s=t;3===s.rank&&(s=t.as4D(1,t.shape[0],t.shape[1],t.shape[2])),assert(4===i.rank,\"Error in conv2dDerFilter: input must be rank 4, but got shape \"+i.shape+\".\"),assert(4===s.rank,\"Error in conv2dDerFilter: dy must be rank 4, but got shape \"+s.shape+\".\"),assert(4===r.length,\"Error in conv2dDerFilter: filterShape must be length 4, but got \"+r+\".\"),assert(i.shape[3]===r[2],\"Error in conv2dDerFilter: depth of input \"+i.shape[3]+\") must match input depth in filter (\"+r[2]+\".\"),assert(s.shape[3]===r[3],\"Error in conv2dDerFilter: depth of dy (\"+s.shape[3]+\") must match output depth for filter (\"+r[3]+\").\"),null!=a&&assert(isInt(o),\"Error in conv2dDerFilter: pad must be an integer when using, dimRoundingMode \"+a+\" but got pad \"+o+\".\");var u=computeConv2DInfo(i.shape,r,n,1,o,a);return ENV.engine.runKernel(function(e){return e.conv2dDerFilter(i,s,u)},{x4D:i,dy4D:s})}function conv2dTranspose_(e,t,r,n,o,a){return conv2dDerInput_(r,convertToTensor(e,\"x\",\"conv2dTranspose\"),convertToTensor(t,\"filter\",\"conv2dTranspose\"),n,o,a)}function depthwiseConv2d_(e,t,r,n,o,a,i){void 0===o&&(o=\"NHWC\"),void 0===a&&(a=[1,1]);var s=convertToTensor(e,\"x\",\"depthwiseConv2d\"),u=convertToTensor(t,\"filter\",\"depthwiseConv2d\"),l=s,c=!1;3===s.rank&&(c=!0,l=s.as4D(1,s.shape[0],s.shape[1],s.shape[2])),assert(4===l.rank,\"Error in depthwiseConv2d: input must be rank 4, but got rank \"+l.rank+\".\"),assert(4===u.rank,\"Error in depthwiseConv2d: filter must be rank 4, but got rank \"+u.rank+\".\"),assert(l.shape[3]===u.shape[2],\"Error in depthwiseConv2d: number of input channels (\"+l.shape[3]+\") must match the inChannels dimension in filter \"+u.shape[2]+\".\"),null==a&&(a=[1,1]),assert(eitherStridesOrDilationsAreOne(r,a),\"Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides \"+r+\" and dilations '\"+a+\"'\"),null!=i&&assert(isInt(n),\"Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode \"+i+\" but got pad \"+n+\".\");var p=computeConv2DInfo(l.shape,u.shape,r,a,n,i,!0),d=ENV.engine.runKernel(function(e){return e.depthwiseConv2D(l,u,p)},{x:l,$filter:u},function(e){return assert(tupleValuesAreOne(a),\"Error in gradient of depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '\"+a+\"'\"),{x:function(){return depthwiseConv2dDerInput(l.shape,e,u,p)},$filter:function(){return depthwiseConv2dDerFilter(l,e,u.shape,p)}}});return c?d.as3D(d.shape[1],d.shape[2],d.shape[3]):d}function separableConv2d_(e,t,r,n,o,a,i){void 0===a&&(a=[1,1]),void 0===i&&(i=\"NHWC\");var s=convertToTensor(e,\"x\",\"separableConv2d\"),u=convertToTensor(t,\"depthwiseFilter\",\"separableConv2d\"),l=convertToTensor(r,\"pointwiseFilter\",\"separableConv2d\"),c=s,p=!1;if(3===s.rank&&(p=!0,c=s.as4D(1,s.shape[0],s.shape[1],s.shape[2])),\"NCHW\"===i)throw new Error(\"separableConv2d currently does not support dataFormat NCHW; only NHWC is supported\");assert(4===c.rank,\"Error in separableConv2d: input must be rank 4, but got rank \"+c.rank+\".\"),assert(4===u.rank,\"Error in separableConv2d: depthwise filter must be rank 4, but got rank \"+u.rank+\".\"),assert(4===l.rank,\"Error in separableConv2d: pointwise filter must be rank 4, but got rank \"+u.rank+\".\"),assert(1===l.shape[0],\"Error in separableConv2d: the first dimension of pointwise filter  must be 1, but got \"+l.shape[0]+\".\"),assert(1===l.shape[1],\"Error in separableConv2d: the second dimension of pointwise filter  must be 1, but got \"+l.shape[1]+\".\");var d=u.shape[2],h=u.shape[3];assert(l.shape[2]===d*h,\"Error in separableConv2d: the third dimension of pointwise filter must be \"+d*h+\", but got \"+l.shape[2]+\".\");var f=depthwiseConv2d(c,u,n,o,i,a),m=conv2d(f,l,1,\"valid\",i);return p?m.as3D(m.shape[1],m.shape[2],m.shape[3]):m}function depthwiseConv2dDerInput(e,t,r,n){var o=t,a=!1;3===t.rank&&(a=!0,o=t.as4D(1,t.shape[0],t.shape[1],t.shape[2]));var i=ENV.engine.runKernel(function(e){return e.depthwiseConv2DDerInput(o,r,n)},{dy4D:o});return a?i.as3D(i.shape[1],i.shape[2],i.shape[3]):i}function depthwiseConv2dDerFilter(e,t,r,n){var o=e;3===e.rank&&(o=e.as4D(1,e.shape[0],e.shape[1],e.shape[2]));var a=t;return 3===a.rank&&(a=t.as4D(1,t.shape[0],t.shape[1],t.shape[2])),ENV.engine.runKernel(function(e){return e.depthwiseConv2DDerFilter(o,a,n)},{x4D:o,dy4D:a})}var conv1d=op({conv1d_:conv1d_}),conv2d=op({conv2d_:conv2d_}),conv2dDerFilter=op({conv2dDerFilter_:conv2dDerFilter_}),depthwiseConv2d=op({depthwiseConv2d_:depthwiseConv2d_}),separableConv2d=op({separableConv2d_:separableConv2d_}),conv2dTranspose=op({conv2dTranspose_:conv2dTranspose_});function reverse1d_(e){var t=convertToTensor(e,\"x\",\"reverse\");return assert(1===t.rank,\"Error in reverse1D: x must be rank 1 but got\\n             rank \"+t.rank+\".\"),reverse(t,0)}function reverse2d_(e,t){var r=convertToTensor(e,\"x\",\"reverse\");return assert(2===r.rank,\"Error in reverse2D: x must be rank 2 but got\\n             rank \"+r.rank+\".\"),reverse(r,t)}function reverse3d_(e,t){var r=convertToTensor(e,\"x\",\"reverse\");return assert(3===r.rank,\"Error in reverse3D: x must be rank 3 but got\\n             rank \"+r.rank+\".\"),reverse(r,t)}function reverse4d_(e,t){var r=convertToTensor(e,\"x\",\"reverse\");return assert(4===r.rank,\"Error in reverse4D: x must be rank 4 but got\\n             rank \"+r.rank+\".\"),reverse(r,t)}function reverse_(e,t){var r=convertToTensor(e,\"x\",\"reverse\");if(0===r.rank)return r.clone();var n=parseAxisParam(t,r.shape);return ENV.engine.runKernel(function(e){return e.reverse(r,n)},{$x:r},function(e){return {$x:function(){return e.reverse(n)}}}).reshapeAs(r)}var reverse=op({reverse_:reverse_}),reverse1d=op({reverse1d_:reverse1d_}),reverse2d=op({reverse2d_:reverse2d_}),reverse3d=op({reverse3d_:reverse3d_}),reverse4d=op({reverse4d_:reverse4d_});function maxPoolImpl_(e,t,r,n,o,a){var i=convertToTensor(e,\"x\",\"maxPool\"),s=i,u=!1;3===i.rank&&(u=!0,s=i.as4D(1,i.shape[0],i.shape[1],i.shape[2])),null==n&&(n=[1,1]),assert(4===s.rank,\"Error in maxPool: input must be rank 4 but got rank \"+s.rank+\".\"),assert(eitherStridesOrDilationsAreOne(r,n),\"Error in maxPool: Either strides or dilations must be 1. Got strides \"+r+\" and dilations '\"+n+\"'\"),null!=a&&assert(isInt(o),\"Error in maxPool: pad must be an integer when using, dimRoundingMode \"+a+\" but got pad \"+o+\".\");var l=computePool2DInfo(s.shape,t,r,n,o,a),c=ENV.engine.runKernel(function(e,t){return t(e.maxPool(s,l))},{x:s},function(e,a){var i=a[0];return {x:function(){return maxPoolBackprop(e,s,i,t,r,n,o)}}});return u?c.as3D(c.shape[1],c.shape[2],c.shape[3]):c}function maxPool_(e,t,r,n,o){return maxPoolImpl_(e,t,r,1,n,o)}function avgPoolImpl_(e,t,r,n,o,a){var i=convertToTensor(e,\"x\",\"avgPool\");assert(\"float32\"===i.dtype,\"The input dtype to avgPool must be float32\"),null==n&&(n=[1,1]),assert(eitherStridesOrDilationsAreOne(r,n),\"Error in avgPool: Either strides or dilations must be 1. Got strides \"+r+\" and dilations '\"+n+\"'\");var s=i,u=!1;3===i.rank&&(u=!0,s=i.as4D(1,i.shape[0],i.shape[1],i.shape[2])),assert(4===s.rank,\"Error in avgPool: x must be rank 4 but got rank \"+s.rank+\".\"),null!=a&&assert(isInt(o),\"Error in avgPool: pad must be an integer when using, dimRoundingMode \"+a+\" but got pad \"+o+\".\");var l=computePool2DInfo(s.shape,t,r,n,o),c=ENV.engine.runKernel(function(e){return e.avgPool(s,l)},{x:s},function(e){return {x:function(){return avgPoolBackprop(e,s,t,r,n,o)}}});return c=c.cast(i.dtype),u?c.as3D(c.shape[1],c.shape[2],c.shape[3]):c}function avgPool_(e,t,r,n,o){return avgPoolImpl_(e,t,r,1,n,o)}function pool_(e,t,r,n,o,a){null==o&&(o=[1,1]),null==a&&(a=1),0===n&&(n=\"valid\");var i=convertToTensor(e,\"x\",\"maxPool\"),s=i,u=!1;3===i.rank&&(u=!0,s=i.as4D(1,i.shape[0],i.shape[1],i.shape[2])),assert(eitherStridesOrDilationsAreOne(a,o),\"Error in pool: Either strides or dilations must be 1. Got strides \"+a+\" and dilations '\"+o+\"'\");var l,c=computePool2DInfo(s.shape,t,a,o,n),p=[c.dilationHeight,c.dilationWidth];l=\"same\"===n?withSpaceToBatchBasePaddings([c.filterHeight,c.filterWidth],p):[[0,0],[0,0]];var d=1===p[0]&&1===p[1],h=requiredSpaceToBatchPaddings([c.inHeight,c.inWidth],p,l),f=h[0],m=h[1],g=d?n:\"valid\",v=d?s:spaceToBatchND(s,p,f),y=(\"avg\"===r?function(){return avgPoolImpl_(v,t,a,1,g)}:function(){return maxPoolImpl_(v,t,a,1,g)})(),x=d?y:batchToSpaceND(y,p,m);return u?x.as3D(x.shape[1],x.shape[2],x.shape[3]):x}function maxPoolBackprop(e,t,r,n,o,a,i,s){var u=convertToTensor(e,\"dy\",\"maxPoolBackprop\"),l=convertToTensor(t,\"input\",\"maxPoolBackprop\"),c=convertToTensor(r,\"output\",\"maxPoolBackprop\");assert(l.rank===u.rank,\"Rank of input (\"+l.rank+\") does not match rank of dy (\"+u.rank+\")\"),null==a&&(a=[1,1]),assert(eitherStridesOrDilationsAreOne(o,a),\"Error in maxPoolBackProp: Either strides or dilations must be 1. Got strides \"+o+\" and dilations '\"+a+\"'\"),assert(4===u.rank,\"Error in maxPoolBackprop: dy must be rank 4 but got rank \"+u.rank+\".\"),assert(4===l.rank,\"Error in maxPoolBackprop: input must be rank 4 but got rank \"+l.rank+\".\"),null!=s&&assert(isInt(i),\"Error in maxPoolBackprop: pad must be an integer when using, dimRoundingMode \"+s+\" but got pad \"+i+\".\");var p=computePool2DInfo(l.shape,n,o,a,i,s);return ENV.engine.runKernel(function(e){return e.maxPoolBackprop(u,l,c,p)},{$dy:u,$input:l})}function avgPoolBackprop(e,t,r,n,o,a){var i=convertToTensor(e,\"dy\",\"avgPoolBackprop\"),s=convertToTensor(t,\"input\",\"avgPoolBackprop\");assert(s.rank===i.rank,\"Rank of input (\"+s.rank+\") does not match rank of dy (\"+i.rank+\")\"),null==o&&(o=[1,1]),assert(eitherStridesOrDilationsAreOne(n,o),\"Error in avgPoolBackprop: Either strides or dilations must be 1. Got strides \"+n+\" and dilations '\"+o+\"'\");var u=s,l=i,c=!1;3===s.rank&&(c=!0,u=s.as4D(1,s.shape[0],s.shape[1],s.shape[2]),l=i.as4D(1,i.shape[0],i.shape[1],i.shape[2])),assert(4===l.rank,\"Error in avgPoolBackprop: dy must be rank 4 but got rank \"+l.rank+\".\"),assert(4===u.rank,\"Error in avgPoolBackprop: input must be rank 4 but got rank \"+u.rank+\".\");var p=computePool2DInfo(u.shape,r,n,o,a),d=ENV.engine.runKernel(function(e){return e.avgPoolBackprop(l,u,p)},{dy4D:l,input4D:u});return c?d.as3D(d.shape[1],d.shape[2],d.shape[3]):d}function requiredSpaceToBatchPaddings(e,t,r){var n=r.map(function(e){return e[0]}),o=r.map(function(e){return e[1]}),a=e.concat(n,o),i=t.map(function(e,t){return (e-a[t]%e)%e}),s=o.map(function(e,t){return e+i[t]});return [t.map(function(e,t){return [n[t],s[t]]}),t.map(function(e,t){return [0,i[t]]})]}function withSpaceToBatchBasePaddings(e,t){var r=e.map(function(e,r){return e+(e-1)*(t[r]-1)}).map(function(e){return e-1}),n=r.map(function(e){return Math.floor(e/2)}),o=r.map(function(e,t){return e-n[t]});return r.map(function(e,t){return [n[t],o[t]]})}var maxPool=op({maxPool_:maxPool_}),avgPool=op({avgPool_:avgPool_}),pool=op({pool_:pool_});function slice1d_(e,t,r){var n=convertToTensor(e,\"x\",\"slice1d\");return assert(1===n.rank,\"slice1d expects a rank-1 tensor, but got a rank-\"+n.rank+\" tensor\"),slice(n,[t],[r])}function slice2d_(e,t,r){var n=convertToTensor(e,\"x\",\"slice2d\");return assert(2===n.rank,\"slice2d expects a rank-2 tensor, but got a rank-\"+n.rank+\" tensor\"),slice(n,t,r)}function slice3d_(e,t,r){var n=convertToTensor(e,\"x\",\"slice3d\");return assert(3===n.rank,\"slice3d expects a rank-3 tensor, but got a rank-\"+n.rank+\" tensor\"),slice(n,t,r)}function slice4d_(e,t,r){var n=convertToTensor(e,\"x\",\"slice4d\");return assert(4===n.rank,\"slice4d expects a rank-4 tensor, but got a rank-\"+n.rank+\" tensor\"),slice(n,t,r)}function slice_(e,t,r){var n,o,a=convertToTensor(e,\"x\",\"slice\");if(0===a.rank)throw new Error(\"Slicing scalar is not possible\");n=\"number\"==typeof t?[t].concat(new Array(a.rank-1).fill(0)):t.length<a.rank?t.concat(new Array(a.rank-t.length).fill(0)):t.slice(),o=(o=null==r?new Array(a.rank).fill(-1):\"number\"==typeof r?[r].concat(new Array(a.rank-1).fill(-1)):r.length<a.rank?r.concat(new Array(a.rank-r.length).fill(-1)):r).map(function(e,t){return e>=0?e:(assert(-1===e,\"Bad value in size\"),a.shape[t]-n[t])}),assertParamsValid(a,n,o);var i=a.shape;return ENV.engine.runKernel(function(e){return e.slice(a,n,o)},{$x:a},function(e){for(var t=[],r=0;r<e.rank;r++)t.push([n[r],i[r]-n[r]-o[r]]);return {$x:function(){return e.pad(t)}}})}var slice=op({slice_:slice_}),slice1d=op({slice1d_:slice1d_}),slice2d=op({slice2d_:slice2d_}),slice3d=op({slice3d_:slice3d_}),slice4d=op({slice4d_:slice4d_});function logSumExp_(e,t,r){void 0===t&&(t=null),void 0===r&&(r=!1);var n=convertToTensor(e,\"x\",\"logSumExp\"),o=parseAxisParam(t,n.shape),a=n.max(o,!0),i=n.sub(a).exp().sum(o).log(),s=a.reshape(i.shape).add(i);if(r){var u=expandShapeToKeepDim(s.shape,o);return s.reshape(u)}return s}function sum_(e,t,r){void 0===t&&(t=null),void 0===r&&(r=!1);var n=convertToTensor(e,\"x\",\"sum\");\"bool\"===n.dtype&&(n=n.toInt());var o=parseAxisParam(t,n.shape);return customGrad(function(e){var t=getAxesPermutation(o,e.rank),n=o,a=e;null!=t&&(a=e.transpose(t),n=getInnerMostAxes(n.length,e.rank));var i=ENV.engine.runKernel(function(e){return e.sum(a,n)},{permutedX:a});if(r){var s=expandShapeToKeepDim(i.shape,o);i=i.reshape(s);}return {value:i,gradFunc:function(t){var r=e.shape.slice();return o.forEach(function(e){r[e]=1;}),t.reshape(r).mul(ones$1(e.shape,\"float32\"))}}})(n)}function prod_(e,t,r){void 0===t&&(t=null),void 0===r&&(r=!1);var n=convertToTensor(e,\"x\",\"prod\");\"bool\"===n.dtype&&(n=n.toInt());var o=parseAxisParam(t,n.shape),a=getAxesPermutation(o,n.rank),i=o,s=n;null!=a&&(s=n.transpose(a),i=getInnerMostAxes(i.length,n.rank));var u=ENV.engine.runKernel(function(e){return e.prod(s,i)},{permutedX:s});if(r){var l=expandShapeToKeepDim(u.shape,o);u=u.reshape(l);}return u}function mean_(e,t,r){void 0===t&&(t=null),void 0===r&&(r=!1);var n=convertToTensor(e,\"x\",\"mean\"),o=parseAxisParam(t,n.shape),a=sizeFromShape(computeOutAndReduceShapes(n.shape,o)[1]);return customGrad(function(e){var n=scalar(a);return {value:(n.dtype===e.dtype?e:e.cast(n.dtype)).div(n).sum(t,r),gradFunc:function(t){var r=e.shape.slice();return o.forEach(function(e){r[e]=1;}),t.reshape(r).mul(ones$1(e.shape,\"float32\")).div(n)}}})(n)}function gradForMinAndMax(e,t,r,n,o){var a=t[0];return a.rank<r.rank&&(a=a.reshape(expandShapeToKeepDim(a.shape,n))),e.rank<r.rank&&(e=e.reshape(expandShapeToKeepDim(e.shape,n))),{$x:function(){var t=e.mul(r.equal(a).cast(e.dtype));return null==o?t:t.transpose(o)}}}function min_(e,t,r){void 0===t&&(t=null),void 0===r&&(r=!1);var n=convertToTensor(e,\"x\",\"min\"),o=n,a=parseAxisParam(t,n.shape),i=a,s=getAxesPermutation(i,n.rank);null!=s&&(n=n.transpose(s),i=getInnerMostAxes(i.length,n.rank));var u=ENV.engine.runKernel(function(e,t){return t(e.min(n,i))},{$x:n},function(e,t){return gradForMinAndMax(e,t,o,a,s)});if(r){var l=expandShapeToKeepDim(u.shape,a);u=u.reshape(l);}return u}function max_(e,t,r){void 0===t&&(t=null),void 0===r&&(r=!1);var n=convertToTensor(e,\"x\",\"max\"),o=n,a=parseAxisParam(t,n.shape),i=a,s=getAxesPermutation(i,n.rank);null!=s&&(n=n.transpose(s),i=getInnerMostAxes(i.length,n.rank));var u=ENV.engine.runKernel(function(e,t){return t(e.max(n,i))},{$x:n},function(e,t){return gradForMinAndMax(e,t,o,a,s)});if(r){var l=expandShapeToKeepDim(u.shape,a);u=u.reshape(l);}return u}function argMin_(e,t){void 0===t&&(t=0);var r=convertToTensor(e,\"x\",\"argMin\");null==t&&(t=0);var n=parseAxisParam(t,r.shape),o=getAxesPermutation(n,r.rank);null!=o&&(r=r.transpose(o),n=getInnerMostAxes(n.length,r.rank));return ENV.engine.runKernel(function(e){return e.argMin(r,n[0])},{$x:r},function(e){return {$x:function(){return zerosLike(r)}}})}function argMax_(e,t){void 0===t&&(t=0);var r=convertToTensor(e,\"x\",\"argMax\");null==t&&(t=0);var n=parseAxisParam(t,r.shape),o=getAxesPermutation(n,r.rank);null!=o&&(r=r.transpose(o),n=getInnerMostAxes(n.length,r.rank));return ENV.engine.runKernel(function(e){return e.argMax(r,n[0])},{$x:r},function(e){return {$x:function(){return zerosLike(r)}}})}function all_(e,t,r){void 0===t&&(t=null),void 0===r&&(r=!1);var n=convertToTensor(e,\"x\",\"all\",\"bool\");assert(\"bool\"===n.dtype,\"Error Tensor must be of type bool. Got: \"+n.dtype);var o=parseAxisParam(t,n.shape),a=o,i=getAxesPermutation(a,n.rank);null!=i&&(n=n.transpose(i),a=getInnerMostAxes(a.length,n.rank));var s=ENV.engine.runKernel(function(e){return e.all(n,a)},{$x:n});if(r){var u=expandShapeToKeepDim(s.shape,o);return s.reshape(u)}return s}function any_(e,t,r){void 0===t&&(t=null),void 0===r&&(r=!1);var n=convertToTensor(e,\"x\",\"any\",\"bool\");assert(\"bool\"===n.dtype,\"Error Tensor must be of type bool. Got: \"+n.dtype);var o=parseAxisParam(t,n.shape),a=o,i=getAxesPermutation(a,n.rank);null!=i&&(n=n.transpose(i),a=getInnerMostAxes(a.length,n.rank));var s=ENV.engine.runKernel(function(e){return e.any(n,a)},{$x:n});if(r){var u=expandShapeToKeepDim(s.shape,o);return s.reshape(u)}return s}function moments_(e,t,r){void 0===t&&(t=null),void 0===r&&(r=!1);var n=parseAxisParam(t,(e=convertToTensor(e,\"x\",\"moments\")).shape),o=e.mean(n,r),a=o.shape;return r||(a=expandShapeToKeepDim(o.shape,n)),{mean:o,variance:e.toFloat().sub(o.reshape(a)).square().mean(n,r)}}var all=op({all_:all_}),any=op({any_:any_}),argMax=op({argMax_:argMax_}),argMin=op({argMin_:argMin_}),logSumExp=op({logSumExp_:logSumExp_}),max=op({max_:max_}),mean=op({mean_:mean_}),min=op({min_:min_}),moments=op({moments_:moments_}),sum$1=op({sum_:sum_}),prod=op({prod_:prod_});function notEqual_(e,t){var r=convertToTensor(e,\"a\",\"notEqual\"),n=convertToTensor(t,\"b\",\"notEqual\");return assertTypesMatch(r,n),assertAndGetBroadcastShape(r.shape,n.shape),ENV.engine.runKernel(function(e){return e.notEqual(r,n)},{$a:r,$b:n})}function notEqualStrict_(e,t){var r=convertToTensor(e,\"a\",\"notEqualStrict\"),n=convertToTensor(t,\"b\",\"notEqualStrict\");return assertShapesMatch(r.shape,n.shape,\"Error in notEqualStrict: \"),r.notEqual(n)}function less_(e,t){var r=convertToTensor(e,\"a\",\"less\"),n=convertToTensor(t,\"b\",\"less\");return assertTypesMatch(r,n),assertAndGetBroadcastShape(r.shape,n.shape),ENV.engine.runKernel(function(e){return e.less(r,n)},{$a:r,$b:n})}function lessStrict_(e,t){var r=convertToTensor(e,\"a\",\"lessStrict\"),n=convertToTensor(t,\"b\",\"lessStrict\");return assertShapesMatch(r.shape,n.shape,\"Error in lessStrict: \"),r.less(n)}function equal_(e,t){var r=convertToTensor(e,\"a\",\"equal\"),n=convertToTensor(t,\"b\",\"equal\");return assertTypesMatch(r,n),assertAndGetBroadcastShape(r.shape,n.shape),ENV.engine.runKernel(function(e){return e.equal(r,n)},{$a:r,$b:n})}function equalStrict_(e,t){var r=convertToTensor(e,\"a\",\"equalStrict\"),n=convertToTensor(t,\"b\",\"equalStrict\");return assertShapesMatch(r.shape,n.shape,\"Error in equalStrict: \"),r.equal(n)}function lessEqual_(e,t){var r=convertToTensor(e,\"a\",\"lessEqual\"),n=convertToTensor(t,\"b\",\"lessEqual\");return assertTypesMatch(r,n),assertAndGetBroadcastShape(r.shape,n.shape),ENV.engine.runKernel(function(e){return e.lessEqual(r,n)},{$a:r,$b:n})}function lessEqualStrict_(e,t){var r=convertToTensor(e,\"a\",\"lessEqualStrict\"),n=convertToTensor(t,\"b\",\"lessEqualStrict\");return assertShapesMatch(r.shape,n.shape,\"Error in lessEqualStrict: \"),r.lessEqual(n)}function greater_(e,t){var r=convertToTensor(e,\"a\",\"greater\"),n=convertToTensor(t,\"b\",\"greater\");return assertTypesMatch(r,n),assertAndGetBroadcastShape(r.shape,n.shape),ENV.engine.runKernel(function(e){return e.greater(r,n)},{$a:r,$b:n})}function greaterStrict_(e,t){var r=convertToTensor(e,\"a\",\"greaterStrict\"),n=convertToTensor(t,\"b\",\"greaterStrict\");return assertShapesMatch(r.shape,n.shape,\"Error in greaterStrict: \"),r.greater(n)}function greaterEqual_(e,t){var r=convertToTensor(e,\"a\",\"greaterEqual\"),n=convertToTensor(t,\"b\",\"greaterEqual\");assertTypesMatch(r,n),assertAndGetBroadcastShape(r.shape,n.shape);return ENV.engine.runKernel(function(e){return e.greaterEqual(r,n)},{$a:r,$b:n},function(e){return {$a:function(){return zerosLike(r)},$b:function(){return zerosLike(n)}}})}function greaterEqualStrict_(e,t){var r=convertToTensor(e,\"a\",\"greaterEqualStrict\"),n=convertToTensor(t,\"b\",\"greaterEqualStrict\");return assertShapesMatch(r.shape,n.shape,\"Error in greaterEqualStrict: \"),r.greaterEqual(n)}var equal=op({equal_:equal_}),equalStrict=op({equalStrict_:equalStrict_}),greater=op({greater_:greater_}),greaterEqual=op({greaterEqual_:greaterEqual_}),greaterEqualStrict=op({greaterEqualStrict_:greaterEqualStrict_}),greaterStrict=op({greaterStrict_:greaterStrict_}),less=op({less_:less_}),lessEqual=op({lessEqual_:lessEqual_}),lessEqualStrict=op({lessEqualStrict_:lessEqualStrict_}),lessStrict=op({lessStrict_:lessStrict_}),notEqual=op({notEqual_:notEqual_}),notEqualStrict=op({notEqualStrict_:notEqualStrict_});function add_(e,t){var r=convertToTensor(e,\"a\",\"add\"),n=convertToTensor(t,\"b\",\"add\");assertTypesMatch(r,n);var o=assertAndGetBroadcastShape(r.shape,n.shape);return ENV.engine.runKernel(function(e){return e.add(r,n)},{$a:r,$b:n},function(e){return {$a:function(){var t=e,n=getReductionAxes(r.shape,o);return n.length>0&&(t=t.sum(n)),t.reshape(r.shape)},$b:function(){var t=e,r=getReductionAxes(n.shape,o);return r.length>0&&(t=t.sum(r)),t.reshape(n.shape)}}})}function addN_(e){assert(Array.isArray(e),function(){return \"The argument passed to tf.addN() must be a list of tensors\"}),assert(e.length>=1,function(){return \"Must pass at least one tensor to tf.addN(), but got \"+e.length});var t=e.map(function(e,t){return convertToTensor(e,\"tensors\"+t,\"addN\")}),r=t[0];t.forEach(function(e){if(e.dtype!==r.dtype)throw new Error(\"All tensors passed to tf.addN() must have the same dtype\")}),t.forEach(function(e){if(!arraysEqual(e.shape,r.shape))throw new Error(\"All tensors passed to tf.addN() must have the same shape\")});var n=t;return ENV.engine.runKernel(function(e){return e.addN(t)},n,function(e){var r={};return t.forEach(function(t,n){r[n]=function(){return e.clone()};}),r})}function addStrict_(e,t){return assertShapesMatch(e.shape,t.shape,\"Error in addStrict: \"),e.add(t)}function sub_(e,t){var r=convertToTensor(e,\"a\",\"sub\"),n=convertToTensor(t,\"b\",\"sub\");assertTypesMatch(r,n);var o=assertAndGetBroadcastShape(r.shape,n.shape);return ENV.engine.runKernel(function(e){return e.subtract(r,n)},{$a:r,$b:n},function(e){return {$a:function(){var t=e,n=getReductionAxes(r.shape,o);return n.length>0&&(t=t.sum(n)),t.reshape(r.shape)},$b:function(){var t=e,r=getReductionAxes(n.shape,o);return r.length>0&&(t=t.sum(r)),t.neg().reshape(n.shape)}}})}function subStrict_(e,t){return assertShapesMatch(e.shape,t.shape,\"Error in subStrict: \"),e.sub(t)}function pow_(e,t){var r=convertToTensor(e,\"base\",\"pow\"),n=convertToTensor(t,\"exp\",\"pow\"),o=assertAndGetBroadcastShape(r.shape,n.shape);e=r.cast(upcastType(r.dtype,n.dtype)),t=n.cast(upcastType(r.dtype,n.dtype));return ENV.engine.runKernel(function(e,t){return t(e.pow(r,n))},{$base:r,$exp:n},function(e,t){var a=t[0];return {$base:function(){var t=n.toFloat(),a=e.mul(t.mul(r.pow(t.sub(scalar(1))))),i=getReductionAxes(r.shape,o);return i.length>0&&(a=a.sum(i)),a.reshape(r.shape)},$exp:function(){var t=e.mul(a.mul(r.log()).toFloat()),i=getReductionAxes(n.shape,o);return i.length>0&&(t=t.sum(i)),t.reshape(n.shape)}}})}function powStrict_(e,t){return assertShapesMatch(e.shape,t.shape,\"Error in powStrict: \"),e.pow(t)}function mul_(e,t){var r=convertToTensor(e,\"a\",\"mul\"),n=convertToTensor(t,\"b\",\"mul\");assertTypesMatch(r,n);var o=assertAndGetBroadcastShape(r.shape,n.shape);return ENV.engine.runKernel(function(e){return e.multiply(r,n)},{$a:r,$b:n},function(e){return {$a:function(){var t=e.mul(n.toFloat()),a=getReductionAxes(r.shape,o);return a.length>0?t.sum(a).reshape(r.shape):t},$b:function(){var t=e.mul(r.toFloat()),a=getReductionAxes(n.shape,o);return a.length>0?t.sum(a).reshape(n.shape):t}}})}function mulStrict_(e,t){return assertShapesMatch(e.shape,t.shape,\"Error in multiplyStrict: \"),e.mul(t)}function div_(e,t){var r,n=convertToTensor(e,\"a\",\"div\"),o=convertToTensor(t,\"b\",\"div\");if(assertTypesMatch(n,o),\"int32\"===n.dtype&&\"int32\"===o.dtype)return floorDiv(n,o);r=function(e){return e.realDivide(n,o)};var a=assertAndGetBroadcastShape(n.shape,o.shape);return ENV.engine.runKernel(r,{$a:n,$b:o},function(e){return {$a:function(){var t=e.div(o.toFloat()),r=getReductionAxes(n.shape,a);return r.length>0?t.sum(r).reshape(n.shape):t},$b:function(){var t=e.mul(n.toFloat()),r=getReductionAxes(o.shape,a);r.length>0&&(t=t.sum(r).reshape(o.shape));var i=o.square();return t.div(i.toFloat()).neg()}}})}function floorDiv_(e,t){var r=convertToTensor(e,\"a\",\"floorDiv\"),n=convertToTensor(t,\"b\",\"floorDiv\");assertTypesMatch(r,n);var o=assertAndGetBroadcastShape(r.shape,n.shape);return ENV.engine.runKernel(function(e){return e.floorDiv(r,n)},{$a:r,$b:n},function(e){return {$a:function(){var t=e.div(n.toFloat()),a=getReductionAxes(r.shape,o);return a.length>0?t.sum(a).reshape(r.shape):t},$b:function(){var t=e.mul(r.toFloat()),a=getReductionAxes(n.shape,o);a.length>0&&(t=t.sum(a).reshape(n.shape));var i=n.square();return t.div(i.toFloat()).neg()}}})}function divStrict_(e,t){return assertShapesMatch(e.shape,t.shape,\"Error in divideStrict: \"),e.div(t)}function mod_(e,t){var r=convertToTensor(e,\"a\",\"mod\"),n=convertToTensor(t,\"b\",\"mod\");assertTypesMatch(r,n);var o=assertAndGetBroadcastShape(r.shape,n.shape);return ENV.engine.runKernel(function(e){return e.mod(r,n)},{$a:r,$b:n},function(e){return {$a:function(){var t=getReductionAxes(r.shape,o);return t.length>0?e.sum(t).reshape(r.shape):e},$b:function(){var t=e.mul(r.div(n).floor().neg()),a=getReductionAxes(n.shape,o);return a.length>0?t.sum(a).reshape(n.shape):t}}})}function modStrict_(e,t){return assertShapesMatch(e.shape,t.shape,\"Error in modStrict: \"),e.mod(t)}function minimum_(e,t){var r=convertToTensor(e,\"a\",\"minimum\"),n=convertToTensor(t,\"b\",\"minimum\");assertTypesMatch(r,n),\"bool\"===r.dtype&&(r=r.toInt()),\"bool\"===n.dtype&&(n=n.toInt()),assertAndGetBroadcastShape(r.shape,n.shape);return ENV.engine.runKernel(function(e){return e.minimum(r,n)},{$a:r,$b:n},function(e){return {$a:function(){return e.mul(r.lessEqual(n).toFloat())},$b:function(){return e.mul(r.greater(n).toFloat())}}})}function minimumStrict_(e,t){return assertShapesMatch(e.shape,t.shape,\"Error in minimumStrict: \"),e.minimum(t)}function maximum_(e,t){var r=convertToTensor(e,\"a\",\"maximum\"),n=convertToTensor(t,\"b\",\"maximum\");assertTypesMatch(r,n),\"bool\"===r.dtype&&(r=r.toInt()),\"bool\"===n.dtype&&(n=n.toInt()),assertAndGetBroadcastShape(r.shape,n.shape);return ENV.engine.runKernel(function(e){return e.maximum(r,n)},{$a:r,$b:n},function(e){return {$a:function(){return e.mul(r.greaterEqual(n).toFloat())},$b:function(){return e.mul(r.less(n).toFloat())}}})}function maximumStrict_(e,t){return assertShapesMatch(e.shape,t.shape,\"Error in maximumStrict: \"),e.maximum(t)}function squaredDifference_(e,t){var r=convertToTensor(e,\"a\",\"squaredDifference\"),n=convertToTensor(t,\"b\",\"squaredDifference\");assertTypesMatch(r,n),assertAndGetBroadcastShape(r.shape,n.shape);return ENV.engine.runKernel(function(e){return e.squaredDifference(r,n)},{$a:r,$b:n},function(e){var t=scalar(2);return {$a:function(){return e.mul(r.sub(n).mul(t))},$b:function(){return e.mul(n.sub(r).mul(t))}}})}function squaredDifferenceStrict_(e,t){return assertShapesMatch(e.shape,t.shape,\"Error in squaredDifferenceStrict: \"),e.squaredDifference(t)}function atan2_(e,t){var r=convertToTensor(e,\"a\",\"atan2\"),n=convertToTensor(t,\"b\",\"atan2\");assertTypesMatch(r,n);var o=assertAndGetBroadcastShape(r.shape,n.shape);return ENV.engine.runKernel(function(e){return e.atan2(r,n)},{$a:r,$b:n},function(e){return {$a:function(){var t=add(r.square(),n.square()),a=e.mul(n.div(t)),i=getReductionAxes(r.shape,o);return i.length>0&&(a=a.sum(i)),a.reshape(r.shape)},$b:function(){var t=add(r.square(),n.square()),a=neg(e.mul(r.div(t))),i=getReductionAxes(n.shape,o);return i.length>0&&(a=a.sum(i)),a.reshape(n.shape)}}})}var add=op({add_:add_}),addN=op({addN_:addN_}),addStrict=op({addStrict_:addStrict_}),atan2=op({atan2_:atan2_}),div=op({div_:div_}),divStrict=op({divStrict_:divStrict_}),floorDiv=op({floorDiv_:floorDiv_}),maximum=op({maximum_:maximum_}),maximumStrict=op({maximumStrict_:maximumStrict_}),minimum=op({minimum_:minimum_}),minimumStrict=op({minimumStrict_:minimumStrict_}),mod=op({mod_:mod_}),modStrict=op({modStrict_:modStrict_}),mul=op({mul_:mul_}),mulStrict=op({mulStrict_:mulStrict_}),pow=op({pow_:pow_}),powStrict=op({powStrict_:powStrict_}),squaredDifference=op({squaredDifference_:squaredDifference_}),squaredDifferenceStrict=op({squaredDifferenceStrict_:squaredDifferenceStrict_}),sub=op({sub_:sub_}),subStrict=op({subStrict_:subStrict_});function logicalNot_(e){var t=convertToTensor(e,\"x\",\"logicalNot\",\"bool\");return assert(\"bool\"===t.dtype,\"Error Array must be of type bool.\"),ENV.engine.runKernel(function(e){return e.logicalNot(t)},{$x:t})}function logicalAnd_(e,t){var r=convertToTensor(e,\"a\",\"logicalAnd\",\"bool\"),n=convertToTensor(t,\"b\",\"logicalAnd\",\"bool\");return assert(\"bool\"===r.dtype&&\"bool\"===n.dtype,\"Error Array must be of type bool.\"),assertAndGetBroadcastShape(r.shape,n.shape),ENV.engine.runKernel(function(e){return e.logicalAnd(r,n)},{$a:r,$b:n})}function logicalOr_(e,t){var r=convertToTensor(e,\"a\",\"logicalOr\",\"bool\"),n=convertToTensor(t,\"b\",\"logicalOr\",\"bool\");return assert(\"bool\"===r.dtype&&\"bool\"===n.dtype,\"Error Array must be of type bool.\"),assertAndGetBroadcastShape(r.shape,n.shape),ENV.engine.runKernel(function(e){return e.logicalOr(r,n)},{$a:r,$b:n})}function logicalXor_(e,t){var r=convertToTensor(e,\"a\",\"logicalXor\",\"bool\"),n=convertToTensor(t,\"b\",\"logicalXor\",\"bool\");return assert(\"bool\"===r.dtype&&\"bool\"===n.dtype,\"Error Array must be of type bool.\"),assertAndGetBroadcastShape(r.shape,n.shape),logicalOr(e,t).logicalAnd(logicalAnd(e,t).logicalNot())}function where_(e,t,r){var n=convertToTensor(t,\"a\",\"where\"),o=convertToTensor(r,\"b\",\"where\"),a=convertToTensor(e,\"condition\",\"where\",\"bool\");assert(\"bool\"===a.dtype,\"Error Condition must be of type bool.\"),assertShapesMatch(n.shape,o.shape,\"Error in where: \"),1===a.rank?assert(a.shape[0]===n.shape[0],\"The first dimension of `a` must match the size of `condition`.\"):assertShapesMatch(a.shape,o.shape,\"Error in where: \");return ENV.engine.runKernel(function(e){return e.select(a,n,o)},{$condition:a,$a:n,$b:o},function(e){return {$condition:function(){return zerosLike(a)},$a:function(){return e.mul(a.cast(n.dtype))},$b:function(){return e.mul(a.logicalNot().cast(o.dtype))}}})}function whereAsync_(e){return __awaiter(this,void 0,void 0,function(){var t,r,n;return __generator(this,function(o){switch(o.label){case 0:return assert(\"bool\"===(t=convertToTensor(e,\"condition\",\"where\",\"bool\")).dtype,\"Condition must be of type bool.\"),[4,t.data()];case 1:return r=o.sent(),n=whereImpl(t.shape,r),e!==t&&t.dispose(),[2,n]}})})}var logicalAnd=op({logicalAnd_:logicalAnd_}),logicalNot=op({logicalNot_:logicalNot_}),logicalOr=op({logicalOr_:logicalOr_}),logicalXor=op({logicalXor_:logicalXor_}),where=op({where_:where_}),whereAsync=whereAsync_;function relu_(e){var t=convertToTensor(e,\"x\",\"relu\");if(\"bool\"===t.dtype)return t.toInt();return ENV.engine.runKernel(function(e){return e.relu(t)},{$x:t},function(e){var r=t.step();return {$x:function(){return e.mulStrict(r.toFloat())}}})}function elu_(e){var t=convertToTensor(e,\"x\",\"elu\");return ENV.engine.runKernel(function(e,r){return r(e.elu(t))},{$x:t},function(e,t){var r=t[0];return {$x:function(){return ENV.engine.runKernel(function(t){return t.eluDer(e,r)},{dy:e,y:r})}}})}function selu_(e){var t=convertToTensor(e,\"x\",\"selu\");return ENV.engine.runKernel(function(e){return e.selu(t)},{$x:t},function(e){return {$x:function(){var r=t.greater(scalar(0)),n=scalar(SELU_SCALEALPHA),o=scalar(SELU_SCALE),a=e.mul(o),i=e.mul(n).mul(t.toFloat().exp());return where(r,a,i)}}})}function leakyRelu_(e,t){void 0===t&&(t=.2);var r=convertToTensor(e,\"x\",\"leakyRelu\");return maximum(scalar(t).mul(r),r)}function prelu_(e,t){var r=convertToTensor(e,\"x\",\"prelu\"),n=convertToTensor(t,\"alpha\",\"prelu\"),o=scalar(0);return maximum(o,r).add(n.mul(minimum(o,r)))}var elu=op({elu_:elu_}),leakyRelu=op({leakyRelu_:leakyRelu_}),prelu=op({prelu_:prelu_}),relu=op({relu_:relu_}),selu=op({selu_:selu_});function transpose_(e,t){var r=convertToTensor(e,\"x\",\"transpose\");if(null==t&&(t=r.shape.map(function(e,t){return t}).reverse()),assert(r.rank===t.length,\"Error in transpose: rank of input \"+r.rank+\" must match length of perm \"+t+\".\"),t.forEach(function(e){assert(e>=0&&e<r.rank,\"All entries in 'perm' must be between 0 and \"+(r.rank-1)+\" but got \"+t);}),r.rank<=1)return r.clone();return ENV.engine.runKernel(function(e){return e.transpose(r,t)},{$x:r},function(e){var r=getUndoAxesPermutation(t);return {$x:function(){return e.transpose(r)}}})}var transpose=op({transpose_:transpose_});function localResponseNormalization_(e,t,r,n,o){void 0===t&&(t=5),void 0===r&&(r=1),void 0===n&&(n=1),void 0===o&&(o=.5);var a=convertToTensor(e,\"x\",\"localResponseNormalization\");assert(4===a.rank||3===a.rank,\"Error in localResponseNormalization: x must be rank 3 or 4 but got\\n               rank \"+a.rank+\".\"),assert(isInt(t),\"Error in localResponseNormalization: depthRadius must be an integer\\n                     but got depthRadius \"+t+\".\");var i=a,s=!1;3===a.rank&&(s=!0,i=a.as4D(1,a.shape[0],a.shape[1],a.shape[2]));var u=ENV.engine.runKernel(function(e,a){return a(e.localResponseNormalization4D(i,t,r,n,o))},{x4D:i},function(e,a){var s=a[0];return {x4D:function(){return ENV.engine.runKernel(function(a){return a.LRNGrad(e,i,s,t,r,n,o)},{})}}});return s?u.as3D(u.shape[1],u.shape[2],u.shape[3]):u}var localResponseNormalization=op({localResponseNormalization_:localResponseNormalization_});function norm_(e,t,r,n){void 0===t&&(t=\"euclidean\"),void 0===r&&(r=null),void 0===n&&(n=!1);var o=normImpl(e=convertToTensor(e,\"x\",\"norm\"),t,r),a=o.shape;if(n){var i=parseAxisParam(r,e.shape);a=expandShapeToKeepDim(o.shape,i);}return o.reshape(a)}function normImpl(e,t,r){if(void 0===r&&(r=null),0===e.rank)return e.abs();if(1!==e.rank&&null===r)return normImpl(e.reshape([-1]),t,r);if(1===e.rank||\"number\"==typeof r||r instanceof Array&&1===r.length){if(1===t)return e.abs().sum(r);if(t===1/0)return e.abs().max(r);if(t===-1/0)return e.abs().min(r);if(\"euclidean\"===t||2===t)return e.abs().pow(scalar(2,\"int32\")).sum(r).sqrt();throw new Error(\"Error in norm: invalid ord value: \"+t)}if(r instanceof Array&&2===r.length){if(1===t)return e.abs().sum(r[0]).max(r[1]-1);if(t===1/0)return e.abs().sum(r[1]).max(r[0]);if(t===-1/0)return e.abs().sum(r[1]).min(r[0]);if(\"fro\"===t||\"euclidean\"===t)return e.square().sum(r).sqrt();throw new Error(\"Error in norm: invalid ord value: \"+t)}throw new Error(\"Error in norm: invalid axis: \"+r)}var norm=op({norm_:norm_});function unsortedSegmentSum_(e,t,r){var n=convertToTensor(e,\"x\",\"unsortedSegmentSum\"),o=convertToTensor(t,\"segmentIds\",\"unsortedSegmentSum\",\"int32\");assert(\"int32\"===o.dtype,\"segmentIds must be of dtype `int32`\"),assert(isInt(r),\"numSegments must be of dtype int\");return ENV.engine.runKernel(function(e){return e.unsortedSegmentSum(n,o,r)},{$x:n},function(e){return {$x:function(){return gatherDropNegatives(e,o)}}})}function gather_(e,t,r){void 0===r&&(r=0);var n=convertToTensor(e,\"x\",\"gather\"),o=convertToTensor(t,\"indices\",\"gather\",\"int32\");assert(\"int32\"===o.dtype,\"Indices must be of dtype `int32`\"),r=parseAxisParam(r,n.shape)[0];return ENV.engine.runKernel(function(e){return e.gather(n,o,r)},{$x:n},function(e){return {$x:function(){if(0===r)return unsortedSegmentSum(e,o,n.shape[r]);var t=n.shape,a=o.size,i=t.slice(0,r),s=i.length,u=t.slice(r,t.length).slice(1),l=u.length,c=arrayRange(0,s),p=arrayRange(s+1,s+1+l),d=arrayConcat([i,[a],u]),h=e.reshape(d),f=o.reshape([a]),m=arrayConcat([[s],c,p]),g=h.transpose(m),v=unsortedSegmentSum(g,f,n.shape[r]),y=getUndoAxesPermutation(m);return v=v.transpose(y)}}})}function arrayRange(e,t){for(var r=[],n=e;n<t;++n)r.push(n);return r}function arrayConcat(e){for(var t=[],r=0;r<e.length;++r)for(var n=0;n<e[r].length;++n)t.push(e[r][n]);return t}function gatherDropNegatives(e,t){for(var r=maximum(t,zerosLike(t)),n=gather(e,r),o=greaterEqual(t,scalar(0,\"int32\")),a=n.rank-o.rank,i=0;i<a;++i)o=expandDims(o,i+1);o=logicalAnd(o,ones$1(n.shape,\"bool\"));var s=zerosLike(n);return where(o,n,s)}var gather=op({gather_:gather_}),unsortedSegmentSum=op({unsortedSegmentSum_:unsortedSegmentSum_});function multiRNNCell_(e,t,r,n){for(var o=convertToTensor(t,\"data\",\"multiRNNCell\"),a=convertToTensorArray(r,\"c\",\"multiRNNCell\"),i=convertToTensorArray(n,\"h\",\"multiRNNCell\"),s=o,u=[],l=0;l<e.length;l++){var c=e[l](s,a[l],i[l]);u.push(c[0]),u.push(c[1]),s=c[1];}var p=[],d=[];for(l=0;l<u.length;l+=2)p.push(u[l]),d.push(u[l+1]);return [p,d]}function basicLSTMCell_(e,t,r,n,o,a){var i=convertToTensor(e,\"forgetBias\",\"basicLSTMCell\"),s=convertToTensor(t,\"lstmKernel\",\"basicLSTMCell\"),u=convertToTensor(r,\"lstmBias\",\"basicLSTMCell\"),l=convertToTensor(n,\"data\",\"basicLSTMCell\"),c=convertToTensor(o,\"c\",\"basicLSTMCell\"),p=convertToTensor(a,\"h\",\"basicLSTMCell\"),d=l.concat(p,1).matMul(s).add(u),h=d.shape[0],f=d.shape[1]/4,m=[h,f],g=d.slice([0,0],m),v=d.slice([0,f],m),y=d.slice([0,2*f],m),x=d.slice([0,3*f],m),T=g.sigmoid().mulStrict(v.tanh()).addStrict(c.mulStrict(i.add(y).sigmoid()));return [T,T.tanh().mulStrict(x.sigmoid())]}var basicLSTMCell=op({basicLSTMCell_:basicLSTMCell_}),multiRNNCell=op({multiRNNCell_:multiRNNCell_});function movingAverage_(e,t,r,n,o){void 0===o&&(o=!0);var a=convertToTensor(e,\"v\",\"movingAverage\"),i=convertToTensor(t,\"x\",\"movingAverage\"),s=convertToTensor(r,\"decay\",\"movingAverage\");assertTypesMatch(a,i),assert(arraysEqual(a.shape,i.shape),\"Shape mismatch in v and x\");var u=scalar(1),l=u.sub(s),c=i.sub(a).mul(l);if(o){assert(null!=n,\"When using zeroDebias: true, step is required.\");var p=convertToTensor(n,\"step\",\"movingAverage\");c=c.div(u.sub(pow(s,p)));}return a.add(c)}var movingAverage=op({movingAverage_:movingAverage_});function stridedSlice_(e,t,r,n,o,a,i,s,u){if(void 0===o&&(o=0),void 0===a&&(a=0),void 0===i&&(i=0),void 0===s&&(s=0),void 0===u&&(u=0),0!==i)throw new Error(\"ellipsis mask is not yet supported\");if(0!==s)throw new Error(\"new axis mask is not yet supported\");var l=convertToTensor(e,\"x\",\"stridedSlice\");return ENV.engine.runKernel(function(e){return e.stridedSlice(l,t,r,n,o,a,i,s,u)},{$x:l})}var stridedSlice=op({stridedSlice_:stridedSlice_});function topk_(e,t,r){void 0===t&&(t=1),void 0===r&&(r=!0);var n=convertToTensor(e,\"x\",\"topk\");if(0===n.rank)throw new Error(\"topk() expects the input to be of rank 1 or higher\");var o=n.shape[n.shape.length-1];if(t>o)throw new Error(\"'k' passed to topk() must be <= the last dimension (\"+o+\") but got \"+t);var a=ENV.engine.runKernel(function(e){return e.topk(n,t,r)},{$x:n});return {values:a[0],indices:a[1]}}var topk=op({topk_:topk_});function scatterND_(e,t,r){var n=convertToTensor(e,\"indices\",\"scatterND\",\"int32\"),o=convertToTensor(t,\"updates\",\"scatterND\");return validateInput(o,n,r),ENV.engine.runKernel(function(e){return e.scatterND(n,o,r)},{$indices:n,$updates:o})}var scatterND=op({scatterND_:scatterND_});function fft_(e){assert(\"complex64\"===e.dtype,\"The dtype for tf.spectral.fft() must be complex64 but got \"+e.dtype+\".\");var t=e.shape[e.shape.length-1],r=e.size/t,n=e.as2D(r,t);return ENV.engine.runKernel(function(e){return e.fft(n)},{input:e}).reshape(e.shape)}function ifft_(e){assert(\"complex64\"===e.dtype,\"The dtype for tf.spectral.ifft() must be complex64 but got \"+e.dtype+\".\");var t=e.shape[e.shape.length-1],r=e.size/t,n=e.as2D(r,t);return ENV.engine.runKernel(function(e){return e.ifft(n)},{input:e}).reshape(e.shape)}function rfft_(e){assert(\"float32\"===e.dtype,\"The dtype for rfft() must be real value but\\n    got \"+e.dtype);var t=e.shape[e.shape.length-1],r=e.size/t,n=e.zerosLike(),o=complex(e,n).as2D(r,t),a=ENV.engine.runKernel(function(e){return e.fft(o)},{complexInput:o}),i=Math.floor(t/2)+1,s=real(a),u=imag(a),l=s.split([i,t-i],s.shape.length-1),c=u.split([i,t-i],u.shape.length-1),p=e.shape.slice();return p[e.shape.length-1]=i,complex(l[0],c[0]).reshape(p)}var fft=op({fft_:fft_}),ifft=op({ifft_:ifft_}),rfft=op({rfft_:rfft_}),spectral_ops=Object.freeze({fft:fft,ifft:ifft,rfft:rfft});function validateInput$1(e,t,r,n){if(\"int32\"!==e.dtype)throw new Error(\"tf.sparseToDense() expects the indices to be int32 type, but the dtype was \"+e.dtype+\".\");if(e.rank>2)throw new Error(\"sparseIndices should be a scalar, vector, or matrix, but got shape \"+e.shape+\".\");var o=e.rank>0?e.shape[0]:1,a=e.rank>1?e.shape[1]:1;if(r.length!==a)throw new Error(\"outputShape has incorrect number of elements:, \"+r.length+\", should be: \"+a+\".\");var i=t.size;if(0!==t.rank&&(1!==t.rank||i!==o))throw new Error(\"sparseValues has incorrect shape \"+t.shape+\", should be [] or [\"+o+\"]\");if(t.dtype!==n.dtype)throw new Error(\"sparseValues.dtype must match defaultValues.dtype\")}function sparseToDense_(e,t,r,n){var o=convertToTensor(e,\"sparseIndices\",\"sparseToDense\",\"int32\"),a=convertToTensor(t,\"sparseValues\",\"sparseToDense\"),i=convertToTensor(n,\"defaultValue\",\"sparseToDense\",a.dtype);return validateInput$1(o,a,r,i),ENV.engine.runKernel(function(e){return e.sparseToDense(o,a,r,i)},{$sparseIndices:o,$sparseValues:a,$defaultValue:i})}var sparseToDense=op({sparseToDense_:sparseToDense_});function gatherND_(e,t){var r=convertToTensor(t,\"indices\",\"gatherND\",\"int32\"),n=convertToTensor(e,\"x\",\"gatherND\");return ENV.engine.runKernel(function(e){return e.gatherND(n,r)},{$x:n,$indices:r})}var Reduction,gatherND=op({gatherND_:gatherND_});function computeWeightedLoss_(e,t,r){void 0===r&&(r=Reduction.SUM_BY_NONZERO_WEIGHTS);var n=convertToTensor(e,\"losses\",\"computeWeightedLoss\"),o=null;null!=t&&(o=convertToTensor(t,\"weights\",\"computeWeightedLoss\"));var a=null==o?n:n.mul(o);if(r===Reduction.NONE)return a;if(r===Reduction.SUM)return a.sum();if(r===Reduction.MEAN){if(null==o)return a.mean();var i=sizeFromShape(n.shape)/sizeFromShape(o.shape),s=a.sum().div(o.sum());return i>1?s.div(scalar(i)):s}if(r===Reduction.SUM_BY_NONZERO_WEIGHTS){if(null==o)return a.sum().div(scalar(n.size));var u=o.mul(ones$1(n.shape)).notEqual(scalar(0)).sum().toFloat();return a.sum().div(u)}throw Error(\"Unknown reduction: \"+r)}function absoluteDifference_(e,t,r,n){void 0===n&&(n=Reduction.SUM_BY_NONZERO_WEIGHTS);var o=convertToTensor(e,\"labels\",\"absoluteDifference\"),a=convertToTensor(t,\"predictions\",\"absoluteDifference\"),i=null;null!=r&&(i=convertToTensor(r,\"weights\",\"absoluteDifference\")),assertShapesMatch(o.shape,a.shape,\"Error in absoluteDifference: \");var s=o.sub(a).abs();return computeWeightedLoss(s,i,n)}function meanSquaredError_(e,t,r,n){void 0===n&&(n=Reduction.SUM_BY_NONZERO_WEIGHTS);var o=convertToTensor(e,\"labels\",\"meanSquaredError\"),a=convertToTensor(t,\"predictions\",\"meanSquaredError\"),i=null;null!=r&&(i=convertToTensor(r,\"weights\",\"meanSquaredError\")),assertShapesMatch(o.shape,a.shape,\"Error in meanSquaredError: \");var s=o.squaredDifference(a);return computeWeightedLoss(s,i,n)}function cosineDistance_(e,t,r,n,o){void 0===o&&(o=Reduction.SUM_BY_NONZERO_WEIGHTS);var a=convertToTensor(e,\"labels\",\"cosineDistance\"),i=convertToTensor(t,\"predictions\",\"cosineDistance\"),s=null;null!=n&&(s=convertToTensor(n,\"weights\",\"cosineDistance\")),assertShapesMatch(a.shape,i.shape,\"Error in cosineDistance: \");var u=scalar(1).sub(a.mul(i).sum(r,!0));return computeWeightedLoss(u,s,o)}function hingeLoss_(e,t,r,n){void 0===n&&(n=Reduction.SUM_BY_NONZERO_WEIGHTS);var o=convertToTensor(e,\"labels\",\"hingeLoss\"),a=convertToTensor(t,\"predictions\",\"hingeLoss\"),i=null;null!=r&&(i=convertToTensor(r,\"weights\",\"hingeLoss\")),assertShapesMatch(o.shape,a.shape,\"Error in hingeLoss: \");var s=scalar(1);o=scalar(2).mul(o).sub(s);var u=s.sub(o.mul(a)).relu();return computeWeightedLoss(u,i,n)}function logLoss_(e,t,r,n,o){void 0===n&&(n=1e-7),void 0===o&&(o=Reduction.SUM_BY_NONZERO_WEIGHTS);var a=convertToTensor(e,\"labels\",\"logLoss\"),i=convertToTensor(t,\"predictions\",\"logLoss\"),s=null;null!=r&&(s=convertToTensor(r,\"weights\",\"logLoss\")),assertShapesMatch(a.shape,i.shape,\"Error in logLoss: \");var u=scalar(1),l=scalar(n),c=a.mul(i.add(l).log()).neg().sub(u.sub(a).mul(u.sub(i).add(l).log()));return computeWeightedLoss(c,s,o)}function sigmoidCrossEntropyWithLogits_(e,t){var r=convertToTensor(e,\"labels\",\"sigmoidCrossEntropyWithLogits\"),n=convertToTensor(t,\"logits\",\"sigmoidCrossEntropyWithLogits\");assertShapesMatch(r.shape,n.shape,\"Error in sigmoidCrossEntropyWithLogits: \");var o=n.relu(),a=n.mul(r),i=n.abs().neg().exp().log1p();return o.sub(a).add(i)}function sigmoidCrossEntropy_(e,t,r,n,o){void 0===n&&(n=0),void 0===o&&(o=Reduction.SUM_BY_NONZERO_WEIGHTS);var a=convertToTensor(e,\"multiClassLabels\",\"sigmoidCrossEntropy\"),i=convertToTensor(t,\"logits\",\"sigmoidCrossEntropy\"),s=null;if(null!=r&&(s=convertToTensor(r,\"weights\",\"sigmoidCrossEntropy\")),assertShapesMatch(a.shape,i.shape,\"Error in sigmoidCrossEntropy: \"),n>0){var u=scalar(n),l=scalar(1),c=scalar(.5);a=a.mul(l.sub(u)).add(c.mul(u));}var p=sigmoidCrossEntropyWithLogits_(a,i);return computeWeightedLoss(p,s,o)}function huberLoss_(e,t,r,n,o){void 0===n&&(n=1),void 0===o&&(o=Reduction.SUM_BY_NONZERO_WEIGHTS);var a=convertToTensor(e,\"labels\",\"huberLoss\"),i=convertToTensor(t,\"predictions\",\"huberLoss\"),s=null;null!=r&&(s=convertToTensor(r,\"weights\",\"huberLoss\")),assertShapesMatch(a.shape,i.shape,\"Error in huberLoss: \");var u=scalar(n),l=i.sub(a).abs(),c=minimum(l,u),p=l.sub(c),d=scalar(.5).mul(c.square()).add(u.mul(p));return computeWeightedLoss(d,s,o)}function softmaxCrossEntropyWithLogits_(e,t,r){if(void 0===r&&(r=-1),-1===r&&(r=t.rank-1),r!==t.rank-1)throw Error(\"Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank \"+t.rank+\" and dim was \"+r);return customGrad(function(e,t){var n=t.logSumExp([r],!0),o=t.toFloat().sub(n);return {value:o.mul(e).neg().sum([r]),gradFunc:function(t){var n=expandShapeToKeepDim(t.shape,[r]);return [t.reshape(n).mul(e.toFloat().sub(o.exp())),t.reshape(n).mul(o.exp().sub(e.toFloat()))]}}})(e,t)}function softmaxCrossEntropy_(e,t,r,n,o){void 0===n&&(n=0),void 0===o&&(o=Reduction.SUM_BY_NONZERO_WEIGHTS);var a=convertToTensor(e,\"onehotLabels\",\"softmaxCrossEntropy\"),i=convertToTensor(t,\"logits\",\"softmaxCrossEntropy\"),s=null;if(null!=r&&(s=convertToTensor(r,\"weights\",\"softmaxCrossEntropy\")),assertShapesMatch(a.shape,i.shape,\"Error in softmaxCrossEntropy: \"),n>0){var u=scalar(n),l=scalar(1),c=scalar(a.shape[1]);a=a.mul(l.sub(u)).add(u.div(c));}var p=softmaxCrossEntropyWithLogits_(a,i);return computeWeightedLoss(p,s,o)}!function(e){e[e.NONE=0]=\"NONE\",e[e.MEAN=1]=\"MEAN\",e[e.SUM=2]=\"SUM\",e[e.SUM_BY_NONZERO_WEIGHTS=3]=\"SUM_BY_NONZERO_WEIGHTS\";}(Reduction||(Reduction={}));var absoluteDifference=op({absoluteDifference_:absoluteDifference_}),computeWeightedLoss=op({computeWeightedLoss_:computeWeightedLoss_}),cosineDistance=op({cosineDistance_:cosineDistance_}),hingeLoss=op({hingeLoss_:hingeLoss_}),huberLoss=op({huberLoss_:huberLoss_}),logLoss=op({logLoss_:logLoss_}),meanSquaredError=op({meanSquaredError_:meanSquaredError_}),sigmoidCrossEntropy=op({sigmoidCrossEntropy_:sigmoidCrossEntropy_}),softmaxCrossEntropy=op({softmaxCrossEntropy_:softmaxCrossEntropy_}),loss_ops=Object.freeze({get Reduction(){return Reduction},absoluteDifference:absoluteDifference,computeWeightedLoss:computeWeightedLoss,cosineDistance:cosineDistance,hingeLoss:hingeLoss,huberLoss:huberLoss,logLoss:logLoss,meanSquaredError:meanSquaredError,sigmoidCrossEntropy:sigmoidCrossEntropy,softmaxCrossEntropy:softmaxCrossEntropy});function gramSchmidt_(e){var t;if(Array.isArray(e)){t=!1,assert(null!=e&&e.length>0,\"Gram-Schmidt process: input must not be null, undefined, or empty\");for(var r=e[0].shape[0],n=1;n<e.length;++n)assert(e[n].shape[0]===r,\"Gram-Schmidt: Non-unique lengths found in the input vectors: (\"+e[n].shape[0]+\" vs. \"+r+\")\");}else t=!0,e=split$1(e,e.shape[0],0).map(function(e){return squeeze(e,[0])});assert(e.length<=e[0].shape[0],\"Gram-Schmidt: Number of vectors (\"+e.length+\") exceeds number of dimensions (\"+e[0].shape[0]+\").\");var o=[],a=e,i=function(e){o.push(ENV.engine.tidy(function(){var t=a[e];if(e>0)for(var r=0;r<e;++r){var n=sum$1(o[r].mulStrict(t)).mul(o[r]);t=t.sub(n);}return t.div(norm(t,\"euclidean\"))}));};for(n=0;n<e.length;++n)i(n);return t?stack(o,0):o}function qr_(e,t){if(void 0===t&&(t=!1),e.rank<2)throw new Error(\"qr() requires input tensor to have a rank >= 2, but got rank \"+e.rank);if(2===e.rank)return qr2d(e,t);var r=e.shape.slice(0,e.shape.length-2).reduce(function(e,t){return e*t}),n=[],o=[];return unstack(e.reshape([r,e.shape[e.shape.length-2],e.shape[e.shape.length-1]]),0).forEach(function(e){var r=qr2d(e,t),a=r[0],i=r[1];n.push(a),o.push(i);}),[stack(n,0).reshape(e.shape),stack(o,0).reshape(e.shape)]}function qr2d(e,t){return void 0===t&&(t=!1),ENV.engine.tidy(function(){if(2!==e.shape.length)throw new Error(\"qr2d() requires a 2D Tensor, but got a \"+e.shape.length+\"D Tensor.\");for(var r=e.shape[0],n=e.shape[1],o=eye(r),a=e.clone(),i=tensor2d([[1]],[1,1]),s=i.clone(),u=r>=n?n:r,l=function(e){var t,u=a,l=s,c=o;t=ENV.engine.tidy(function(){var t=a.slice([e,e],[r-e,1]),u=t.norm(),l=a.slice([e,e],[1,1]),c=l.sign().neg(),p=l.sub(c.mul(u)),d=t.div(p);s=1===d.shape[0]?i.clone():i.concat(d.slice([1,0],[d.shape[0]-1,d.shape[1]]),0);var h=c.matMul(p).div(u).neg(),f=a.slice([e,0],[r-e,n]),m=h.mul(s);a=0===e?f.sub(m.matMul(s.transpose().matMul(f))):a.slice([0,0],[e,n]).concat(f.sub(m.matMul(s.transpose().matMul(f))),0);var g=o.slice([0,e],[r,o.shape[1]-e]);return o=0===e?g.sub(g.matMul(s).matMul(m.transpose())):o.slice([0,0],[r,e]).concat(g.sub(g.matMul(s).matMul(m.transpose())),1),[s,a,o]}),s=t[0],a=t[1],o=t[2],dispose([u,l,c]);},c=0;c<u;++c)l(c);return !t&&r>n&&(o=o.slice([0,0],[r,n]),a=a.slice([0,0],[n,n])),[o,a]})}var gramSchmidt=op({gramSchmidt_:gramSchmidt_}),qr=op({qr_:qr_}),linalg_ops=Object.freeze({gramSchmidt:gramSchmidt,qr:qr});function resizeBilinear_(e,t,r){void 0===r&&(r=!1);var n=convertToTensor(e,\"images\",\"resizeBilinear\");assert(3===n.rank||4===n.rank,\"Error in resizeBilinear: x must be rank 3 or 4, but got rank \"+n.rank+\".\"),assert(2===t.length,\"Error in resizeBilinear: new shape must 2D, but got shape \"+t+\".\");var o=n,a=!1;3===n.rank&&(a=!0,o=n.as4D(1,n.shape[0],n.shape[1],n.shape[2]));var i=t[0],s=t[1],u=ENV.engine.runKernel(function(e,t){return e.resizeBilinear(o,i,s,r)},{batchImages:o},function(e,t){return {batchImages:function(){return ENV.engine.runKernel(function(t){return t.resizeBilinearBackprop(e,o,r)},{})}}});return a?u.as3D(u.shape[1],u.shape[2],u.shape[3]):u}function resizeNearestNeighbor_(e,t,r){void 0===r&&(r=!1);var n=convertToTensor(e,\"images\",\"resizeNearestNeighbor\");assert(3===n.rank||4===n.rank,\"Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank \"+n.rank+\".\"),assert(2===t.length,\"Error in resizeNearestNeighbor: new shape must 2D, but got shape \"+t+\".\"),assert(\"float32\"===n.dtype||\"int32\"===n.dtype,\"`images` must have `int32` or `float32` as dtype\");var o=n,a=!1;3===n.rank&&(a=!0,o=n.as4D(1,n.shape[0],n.shape[1],n.shape[2]));var i=t[0],s=t[1],u=ENV.engine.runKernel(function(e,t){return e.resizeNearestNeighbor(o,i,s,r)},{batchImages:o},function(e,t){return {batchImages:function(){return ENV.engine.runKernel(function(t){return t.resizeNearestNeighborBackprop(e,o,r)},{})}}});return a?u.as3D(u.shape[1],u.shape[2],u.shape[3]):u}function nonMaxSuppression_(e,t,r,n,o){void 0===n&&(n=.5),void 0===o&&(o=Number.NEGATIVE_INFINITY);var a=convertToTensor(e,\"boxes\",\"nonMaxSuppression\"),i=convertToTensor(t,\"scores\",\"nonMaxSuppression\"),s=nonMaxSuppSanityCheck(a,i,r,n,o);return r=s.maxOutputSize,n=s.iouThreshold,o=s.scoreThreshold,ENV.engine.runKernel(function(e){return e.nonMaxSuppression(a,i,r,n,o)},{$boxes:a})}function nonMaxSuppressionAsync_(e,t,r,n,o){return void 0===n&&(n=.5),void 0===o&&(o=Number.NEGATIVE_INFINITY),__awaiter(this,void 0,void 0,function(){var a,i,s,u,l,c;return __generator(this,function(p){switch(p.label){case 0:return a=convertToTensor(e,\"boxes\",\"nonMaxSuppressionAsync\"),i=convertToTensor(t,\"scores\",\"nonMaxSuppressionAsync\"),s=nonMaxSuppSanityCheck(a,i,r,n,o),r=s.maxOutputSize,n=s.iouThreshold,o=s.scoreThreshold,[4,a.data()];case 1:return u=p.sent(),[4,i.data()];case 2:return l=p.sent(),c=nonMaxSuppressionImpl(u,l,r,n,o),a!==e&&a.dispose(),i!==t&&i.dispose(),[2,c]}})})}function nonMaxSuppSanityCheck(e,t,r,n,o){null==n&&(n=.5),null==o&&(o=Number.NEGATIVE_INFINITY);var a=e.shape[0];return r=Math.min(r,a),assert(0<=n&&n<=1,\"iouThreshold must be in [0, 1], but was '\"+n+\"'\"),assert(2===e.rank,\"boxes must be a 2D tensor, but was of rank '\"+e.rank+\"'\"),assert(4===e.shape[1],\"boxes must have 4 columns, but 2nd dimension was \"+e.shape[1]),assert(1===t.rank,\"scores must be a 1D tensor\"),assert(t.shape[0]===a,\"scores has incompatible shape with boxes. Expected \"+a+\", but was \"+t.shape[0]),{maxOutputSize:r,iouThreshold:n,scoreThreshold:o}}function cropAndResize_(e,t,r,n,o,a){var i=convertToTensor(e,\"image\",\"cropAndResize\",\"float32\"),s=convertToTensor(t,\"boxes\",\"cropAndResize\",\"float32\"),u=convertToTensor(r,\"boxInd\",\"cropAndResize\",\"int32\");o=o||\"bilinear\",a=a||0;var l=s.shape[0];assert(4===i.rank,\"Error in cropAndResize: image must be rank 4,but got rank \"+i.rank+\".\"),assert(2===s.rank&&4===s.shape[1],\"Error in cropAndResize: boxes must be have size [\"+l+\",4] but had shape \"+s.shape+\".\"),assert(1===u.rank&&u.shape[0]===l,\"Error in cropAndResize: boxInd must be have size [\"+l+\"] but had shape \"+s.shape+\".\"),assert(\"int32\"===u.dtype,\"Error in cropAndResize: boxInd must be of dtype int32, but got dtype \"+u.dtype+\".\"),assert(2===n.length,\"Error in cropAndResize: cropSize must be of length 2, but got length \"+n.length+\".\"),assert(n[0]>=1&&n[1]>=1,\"cropSize must be atleast [1,1], but was \"+n),assert(\"bilinear\"===o||\"nearest\"===o,\"method must be bilinear or nearest, but was \"+o);return ENV.engine.runKernel(function(e,t){return e.cropAndResize(i,s,u,n,o,a)},{$image:i,$boxes:s})}var resizeBilinear=op({resizeBilinear_:resizeBilinear_}),resizeNearestNeighbor=op({resizeNearestNeighbor_:resizeNearestNeighbor_}),nonMaxSuppression=op({nonMaxSuppression_:nonMaxSuppression_}),nonMaxSuppressionAsync=nonMaxSuppressionAsync_,cropAndResize=cropAndResize_,image_ops=Object.freeze({resizeBilinear:resizeBilinear,resizeNearestNeighbor:resizeNearestNeighbor,nonMaxSuppression:nonMaxSuppression,nonMaxSuppressionAsync:nonMaxSuppressionAsync,cropAndResize:cropAndResize}),ops=Object.freeze({image:image_ops,linalg:linalg_ops,losses:loss_ops,spectral:spectral_ops,op:op,batchNormalization2d:batchNormalization2d,batchNormalization3d:batchNormalization3d,batchNormalization4d:batchNormalization4d,batchNormalization:batchNormalization,complex:complex,real:real,imag:imag,concat:concat,concat1d:concat1d,concat2d:concat2d,concat3d:concat3d,concat4d:concat4d,split:split$1,conv1d:conv1d,conv2d:conv2d,conv2dDerFilter:conv2dDerFilter,depthwiseConv2d:depthwiseConv2d,separableConv2d:separableConv2d,conv2dTranspose:conv2dTranspose,matMul:matMul,dot:dot,outerProduct:outerProduct,reverse:reverse,reverse1d:reverse1d,reverse2d:reverse2d,reverse3d:reverse3d,reverse4d:reverse4d,maxPool:maxPool,avgPool:avgPool,pool:pool,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$1,log1p:log1p,logSigmoid:logSigmoid,neg:neg,reciprocal:reciprocal,round:round,rsqrt:rsqrt,sigmoid:sigmoid,sign:sign,sin:sin,sinh:sinh,softplus:softplus,sqrt:sqrt,square:square,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,divStrict:divStrict,floorDiv:floorDiv,maximum:maximum,maximumStrict:maximumStrict,minimum:minimum,minimumStrict:minimumStrict,mod:mod,modStrict:modStrict,mul:mul,mulStrict:mulStrict,pow:pow,powStrict:powStrict,squaredDifference:squaredDifference,squaredDifferenceStrict:squaredDifferenceStrict,sub:sub,subStrict:subStrict,elu:elu,leakyRelu:leakyRelu,prelu:prelu,relu:relu,selu:selu,logicalAnd:logicalAnd,logicalNot:logicalNot,logicalOr:logicalOr,logicalXor:logicalXor,where:where,whereAsync:whereAsync,buffer:buffer,toPixels:toPixels,print:print,batchToSpaceND:batchToSpaceND,cast:cast,clone:clone,cumsum:cumsum,depthToSpace:depthToSpace,expandDims:expandDims,eye:eye,fromPixels:fromPixels,multinomial:multinomial,oneHot:oneHot,pad:pad,pad1d:pad1d,pad2d:pad2d,pad3d:pad3d,pad4d:pad4d,rand:rand,randomNormal:randomNormal,randomUniform:randomUniform,reshape:reshape,spaceToBatchND:spaceToBatchND,squeeze:squeeze,stack:stack,tile:tile,truncatedNormal:truncatedNormal,unstack:unstack,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,zeros:zeros,onesLike:onesLike,zerosLike:zerosLike,transpose:transpose,softmax:softmax,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,sparseToDense:sparseToDense,gatherND:gatherND}),MathBackendCPU=function(){function e(){this.blockSize=48,this.firstUse=!0,ENV.get(\"IS_BROWSER\")&&(this.fromPixels2DContext=document.createElement(\"canvas\").getContext(\"2d\"));}return e.prototype.setDataMover=function(e){this.data=new DataStorage(e);},e.prototype.register=function(e,t,r){if(this.firstUse&&(this.firstUse=!1,ENV.get(\"IS_NODE\")&&warn(\"\\n============================\\nHi 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============================\\n\")),this.data.has(e))throw new Error(\"Data buffer is already registered\");this.data.set(e,{dtype:r});},e.prototype.write=function(e,t){if(null==t)throw new Error(\"MathBackendCPU.write(): values can not be null\");this.data.get(e).values=t;},e.prototype.fromPixels=function(e,t){if(null==e)throw new Error(\"pixels passed to tf.fromPixels() can not be null\");var r,n;if(ENV.get(\"IS_NODE\")&&null==e.getContext)throw new Error(\"When running in node, pixels must be an HTMLCanvasElement like the one returned by the `canvas` npm package\");if(null!=e.getContext)r=e.getContext(\"2d\").getImageData(0,0,e.width,e.height).data;else if(e instanceof ImageData)r=e.data;else{if(!(e instanceof HTMLImageElement||e instanceof HTMLVideoElement))throw new Error(\"pixels passed to tf.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement or ImageData, but was \"+e.constructor.name);if(null==this.fromPixels2DContext)throw new Error(\"Can't read pixels from HTMLImageElement outside the browser.\");this.fromPixels2DContext.canvas.width=e.width,this.fromPixels2DContext.canvas.height=e.height,this.fromPixels2DContext.drawImage(e,0,0,e.width,e.height),r=this.fromPixels2DContext.getImageData(0,0,e.width,e.height).data;}if(4===t)n=new Int32Array(r);else{var o=e.width*e.height;n=new Int32Array(o*t);for(var a=0;a<o;a++)for(var i=0;i<t;++i)n[a*t+i]=r[4*a+i];}return tensor3d(n,[e.height,e.width,t],\"int32\")},e.prototype.read=function(e){return __awaiter(this,void 0,void 0,function(){return __generator(this,function(t){return [2,this.readSync(e)]})})},e.prototype.readSync=function(e){var t=this.data.get(e),r=t.dtype,n=t.complexTensors;return \"complex64\"===r?mergeRealAndImagArrays(n.real.dataSync(),n.imag.dataSync()):this.data.get(e).values},e.prototype.disposeData=function(e){if(this.data.has(e)){var t=this.data.get(e).complexTensors;null!=t&&(t.real.dispose(),t.imag.dispose()),this.data.delete(e);}},e.prototype.time=function(e){return __awaiter(this,void 0,void 0,function(){var t;return __generator(this,function(r){return t=now(),e(),[2,{kernelMs:now()-t}]})})},e.prototype.memory=function(){return {unreliable:!0}},e.prototype.complex=function(e,t){var r=Tensor.make(e.shape,{},\"complex64\");return this.data.get(r.dataId).complexTensors={real:ENV.engine.keep(e.clone()),imag:ENV.engine.keep(t.clone())},r},e.prototype.real=function(e){return this.data.get(e.dataId).complexTensors.real.clone()},e.prototype.imag=function(e){return this.data.get(e.dataId).complexTensors.imag.clone()},e.prototype.assertNotComplex=function(e,t){Array.isArray(e)||(e=[e]),e.forEach(function(e){null!=e&&assert(\"complex64\"!==e.dtype,t+\" does not support complex64 tensors.\");});},e.prototype.slice=function(e,t,r){this.assertNotComplex(e,\"slice\");for(var n=buffer(r,e.dtype),o=0;o<n.size;++o){var a=n.indexToLoc(o),i=a.map(function(e,r){return e+t[r]});n.set.apply(n,[e.get.apply(e,i)].concat(a));}return n.toTensor()},e.prototype.stridedSlice=function(e,t,r,n,o,a,i,s,u){this.assertNotComplex(e,\"stridedSlice\");var l=getStridedSlicedInfo(e.shape,t,r,n,o,a,i,s,u),c=l[0],p=l[1],d=l[2],h=p.filter(function(e,t){return -1===d.indexOf(t)});if(h.some(function(e){return 0===e}))return tensor([],h);for(var f=buffer(p,e.dtype),m=0;m<f.size;m++){for(var g=f.indexToLoc(m),v=new Array(g.length),y=0;y<v.length;y++)v[y]=g[y]*n[y]+c[y];f.set.apply(f,[e.get.apply(e,v)].concat(g));}return f.toTensor().reshape(h)},e.prototype.reverse=function(e,t){this.assertNotComplex(e,\"reverse\");for(var r=buffer(e.shape,e.dtype),n=e.buffer(),o=function(o){var a=r.indexToLoc(o),i=a.slice();t.forEach(function(t){return i[t]=e.shape[t]-1-i[t]}),r.set.apply(r,[n.get.apply(n,i)].concat(a));},a=0;a<r.size;a++)o(a);return r.toTensor()},e.prototype.concat=function(e,t){this.assertNotComplex(e,\"concat\");var r=e.map(function(e){var r=sizeFromShape(e.shape.slice(t));return e.as2D(-1,r)}),n=computeOutShape(r.map(function(e){return e.shape}),1),o=buffer(n,e[0].dtype).values;if(1===r[0].shape[0]){var a=0;r.forEach(function(e){o.set(e.dataSync(),a),a+=e.size;});}else{var i=0;r.forEach(function(e){for(var t=e.dataSync(),r=0,a=0;a<e.shape[0];++a)for(var s=a*n[1]+i,u=0;u<e.shape[1];++u)o[s+u]=t[r++];i+=e.shape[1];});}var s=computeOutShape(e.map(function(e){return e.shape}),t);return tensor(o,s,e[0].dtype)},e.prototype.neg=function(e){return this.assertNotComplex(e,\"neg\"),this.multiply(scalar(-1),e)},e.prototype.add=function(e,t){return \"complex64\"===e.dtype||\"complex64\"===t.dtype?this.broadcastedBinaryComplexOp(e.cast(\"complex64\"),t.cast(\"complex64\"),function(e,t,r,n){return {real:e+r,imag:t+n}}):this.broadcastedBinaryOp(e,t,upcastType(e.dtype,t.dtype),function(e,t){return e+t})},e.prototype.addN=function(e){this.assertNotComplex(e,\"addN\");for(var t=e.map(function(e){return e.dataSync()}),r=buffer(e[0].shape,e[0].dtype),n=r.values,o=0;o<e.length;o++)for(var a=t[o],i=0;i<n.length;i++)n[i]+=a[i];return r.toTensor()},e.prototype.subtract=function(e,t){return \"complex64\"===e.dtype||\"complex64\"===t.dtype?this.broadcastedBinaryComplexOp(e.cast(\"complex64\"),t.cast(\"complex64\"),function(e,t,r,n){return {real:e-r,imag:t-n}}):this.broadcastedBinaryOp(e,t,upcastType(e.dtype,t.dtype),function(e,t){return e-t})},e.prototype.pow=function(e,t){return this.assertNotComplex([e,t],\"pow\"),this.broadcastedBinaryOp(e,t,e.dtype,function(e,t){return Math.pow(e,t)})},e.prototype.batchMatMul=function(e,t,r,n){this.assertNotComplex([e,t],\"matMul\");for(var o=r?e.shape[1]:e.shape[2],a=r?e.shape[2]:e.shape[1],i=n?t.shape[1]:t.shape[2],s=e.shape[0],u=e.dataSync(),l=t.dataSync(),c=r?[e.strides[0],1,e.strides[1]]:[e.strides[0],e.strides[1],1],p=c[0],d=c[1],h=c[2],f=n?[1,t.strides[1],t.strides[0]]:[t.strides[1],1,t.strides[0]],m=f[0],g=f[1],v=f[2],y=a*i,x=new Float32Array(s*y),T=this.blockSize,E=0;E<s;E++)for(var w=0;w<a;w+=T)for(var S=0;S<i;S+=T)for(var b=0;b<o;b+=T)for(var C=Math.min(w+T,a),_=Math.min(S+T,i),A=Math.min(b+T,o),N=w;N<C;N++)for(var R=S;R<_;R++){for(var k=0,I=b;I<A;I++)k+=u[E*p+N*d+I*h]*l[I*m+R*g+E*v];x[E*y+(N*i+R)]+=k;}return tensor3d(x,[s,a,i])},e.prototype.multiply=function(e,t){return \"complex64\"===e.dtype||\"complex64\"===t.dtype?this.broadcastedBinaryComplexOp(e.cast(\"complex64\"),t.cast(\"complex64\"),function(e,t,r,n){return {real:e*r-t*n,imag:e*n+t*r}}):this.broadcastedBinaryOp(e,t,upcastType(e.dtype,t.dtype),function(e,t){return e*t})},e.prototype.realDivide=function(e,t){this.assertNotComplex([e,t],\"realDivide\");return this.broadcastedBinaryOp(e,t,\"float32\",function(e,t){return e/t})},e.prototype.floorDiv=function(e,t){this.assertNotComplex([e,t],\"floorDiv\");return this.broadcastedBinaryOp(e,t,\"int32\",function(e,t){return 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r=[t];assertAxesAreInnerMostDims(\"argMin\",r,e.rank);for(var n=computeOutAndReduceShapes(e.shape,r),o=n[0],a=n[1],i=zeros(o,\"int32\"),s=sizeFromShape(a),u=i.dataSync(),l=e.dataSync(),c=0;c<u.length;++c){for(var p=c*s,d=l[p],h=0,f=0;f<s;++f){var m=l[p+f];m<d&&(d=m,h=f);}u[c]=h;}return i},e.prototype.argMax=function(e,t){this.assertNotComplex(e,\"argMax\");var r=[t];assertAxesAreInnerMostDims(\"argMax\",r,e.rank);for(var n=computeOutAndReduceShapes(e.shape,r),o=n[0],a=n[1],i=zeros(o,\"int32\"),s=sizeFromShape(a),u=i.dataSync(),l=e.dataSync(),c=0;c<u.length;++c){for(var p=c*s,d=l[p],h=0,f=0;f<s;++f){var m=l[p+f];m>d&&(d=m,h=f);}u[c]=h;}return i},e.prototype.cumsum=function(e,t,r,n){if(this.assertNotComplex(e,\"cumsum\"),t!==e.rank-1)throw new Error(\"backend.cumsum in CPU expects an inner-most axis=\"+(e.rank-1)+\" but got axis=\"+t);for(var o=upcastType(e.dtype,\"int32\"),a=zeros(e.shape,o),i=a.dataSync(),s=e.dataSync(),u=e.shape[e.rank-1],l=n?function(e,t){return e+u-t-1}:function(e,t){return e+t},c=0;c<s.length;c+=u)for(var p=0;p<u;p++){var d=l(c,p);if(0===p)i[d]=r?0:s[d];else{var h=l(c,p-1);i[d]=r?s[h]+i[h]:s[d]+i[h];}}return a},e.prototype.equal=function(e,t){return this.assertNotComplex([e,t],\"equal\"),this.broadcastedBinaryOp(e,t,\"bool\",function(e,t){return e===t?1:0})},e.prototype.notEqual=function(e,t){return this.assertNotComplex([e,t],\"notEqual\"),this.broadcastedBinaryOp(e,t,\"bool\",function(e,t){return e!==t?1:0})},e.prototype.less=function(e,t){return this.assertNotComplex([e,t],\"less\"),this.broadcastedBinaryOp(e,t,\"bool\",function(e,t){return e<t?1:0})},e.prototype.lessEqual=function(e,t){return this.assertNotComplex([e,t],\"lessEqual\"),this.broadcastedBinaryOp(e,t,\"bool\",function(e,t){return e<=t?1:0})},e.prototype.greater=function(e,t){return this.assertNotComplex([e,t],\"greater\"),this.broadcastedBinaryOp(e,t,\"bool\",function(e,t){return e>t?1:0})},e.prototype.greaterEqual=function(e,t){return this.assertNotComplex([e,t],\"greaterEqual\"),this.broadcastedBinaryOp(e,t,\"bool\",function(e,t){return e>=t?1:0})},e.prototype.logicalNot=function(e){this.assertNotComplex(e,\"logicalNot\");for(var t=e.dataSync(),r=new Int32Array(t.length),n=0;n<t.length;++n)r[n]=t[n]?0:1;return Tensor.make(e.shape,{values:r},\"bool\")},e.prototype.logicalAnd=function(e,t){return this.assertNotComplex([e,t],\"logicalAnd\"),this.broadcastedBinaryOp(e,t,\"bool\",function(e,t){return e&&t})},e.prototype.logicalOr=function(e,t){return this.assertNotComplex([e,t],\"logicalOr\"),this.broadcastedBinaryOp(e,t,\"bool\",function(e,t){return e||t})},e.prototype.select=function(e,t,r){this.assertNotComplex([e,t,r],\"select\");for(var n=e.dataSync(),o=t.dataSync(),a=r.dataSync(),i=zeros(t.shape,upcastType(t.dtype,r.dtype)),s=i.dataSync(),u=0,l=0===e.rank||e.rank>1||1===t.rank?1:t.shape[1],c=0;c<n.length;c++)for(var p=0;p<l;p++)1===n[c]?s[u++]=o[c]:s[u++]=a[c];return i},e.prototype.where=function(e){this.assertNotComplex([e],\"where\");var t=e.dataSync();return whereImpl(e.shape,t)},e.prototype.topk=function(e,t,r){return this.assertNotComplex(e,\"topk\"),topkImpl(e.dataSync(),e.shape,e.dtype,t,r)},e.prototype.min=function(e,t){this.assertNotComplex(e,\"min\"),assertAxesAreInnerMostDims(\"min\",t,e.rank);for(var r=computeOutAndReduceShapes(e.shape,t),n=r[0],o=r[1],a=zeros(n,e.dtype),i=sizeFromShape(o),s=a.dataSync(),u=e.dataSync(),l=0;l<s.length;++l){for(var c=l*i,p=u[c],d=0;d<i;++d){var h=u[c+d];h<p&&(p=h);}s[l]=p;}return a},e.prototype.minimum=function(e,t){return this.assertNotComplex([e,t],\"minimum\"),this.broadcastedBinaryOp(e,t,e.dtype,function(e,t){return Math.min(e,t)})},e.prototype.mod=function(e,t){return this.assertNotComplex([e,t],\"mod\"),this.broadcastedBinaryOp(e,t,e.dtype,function(e,t){var r=e%t;return 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r=computeOutAndReduceShapes(e.shape,t),n=r[0],o=r[1],a=zeros(n,e.dtype),i=sizeFromShape(o),s=a.dataSync(),u=e.dataSync(),l=0;l<s.length;++l){for(var c=l*i,p=u[c],d=0;d<i;++d){var h=u[c+d];p=p||h;}s[l]=p;}return a},e.prototype.squaredDifference=function(e,t){return this.assertNotComplex([e,t],\"squaredDifference\"),this.broadcastedBinaryOp(e,t,e.dtype,function(e,t){var r=e-t;return r*r})},e.prototype.ceil=function(e){this.assertNotComplex(e,\"ceil\");for(var t=e.dataSync(),r=new Float32Array(t.length),n=0;n<t.length;++n)r[n]=Math.ceil(t[n]);return Tensor.make(e.shape,{values:r})},e.prototype.floor=function(e){this.assertNotComplex(e,\"floor\");for(var t=e.dataSync(),r=new Float32Array(t.length),n=0;n<t.length;++n)r[n]=Math.floor(t[n]);return Tensor.make(e.shape,{values:r})},e.prototype.sign=function(e){this.assertNotComplex(e,\"x\");for(var t=e.dataSync(),r=new Float32Array(t.length),n=0;n<t.length;++n)t[n]<0?r[n]=-1:t[n]>0?r[n]=1:r[n]=0;return Tensor.make(e.shape,{values:r})},e.prototype.round=function(e){this.assertNotComplex(e,\"round\");for(var t=e.dataSync(),r=new Float32Array(t.length),n=0;n<t.length;++n){var o=Math.floor(t[n]);t[n]-o<.5?r[n]=Math.floor(t[n]):t[n]-o>.5?r[n]=Math.ceil(t[n]):r[n]=o%2==0?o:o+1;}return Tensor.make(e.shape,{values:r})},e.prototype.exp=function(e){this.assertNotComplex(e,\"exp\");for(var t=e.dataSync(),r=new Float32Array(t.length),n=0;n<t.length;++n)r[n]=Math.exp(t[n]);return Tensor.make(e.shape,{values:r})},e.prototype.expm1=function(e){this.assertNotComplex(e,\"expm1\");for(var t=e.dataSync(),r=new Float32Array(t.length),n=0;n<t.length;++n)r[n]=Math.expm1(t[n]);return Tensor.make(e.shape,{values:r})},e.prototype.log=function(e){this.assertNotComplex(e,\"log\");for(var t=e.dataSync(),r=new Float32Array(t.length),n=0;n<t.length;++n){var o=t[n];r[n]=Math.log(o);}return Tensor.make(e.shape,{values:r})},e.prototype.log1p=function(e){this.assertNotComplex(e,\"log1p\");for(var t=e.dataSync(),r=new Float32Array(t.length),n=0;n<t.length;++n){var o=t[n];r[n]=Math.log1p(o);}return Tensor.make(e.shape,{values:r})},e.prototype.sqrt=function(e){this.assertNotComplex(e,\"sqrt\");for(var t=e.dataSync(),r=new Float32Array(t.length),n=0;n<t.length;++n){var o=t[n];r[n]=Math.sqrt(o);}return Tensor.make(e.shape,{values:r})},e.prototype.rsqrt=function(e){this.assertNotComplex(e,\"rsqrt\");for(var t=e.dataSync(),r=new Float32Array(t.length),n=0;n<t.length;++n){var o=t[n];r[n]=1/Math.sqrt(o);}return Tensor.make(e.shape,{values:r})},e.prototype.square=function(e){this.assertNotComplex(e,\"square\");for(var t=e.dataSync(),r=new Float32Array(t.length),n=0;n<t.length;++n){var o=t[n];r[n]=o*o;}return Tensor.make(e.shape,{values:r})},e.prototype.reciprocal=function(e){this.assertNotComplex(e,\"reciprocal\");for(var t=e.dataSync(),r=new Float32Array(t.length),n=0;n<t.length;++n)r[n]=1/t[n];return Tensor.make(e.shape,{values:r})},e.prototype.relu=function(e){this.assertNotComplex(e,\"relu\");for(var t=zeros(e.shape,e.dtype),r=t.dataSync(),n=e.dataSync(),o=0;o<n.length;++o)r[o]=Math.max(0,n[o]);return t},e.prototype.elu=function(e){this.assertNotComplex(e,\"elu\");for(var t=new Float32Array(e.size),r=e.dataSync(),n=0;n<r.length;++n){var o=r[n];t[n]=o>=0?o:Math.exp(o)-1;}return Tensor.make(e.shape,{values:t})},e.prototype.eluDer=function(e,t){this.assertNotComplex([e,t],\"eluDer\");for(var r=new Float32Array(t.size),n=t.dataSync(),o=e.dataSync(),a=0;a<n.length;++a){var i=n[a];r[a]=i>=1?o[a]:o[a]*(i+1);}return Tensor.make(t.shape,{values:r})},e.prototype.selu=function(e){this.assertNotComplex(e,\"selu\");for(var t=SELU_SCALEALPHA,r=SELU_SCALE,n=new Float32Array(e.size),o=e.dataSync(),a=0;a<o.length;++a){var i=o[a];n[a]=i>=0?r*i:t*(Math.exp(i)-1);}return Tensor.make(e.shape,{values:n})},e.prototype.clip=function(e,t,r){this.assertNotComplex(e,\"clip\");for(var n=new Float32Array(e.size),o=e.dataSync(),a=0;a<o.length;++a){var i=o[a];n[a]=i>r?r:i<t?t:i;}return Tensor.make(e.shape,{values:n})},e.prototype.abs=function(e){for(var t=new Float32Array(e.size),r=e.dataSync(),n=0;n<r.length;++n)t[n]=Math.abs(r[n]);return Tensor.make(e.shape,{values:t})},e.prototype.complexAbs=function(e){for(var t=new Float32Array(e.size),r=e.dataSync(),n=0;n<e.size;++n){var o=r[2*n],a=r[2*n+1];t[n]=Math.sqrt(o*o+a*a);}return Tensor.make(e.shape,{values:t})},e.prototype.int=function(e){this.assertNotComplex(e,\"int\");for(var t=new Int32Array(e.size),r=e.dataSync(),n=0;n<r.length;++n)t[n]=r[n];return Tensor.make(e.shape,{values:t},\"int32\")},e.prototype.sigmoid=function(e){this.assertNotComplex(e,\"sigmoid\");for(var t=new Float32Array(e.size),r=e.dataSync(),n=0;n<r.length;++n)t[n]=1/(1+Math.exp(-r[n]));return Tensor.make(e.shape,{values:t})},e.prototype.softplus=function(e){this.assertNotComplex(e,\"softplus\");for(var t=Math.log(1.1920928955078125e-7)+2,r=new Float32Array(e.size),n=e.dataSync(),o=0;o<n.length;++o){var a=n[o]>-t,i=n[o]<t,s=Math.exp(n[o]),u=void 0;u=i?s:a?n[o]:Math.log(1+s),r[o]=u;}return Tensor.make(e.shape,{values:r})},e.prototype.sin=function(e){this.assertNotComplex(e,\"sin\");for(var t=new Float32Array(e.size),r=e.dataSync(),n=0;n<r.length;++n)t[n]=Math.sin(r[n]);return Tensor.make(e.shape,{values:t})},e.prototype.cos=function(e){this.assertNotComplex(e,\"cos\");for(var t=new Float32Array(e.size),r=e.dataSync(),n=0;n<r.length;++n)t[n]=Math.cos(r[n]);return Tensor.make(e.shape,{values:t})},e.prototype.tan=function(e){this.assertNotComplex(e,\"tan\");for(var t=new Float32Array(e.size),r=e.dataSync(),n=0;n<r.length;++n)t[n]=Math.tan(r[n]);return Tensor.make(e.shape,{values:t})},e.prototype.asin=function(e){this.assertNotComplex(e,\"asin\");for(var t=new Float32Array(e.size),r=e.dataSync(),n=0;n<r.length;++n)t[n]=Math.asin(r[n]);return Tensor.make(e.shape,{values:t})},e.prototype.acos=function(e){this.assertNotComplex(e,\"acos\");for(var t=new Float32Array(e.size),r=e.dataSync(),n=0;n<r.length;++n)t[n]=Math.acos(r[n]);return Tensor.make(e.shape,{values:t})},e.prototype.atan=function(e){this.assertNotComplex(e,\"atan\");for(var t=new Float32Array(e.size),r=e.dataSync(),n=0;n<r.length;++n)t[n]=Math.atan(r[n]);return Tensor.make(e.shape,{values:t})},e.prototype.atan2=function(e,t){return this.assertNotComplex([e,t],\"atan2\"),this.broadcastedBinaryOp(e,t,e.dtype,function(e,t){return Math.atan2(e,t)})},e.prototype.sinh=function(e){this.assertNotComplex(e,\"sinh\");for(var t=new Float32Array(e.size),r=e.dataSync(),n=0;n<r.length;++n)t[n]=Math.sinh(r[n]);return Tensor.make(e.shape,{values:t})},e.prototype.cosh=function(e){this.assertNotComplex(e,\"cosh\");for(var t=new Float32Array(e.size),r=e.dataSync(),n=0;n<r.length;++n)t[n]=Math.cosh(r[n]);return Tensor.make(e.shape,{values:t})},e.prototype.tanh=function(e){this.assertNotComplex(e,\"tanh\");for(var t=new Float32Array(e.size),r=e.dataSync(),n=0;n<r.length;++n)t[n]=tanh(r[n]);return Tensor.make(e.shape,{values:t})},e.prototype.asinh=function(e){this.assertNotComplex(e,\"asinh\");for(var t=new Float32Array(e.size),r=e.dataSync(),n=0;n<r.length;++n)t[n]=Math.asinh(r[n]);return Tensor.make(e.shape,{values:t})},e.prototype.acosh=function(e){this.assertNotComplex(e,\"acosh\");for(var t=new Float32Array(e.size),r=e.dataSync(),n=0;n<r.length;++n)t[n]=Math.acosh(r[n]);return Tensor.make(e.shape,{values:t})},e.prototype.atanh=function(e){this.assertNotComplex(e,\"atanh\");for(var t=new Float32Array(e.size),r=e.dataSync(),n=0;n<r.length;++n)t[n]=Math.atanh(r[n]);return Tensor.make(e.shape,{values:t})},e.prototype.erf=function(e){this.assertNotComplex(e,\"erf\");for(var t=new Float32Array(e.size),r=e.dataSync(),n=ERF_P,o=ERF_A1,a=ERF_A2,i=ERF_A3,s=ERF_A4,u=ERF_A5,l=0;l<r.length;++l){var c=r[l],p=1/(1+n*c);t[l]=1-((((u*p+s)*p+i)*p+a)*p+o)*p*Math.exp(-c*c);}return Tensor.make(e.shape,{values:t})},e.prototype.step=function(e,t){void 0===t&&(t=0),this.assertNotComplex(e,\"step\");for(var r=new Float32Array(e.size),n=e.dataSync(),o=0;o<n.length;++o){var a=n[o];isNaN(a)?r[o]=NaN:r[o]=a>0?1:t;}return Tensor.make(e.shape,{values:r})},e.prototype.conv2d=function(e,t,r){this.assertNotComplex([e,t],\"conv2d\");for(var n=r.filterHeight,o=r.filterWidth,a=r.dilationHeight,i=r.dilationWidth,s=r.padInfo.left,u=r.padInfo.top,l=buffer(r.outShape,e.dtype),c=e.dataSync(),p=t.dataSync(),d=l.values,h=0;h<r.batchSize;++h)for(var f=h*e.strides[0],m=h*l.strides[0],g=0;g<r.outHeight;++g)for(var v=m+g*l.strides[1],y=g*r.strideHeight-s,x=0;x<n;x++){var T=y+x*a;if(!(T<0||T>=r.inHeight))for(var E=x*t.strides[0],w=f+T*e.strides[1],S=0;S<r.outWidth;++S)for(var b=v+S*r.outChannels,C=S*r.strideWidth-u,_=0;_<o;_++){var A=C+_*i;if(!(A<0||A>=r.inWidth))for(var N=E+_*t.strides[1],R=w+A*r.inChannels,k=N,I=0;I<r.inChannels;++I){for(var D=c[R+I],M=0;M<r.outChannels;++M)d[b+M]+=D*p[k+M];k+=r.outChannels;}}}return l.toTensor()},e.prototype.conv2dDerInput=function(e,t,r){this.assertNotComplex([e,t],\"conv2dDerInput\");for(var n=buffer(r.inShape,\"float32\"),o=n.values,a=n.strides,i=a[0],s=a[1],u=a[2],l=e.dataSync(),c=e.strides,p=c[0],d=c[1],h=c[2],f=t.dataSync(),m=t.strides,g=m[0],v=m[1],y=m[2],x=r.batchSize,T=r.filterHeight,E=r.filterWidth,w=r.inChannels,S=r.inHeight,b=r.inWidth,C=r.outChannels,_=r.outHeight,A=r.outWidth,N=r.strideHeight,R=r.strideWidth,k=T-1-r.padInfo.top,I=E-1-r.padInfo.left,D=0;D<x;++D)for(var M=0;M<w;++M)for(var O=0;O<S;++O)for(var P=O-k,F=Math.max(0,Math.ceil(P/N)),L=Math.min(_,(T+P)/N),B=0;B<b;++B){for(var U=B-I,z=Math.max(0,Math.ceil(U/R)),V=Math.min(A,(E+U)/R),H=0,G=F;G<L;++G)for(var W=G*N-P,q=z;q<V;++q)for(var $=p*D+d*G+h*q,X=g*(T-1-W)+v*(E-1-(q*R-U))+y*M,K=0;K<C;++K){H+=l[$+K]*f[X+K];}o[i*D+s*O+u*B+M]=H;}return n.toTensor()},e.prototype.conv2dDerFilter=function(e,t,r){this.assertNotComplex([e,t],\"conv2dDerFilter\");for(var n=r.strideHeight,o=r.strideWidth,a=r.filterHeight,i=r.filterWidth,s=buffer(r.filterShape,\"float32\"),u=r.padInfo.left,l=r.padInfo.top,c=0;c<a;++c)for(var p=Math.max(0,Math.ceil((l-c)/n)),d=Math.min(r.outHeight,(r.inHeight+l-c)/n),h=0;h<i;++h)for(var f=Math.max(0,Math.ceil((u-h)/o)),m=Math.min(r.outWidth,(r.inWidth+u-h)/o),g=0;g<r.inChannels;++g)for(var v=0;v<r.outChannels;++v){for(var y=0,x=0;x<r.batchSize;++x)for(var T=p;T<d;++T)for(var E=c+T*n-l,w=f;w<m;++w){var S=h+w*o-u;y+=e.get(x,E,S,g)*t.get(x,T,w,v);}s.set(y,c,h,g,v);}return s.toTensor()},e.prototype.depthwiseConv2D=function(e,t,r){this.assertNotComplex([e,t],\"depthwiseConv2D\");for(var n=r.filterHeight,o=r.filterWidth,a=r.dilationHeight,i=r.dilationWidth,s=r.padInfo.left,u=r.padInfo.top,l=r.outChannels/r.inChannels,c=buffer(r.outShape,e.dtype),p=e.dataSync(),d=t.dataSync(),h=c.values,f=0;f<r.batchSize;++f)for(var m=f*e.strides[0],g=f*c.strides[0],v=0;v<r.outHeight;++v)for(var y=g+v*c.strides[1],x=v*r.strideHeight-s,T=0;T<n;++T){var E=x+T*a;if(!(E<0||E>=r.inHeight))for(var w=T*t.strides[0],S=m+E*e.strides[1],b=0;b<r.outWidth;++b)for(var C=y+b*c.strides[2],_=b*r.strideWidth-u,A=0;A<o;++A){var N=_+A*i;if(!(N<0||N>=r.inWidth))for(var R=w+A*t.strides[1],k=S+N*r.inChannels,I=C,D=R,M=0;M<r.inChannels;++M){for(var O=p[k+M],P=0;P<l;++P)h[I+P]+=O*d[D+P];I+=l,D+=l;}}}return c.toTensor()},e.prototype.depthwiseConv2DDerInput=function(e,t,r){this.assertNotComplex([e,t],\"depthwiseConv2DDerInput\");for(var n=buffer(r.inShape,\"float32\"),o=n.values,a=n.strides,i=a[0],s=a[1],u=a[2],l=e.dataSync(),c=e.strides,p=c[0],d=c[1],h=c[2],f=t.dataSync(),m=t.strides,g=m[0],v=m[1],y=m[2],x=r.batchSize,T=r.filterHeight,E=r.filterWidth,w=r.inChannels,S=r.inHeight,b=r.inWidth,C=r.outChannels,_=r.outHeight,A=r.outWidth,N=r.strideHeight,R=r.strideWidth,k=T-1-r.padInfo.top,I=E-1-r.padInfo.left,D=C/w,M=0;M<x;++M)for(var O=0;O<w;++O)for(var P=0;P<S;++P)for(var F=P-k,L=Math.max(0,Math.ceil(F/N)),B=Math.min(_,(T+F)/N),U=0;U<b;++U){for(var z=U-I,V=Math.max(0,Math.ceil(z/R)),H=Math.min(A,(E+z)/R),G=0,W=L;W<B;++W)for(var q=W*N-F,$=V;$<H;++$)for(var X=p*M+d*W+h*$,K=g*(T-1-q)+v*(E-1-($*R-z))+y*O,j=0;j<D;++j){G+=l[X+(O*D+j)]*f[K+j];}o[i*M+s*P+u*U+O]=G;}return n.toTensor()},e.prototype.depthwiseConv2DDerFilter=function(e,t,r){this.assertNotComplex([e,t],\"depthwiseConv2DDerFilter\");for(var n=r.strideHeight,o=r.strideWidth,a=r.filterHeight,i=r.filterWidth,s=buffer(r.filterShape,\"float32\"),u=r.padInfo.left,l=r.padInfo.top,c=r.outChannels/r.inChannels,p=0;p<a;++p)for(var d=Math.max(0,Math.ceil((l-p)/n)),h=Math.min(r.outHeight,(r.inHeight+l-p)/n),f=0;f<i;++f)for(var m=Math.max(0,Math.ceil((u-f)/o)),g=Math.min(r.outWidth,(r.inWidth+u-f)/o),v=0;v<r.outChannels;++v){for(var y=Math.trunc(v/c),x=v%c,T=0,E=0;E<r.batchSize;++E)for(var w=d;w<h;++w)for(var S=p+w*n-l,b=m;b<g;++b){var C=f+b*o-u;T+=e.get(E,S,C,y)*t.get(E,w,b,v);}s.set(T,p,f,y,x);}return s.toTensor()},e.prototype.tile=function(e,t){this.assertNotComplex(e,\"tile\");for(var r=new Array(e.rank),n=0;n<r.length;n++)r[n]=e.shape[n]*t[n];var o=buffer(r,e.dtype),a=e.buffer();for(n=0;n<o.values.length;++n){for(var i=o.indexToLoc(n),s=new Array(e.rank),u=0;u<s.length;u++)s[u]=i[u]%e.shape[u];var l=a.locToIndex(s);o.values[n]=a.values[l];}return o.toTensor()},e.prototype.pad=function(e,t,r){this.assertNotComplex(e,\"pad\");var n=t.map(function(t,r){return t[0]+e.shape[r]+t[1]}),o=t.map(function(e){return e[0]}),a=e.buffer(),i=buffer(n,e.dtype);0!==r&&i.values.fill(r);for(var s=0;s<e.size;s++){var u=a.indexToLoc(s),l=u.map(function(e,t){return e+o[t]});i.set.apply(i,[e.get.apply(e,u)].concat(l));}return i.toTensor()},e.prototype.transpose=function(e,t){this.assertNotComplex(e,\"transpose\");for(var r=new Array(e.rank),n=0;n<r.length;n++)r[n]=e.shape[t[n]];var o=e.dataSync(),a=buffer(r,e.dtype),i=e.buffer();for(n=0;n<e.size;++n){for(var s=i.indexToLoc(n),u=new Array(s.length),l=0;l<u.length;l++)u[l]=s[t[l]];var c=a.locToIndex(u);a.values[c]=o[n];}return a.toTensor()},e.prototype.gather=function(e,t,r){this.assertNotComplex([e,t],\"gather\");var n=e.shape.slice(),o=t.dataSync();n[r]=o.length;for(var a=buffer(n,e.dtype),i=e.buffer(),s=0;s<a.size;++s){var u=a.indexToLoc(s),l=u.slice();l[r]=o[u[r]];var c=i.locToIndex(l);a.values[s]=i.values[c];}return a.toTensor()},e.prototype.batchToSpaceND=function(e,t,r){this.assertNotComplex([e],\"batchToSpaceND\");var n=t.reduce(function(e,t){return e*t}),o=getReshaped(e.shape,t,n),a=getPermuted(o.length,t.length),i=getReshapedPermuted(e.shape,t,n),s=getSliceBeginCoords(r,t.length),u=getSliceSize(i,r,t.length);return e.reshape(o).transpose(a).reshape(i).slice(s,u)},e.prototype.spaceToBatchND=function(e,t,r){this.assertNotComplex([e],\"spaceToBatchND\");var n=t.reduce(function(e,t){return e*t}),o=[[0,0]];o.push.apply(o,r);for(var a=1+t.length;a<e.shape.length;++a)o.push([0,0]);var i=e.pad(o),s=getReshaped(i.shape,t,n,!1),u=getPermuted(s.length,t.length,!1),l=getReshapedPermuted(i.shape,t,n,!1);return i.reshape(s).transpose(u).reshape(l)},e.prototype.pool=function(e,t,r){this.assertNotComplex(e,\"pool\");for(var n=t.strideHeight,o=t.strideWidth,a=t.dilationHeight,i=t.dilationWidth,s=t.effectiveFilterHeight,u=t.effectiveFilterWidth,l=t.padInfo.top,c=t.padInfo.left,p=\"max\"===r?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,d=e.dataSync(),h=buffer(t.outShape,e.dtype),f=h.values,m=t.outShape[1]*t.outShape[2]*t.outShape[3],g=t.outShape[2]*t.outShape[3],v=t.outShape[3],y=0;y<t.batchSize;++y)for(var x=y*m,T=y*e.strides[0],E=0;E<t.inChannels;++E)for(var w=0;w<t.outHeight;++w)for(var S=w*n-l,b=Math.max(0,S),C=Math.min(t.inHeight,s+S),_=x+w*g,A=0;A<t.outWidth;++A){for(var N=A*o-c,R=Math.max(0,N),k=Math.min(t.inWidth,u+N),I=p,D=0,M=0,O=b;O<C;O+=a){for(var P=T+O*e.strides[1],F=R;F<k;F+=i){var L=d[P+F*e.strides[2]+E];\"max\"===r&&L>I?I=L:\"avg\"===r&&(D+=L,M++);}if(isNaN(I))break}f[_+A*v+E]=\"avg\"===r?D/M:I;}return h.toTensor()},e.prototype.maxPool=function(e,t){return this.pool(e,t,\"max\")},e.prototype.maxPoolPositions=function(e,t){for(var r=buffer(t.outShape,\"int32\"),n=t.strideHeight,o=t.strideWidth,a=t.dilationHeight,i=t.dilationWidth,s=t.effectiveFilterHeight,u=t.effectiveFilterWidth,l=t.padInfo.top,c=t.padInfo.left,p=0;p<t.batchSize;++p)for(var d=0;d<t.inChannels;++d)for(var h=0;h<t.outHeight;++h){for(var f=h*n-l,m=f;m<0;)m+=a;for(var g=Math.min(t.inHeight,s+f),v=0;v<t.outWidth;++v){for(var y=v*o-c,x=y;x<0;)x+=i;for(var T=Math.min(t.inWidth,u+y),E=Number.NEGATIVE_INFINITY,w=-1,S=m;S<g;S+=a)for(var b=S-f,C=x;C<T;C+=i){var _=C-y,A=e.get(p,S,C,d);A>E&&(E=A,w=b*u+_);}r.set(w,p,h,v,d);}}return r.toTensor()},e.prototype.maxPoolBackprop=function(e,t,r,n){this.assertNotComplex([t,r],\"maxPoolBackprop\");for(var o=this.maxPoolPositions(t,n),a=n.strideHeight,i=n.strideWidth,s=n.dilationHeight,u=n.dilationWidth,l=n.effectiveFilterHeight,c=n.effectiveFilterWidth,p=c-1-n.padInfo.left,d=l-1-n.padInfo.top,h=buffer(t.shape,\"float32\"),f=0;f<n.batchSize;++f)for(var m=0;m<n.inChannels;++m)for(var g=0;g<n.inHeight;++g)for(var v=0;v<n.inWidth;++v){for(var y=g-d,x=v-p,T=0,E=0;E<l;E+=s){var w=(y+E)/a;if(!(w<0||w>=n.outHeight||Math.floor(w)!==w))for(var S=0;S<c;S+=u){var b=(x+S)/i;if(!(b<0||b>=n.outWidth||Math.floor(b)!==b)){var C=l*c-1-o.get(f,w,b,m)===E*c+S?1:0;if(0!==C)T+=e.get(f,w,b,m)*C;}}}h.set(T,f,g,v,m);}return h.toTensor()},e.prototype.avgPoolBackprop=function(e,t,r){this.assertNotComplex([e,t],\"avgPoolBackprop\");for(var n=r.strideHeight,o=r.strideWidth,a=r.filterHeight,i=r.filterWidth,s=r.dilationHeight,u=r.dilationWidth,l=r.effectiveFilterHeight,c=r.effectiveFilterWidth,p=c-1-r.padInfo.left,d=l-1-r.padInfo.top,h=buffer(t.shape,\"float32\"),f=1/(a*i),m=0;m<r.batchSize;++m)for(var g=0;g<r.inChannels;++g)for(var v=0;v<r.inHeight;++v)for(var y=0;y<r.inWidth;++y){for(var x=v-d,T=y-p,E=0,w=0;w<l;w+=s){var S=(x+w)/n;if(!(S<0||S>=r.outHeight||Math.floor(S)!==S))for(var b=0;b<c;b+=u){var C=(T+b)/o;if(!(C<0||C>=r.outWidth||Math.floor(C)!==C))E+=e.get(m,S,C,g);}}h.set(E*f,m,v,y,g);}return h.toTensor()},e.prototype.cast=function(e,t){return castTensor(e,t,this)},e.prototype.reshape=function(e,t){return reshapeTensor(e,t)},e.prototype.avgPool=function(e,t){return this.assertNotComplex(e,\"avgPool\"),this.pool(e,t,\"avg\").toFloat()},e.prototype.resizeBilinear=function(e,t,r,n){this.assertNotComplex(e,\"resizeBilinear\");for(var o=e.shape,a=o[0],i=o[1],s=o[2],u=o[3],l=e.dataSync(),c=new Float32Array(sizeFromShape([a,t,r,u])),p=[n&&t>1?i-1:i,n&&r>1?s-1:s],d=[n&&t>1?t-1:t,n&&r>1?r-1:r],h=0,f=p[0]/d[0],m=p[1]/d[1],g=0;g<a;g++)for(var v=0;v<t;v++)for(var y=f*v,x=Math.floor(y),T=y-x,E=Math.min(i-1,Math.ceil(y)),w=g*e.strides[0]+x*e.strides[1],S=g*e.strides[0]+E*e.strides[1],b=0;b<r;b++)for(var C=m*b,_=Math.floor(C),A=C-_,N=Math.min(s-1,Math.ceil(C)),R=w+_*e.strides[2],k=S+_*e.strides[2],I=w+ +N*e.strides[2],D=S+N*e.strides[2],M=0;M<u;M++){var O=l[R+M],P=l[k+M],F=O+(l[I+M]-O)*A,L=F+(P+(l[D+M]-P)*A-F)*T;c[h++]=L;}return tensor(c,[a,t,r,u])},e.prototype.resizeBilinearBackprop=function(e,t,r){this.assertNotComplex([e,t],\"resizeBilinearBackprop\");for(var n=t.shape,o=n[0],a=n[1],i=n[2],s=n[3],u=e.shape,l=u[1],c=u[2],p=new Float32Array(o*a*i*s),d=[r&&l>1?a-1:a,r&&c>1?i-1:i],h=[r&&l>1?l-1:l,r&&c>1?c-1:c],f=d[0]/h[0],m=d[1]/h[1],g=e.dataSync(),v=0,y=0;y<o;y++)for(var x=y*t.strides[0],T=0;T<l;T++)for(var E=T*f,w=Math.floor(E),S=Math.min(Math.ceil(E),a-1),b=x+w*t.strides[1],C=x+S*t.strides[1],_=E-w,A=1-_,N=0;N<c;N++)for(var R=N*m,k=Math.floor(R),I=Math.min(Math.ceil(R),i-1),D=R-k,M=1-D,O=b+k*t.strides[2],P=b+I*t.strides[2],F=C+k*t.strides[2],L=C+I*t.strides[2],B=A*M,U=A*D,z=_*M,V=_*D,H=0;H<s;H++){var G=g[v++];p[O+H]+=G*B,p[P+H]+=G*U,p[F+H]+=G*z,p[L+H]+=G*V;}return tensor4d(p,[o,i,a,s],t.dtype)},e.prototype.resizeNearestNeighbor=function(e,t,r,n){this.assertNotComplex(e,\"resizeNearestNeighbor\");for(var o=e.shape,a=o[0],i=o[1],s=o[2],u=o[3],l=e.dataSync(),c=new Float32Array(a*t*r*u),p=[n&&t>1?i-1:i,n&&r>1?s-1:s],d=[n&&t>1?t-1:t,n&&r>1?r-1:r],h=p[0]/d[0],f=p[1]/d[1],m=0,g=0;g<a;g++)for(var v=g*e.strides[0],y=0;y<t;y++)for(var x=h*y,T=v+Math.min(i-1,n?Math.round(x):Math.floor(x))*e.strides[1],E=0;E<r;E++)for(var w=f*E,S=T+Math.min(s-1,n?Math.round(w):Math.floor(w))*e.strides[2],b=0;b<u;b++){var C=l[S+b];c[m++]=C;}return tensor(c,[a,t,r,u],e.dtype)},e.prototype.resizeNearestNeighborBackprop=function(e,t,r){this.assertNotComplex([e,t],\"resizeNearestNeighborBackprop\");for(var n=t.shape,o=n[0],a=n[1],i=n[2],s=n[3],u=e.shape,l=u[1],c=u[2],p=new Float32Array(o*a*i*s),d=e.dataSync(),h=[r&&l>1?a-1:a,r&&c>1?i-1:i],f=[r&&l>1?l-1:l,r&&c>1?c-1:c],m=h[0]/f[0],g=h[1]/f[1],v=1/m,y=1/g,x=2*Math.ceil(v)+2,T=2*Math.ceil(y)+2,E=0;E<o;E++)for(var w=E*t.strides[0],S=0;S<a;S++)for(var b=w+S*t.strides[1],C=Math.floor(S*v),_=Math.floor(C-x/2),A=0;A<i;A++)for(var N=b+A*t.strides[2],R=Math.floor(A*y),k=Math.floor(R-T/2),I=0;I<s;I++){for(var D=0,M=0;M<x;M++){var O=M+_;if(!(O<0||O>=l)){var P=w+O*e.strides[1],F=O*m;if(S===Math.min(a-1,r?Math.round(F):Math.floor(F)))for(var L=0;L<T;L++){var B=L+k;if(!(B<0||B>=c)){var U=P+B*e.strides[2],z=B*g;A===Math.min(i-1,r?Math.round(z):Math.floor(z))&&(D+=d[U+I]);}}}}p[N+I]=D;}return tensor4d(p,t.shape,t.dtype)},e.prototype.batchNormalization=function(e,t,r,n,o,a){this.assertNotComplex([e,t,r,o,a],\"batchNormalization\");for(var i=e.dataSync(),s=t.dataSync(),u=r.dataSync(),l=o?o.dataSync():new Float32Array([1]),c=a?a.dataSync():new Float32Array([0]),p=new Float32Array(i.length),d=c.length,h=l.length,f=u.length,m=s.length,g=0,v=0,y=0,x=0,T=0;T<i.length;++T)p[T]=c[g++]+(i[T]-s[v++])*l[y++]/Math.sqrt(u[x++]+n),g>=d&&(g=0),v>=m&&(v=0),y>=h&&(y=0),x>=f&&(x=0);return tensor4d(p,e.shape)},e.prototype.localResponseNormalization4D=function(e,t,r,n,o){this.assertNotComplex(e,\"localResponseNormalization4D\");var a=e.shape[3],i=a-1,s=e.dataSync(),u=sizeFromShape(e.shape),l=new Float32Array(u);function c(e){for(var r=e%a,n=e-r+Math.max(0,r-t),o=e-r+Math.min(r+t,i),u=0;n<=o;n++){var l=s[n];u+=l*l;}return u}for(var p=0;p<u;p++){var d=c(p),h=s[p]*Math.pow(r+n*d,-o);l[p]=h;}return tensor4d(l,e.shape)},e.prototype.LRNGrad=function(e,t,r,n,o,a,i){this.assertNotComplex(e,\"LRNGrad\");for(var s=e.shape[3],u=e.dataSync(),l=t.dataSync(),c=r.dataSync(),p=new Float32Array(sizeFromShape(e.shape)),d=sizeFromShape(e.shape),h=0;h<d;h++){for(var f=h%s,m=h-f+Math.max(0,f-n),g=h-f+Math.min(s,f+n+1),v=0,y=m;y<g;y++)v+=Math.pow(l[y],2);v=a*v+o;for(y=m;y<g;y++){var x=-2*a*i*l[y]*c[h]/v;h===y&&(x+=Math.pow(v,-i)),x*=u[h],p[y]+=x;}}return tensor4d(p,e.shape)},e.prototype.multinomial=function(e,t,r,n){this.assertNotComplex(e,\"multinomial\");for(var o=t?e:softmax(e),a=o.shape[0],i=o.shape[1],s=zeros([a,r],\"int32\"),u=s.dataSync(),l=o.dataSync(),c=0;c<a;++c){var p=c*i,d=new Float32Array(i-1);d[0]=l[p];for(var h=1;h<d.length;++h)d[h]=d[h-1]+l[p+h];for(var f=seedrandom_1(n.toString()),m=c*r,g=0;g<r;++g){var v=f();u[m+g]=d.length;for(var y=0;y<d.length;y++)if(v<d[y]){u[m+g]=y;break}}}return s},e.prototype.oneHot=function(e,t,r,n){this.assertNotComplex(e,\"oneHot\");var o=new Float32Array(e.size*t);o.fill(n);for(var a=0;a<e.size;++a)e.get(a)>=0&&e.get(a)<t&&(o[a*t+e.get(a)]=r);return tensor2d(o,[e.size,t],\"int32\")},e.prototype.nonMaxSuppression=function(e,t,r,n,o){return this.assertNotComplex(e,\"nonMaxSuppression\"),nonMaxSuppressionImpl(e.dataSync(),t.dataSync(),r,n,o)},e.prototype.fft=function(e){if(1!==e.shape[0])throw new Error(\"tf.fft() on CPU only supports vectors.\");return this.fftImpl(e,!1)},e.prototype.ifft=function(e){if(1!==e.shape[0])throw new Error(\"tf.ifft() on CPU only supports vectors.\");return this.fftImpl(e,!0)},e.prototype.fftImpl=function(e,t){var r=e.as1D(),n=r.size;if(this.isExponentOf2(n)){var o=this.fftRadix2(r,n,t).as2D(e.shape[0],e.shape[1]);return t&&(o=complex(real(o).div(scalar(n)),imag(o).div(scalar(n)))),o}var a=e.dataSync(),i=splitRealAndImagArrays(this.fourierTransformByMatmul(a,n,t));return complex(i.real,i.imag).as2D(e.shape[0],e.shape[1])},e.prototype.isExponentOf2=function(e){return 0==(e&e-1)},e.prototype.fftRadix2=function(e,t,r){if(1===t)return e;var n=e.dataSync(),o=t/2,a=complexWithEvenIndex(n),i=complex(a.real,a.imag).as1D(),s=complexWithOddIndex(n),u=complex(s.real,s.imag).as1D();i=this.fftRadix2(i,o,r),u=this.fftRadix2(u,o,r);var l=exponents(t,r),c=complex(l.real,l.imag).mul(u),p=i.add(c),d=i.sub(c),h=real(p).concat(real(d)),f=imag(p).concat(imag(d));return complex(h,f).as1D()},e.prototype.fourierTransformByMatmul=function(e,t,r){for(var n=new Float32Array(2*t),o=0;o<t;o++){for(var a=0,i=0,s=0;s<t;s++){var u=exponent(o*s,t,r),l=getComplexWithIndex(e,s);a+=l.real*u.real-l.imag*u.imag,i+=l.real*u.imag+l.imag*u.real;}r&&(a/=t,i/=t),assignToTypedArray(n,a,i,o);}return n},e.prototype.depthToSpace=function(e,t,r){assert(\"NHWC\"===r,\"Only NHWC dataFormat supported on CPU for depthToSpace. Got \"+r),assert(t>1,\"blockSize should be > 1 for depthToSpace, but was: \"+t);for(var n=e.shape[0],o=e.shape[1],a=e.shape[2],i=e.shape[3],s=o*t,u=a*t,l=i/(t*t),c=e.dataSync(),p=new Float32Array(n*s*u*l),d=0,h=0;h<n;++h)for(var f=0;f<s;++f)for(var m=Math.floor(f/t),g=f%t,v=0;v<u;++v)for(var y=Math.floor(v/t),x=(g*t+v%t)*l,T=0;T<l;++T){var E=T+x+i*(y+a*(m+o*h));p[d++]=c[E];}return tensor4d(p,[n,s,u,l])},e.prototype.broadcastedBinaryOp=function(e,t,r,n){var o=assertAndGetBroadcastShape(e.shape,t.shape),a=buffer(o,r),i=e.dataSync(),s=t.dataSync(),u=getBroadcastDims(e.shape,o),l=getBroadcastDims(t.shape,o),c=a.values;if(u.length+l.length===0)for(var p=0;p<c.length;++p)c[p]=n(i[p%i.length],s[p%s.length]);else{var d=e.buffer(),h=t.buffer(),f=function(r){var o=a.indexToLoc(r),p=o.slice(-e.rank);u.forEach(function(e){return p[e]=0});var f=d.locToIndex(p),m=o.slice(-t.rank);l.forEach(function(e){return m[e]=0});var g=h.locToIndex(m);c[r]=n(i[f],s[g]);};for(p=0;p<c.length;++p)f(p);}return a.toTensor()},e.prototype.broadcastedBinaryComplexOp=function(e,t,r){var n=assertAndGetBroadcastShape(e.shape,t.shape),o=buffer(n,\"float32\"),a=buffer(n,\"float32\"),i=e.dataSync(),s=t.dataSync(),u=getBroadcastDims(e.shape,n),l=getBroadcastDims(t.shape,n),c=o.values,p=a.values;if(u.length+l.length===0)for(var d=0;d<c.length;d++){var h=d%i.length,f=d%s.length,m=r(i[2*h],i[2*h+1],s[2*f],s[2*f+1]);c[d]=m.real,p[d]=m.imag;}else{var g=this.data.get(e.dataId).complexTensors.real.buffer(),v=this.data.get(t.dataId).complexTensors.real.buffer(),y=function(n){var a=o.indexToLoc(n),d=a.slice(-e.rank);u.forEach(function(e){return d[e]=0});var h=g.locToIndex(d),f=a.slice(-t.rank);l.forEach(function(e){return f[e]=0});var m=v.locToIndex(f),y=r(i[2*h],i[2*h+1],s[2*m],s[2*m+1]);c[n]=y.real,p[n]=y.imag;};for(d=0;d<c.length;d++)y(d);}return this.complex(o.toTensor(),a.toTensor())},e.prototype.split=function(e,t,r){return split(e,t,r)},e.prototype.dispose=function(){},e.prototype.floatPrecision=function(){return 32},e.prototype.cropAndResize=function(e,t,r,n,o,a){for(var i=e.shape,s=i[0],u=i[1],l=i[2],c=i[3],p=t.shape[0],d=n[0],h=n[1],f=buffer([p,d,h,c]),m=t.dataSync(),g=r.dataSync(),v=e.dataSync(),y=e.strides,x=f.strides,T=0;T<p;T++){var E=4*T,w=m[E],S=m[E+1],b=m[E+2],C=m[E+3],_=g[T];if(!(_>=s))for(var A=d>1?(b-w)*(u-1)/(d-1):0,N=h>1?(C-S)*(l-1)/(h-1):0,R=0;R<d;R++){var k=d>1?w*(u-1)+R*A:.5*(w+b)*(u-1);if(k<0||k>u-1)for(var I=0;I<h;I++)for(var D=0;D<c;D++){var M=D+I*x[2]+R*x[1]+T*x[0];f.values[M]=a;}else if(\"bilinear\"===o){var O=Math.floor(k),P=Math.ceil(k),F=k-O;for(I=0;I<h;I++){if((q=h>1?S*(l-1)+I*N:.5*(S+C)*(l-1))<0||q>l-1)for(D=0;D<c;D++){M=D+I*x[2]+R*x[1]+T*x[0];f.values[M]=a;}else{var L=Math.floor(q),B=Math.ceil(q),U=q-L;for(D=0;D<c;D++){var z=v[M=D+L*y[2]+O*y[1]+_*y[0]],V=v[M=D+B*y[2]+O*y[1]+_*y[0]],H=v[M=D+L*y[2]+P*y[1]+_*y[0]],G=z+(V-z)*U,W=H+(v[M=D+B*y[2]+P*y[1]+_*y[0]]-H)*U;M=D+I*x[2]+R*x[1]+T*x[0],f.values[M]=G+(W-G)*F;}}}}else for(I=0;I<h;++I){var q;if((q=h>1?S*(l-1)+I*N:.5*(S+C)*(l-1))<0||q>l-1)for(D=0;D<c;D++){M=D+I*x[2]+R*x[1]+T*x[0];f.values[M]=a;}else{var $=Math.round(q),X=Math.round(k);for(D=0;D<c;D++){var K=D+$*y[2]+X*y[1]+_*y[0],j=D+I*x[2]+R*x[1]+T*x[0];f.values[j]=v[K];}}}}}return f.toTensor()},e.prototype.sparseToDense=function(e,t,r,n){var o=calculateShapes(t,e,r),a=o.sliceRank,i=o.numUpdates,s=o.sliceSize,u=o.strides,l=o.outputSize;return this.scatter(e,t,r,l,s,i,a,u,n,!1)},e.prototype.gatherND=function(e,t){var r=t.shape,n=r[r.length-1],o=prepareAndValidate(e,t),a=o[0],i=o[1],s=o[2],u=o[3];if(0===i)return tensor([],a,e.dtype);for(var l=new TensorBuffer([i,s],e.dtype),c=t.dataSync(),p=e.dataSync(),d=0;d<i;d++){for(var h=[],f=0,m=0;m<n;m++){var g=c[d*n+m];f+=g*u[m],h.push(g);}if(f<0||f>=e.size/s)throw new Error(\"Invalid indices: \"+h+\" does not index into \"+e.shape);for(var v=0;v<s;v++)l.values[d*s+v]=p[f*s+v];}return l.toTensor().reshape(a)},e.prototype.scatterND=function(e,t,r){var n=calculateShapes(t,e,r),o=n.sliceRank,a=n.numUpdates,i=n.sliceSize,s=n.strides,u=n.outputSize,l=scalar(0);return this.scatter(e,t,r,u,i,a,o,s,l,!0)},e.prototype.scatter=function(e,t,r,n,o,a,i,s,u,l){var c=[n/o,o],p=e.dataSync(),d=t.dataSync();if(0===n)return tensor([],r,t.dtype);var h=new TensorBuffer(c,t.dtype);h.values.fill(u.dataSync()[0]);for(var f=0;f<a;f++){for(var m=[],g=0,v=0;v<i;v++){var y=p[f*i+v];m.push(y),g+=y*s[v];}if(g<0||g>=n/o)throw new Error(\"Invalid indices: \"+m+\" does not index into \"+r);for(var x=0;x<o;x++)l?h.values[g*o+x]+=d[f*o+x]:h.values[g*o+x]=0===t.rank?d[0]:d[f*o+x];}return h.toTensor().reshape(r)},e}();ENV.registerBackend(\"cpu\",function(){return new MathBackendCPU},1,setTensorTracker);var delayCallback=\"undefined\"!=typeof requestAnimationFrame?requestAnimationFrame:setImmediate;function nextFrame(){return new Promise(function(e){return delayCallback(function(){return e()})})}var DTYPE_VALUE_SIZE_MAP={float32:4,int32:4,uint16:2,uint8:1,bool:1};function encodeWeights(e){return __awaiter(this,void 0,void 0,function(){var t,r,n,o;return __generator(this,function(a){switch(a.label){case 0:for(n in t=[],r=[],e){if(\"float32\"!==(o=e[n]).dtype&&\"int32\"!==o.dtype&&\"bool\"!==o.dtype)throw new Error(\"Unsupported dtype in weight '\"+n+\"': \"+o.dtype);t.push({name:n,shape:o.shape,dtype:o.dtype}),r.push(o.data());}return [4,Promise.all(r)];case 1:return [2,{data:concatenateTypedArrays(a.sent()),specs:t}]}})})}function decodeWeights(e,t){for(var r={},n=0,o=function(t){var o=t.name,a=t.dtype,i=t.shape,s=sizeFromShape(i),u=void 0;if(\"quantization\"in t){var l=t.quantization;if(\"uint8\"!==l.dtype&&\"uint16\"!==l.dtype)throw new Error(\"Weight \"+t.name+\" has unknown quantization dtype \"+l.dtype+\". Supported quantization dtypes are: 'uint8' and 'uint16'.\");var c=DTYPE_VALUE_SIZE_MAP[l.dtype],p=e.slice(n,n+s*c),d=\"uint8\"===l.dtype?new Uint8Array(p):new Uint16Array(p);if(\"float32\"===a)u=Float32Array.from(d,function(e){return e*l.scale+l.min});else{if(\"int32\"!==a)throw new Error(\"Unsupported dtype in weight '\"+o+\"': \"+a);u=Int32Array.from(d,function(e){return Math.round(e*l.scale+l.min)});}n+=s*c;}else{var h=DTYPE_VALUE_SIZE_MAP[a];p=e.slice(n,n+s*h);if(\"float32\"===a)u=new Float32Array(p);else if(\"int32\"===a)u=new Int32Array(p);else{if(\"bool\"!==a)throw new Error(\"Unsupported dtype in weight '\"+o+\"': \"+a);u=new Uint8Array(p);}n+=s*h;}var f=void 0;if(\"float32\"===a)f=tensor(u,i,\"float32\");else if(\"int32\"===a)f=tensor(u,i,\"int32\");else{if(\"bool\"!==a)throw new Error(\"Unsupported dtype in weight '\"+o+\"': \"+a);f=tensor(u,i,\"bool\");}r[o]=f;},a=0,i=t;a<i.length;a++){o(i[a]);}return r}function concatenateTypedArrays(e){if(null===e)throw new Error(\"Invalid input value: \"+JSON.stringify(e));var t=0,r=[];e.forEach(function(e){if(t+=e.byteLength,r.push(e.byteLength===e.buffer.byteLength?e:new e.constructor(e)),!(e instanceof Float32Array||e instanceof Int32Array||e instanceof Uint8Array))throw new Error(\"Unsupported TypedArray subtype: \"+e.constructor.name)});var n=new Uint8Array(t),o=0;return r.forEach(function(e){n.set(new Uint8Array(e.buffer),o),o+=e.byteLength;}),n.buffer}var useNodeBuffer=\"undefined\"!=typeof Buffer&&(\"undefined\"==typeof Blob||\"undefined\"==typeof atob||\"undefined\"==typeof btoa);function stringByteLength(e){return useNodeBuffer?Buffer.byteLength(e):new Blob([e]).size}function arrayBufferToBase64String(e){return useNodeBuffer?Buffer.from(e).toString(\"base64\"):btoa(String.fromCharCode.apply(null,new Uint8Array(e)))}function base64StringToArrayBuffer(e){if(useNodeBuffer){var t=Buffer.from(e,\"base64\");return t.buffer.slice(t.byteOffset,t.byteOffset+t.byteLength)}for(var r=atob(e),n=new Uint8Array(r.length),o=0;o<r.length;++o)n.set([r.charCodeAt(o)],o);return n.buffer}function concatenateArrayBuffers(e){var t=0;e.forEach(function(e){t+=e.byteLength;});var r=new Uint8Array(t),n=0;return e.forEach(function(e){r.set(new Uint8Array(e),n),n+=e.byteLength;}),r.buffer}function basename(e){for(e=e.trim();e.endsWith(\"/\");)e=e.slice(0,e.length-1);var t=e.split(\"/\");return t[t.length-1]}function getModelArtifactsInfoForJSON(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error(\"Expected JSON model topology, received ArrayBuffer.\");return {dateSaved:new Date,modelTopologyType:\"JSON\",modelTopologyBytes:null==e.modelTopology?0:stringByteLength(JSON.stringify(e.modelTopology)),weightSpecsBytes:null==e.weightSpecs?0:stringByteLength(JSON.stringify(e.weightSpecs)),weightDataBytes:null==e.weightData?0:e.weightData.byteLength}}var IORouterRegistry=function(){function e(){this.saveRouters=[],this.loadRouters=[];}return e.getInstance=function(){return null==e.instance&&(e.instance=new e),e.instance},e.registerSaveRouter=function(t){e.getInstance().saveRouters.push(t);},e.registerLoadRouter=function(t){e.getInstance().loadRouters.push(t);},e.getSaveHandlers=function(t){return e.getHandlers(t,\"save\")},e.getLoadHandlers=function(t){return e.getHandlers(t,\"load\")},e.getHandlers=function(e,t){var r=[];return (\"load\"===t?this.getInstance().loadRouters:this.getInstance().saveRouters).forEach(function(t){var n=t(e);null!==n&&r.push(n);}),r},e}(),URL_SCHEME_SUFFIX=\"://\",ModelStoreManagerRegistry=function(){function e(){this.managers={};}return e.getInstance=function(){return null==e.instance&&(e.instance=new e),e.instance},e.registerManager=function(t,r){assert(null!=t,\"scheme must not be undefined or null.\"),t.endsWith(URL_SCHEME_SUFFIX)&&(t=t.slice(0,t.indexOf(URL_SCHEME_SUFFIX))),assert(t.length>0,\"scheme must not be an empty string.\");var n=e.getInstance();assert(null==n.managers[t],\"A model store manager is already registered for scheme '\"+t+\"'.\"),n.managers[t]=r;},e.getManager=function(e){var t=this.getInstance().managers[e];if(null==t)throw new Error(\"Cannot find model manager for scheme '\"+e+\"'\");return t},e.getSchemes=function(){return Object.keys(this.getInstance().managers)},e}();function parseURL(e){if(-1===e.indexOf(URL_SCHEME_SUFFIX))throw new Error(\"The url string provided does not contain a scheme. Supported schemes are: \"+ModelStoreManagerRegistry.getSchemes().join(\",\"));return {scheme:e.split(URL_SCHEME_SUFFIX)[0],path:e.split(URL_SCHEME_SUFFIX)[1]}}function cloneModelInternal(e,t,r){return void 0===r&&(r=!1),__awaiter(this,void 0,void 0,function(){var n,o,a,i,s,u,l,c,p;return __generator(this,function(d){switch(d.label){case 0:return assert(e!==t,\"Old path and new path are the same: '\"+e+\"'\"),assert((n=IORouterRegistry.getLoadHandlers(e)).length>0,\"Copying failed because no load handler is found for source URL \"+e+\".\"),assert(n.length<2,\"Copying failed because more than one (\"+n.length+\") load handlers for source URL \"+e+\".\"),o=n[0],assert((a=IORouterRegistry.getSaveHandlers(t)).length>0,\"Copying failed because no save handler is found for destination URL \"+t+\".\"),assert(a.length<2,\"Copying failed because more than one (\"+n.length+\") save handlers for destination URL \"+t+\".\"),i=a[0],s=parseURL(e).scheme,u=parseURL(e).path,l=s===parseURL(e).scheme,[4,o.load()];case 1:return c=d.sent(),r&&l?[4,ModelStoreManagerRegistry.getManager(s).removeModel(u)]:[3,3];case 2:d.sent(),d.label=3;case 3:return [4,i.save(c)];case 4:return p=d.sent(),!r||l?[3,6]:[4,ModelStoreManagerRegistry.getManager(s).removeModel(u)];case 5:d.sent(),d.label=6;case 6:return [2,p.modelArtifactsInfo]}})})}function listModels(){return __awaiter(this,void 0,void 0,function(){var e,t,r,n,o,a,i;return __generator(this,function(s){switch(s.label){case 0:e=ModelStoreManagerRegistry.getSchemes(),t={},r=0,n=e,s.label=1;case 1:return r<n.length?(o=n[r],[4,ModelStoreManagerRegistry.getManager(o).listModels()]):[3,4];case 2:for(i in a=s.sent())t[o+URL_SCHEME_SUFFIX+i]=a[i];s.label=3;case 3:return r++,[3,1];case 4:return [2,t]}})})}function removeModel(e){return __awaiter(this,void 0,void 0,function(){var t;return __generator(this,function(r){switch(r.label){case 0:return t=parseURL(e),[4,ModelStoreManagerRegistry.getManager(t.scheme).removeModel(t.path)];case 1:return [2,r.sent()]}})})}function copyModel(e,t){return __awaiter(this,void 0,void 0,function(){return __generator(this,function(r){switch(r.label){case 0:return [4,cloneModelInternal(e,t,!1)];case 1:return [2,r.sent()]}})})}function moveModel(e,t){return __awaiter(this,void 0,void 0,function(){return __generator(this,function(r){switch(r.label){case 0:return [4,cloneModelInternal(e,t,!0)];case 1:return [2,r.sent()]}})})}var DATABASE_NAME=\"tensorflowjs\",DATABASE_VERSION=1,MODEL_STORE_NAME=\"models_store\",INFO_STORE_NAME=\"model_info_store\";function getIndexedDBFactory(){if(!ENV.get(\"IS_BROWSER\"))throw new Error(\"Failed to obtain IndexedDB factory because the current environmentis not a web browser.\");var e=window,t=e.indexedDB||e.mozIndexedDB||e.webkitIndexedDB||e.msIndexedDB||e.shimIndexedDB;if(null==t)throw new Error(\"The current browser does not appear to support IndexedDB.\");return t}function setUpDatabase(e){var t=e.result;t.createObjectStore(MODEL_STORE_NAME,{keyPath:\"modelPath\"}),t.createObjectStore(INFO_STORE_NAME,{keyPath:\"modelPath\"});}var BrowserIndexedDB=function(){function e(e){if(this.indexedDB=getIndexedDBFactory(),null==e||!e)throw new Error(\"For IndexedDB, modelPath must not be null, undefined or empty.\");this.modelPath=e;}return e.prototype.save=function(e){return __awaiter(this,void 0,void 0,function(){return __generator(this,function(t){if(e.modelTopology instanceof ArrayBuffer)throw new Error(\"BrowserLocalStorage.save() does not support saving model topology in binary formats yet.\");return [2,this.databaseAction(this.modelPath,e)]})})},e.prototype.load=function(){return __awaiter(this,void 0,void 0,function(){return __generator(this,function(e){return [2,this.databaseAction(this.modelPath)]})})},e.prototype.databaseAction=function(e,t){var r=this;return new Promise(function(e,n){var o=r.indexedDB.open(DATABASE_NAME,DATABASE_VERSION);o.onupgradeneeded=function(){return setUpDatabase(o)},o.onsuccess=function(){var a=o.result;if(null==t){var i=a.transaction(MODEL_STORE_NAME,\"readonly\"),s=i.objectStore(MODEL_STORE_NAME).get(r.modelPath);s.onsuccess=function(){if(null==s.result)return a.close(),n(new Error(\"Cannot find model with path '\"+r.modelPath+\"' in IndexedDB.\"));e(s.result.modelArtifacts);},s.onerror=function(e){return a.close(),n(s.error)},i.oncomplete=function(){return a.close()};}else{var u,l=getModelArtifactsInfoForJSON(t),c=a.transaction(INFO_STORE_NAME,\"readwrite\"),p=c.objectStore(INFO_STORE_NAME),d=p.put({modelPath:r.modelPath,modelArtifactsInfo:l});d.onsuccess=function(){var o=(u=a.transaction(MODEL_STORE_NAME,\"readwrite\")).objectStore(MODEL_STORE_NAME).put({modelPath:r.modelPath,modelArtifacts:t,modelArtifactsInfo:l});o.onsuccess=function(){return e({modelArtifactsInfo:l})},o.onerror=function(e){var t=(p=c.objectStore(INFO_STORE_NAME)).delete(r.modelPath);t.onsuccess=function(){return a.close(),n(o.error)},t.onerror=function(e){return a.close(),n(o.error)};};},d.onerror=function(e){return a.close(),n(d.error)},c.oncomplete=function(){null==u?a.close():u.oncomplete=function(){return a.close()};};}},o.onerror=function(e){return n(o.error)};})},e.URL_SCHEME=\"indexeddb://\",e}(),indexedDBRouter=function(e){return ENV.get(\"IS_BROWSER\")&&!Array.isArray(e)&&e.startsWith(BrowserIndexedDB.URL_SCHEME)?browserIndexedDB(e.slice(BrowserIndexedDB.URL_SCHEME.length)):null};function browserIndexedDB(e){return new BrowserIndexedDB(e)}function maybeStripScheme(e){return e.startsWith(BrowserIndexedDB.URL_SCHEME)?e.slice(BrowserIndexedDB.URL_SCHEME.length):e}IORouterRegistry.registerSaveRouter(indexedDBRouter),IORouterRegistry.registerLoadRouter(indexedDBRouter);var BrowserIndexedDBManager=function(){function e(){this.indexedDB=getIndexedDBFactory();}return e.prototype.listModels=function(){return __awaiter(this,void 0,void 0,function(){var e=this;return __generator(this,function(t){return [2,new Promise(function(t,r){var n=e.indexedDB.open(DATABASE_NAME,DATABASE_VERSION);n.onupgradeneeded=function(){return setUpDatabase(n)},n.onsuccess=function(){var e=n.result,o=e.transaction(INFO_STORE_NAME,\"readonly\"),a=o.objectStore(INFO_STORE_NAME).getAll();a.onsuccess=function(){for(var e={},r=0,n=a.result;r<n.length;r++){var o=n[r];e[o.modelPath]=o.modelArtifactsInfo;}t(e);},a.onerror=function(t){return e.close(),r(a.error)},o.oncomplete=function(){return e.close()};},n.onerror=function(e){return r(n.error)};})]})})},e.prototype.removeModel=function(e){return __awaiter(this,void 0,void 0,function(){var t=this;return __generator(this,function(r){return e=maybeStripScheme(e),[2,new Promise(function(r,n){var o=t.indexedDB.open(DATABASE_NAME,DATABASE_VERSION);o.onupgradeneeded=function(){return setUpDatabase(o)},o.onsuccess=function(){var t,a=o.result,i=a.transaction(INFO_STORE_NAME,\"readwrite\"),s=i.objectStore(INFO_STORE_NAME),u=s.get(e);u.onsuccess=function(){if(null==u.result)return a.close(),n(new Error(\"Cannot find model with path '\"+e+\"' in IndexedDB.\"));var o=s.delete(e),i=function(){var o=(t=a.transaction(MODEL_STORE_NAME,\"readwrite\")).objectStore(MODEL_STORE_NAME).delete(e);o.onsuccess=function(){return r(u.result.modelArtifactsInfo)},o.onerror=function(e){return n(u.error)};};o.onsuccess=i,o.onerror=function(e){return i(),a.close(),n(u.error)};},u.onerror=function(e){return a.close(),n(u.error)},i.oncomplete=function(){null==t?a.close():t.oncomplete=function(){return a.close()};};},o.onerror=function(e){return n(o.error)};})]})})},e}();if(ENV.get(\"IS_BROWSER\"))try{ModelStoreManagerRegistry.registerManager(BrowserIndexedDB.URL_SCHEME,new BrowserIndexedDBManager);}catch(e){}var PATH_SEPARATOR=\"/\",PATH_PREFIX=\"tensorflowjs_models\",INFO_SUFFIX=\"info\",MODEL_TOPOLOGY_SUFFIX=\"model_topology\",WEIGHT_SPECS_SUFFIX=\"weight_specs\",WEIGHT_DATA_SUFFIX=\"weight_data\";function getModelKeys(e){return {info:[PATH_PREFIX,e,INFO_SUFFIX].join(PATH_SEPARATOR),topology:[PATH_PREFIX,e,MODEL_TOPOLOGY_SUFFIX].join(PATH_SEPARATOR),weightSpecs:[PATH_PREFIX,e,WEIGHT_SPECS_SUFFIX].join(PATH_SEPARATOR),weightData:[PATH_PREFIX,e,WEIGHT_DATA_SUFFIX].join(PATH_SEPARATOR)}}function getModelPathFromKey(e){var t=e.split(PATH_SEPARATOR);if(t.length<3)throw new Error(\"Invalid key format: \"+e);return t.slice(1,t.length-1).join(PATH_SEPARATOR)}function maybeStripScheme$1(e){return e.startsWith(BrowserLocalStorage.URL_SCHEME)?e.slice(BrowserLocalStorage.URL_SCHEME.length):e}var BrowserLocalStorage=function(){function e(e){if(!ENV.get(\"IS_BROWSER\")||void 0===window.localStorage)throw new Error(\"The current environment does not support local storage.\");if(this.LS=window.localStorage,null==e||!e)throw new Error(\"For local storage, modelPath must not be null, undefined or empty.\");this.modelPath=e,this.keys=getModelKeys(this.modelPath);}return e.prototype.save=function(e){return __awaiter(this,void 0,void 0,function(){var t,r,n,o;return __generator(this,function(a){if(e.modelTopology instanceof ArrayBuffer)throw new Error(\"BrowserLocalStorage.save() does not support saving model topology in binary formats yet.\");t=JSON.stringify(e.modelTopology),r=JSON.stringify(e.weightSpecs),n=getModelArtifactsInfoForJSON(e);try{return this.LS.setItem(this.keys.info,JSON.stringify(n)),this.LS.setItem(this.keys.topology,t),this.LS.setItem(this.keys.weightSpecs,r),this.LS.setItem(this.keys.weightData,arrayBufferToBase64String(e.weightData)),[2,{modelArtifactsInfo:n}]}catch(e){for(o in this.keys)this.LS.removeItem(this.keys[o]);throw new Error(\"Failed to save model '\"+this.modelPath+\"' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=\"+n.modelTopologyBytes+\", weightSpecsBytes=\"+n.weightSpecsBytes+\", weightDataBytes=\"+n.weightDataBytes+\".\")}return [2]})})},e.prototype.load=function(){return __awaiter(this,void 0,void 0,function(){var e,t,r,n,o;return __generator(this,function(a){if(null==(e=JSON.parse(this.LS.getItem(this.keys.info))))throw new Error(\"In local storage, there is no model with name '\"+this.modelPath+\"'\");if(\"JSON\"!==e.modelTopologyType)throw new Error(\"BrowserLocalStorage does not support loading non-JSON model topology yet.\");if(t={},null==(r=JSON.parse(this.LS.getItem(this.keys.topology))))throw new Error(\"In local storage, the topology of model '\"+this.modelPath+\"' is missing.\");if(t.modelTopology=r,null==(n=JSON.parse(this.LS.getItem(this.keys.weightSpecs))))throw new Error(\"In local storage, the weight specs of model '\"+this.modelPath+\"' are missing.\");if(t.weightSpecs=n,null==(o=this.LS.getItem(this.keys.weightData)))throw new Error(\"In local storage, the binary weight values of model '\"+this.modelPath+\"' are missing.\");return t.weightData=base64StringToArrayBuffer(o),[2,t]})})},e.URL_SCHEME=\"localstorage://\",e}(),localStorageRouter=function(e){return ENV.get(\"IS_BROWSER\")&&!Array.isArray(e)&&e.startsWith(BrowserLocalStorage.URL_SCHEME)?browserLocalStorage(e.slice(BrowserLocalStorage.URL_SCHEME.length)):null};function browserLocalStorage(e){return new BrowserLocalStorage(e)}IORouterRegistry.registerSaveRouter(localStorageRouter),IORouterRegistry.registerLoadRouter(localStorageRouter);var BrowserLocalStorageManager=function(){function e(){assert(ENV.get(\"IS_BROWSER\"),\"Current environment is not a web browser\"),assert(void 0!==window.localStorage,\"Current browser does not appear to support localStorage\"),this.LS=window.localStorage;}return e.prototype.listModels=function(){return __awaiter(this,void 0,void 0,function(){var e,t,r,n,o,a;return __generator(this,function(i){for(e={},t=PATH_PREFIX+PATH_SEPARATOR,r=PATH_SEPARATOR+INFO_SUFFIX,n=0;n<this.LS.length;++n)(o=this.LS.key(n)).startsWith(t)&&o.endsWith(r)&&(a=getModelPathFromKey(o),e[a]=JSON.parse(this.LS.getItem(o)));return [2,e]})})},e.prototype.removeModel=function(e){return __awaiter(this,void 0,void 0,function(){var t,r;return __generator(this,function(n){if(e=maybeStripScheme$1(e),t=getModelKeys(e),null==this.LS.getItem(t.info))throw new Error(\"Cannot find model at path '\"+e+\"'\");return r=JSON.parse(this.LS.getItem(t.info)),this.LS.removeItem(t.info),this.LS.removeItem(t.topology),this.LS.removeItem(t.weightSpecs),this.LS.removeItem(t.weightData),[2,r]})})},e}();if(ENV.get(\"IS_BROWSER\"))try{ModelStoreManagerRegistry.registerManager(BrowserLocalStorage.URL_SCHEME,new BrowserLocalStorageManager);}catch(e){}var DEFAULT_FILE_NAME_PREFIX=\"model\",DEFAULT_JSON_EXTENSION_NAME=\".json\",DEFAULT_WEIGHT_DATA_EXTENSION_NAME=\".weights.bin\",BrowserDownloads=function(){function e(t){if(!ENV.get(\"IS_BROWSER\"))throw new Error(\"triggerDownloads() cannot proceed because the current environment is not a browser.\");t.startsWith(e.URL_SCHEME)&&(t=t.slice(e.URL_SCHEME.length)),null!=t&&0!==t.length||(t=DEFAULT_FILE_NAME_PREFIX),this.modelTopologyFileName=t+DEFAULT_JSON_EXTENSION_NAME,this.weightDataFileName=t+DEFAULT_WEIGHT_DATA_EXTENSION_NAME;}return e.prototype.save=function(e){return __awaiter(this,void 0,void 0,function(){var t,r,n,o,a,i;return __generator(this,function(s){if(t=window.URL.createObjectURL(new Blob([e.weightData],{type:\"application/octet-stream\"})),e.modelTopology instanceof ArrayBuffer)throw new Error(\"DownloadTrigger.save() does not support saving model topology in binary formats yet.\");return r=[{paths:[\"./\"+this.weightDataFileName],weights:e.weightSpecs}],n={modelTopology:e.modelTopology,weightsManifest:r},o=window.URL.createObjectURL(new Blob([JSON.stringify(n)],{type:\"application/json\"})),(a=null==this.jsonAnchor?document.createElement(\"a\"):this.jsonAnchor).download=this.modelTopologyFileName,a.href=o,a.click(),null!=e.weightData&&((i=null==this.weightDataAnchor?document.createElement(\"a\"):this.weightDataAnchor).download=this.weightDataFileName,i.href=t,i.click()),[2,{modelArtifactsInfo:getModelArtifactsInfoForJSON(e)}]})})},e.URL_SCHEME=\"downloads://\",e}(),BrowserFiles=function(){function e(e){if(null==e||e.length<1)throw new Error(\"When calling browserFiles, at least 1 file is required, but received \"+e);this.files=e;}return e.prototype.load=function(){return __awaiter(this,void 0,void 0,function(){var e,t,r=this;return __generator(this,function(n){return e=this.files[0],t=this.files.slice(1),[2,new Promise(function(n,o){var a=new FileReader;a.onload=function(a){var i=JSON.parse(a.target.result),s=i.modelTopology;if(null!=s){0===t.length&&n({modelTopology:s});var u=i.weightsManifest;if(null!=u){var l;try{l=r.checkManifestAndWeightFiles(u,t);}catch(e){return void o(e)}var c=[],p=[],d=[];u.forEach(function(e){e.paths.forEach(function(e){p.push(e),d.push(null);}),c.push.apply(c,e.weights);}),u.forEach(function(e){e.paths.forEach(function(e){var t=new FileReader;t.onload=function(t){var r=t.target.result,o=p.indexOf(e);d[o]=r,-1===d.indexOf(null)&&n({modelTopology:s,weightSpecs:c,weightData:concatenateArrayBuffers(d)});},t.onerror=function(t){o(\"Failed to weights data from file of path '\"+e+\"'.\");},t.readAsArrayBuffer(l[e]);});});}else o(new Error(\"weightManifest field is missing from file \"+e.name));}else o(new Error(\"modelTopology field is missing from file \"+e.name));},a.onerror=function(t){o(\"Failed to read model topology and weights manifest JSON from file '\"+e.name+\"'. BrowserFiles supports loading Keras-style tf.Model artifacts only.\");},a.readAsText(e);})]})})},e.prototype.checkManifestAndWeightFiles=function(e,t){for(var r=[],n=t.map(function(e){return basename(e.name)}),o={},a=0,i=e;a<i.length;a++){i[a].paths.forEach(function(e){var a=basename(e);if(-1!==r.indexOf(a))throw new Error(\"Duplicate file basename found in weights manifest: '\"+a+\"'\");if(r.push(a),-1===n.indexOf(a))throw new Error(\"Weight file with basename '\"+a+\"' is not provided.\");o[e]=t[n.indexOf(a)];});}if(r.length!==t.length)throw new Error(\"Mismatch in the number of files in weights manifest (\"+r.length+\") and the number of weight files provided (\"+t.length+\").\");return o},e}(),browserDownloadsRouter=function(e){return ENV.get(\"IS_BROWSER\")&&!Array.isArray(e)&&e.startsWith(BrowserDownloads.URL_SCHEME)?browserDownloads(e.slice(BrowserDownloads.URL_SCHEME.length)):null};function browserDownloads(e){return void 0===e&&(e=\"model\"),new BrowserDownloads(e)}function browserFiles(e){return new BrowserFiles(e)}function loadWeightsAsArrayBuffer(e,t){return __awaiter(this,void 0,void 0,function(){var r,n;return __generator(this,function(o){switch(o.label){case 0:return r=e.map(function(e){return fetch(e,t)}),[4,Promise.all(r)];case 1:return n=o.sent(),[4,Promise.all(n.map(function(e){return e.arrayBuffer()}))];case 2:return [2,o.sent()]}})})}function loadWeights(e,t,r,n){return void 0===t&&(t=\"\"),__awaiter(this,void 0,void 0,function(){var o,a,i,s,u,l,c,p,d,h;return __generator(this,function(f){switch(f.label){case 0:if(o=e.map(function(){return !1}),a={},i=null!=r?r.map(function(){return !1}):[],s=[],e.forEach(function(e,t){var n=0;e.weights.forEach(function(e){var u=\"quantization\"in e?e.quantization.dtype:e.dtype,l=DTYPE_VALUE_SIZE_MAP[u]*sizeFromShape(e.shape),c=function(){o[t]=!0,null==a[t]&&(a[t]=[]),a[t].push({manifestEntry:e,groupOffset:n,sizeBytes:l});};null!=r?r.forEach(function(t,r){t===e.name&&(c(),i[r]=!0);}):c(),s.push(e.name),n+=l;});}),!i.every(function(e){return e}))throw u=r.filter(function(e,t){return !i[t]}),new Error(\"Could not find weights in manifest with names: \"+u.join(\", \")+\". \\nManifest JSON has weights with names: \"+s.join(\", \")+\".\");return l=o.reduce(function(e,t,r){return t&&e.push(r),e},[]),c=[],l.forEach(function(r){e[r].paths.forEach(function(e){var r=t+(t.endsWith(\"/\")?\"\":\"/\")+e;c.push(r);});}),[4,loadWeightsAsArrayBuffer(c,n)];case 1:return p=f.sent(),d={},h=0,l.forEach(function(t){for(var r=e[t].paths.length,n=0,o=0;o<r;o++)n+=p[h+o].byteLength;for(var i=new ArrayBuffer(n),s=new Uint8Array(i),u=0,l=0;l<r;l++){var c=new Uint8Array(p[h+l]);s.set(c,u),u+=c.byteLength;}a[t].forEach(function(e){var t=decodeWeights(i.slice(e.groupOffset,e.groupOffset+e.sizeBytes),[e.manifestEntry]);for(var r in t)d[r]=t[r];}),h+=r;}),[2,d]}})})}IORouterRegistry.registerSaveRouter(browserDownloadsRouter);var BrowserHTTPRequest=function(){function e(e,t,r){if(this.weightPathPrefix=r,this.DEFAULT_METHOD=\"POST\",\"undefined\"==typeof fetch)throw new Error(\"browserHTTPRequest is not supported outside the web browser without a fetch polyfill.\");if(assert(null!=e&&e.length>0,\"URL path for browserHTTPRequest must not be null, undefined or empty.\"),Array.isArray(e)&&assert(2===e.length,\"URL paths for browserHTTPRequest must have a length of 2, (actual length is \"+e.length+\").\"),this.path=e,null!=t&&null!=t.body)throw new Error(\"requestInit is expected to have no pre-existing body, but has one.\");this.requestInit=t||{};}return e.prototype.save=function(e){return __awaiter(this,void 0,void 0,function(){var t,r,n,o;return __generator(this,function(a){switch(a.label){case 0:if(e.modelTopology instanceof ArrayBuffer)throw new Error(\"BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.\");return (t=Object.assign({method:this.DEFAULT_METHOD},this.requestInit)).body=new FormData,r=[{paths:[\"./model.weights.bin\"],weights:e.weightSpecs}],n={modelTopology:e.modelTopology,weightsManifest:r},t.body.append(\"model.json\",new Blob([JSON.stringify(n)],{type:\"application/json\"}),\"model.json\"),null!=e.weightData&&t.body.append(\"model.weights.bin\",new Blob([e.weightData],{type:\"application/octet-stream\"}),\"model.weights.bin\"),[4,fetch(this.path,t)];case 1:if(200===(o=a.sent()).status)return [2,{modelArtifactsInfo:getModelArtifactsInfoForJSON(e),responses:[o]}];throw new Error(\"BrowserHTTPRequest.save() failed due to HTTP response status \"+o.status+\".\")}})})},e.prototype.load=function(){return __awaiter(this,void 0,void 0,function(){return __generator(this,function(e){return [2,Array.isArray(this.path)?this.loadBinaryModel():this.loadJSONModel()]})})},e.prototype.loadBinaryTopology=function(){return __awaiter(this,void 0,void 0,function(){var e;return __generator(this,function(t){switch(t.label){case 0:return t.trys.push([0,3,,4]),[4,fetch(this.path[0],this.requestInit)];case 1:return [4,t.sent().arrayBuffer()];case 2:return [2,t.sent()];case 3:throw e=t.sent(),new Error(this.path[0]+\" not found. \"+e);case 4:return [2]}})})},e.prototype.loadBinaryModel=function(){return __awaiter(this,void 0,void 0,function(){var e,t,r,n,o,a,i,s;return __generator(this,function(u){switch(u.label){case 0:return e=this.loadBinaryTopology(),[4,fetch(this.path[1],this.requestInit)];case 1:return t=u.sent(),[4,Promise.all([e,t])];case 2:return r=u.sent(),n=r[0],[4,r[1].json()];case 3:return null==(o=u.sent())?[3,5]:[4,this.loadWeights(o)];case 4:s=u.sent(),a=s[0],i=s[1],u.label=5;case 5:return [2,{modelTopology:n,weightSpecs:a,weightData:i}]}})})},e.prototype.loadJSONModel=function(){return __awaiter(this,void 0,void 0,function(){var e,t,r,n,o,a,i;return __generator(this,function(s){switch(s.label){case 0:return [4,fetch(this.path,this.requestInit)];case 1:return [4,s.sent().json()];case 2:if(e=s.sent(),t=e.modelTopology,r=e.weightsManifest,null==t&&null==r)throw new Error(\"The JSON from HTTP path \"+this.path+\" contains neither model topology or manifest for weights.\");return null==r?[3,4]:(a=e.weightsManifest,[4,this.loadWeights(a)]);case 3:i=s.sent(),n=i[0],o=i[1],s.label=4;case 4:return [2,{modelTopology:t,weightSpecs:n,weightData:o}]}})})},e.prototype.loadWeights=function(e){return __awaiter(this,void 0,void 0,function(){var t,r,n,o,a,i,s,u,l;return __generator(this,function(c){switch(c.label){case 0:for(null==(t=this.weightPathPrefix)&&(r=Array.isArray(this.path)?this.path[1]:this.path,(t=r.substring(0,r.lastIndexOf(\"/\"))).endsWith(\"/\")||(t+=\"/\")),n=[],o=0,a=e;o<a.length;o++)i=a[o],n.push.apply(n,i.weights);return s=[],e.forEach(function(e){e.paths.forEach(function(e){s.push(t+e);});}),u=[n],l=concatenateArrayBuffers,[4,loadWeightsAsArrayBuffer(s,this.requestInit)];case 1:return [2,u.concat([l.apply(void 0,[c.sent()])])]}})})},e.URL_SCHEME_REGEX=/^https?:\\/\\//,e}();function isHTTPScheme(e){return null!=e.match(BrowserHTTPRequest.URL_SCHEME_REGEX)}var httpRequestRouter=function(e){if(\"undefined\"==typeof fetch)return null;return (Array.isArray(e)?e.every(function(e){return isHTTPScheme(e)}):isHTTPScheme(e))?browserHTTPRequest(e):null};function browserHTTPRequest(e,t,r){return new BrowserHTTPRequest(e,t,r)}IORouterRegistry.registerSaveRouter(httpRequestRouter),IORouterRegistry.registerLoadRouter(httpRequestRouter);var PassthroughLoader=function(){function e(e,t,r){this.modelTopology=e,this.weightSpecs=t,this.weightData=r;}return e.prototype.load=function(){return __awaiter(this,void 0,void 0,function(){var e;return __generator(this,function(t){return e={},null!=this.modelTopology&&(e=__assign({modelTopology:this.modelTopology},e)),null!=this.weightSpecs&&this.weightSpecs.length>0&&(e=__assign({weightSpecs:this.weightSpecs},e)),null!=this.weightData&&this.weightData.byteLength>0&&(e=__assign({weightData:this.weightData},e)),[2,e]})})},e}(),PassthroughSaver=function(){function e(e){this.saveHandler=e;}return e.prototype.save=function(e){return __awaiter(this,void 0,void 0,function(){return __generator(this,function(t){return [2,this.saveHandler(e)]})})},e}();function fromMemory(e,t,r){return new PassthroughLoader(e,t,r)}function withSaveHandler(e){return new PassthroughSaver(e)}var registerSaveRouter=IORouterRegistry.registerSaveRouter,registerLoadRouter=IORouterRegistry.registerLoadRouter,getSaveHandlers=IORouterRegistry.getSaveHandlers,getLoadHandlers=IORouterRegistry.getLoadHandlers,io=Object.freeze({browserFiles:browserFiles,browserHTTPRequest:browserHTTPRequest,concatenateArrayBuffers:concatenateArrayBuffers,decodeWeights:decodeWeights,encodeWeights:encodeWeights,fromMemory:fromMemory,getLoadHandlers:getLoadHandlers,getModelArtifactsInfoForJSON:getModelArtifactsInfoForJSON,getSaveHandlers:getSaveHandlers,loadWeights:loadWeights,registerLoadRouter:registerLoadRouter,registerSaveRouter:registerSaveRouter,withSaveHandler:withSaveHandler,copyModel:copyModel,listModels:listModels,moveModel:moveModel,removeModel:removeModel});function confusionMatrix_(e,t,r){var n=convertToTensor(e,\"label\",\"confusionMatrix\",\"int32\"),o=convertToTensor(t,\"label\",\"confusionMatrix\",\"int32\");assert(null==r||r>0&&Number.isInteger(r),\"If provided, numClasses must be a positive integer, but got \"+r),assert(1===n.rank,\"Expected the rank of labels to be 1, but got \"+n.rank),assert(1===o.rank,\"Expected the rank of predictions to be 1, but got \"+o.rank),assert(n.shape[0]===o.shape[0],\"Mismatch in the number of examples: \"+n.shape[0]+\" vs. \"+o.shape[0]+\". Labels and predictions should have the same number of elements.\"),assert(r>0&&Number.isInteger(r),\"numClasses is required to be a positive integer, but got \"+r);var a=oneHot(n.asType(\"int32\"),r),i=oneHot(o.asType(\"int32\"),r);return a.transpose().matMul(i).asType(\"int32\")}var confusionMatrix=op({confusionMatrix_:confusionMatrix_}),math=Object.freeze({confusionMatrix:confusionMatrix}),Serializable=function(){function e(){}return e.prototype.getClassName=function(){return this.constructor.className},e.fromConfig=function(e,t){return new e(t)},e}(),SerializationMap=function(){function e(){this.classNameMap={};}return e.getMap=function(){return null==e.instance&&(e.instance=new e),e.instance},e.register=function(t){e.getMap().classNameMap[t.className]=[t,t.fromConfig];},e}();function registerClass(e){assert(null!=e.className,\"Class being registered does not have the static className property defined.\"),assert(\"string\"==typeof e.className,\"className is required to be a string, but got type \"+typeof e.className),assert(e.className.length>0,\"Class being registered has an empty-string as its className, which is disallowed.\"),SerializationMap.register(e);}var serialization=Object.freeze({Serializable:Serializable,SerializationMap:SerializationMap,registerClass:registerClass}),WEBGL_ENVS={HAS_WEBGL:!0},NODE_ENVS={IS_NODE:!0},CHROME_ENVS={IS_CHROME:!0},BROWSER_ENVS={IS_BROWSER:!0},CPU_ENVS={HAS_WEBGL:!1},BROWSER_CPU_ENVS={BACKEND:\"test-cpu\"},ALL_ENVS={};function expectArraysClose(e,t,r){if(null==r&&(r=ENV.get(\"TEST_EPSILON\")),e instanceof Tensor||t instanceof Tensor){if(e instanceof Tensor&&t instanceof Tensor){if(e.dtype!==t.dtype)throw new Error(\"Arrays are of different type actual: \"+e.dtype+\" vs expected: \"+t.dtype+\".\");if(!arraysEqual(e.shape,t.shape))throw new Error(\"Arrays are of different shape actual: \"+e.shape+\" vs expected: \"+t.shape+\".\")}}else{var n=e.constructor.name,o=t.constructor.name;if(n!==o)throw new Error(\"Arrays are of different type actual: \"+n+\" vs expected: \"+o)}var a,i;if(a=e instanceof Tensor?e.dataSync():e,i=t instanceof Tensor?t.dataSync():t,a.length!==i.length)throw new Error(\"Arrays have different lengths actual: \"+a.length+\" vs expected: \"+i.length+\".\\nActual:   \"+a+\".\\nExpected: \"+i+\".\");for(var s=0;s<i.length;++s){var u=a[s],l=i[s];if(!areClose(u,Number(l),r))throw new Error(\"Arrays differ: actual[\"+s+\"] = \"+u+\", expected[\"+s+\"] = \"+l+\".\\nActual:   \"+a+\".\\nExpected: \"+i+\".\")}}function expectPromiseToFail(e,t){e().then(function(){return t.fail()},function(){return t()});}function expectArraysEqual(e,t){return expectArraysClose(e,t,0)}function expectNumbersClose(e,t,r){if(null==r&&(r=ENV.get(\"TEST_EPSILON\")),!areClose(e,t,r))throw new Error(\"Numbers differ: actual === \"+e+\", expected === \"+t)}function areClose(e,t,r){return !(!isNaN(e)||!isNaN(t))||!(isNaN(e)||isNaN(t)||Math.abs(e-t)>r)}function expectValuesInRange(e,t,r){var n;n=e instanceof Tensor?e.dataSync():e;for(var o=0;o<n.length;o++)if(n[o]<t||n[o]>r)throw new Error(\"Value out of range:\"+n[o]+\" low: \"+t+\", high: \"+r)}function expectArrayBuffersEqual(e,t){expect(new Float32Array(e)).toEqual(new Float32Array(t));}var test_util=Object.freeze({WEBGL_ENVS:WEBGL_ENVS,NODE_ENVS:NODE_ENVS,CHROME_ENVS:CHROME_ENVS,BROWSER_ENVS:BROWSER_ENVS,CPU_ENVS:CPU_ENVS,BROWSER_CPU_ENVS:BROWSER_CPU_ENVS,ALL_ENVS:ALL_ENVS,expectArraysClose:expectArraysClose,expectPromiseToFail:expectPromiseToFail,expectArraysEqual:expectArraysEqual,expectNumbersClose:expectNumbersClose,expectValuesInRange:expectValuesInRange,expectArrayBuffersEqual:expectArrayBuffersEqual}),version$1=\"0.13.10\",webgl=Object.freeze({gpgpu_util:gpgpu_util,webgl_util:webgl_util,MathBackendWebGL:MathBackendWebGL,GPGPUContext:GPGPUContext}),Optimizer=function(e){function t(){return null!==e&&e.apply(this,arguments)||this}return __extends(t,e),t.prototype.minimize=function(e,t,r){void 0===t&&(t=!1);var n=this.computeGradients(e,r),o=n.value,a=n.grads;return this.applyGradients(a),Object.keys(a).forEach(function(e){return a[e].dispose()}),t?o:(o.dispose(),null)},t.prototype.computeGradients=function(e,t){return variableGrads(e,t)},t}(Serializable),AdadeltaOptimizer=function(e){function t(t,r,n){void 0===n&&(n=null);var o=e.call(this)||this;return o.learningRate=t,o.rho=r,o.epsilon=n,o.accumulatedGrads={},o.accumulatedUpdates={},o.c=keep(scalar(-t)),o.rhoScalar=keep(scalar(r)),o.oneMinusRho=keep(scalar(1-r)),null===n&&(n=ENV.get(\"EPSILON\")),o.epsilonScalar=keep(scalar(n)),o}return __extends(t,e),t.prototype.applyGradients=function(e){var t=this,r=function(r){var o=ENV.engine.registeredVariables[r];if(null==n.accumulatedGrads[r]){tidy(function(){t.accumulatedGrads[r]=zerosLike(o).variable(!1);});}if(null==n.accumulatedUpdates[r]){tidy(function(){t.accumulatedUpdates[r]=zerosLike(o).variable(!1);});}var a=e[r],i=n.accumulatedGrads[r],s=n.accumulatedUpdates[r];tidy(function(){var e=t.rhoScalar.mul(i).add(t.oneMinusRho.mul(a.square())),n=s.add(t.epsilonScalar).sqrt().div(i.add(t.epsilonScalar).sqrt()).mul(a),u=t.rhoScalar.mul(s).add(t.oneMinusRho.mul(n.square()));t.accumulatedGrads[r].assign(e),t.accumulatedUpdates[r].assign(u);var l=t.c.mul(n).add(o);o.assign(l);});},n=this;for(var o in e)r(o);},t.prototype.dispose=function(){var e=this;this.c.dispose(),this.epsilonScalar.dispose(),this.rhoScalar.dispose(),this.oneMinusRho.dispose(),null!=this.accumulatedUpdates&&(Object.keys(this.accumulatedUpdates).forEach(function(t){return e.accumulatedUpdates[t].dispose()}),Object.keys(this.accumulatedGrads).forEach(function(t){return e.accumulatedGrads[t].dispose()}));},t.prototype.getConfig=function(){return {learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}},t.fromConfig=function(e,t){return new e(t.learningRate,t.rho,t.epsilon)},t.className=\"AdadeltaOptimizer\",t}(Optimizer);registerClass(AdadeltaOptimizer);var AdagradOptimizer=function(e){function t(t,r){void 0===r&&(r=.1);var n=e.call(this)||this;return n.learningRate=t,n.initialAccumulatorValue=r,n.accumulatedGrads={},n.c=keep(scalar(-t)),n.epsilon=keep(scalar(ENV.get(\"EPSILON\"))),n}return __extends(t,e),t.prototype.applyGradients=function(e){var t=this,r=function(r){var o=ENV.engine.registeredVariables[r];if(null==n.accumulatedGrads[r]){tidy(function(){t.accumulatedGrads[r]=fill(o.shape,t.initialAccumulatorValue).variable(!1);});}var a=e[r],i=n.accumulatedGrads[r];tidy(function(){var e=i.add(a.square());t.accumulatedGrads[r].assign(e);var n=t.c.mul(a.div(e.add(t.epsilon).sqrt())).add(o);o.assign(n);});},n=this;for(var o in e)r(o);},t.prototype.dispose=function(){var e=this;this.epsilon.dispose(),this.c.dispose(),null!=this.accumulatedGrads&&Object.keys(this.accumulatedGrads).forEach(function(t){return e.accumulatedGrads[t].dispose()});},t.prototype.getConfig=function(){return {learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}},t.fromConfig=function(e,t){return new e(t.learningRate,t.initialAccumulatorValue)},t.className=\"AdagradOptimizer\",t}(Optimizer);registerClass(AdagradOptimizer);var AdamOptimizer=function(e){function t(t,r,n,o){void 0===o&&(o=null);var a=e.call(this)||this;return a.learningRate=t,a.beta1=r,a.beta2=n,a.epsilon=o,a.accumulatedFirstMoment={},a.accumulatedSecondMoment={},a.c=keep(scalar(-t)),a.beta1Scalar=keep(scalar(r)),a.beta2Scalar=keep(scalar(n)),tidy(function(){a.accBeta1=scalar(r).variable(),a.accBeta2=scalar(n).variable();}),a.oneMinusBeta1=keep(scalar(1-r)),a.oneMinusBeta2=keep(scalar(1-n)),a.one=keep(scalar(1)),null===o&&(o=ENV.get(\"EPSILON\")),a.epsScalar=keep(scalar(o)),a}return __extends(t,e),t.prototype.applyGradients=function(e){var t=this;tidy(function(){var r=t.one.sub(t.accBeta1),n=t.one.sub(t.accBeta2);for(var o in e){var a=ENV.engine.registeredVariables[o];if(null==t.accumulatedFirstMoment[o]){var i=!1;t.accumulatedFirstMoment[o]=zerosLike(a).variable(i);}if(null==t.accumulatedSecondMoment[o]){i=!1;t.accumulatedSecondMoment[o]=zerosLike(a).variable(i);}var s=e[o],u=t.accumulatedFirstMoment[o],l=t.accumulatedSecondMoment[o],c=t.beta1Scalar.mul(u).add(t.oneMinusBeta1.mul(s)),p=t.beta2Scalar.mul(l).add(t.oneMinusBeta2.mul(s.square())),d=c.div(r),h=p.div(n);t.accumulatedFirstMoment[o].assign(c),t.accumulatedSecondMoment[o].assign(p);var f=t.c.mul(d.div(t.epsScalar.add(h.sqrt()))).add(a);a.assign(f);}t.accBeta1.assign(t.accBeta1.mul(t.beta1Scalar)),t.accBeta2.assign(t.accBeta2.mul(t.beta2Scalar));});},t.prototype.dispose=function(){var e=this;this.c.dispose(),this.epsScalar.dispose(),this.beta1Scalar.dispose(),this.beta2Scalar.dispose(),this.accBeta1.dispose(),this.accBeta2.dispose(),this.oneMinusBeta1.dispose(),this.oneMinusBeta2.dispose(),this.one.dispose(),null!=this.accumulatedFirstMoment&&Object.keys(this.accumulatedFirstMoment).forEach(function(t){return e.accumulatedFirstMoment[t].dispose()}),null!=this.accumulatedSecondMoment&&Object.keys(this.accumulatedSecondMoment).forEach(function(t){return e.accumulatedSecondMoment[t].dispose()});},t.prototype.getConfig=function(){return {learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}},t.fromConfig=function(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)},t.className=\"AdamOptimizer\",t}(Optimizer);registerClass(AdamOptimizer);var AdamaxOptimizer=function(e){function t(t,r,n,o,a){void 0===o&&(o=null),void 0===a&&(a=0);var i=e.call(this)||this;return i.learningRate=t,i.beta1=r,i.beta2=n,i.epsilon=o,i.decay=a,i.accumulatedFirstMoment={},i.accumulatedWeightedInfNorm={},i.c=keep(scalar(-t)),i.beta1Scalar=keep(scalar(r)),i.beta2Scalar=keep(scalar(n)),i.decayScalar=keep(scalar(a)),tidy(function(){i.iteration=scalar(0).variable(),i.accBeta1=scalar(r).variable();}),i.oneMinusBeta1=keep(scalar(1-r)),i.one=keep(scalar(1)),null===o&&(o=ENV.get(\"EPSILON\")),i.epsScalar=keep(scalar(o)),i}return __extends(t,e),t.prototype.applyGradients=function(e){var t=this;tidy(function(){var r=t.one.sub(t.accBeta1),n=t.c.div(t.one.add(t.decayScalar.mul(t.iteration)));for(var o in e){var a=ENV.engine.registeredVariables[o];if(null==t.accumulatedFirstMoment[o]){var i=!1;t.accumulatedFirstMoment[o]=zerosLike(a).variable(i);}if(null==t.accumulatedWeightedInfNorm[o]){i=!1;t.accumulatedWeightedInfNorm[o]=zerosLike(a).variable(i);}var s=e[o],u=t.accumulatedFirstMoment[o],l=t.accumulatedWeightedInfNorm[o],c=t.beta1Scalar.mul(u).add(t.oneMinusBeta1.mul(s)),p=t.beta2Scalar.mul(l),d=s.abs(),h=p.maximum(d);t.accumulatedFirstMoment[o].assign(c),t.accumulatedWeightedInfNorm[o].assign(h);var f=n.div(r).mul(c.div(t.epsScalar.add(h))).add(a);a.assign(f);}t.iteration.assign(t.iteration.add(t.one)),t.accBeta1.assign(t.accBeta1.mul(t.beta1Scalar));});},t.prototype.dispose=function(){var e=this;this.c.dispose(),this.epsScalar.dispose(),this.accBeta1.dispose(),this.beta1Scalar.dispose(),this.beta2Scalar.dispose(),this.oneMinusBeta1.dispose(),this.decayScalar.dispose(),this.iteration.dispose(),this.one.dispose(),null!=this.accumulatedFirstMoment&&Object.keys(this.accumulatedFirstMoment).forEach(function(t){return e.accumulatedFirstMoment[t].dispose()}),null!=this.accumulatedWeightedInfNorm&&Object.keys(this.accumulatedWeightedInfNorm).forEach(function(t){return e.accumulatedWeightedInfNorm[t].dispose()});},t.prototype.getConfig=function(){return {learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}},t.fromConfig=function(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)},t.className=\"AdamaxOptimizer\",t}(Optimizer);registerClass(AdamaxOptimizer);var SGDOptimizer=function(e){function t(t){var r=e.call(this)||this;return r.learningRate=t,r.setLearningRate(t),r}return __extends(t,e),t.prototype.applyGradients=function(e){var t=this;Object.keys(e).forEach(function(r){var n=e[r],o=ENV.engine.registeredVariables[r];tidy(function(){var e=t.c.mul(n).add(o);o.assign(e);});});},t.prototype.setLearningRate=function(e){this.learningRate=e,null!=this.c&&this.c.dispose(),this.c=keep(scalar(-e));},t.prototype.dispose=function(){this.c.dispose();},t.prototype.getConfig=function(){return {learningRate:this.learningRate}},t.fromConfig=function(e,t){return new e(t.learningRate)},t.className=\"SGDOptimizer\",t}(Optimizer);registerClass(SGDOptimizer);var MomentumOptimizer=function(e){function t(t,r,n){void 0===n&&(n=!1);var o=e.call(this,t)||this;return o.learningRate=t,o.momentum=r,o.useNesterov=n,o.m=scalar(o.momentum),o.accumulations={},o}return __extends(t,e),t.prototype.applyGradients=function(e){var t=this,r=function(r){var o=ENV.engine.registeredVariables[r];if(null==n.accumulations[r]){tidy(function(){t.accumulations[r]=zerosLike(o).variable(!1);});}var a=n.accumulations[r],i=e[r];tidy(function(){var e,n=t.m.mul(a).add(i);e=t.useNesterov?t.c.mul(i.add(n.mul(t.m))).add(o):t.c.mul(n).add(o),t.accumulations[r].assign(n),o.assign(e);});},n=this;for(var o in e)r(o);},t.prototype.dispose=function(){if(e.prototype.dispose.call(this),this.m.dispose(),null!=this.accumulations)for(var t in this.accumulations)this.accumulations[t].dispose();},t.prototype.setMomentum=function(e){this.momentum=e;},t.prototype.getConfig=function(){return {learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}},t.fromConfig=function(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)},t.className=\"MomentumOptimizer\",t}(SGDOptimizer);registerClass(MomentumOptimizer);var RMSPropOptimizer=function(e){function t(t,r,n,o,a){void 0===r&&(r=.9),void 0===n&&(n=0),void 0===o&&(o=null),void 0===a&&(a=!1);var i=e.call(this)||this;return i.learningRate=t,i.decay=r,i.momentum=n,i.epsilon=o,i.accumulatedMeanSquares={},i.accumulatedMeanGrads={},i.accumulatedMoments={},i.c=keep(scalar(t)),i.decayScalar=keep(scalar(r)),i.momentumScalar=keep(scalar(n)),i.oneMinusDecay=keep(scalar(1-r)),i.centered=a,null===o&&(o=ENV.get(\"EPSILON\")),i.epsilonScalar=keep(scalar(o)),i}return __extends(t,e),t.prototype.applyGradients=function(e){var t=this,r=function(r){var o=ENV.engine.registeredVariables[r];if(null==n.accumulatedMeanSquares[r]){tidy(function(){t.accumulatedMeanSquares[r]=zerosLike(o).variable(!1);});}if(null==n.accumulatedMeanGrads[r]&&n.centered){tidy(function(){t.accumulatedMeanGrads[r]=zerosLike(o).variable(!1);});}if(null==n.accumulatedMoments[r]){tidy(function(){t.accumulatedMoments[r]=zerosLike(o).variable(!1);});}var a=n.accumulatedMeanSquares[r],i=n.accumulatedMeanGrads[r],s=n.accumulatedMoments[r],u=e[r];tidy(function(){var e=t.decayScalar.mul(a).add(t.oneMinusDecay.mul(u.square()));if(t.centered){var n=t.decayScalar.mul(i).add(t.oneMinusDecay.mul(u)),l=t.momentumScalar.mul(s).add(t.c.mul(u).div(e.sub(n.square().add(t.epsilonScalar)).sqrt()));t.accumulatedMeanSquares[r].assign(e),t.accumulatedMeanGrads[r].assign(n),t.accumulatedMoments[r].assign(l);var c=o.sub(l);o.assign(c);}else{var p=t.decayScalar.mul(a).add(t.oneMinusDecay.mul(u.square()));l=t.momentumScalar.mul(s).add(t.c.mul(u).div(p.add(t.epsilonScalar).sqrt()));t.accumulatedMeanSquares[r].assign(p),t.accumulatedMoments[r].assign(l);c=o.sub(l);o.assign(c);}});},n=this;for(var o in e)r(o);},t.prototype.dispose=function(){var e=this;this.c.dispose(),this.epsilonScalar.dispose(),this.decayScalar.dispose(),this.momentumScalar.dispose(),this.oneMinusDecay.dispose(),null!=this.accumulatedMeanSquares&&Object.keys(this.accumulatedMeanSquares).forEach(function(t){return e.accumulatedMeanSquares[t].dispose()}),null!=this.accumulatedMeanGrads&&this.centered&&Object.keys(this.accumulatedMeanGrads).forEach(function(t){return e.accumulatedMeanGrads[t].dispose()}),null!=this.accumulatedMoments&&Object.keys(this.accumulatedMoments).forEach(function(t){return e.accumulatedMoments[t].dispose()});},t.prototype.getConfig=function(){return {learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}},t.fromConfig=function(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)},t.className=\"RMSPropOptimizer\",t}(Optimizer);registerClass(RMSPropOptimizer);var OptimizerConstructors=function(){function e(){}return e.sgd=function(e){return new SGDOptimizer(e)},e.momentum=function(e,t,r){return void 0===r&&(r=!1),new MomentumOptimizer(e,t,r)},e.rmsprop=function(e,t,r,n,o){return void 0===t&&(t=.9),void 0===r&&(r=0),void 0===n&&(n=null),void 0===o&&(o=!1),new RMSPropOptimizer(e,t,r,n,o)},e.adam=function(e,t,r,n){return void 0===e&&(e=.001),void 0===t&&(t=.9),void 0===r&&(r=.999),void 0===n&&(n=null),new AdamOptimizer(e,t,r,n)},e.adadelta=function(e,t,r){return void 0===e&&(e=.001),void 0===t&&(t=.95),void 0===r&&(r=null),new AdadeltaOptimizer(e,t,r)},e.adamax=function(e,t,r,n,o){return void 0===e&&(e=.002),void 0===t&&(t=.9),void 0===r&&(r=.999),void 0===n&&(n=null),void 0===o&&(o=0),new AdamaxOptimizer(e,t,r,n,o)},e.adagrad=function(e,t){return void 0===t&&(t=.1),new AdagradOptimizer(e,t)},e}(),train={sgd:OptimizerConstructors.sgd,momentum:OptimizerConstructors.momentum,adadelta:OptimizerConstructors.adadelta,adagrad:OptimizerConstructors.adagrad,rmsprop:OptimizerConstructors.rmsprop,adamax:OptimizerConstructors.adamax,adam:OptimizerConstructors.adam},setBackend=Environment.setBackend,getBackend=Environment.getBackend,disposeVariables=Environment.disposeVariables,memory=Environment.memory;setOpHandler(ops);\n\n            /**\n             * @license\n             * Copyright 2018 Google LLC. All Rights Reserved.\n             * Licensed under the Apache License, Version 2.0 (the \"License\");\n             * you may not use this file except in compliance with the License.\n             * You may obtain a copy of the License at\n             *\n             * http://www.apache.org/licenses/LICENSE-2.0\n             *\n             * Unless required by applicable law or agreed to in writing, software\n             * distributed under the License is distributed on an \"AS IS\" BASIS,\n             * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n             * See the License for the specific language governing permissions and\n             * limitations under the License.\n             * =============================================================================\n             */\n            var extendStatics$1=function(e,t){return (extendStatics$1=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t;}||function(e,t){for(var n in t)t.hasOwnProperty(n)&&(e[n]=t[n]);})(e,t)};function __extends$1(e,t){function n(){this.constructor=e;}extendStatics$1(e,t),e.prototype=null===t?Object.create(t):(n.prototype=t.prototype,new n);}var __assign$1=function(){return (__assign$1=Object.assign||function(e){for(var t,n=1,r=arguments.length;n<r;n++)for(var i in t=arguments[n])Object.prototype.hasOwnProperty.call(t,i)&&(e[i]=t[i]);return e}).apply(this,arguments)};function __awaiter$1(e,t,n,r){return new(n||(n=Promise))(function(i,a){function o(e){try{l(r.next(e));}catch(e){a(e);}}function s(e){try{l(r.throw(e));}catch(e){a(e);}}function l(e){e.done?i(e.value):new n(function(t){t(e.value);}).then(o,s);}l((r=r.apply(e,t||[])).next());})}function __generator$1(e,t){var n,r,i,a,o={label:0,sent:function(){if(1&i[0])throw i[1];return i[1]},trys:[],ops:[]};return a={next:s(0),throw:s(1),return:s(2)},\"function\"==typeof Symbol&&(a[Symbol.iterator]=function(){return 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_nextUniqueTensorId=0;function getNextUniqueTensorId(){return _nextUniqueTensorId++}var _uidPrefixes={};function getUid(e){return void 0===e&&(e=\"\"),e in _uidPrefixes||(_uidPrefixes[e]=0),_uidPrefixes[e]+=1,e+_uidPrefixes[e].toString()}var scalarCache={float32:{},int32:{}},DEFAULT_DTYPE=\"float32\";function getScalar(e,t){return void 0===t&&(t=DEFAULT_DTYPE),null==scalarCache[t][e]&&(scalarCache[t][e]=scalar(e,t),keep(scalarCache[t][e])),scalarCache[t][e]}var AttributeError=function(e){function t(n){var r=e.call(this,n)||this;return Object.setPrototypeOf(r,t.prototype),r}return __extends$1(t,e),t}(Error),RuntimeError=function(e){function t(n){var r=e.call(this,n)||this;return Object.setPrototypeOf(r,t.prototype),r}return __extends$1(t,e),t}(Error),ValueError=function(e){function t(n){var r=e.call(this,n)||this;return Object.setPrototypeOf(r,t.prototype),r}return __extends$1(t,e),t}(Error),NotImplementedError=function(e){function t(n){var r=e.call(this,n)||this;return Object.setPrototypeOf(r,t.prototype),r}return __extends$1(t,e),t}(Error),AssertionError=function(e){function t(n){var r=e.call(this,n)||this;return Object.setPrototypeOf(r,t.prototype),r}return __extends$1(t,e),t}(Error),IndexError=function(e){function t(n){var r=e.call(this,n)||this;return Object.setPrototypeOf(r,t.prototype),r}return __extends$1(t,e),t}(Error);function pyListRepeat(e,t){if(Array.isArray(e)){for(var n=[],r=0;r<t;r++)n=n.concat(e);return n}return (n=new Array(t)).fill(e),n}function assert$1(e,t){if(!e)throw new AssertionError(t)}function count(e,t){for(var n=0,r=0,i=e;r<i.length;r++){i[r]===t&&n++;}return n}function singletonOrArray(e){return 1===e.length?e[0]:e}function toList(e){return Array.isArray(e)?e:[e]}function toSnakeCase(e){var t=e.replace(/(.)([A-Z][a-z0-9]+)/g,\"$1_$2\").replace(/([a-z])([A-Z])/g,\"$1_$2\").toLowerCase();return \"_\"!==t[0]?t:\"private\"+t}function toCamelCase(e){return e.length<=1?e:-1===e.indexOf(\"_\")?e:e.replace(/[_]+(\\w|$)/g,function(e,t){return t.toUpperCase()})}var _GLOBAL_CUSTOM_OBJECTS={};function serializeKerasObject(e){return null===e||void 0===e?null:{className:e.getClassName(),config:e.getConfig()}}function deserializeKerasObject(e,t,n,r){if(void 0===t&&(t={}),void 0===n&&(n={}),void 0===r&&(r=\"object\"),\"string\"==typeof e){var i=e,a=void 0;if(i in n)a=n[i];else if(i in _GLOBAL_CUSTOM_OBJECTS)a=_GLOBAL_CUSTOM_OBJECTS[i];else if(null==(a=t[i]))throw new ValueError(\"Unknown \"+r+\": \"+e+\". 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The custom \"+r+\" is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().\");if(null!=u){for(var c={},p=0,h=Object.keys(_GLOBAL_CUSTOM_OBJECTS);p<h.length;p++){c[v=h[p]]=_GLOBAL_CUSTOM_OBJECTS[v];}for(var d=0,f=Object.keys(n);d<f.length;d++){c[v=f[d]]=n[v];}o.config.customObjects=c;for(var g=__assign$1({},_GLOBAL_CUSTOM_OBJECTS),m=0,y=Object.keys(n);m<y.length;m++){var v=y[m];_GLOBAL_CUSTOM_OBJECTS[v]=n[v];}var b=u(l,o.config);return _GLOBAL_CUSTOM_OBJECTS=__assign$1({},g),b}g=__assign$1({},_GLOBAL_CUSTOM_OBJECTS);for(var w=0,z=Object.keys(n);w<z.length;w++){v=z[w];_GLOBAL_CUSTOM_OBJECTS[v]=n[v];}var S,I,C;b=new l(o.config);return _GLOBAL_CUSTOM_OBJECTS=__assign$1({},g),b}function numberCompare(e,t){return e<t?-1:e>t?1:0}function reverseNumberCompare(e,t){return -1*numberCompare(e,t)}function stringToDType(e){switch(e){case\"float32\":return \"float32\";default:throw new ValueError(\"Invalid dtype: \"+e)}}function unique(e){if(null==e)return e;for(var t=[],n=0,r=e;n<r.length;n++){var i=r[n];-1===t.indexOf(i)&&t.push(i);}return t}function isObjectEmpty(e){if(null==e)throw new ValueError(\"Invalid value in obj: \"+JSON.stringify(e));for(var t in e)if(e.hasOwnProperty(t))return !1;return !0}function checkStringTypeUnionValue(e,t,n){if(null!=n&&e.indexOf(n)<0)throw new ValueError(n+\" is not a valid \"+t+\".  Valid values are \"+e+\" or null/undefined.\")}function checkArrayTypeAndLength(e,t,n,r){return void 0===n&&(n=0),void 0===r&&(r=1/0),assert$1(n>=0),assert$1(r>=n),Array.isArray(e)&&e.length>=n&&e.length<=r&&e.every(function(e){return typeof e===t})}function calcL2Norms(e,t){return tidy(function(){return sqrt(sum$1(mulStrict(e,e),t,!0))})}var Constraint=function(e){function t(){return null!==e&&e.apply(this,arguments)||this}return __extends$1(t,e),t.prototype.getConfig=function(){return {}},t}(serialization.Serializable),MaxNorm=function(e){function t(t){var n=e.call(this)||this;return n.defaultMaxValue=2,n.defaultAxis=0,n.maxValue=null!=t.maxValue?t.maxValue:n.defaultMaxValue,n.axis=null!=t.axis?t.axis:n.defaultAxis,n}return __extends$1(t,e),t.prototype.apply=function(e){var t=this;return tidy(function(){var n=calcL2Norms(e,t.axis),r=clipByValue(n,0,t.maxValue);return mul(e,div(r,add(getScalar(epsilon()),n)))})},t.prototype.getConfig=function(){return {maxValue:this.maxValue,axis:this.axis}},t.className=\"MaxNorm\",t}(Constraint);serialization.registerClass(MaxNorm);var UnitNorm=function(e){function t(t){var n=e.call(this)||this;return n.defaultAxis=0,n.axis=null!=t.axis?t.axis:n.defaultAxis,n}return __extends$1(t,e),t.prototype.apply=function(e){var t=this;return tidy(function(){return div(e,add(getScalar(epsilon()),calcL2Norms(e,t.axis)))})},t.prototype.getConfig=function(){return {axis:this.axis}},t.className=\"UnitNorm\",t}(Constraint);serialization.registerClass(UnitNorm);var NonNeg=function(e){function t(){return null!==e&&e.apply(this,arguments)||this}return __extends$1(t,e),t.prototype.apply=function(e){return relu(e)},t.className=\"NonNeg\",t}(Constraint);serialization.registerClass(NonNeg);var MinMaxNorm=function(e){function t(t){var n=e.call(this)||this;return n.defaultMinValue=0,n.defaultMaxValue=1,n.defaultRate=1,n.defaultAxis=0,n.minValue=null!=t.minValue?t.minValue:n.defaultMinValue,n.maxValue=null!=t.maxValue?t.maxValue:n.defaultMaxValue,n.rate=null!=t.rate?t.rate:n.defaultRate,n.axis=null!=t.axis?t.axis:n.defaultAxis,n}return __extends$1(t,e),t.prototype.apply=function(e){var t=this;return tidy(function(){var n=calcL2Norms(e,t.axis),r=add(mul(getScalar(t.rate),clipByValue(n,t.minValue,t.maxValue)),mul(getScalar(1-t.rate),n));return mul(e,div(r,add(getScalar(epsilon()),n)))})},t.prototype.getConfig=function(){return {minValue:this.minValue,maxValue:this.maxValue,rate:this.rate,axis:this.axis}},t.className=\"MinMaxNorm\",t}(Constraint);serialization.registerClass(MinMaxNorm);var CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP={maxNorm:\"MaxNorm\",minMaxNorm:\"MinMaxNorm\",nonNeg:\"NonNeg\",unitNorm:\"UnitNorm\"};function serializeConstraint(e){return serializeKerasObject(e)}function deserializeConstraint(e,t){return void 0===t&&(t={}),deserializeKerasObject(e,serialization.SerializationMap.getMap().classNameMap,t,\"constraint\")}function getConstraint(e){return null==e?null:\"string\"==typeof e?deserializeConstraint({className:e in CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP?CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP[e]:e,config:{}}):e instanceof Constraint?e:deserializeConstraint(e)}function maxNorm(e){return new MaxNorm(e)}function unitNorm(e){return new UnitNorm(e)}function nonNeg(){return new NonNeg}function minMaxNorm(e){return new MinMaxNorm(e)}var exports_constraints=Object.freeze({maxNorm:maxNorm,unitNorm:unitNorm,nonNeg:nonNeg,minMaxNorm:minMaxNorm}),nameMap=new Map,VALID_DATA_FORMAT_VALUES=[\"channelsFirst\",\"channelsLast\"];function checkDataFormat(e){checkStringTypeUnionValue(VALID_DATA_FORMAT_VALUES,\"DataFormat\",e);}var VALID_PADDING_MODE_VALUES=[\"valid\",\"same\",\"causal\"];function checkPaddingMode(e){checkStringTypeUnionValue(VALID_PADDING_MODE_VALUES,\"PaddingMode\",e);}var VALID_POOL_MODE_VALUES=[\"max\",\"avg\"];function checkPoolMode(e){checkStringTypeUnionValue(VALID_POOL_MODE_VALUES,\"PoolMode\",e);}var _nameScopeStack=[],_nameScopeDivider=\"/\";function nameScope(e,t){_nameScopeStack.push(e);try{var n=t();return _nameScopeStack.pop(),n}catch(e){throw _nameScopeStack.pop(),e}}function currentNameScopePrefix(){return 0===_nameScopeStack.length?\"\":_nameScopeStack.join(_nameScopeDivider)+_nameScopeDivider}function getScopedTensorName(e){if(!isValidTensorName(e))throw new Error(\"Not a valid tensor name: '\"+e+\"'\");return currentNameScopePrefix()+e}function getUniqueTensorName(e){if(!isValidTensorName(e))throw new Error(\"Not a valid tensor name: '\"+e+\"'\");nameMap.has(e)||nameMap.set(e,0);var t=nameMap.get(e);if(nameMap.set(e,nameMap.get(e)+1),t>0){var n=e+\"_\"+t;return nameMap.set(n,1),n}return e}var tensorNameRegex=new RegExp(/^[A-Za-z][-A-Za-z0-9\\._\\/]*$/);function isValidTensorName(e){return !!e.match(tensorNameRegex)}function isInteger(e){return e===parseInt(e.toString(),10)}function arrayProd(e,t,n){null==t&&(t=0),null==n&&(n=e.length);for(var r=1,i=t;i<n;++i)r*=e[i];return r}function toArray1D(e){return e=Array.isArray(e)?new Float32Array(e):e,tensor1d(e)}function min$1(e){return min(toArray1D(e)).dataSync()[0]}function max$1(e){return max(toArray1D(e)).dataSync()[0]}function range$1(e,t){if(t<e)throw new ValueError(\"end (\"+t+\") < begin (\"+e+\") is forbidden.\");for(var n=[],r=e;r<t;++r)n.push(r);return n}function cast$1(e,t){return e.asType(t)}function expandDims$1(e,t){void 0===t&&(t=-1);var n=e.shape.slice();return t<0&&(t=n.length+t+1),n.splice(t,0,1),e.reshape(n)}function repeat(e,t){return tidy(function(){if(2!==e.shape.length)throw new ValueError(\"repeat() expects a rank-2 tensor, but received a rank-\"+e.shape.length+\" tensor.\");return tile$1(expandDims$1(e,1),[1,t,1])})}function flatten$1(e){var t=[arrayProd(e.shape)];return e.reshape(t)}function batchFlatten(e){if(e.rank<=1)throw new ValueError(\"batchFlatten requires a minimum rank of 2. Got rank: \"+e.rank+\".\");var t=[e.shape[0],arrayProd(e.shape,1)];return e.reshape(t)}function sliceAlongFirstAxis(e,t,n){return tidy(function(){switch(e.rank){case 1:return slice1d(e,t,n);case 2:return slice2d(e,[t,0],[n,e.shape[1]]);case 3:return slice3d(e,[t,0,0],[n,e.shape[1],e.shape[2]]);case 4:return slice4d(e,[t,0,0,0],[n,e.shape[1],e.shape[2],e.shape[3]]);default:throw new ValueError(\"sliceAlongFirstAxis() received an unsupported tensor rank: \"+e.rank)}})}function sliceAlongLastAxis(e,t,n){return tidy(function(){switch(e.rank){case 1:return slice1d(e,t,n);case 2:return slice2d(e,[0,t],[e.shape[0],n]);case 3:return slice3d(e,[0,0,t],[e.shape[0],e.shape[1],n]);case 4:return slice4d(e,[0,0,0,t],[e.shape[0],e.shape[1],e.shape[2],n]);default:throw new ValueError(\"sliceAlongLastAxis() received an unsupported tensor rank: \"+e.rank)}})}function sliceAlongAxis(e,t,n,r){return tidy(function(){switch(e.rank){case 1:return slice1d(e,t,n);case 2:switch(r){case 1:return sliceAlongFirstAxis(e,t,n);case 2:return sliceAlongLastAxis(e,t,n);default:throw new ValueError(\"The axis is not within the rank of the tensor \"+r)}case 3:switch(r){case 1:return sliceAlongFirstAxis(e,t,n);case 2:return slice3d(e,[0,t,0],[e.shape[0],n,e.shape[2]]);case 3:return sliceAlongLastAxis(e,t,n);default:throw new ValueError(\"The axis is not within the rank of the tensor \"+r)}case 4:switch(r){case 1:return sliceAlongFirstAxis(e,t,n);case 2:return slice4d(e,[0,t,0,0],[e.shape[0],n,e.shape[2],e.shape[3]]);case 3:return slice4d(e,[0,0,t,0],[e.shape[0],e.shape[1],n,e.shape[3]]);case 4:return sliceAlongLastAxis(e,t,n);default:throw new ValueError(\"The axis is not within the rank of the tensor \"+r)}default:throw new ValueError(\"sliceAlongLastAxis() received an unsupported tensor rank: \"+e.rank)}})}function concatenate(e,t){var n;return void 0===t&&(t=-1),t<0&&(t=0!==(n=e[0].rank)?n:0),t===e[0].rank&&(t=-1),concat(e,t)}function concatAlongFirstAxis(e,t){switch(e.rank){case 1:return concat1d([e,t]);case 2:return concat2d([e,t],0);case 3:return concat3d([e,t],0);case 4:return concat4d([e,t],0);default:throw new ValueError(\"concatAlongFirstAxis() received an unsupported tensor rank: \"+e.rank)}}function tile$1(e,t){if(Array.isArray(t)||(t=[t]),e.rank!==t.length)throw new ValueError(\"The length of input n (\"+t.length+\") does not match the number of dimensions in input x (\"+e.rank+\")\");return tile(e,t)}function randomNormal$1(e,t,n,r,i){return void 0===t&&(t=0),void 0===n&&(n=1),randomNormal(e,t,n,r,i)}function dot$1(e,t){if(e.rank<2||t.rank<2)throw new NotImplementedError(\"dot requires both inputs to be rank >= 2 but got x shape = \"+e.shape+\" and y shape = \"+t.shape);if(t.rank>=3&&(r=e.shape.slice(-1)[0])!==(o=t.shape.slice(-2)[0]))throw new NotImplementedError(\"If rank y >= 3, then the second last dim of y must equal the last dim of x but got x shape = \"+e.shape+\" and  y shape = \"+t.shape);if(2===e.rank&&2===t.rank)return matMul(e,t);var n=e.shape.slice(),r=n.pop();e=e.reshape([-1,r]);var i=t.shape.slice(),a=i.pop(),o=i.pop(),s=i.concat([a]),l=Array.from({length:t.rank},function(e,n){return 0===n?t.rank-2:n<=t.rank-2?n-1:n});t=t.transpose(l).reshape([o,-1]);var u=n.concat(s);return matMul(e,t).reshape(u)}function gather$1(e,t,n){return tidy(function(){return t=Array.isArray(t)?tensor1d(t,\"int32\"):t.toInt(),gather(e,t,n)})}function square$1(e){return mulStrict(e,e)}function biasAdd(e,t,n){return tidy(function(){if(null==n&&(n=imageDataFormat()),checkDataFormat(n),1!==t.rank&&t.rank!==e.rank)throw new ValueError(\"Unexpected bias dimensions: \"+t.rank+\"; expected it to be 1 or \"+e.rank);var r,i=t.shape;if(5===e.rank)\"channelsFirst\"===n?r=1===i.length?e.add(t.reshape([1,i[0],1,1,1])):e.add(t.reshape([1,i[3],i[0],i[1],i[2]])):\"channelsLast\"===n&&(r=1===i.length?e.add(t.reshape([1,1,1,1,i[0]])):e.add(t.reshape([1].concat(i))));else if(4===e.rank)\"channelsFirst\"===n?r=1===i.length?e.add(t.reshape([1,i[0],1,1])):e.add(t.reshape([1,i[2],i[0],i[1]])):\"channelsLast\"===n&&(r=1===i.length?e.add(t.reshape([1,1,1,i[0]])):e.add(t.reshape([1].concat(i))));else if(3===e.rank)\"channelsFirst\"===n?r=1===i.length?e.add(t.reshape([1,i[0],1])):e.add(t.reshape([1,i[1],i[0]])):\"channelsLast\"===n&&(r=1===i.length?e.add(t.reshape([1,1,i[0]])):e.add(t.reshape([1].concat(i))));else{if(!(e.rank<3))throw new ValueError(\"Unsupported input rank by biasAdd: \"+e.rank);r=e.add(t);}return r})}function elu$1(e,t){if(void 0===t&&(t=1),1!==t)throw new NotImplementedError(\"Support for alpha values other than 1 (\"+t+\") is not implemented yet.\");return elu(e)}function softsign(e){return tidy(function(){return div(e,add(getScalar(1),abs(e)))})}function dropout(e,t,n,r){return tidy(function(){if(null!=n&&!util.arraysEqual(e.shape,n))throw new NotImplementedError(\"Non-default noise shape is not implemented yet: \"+JSON.stringify(n));if(null!=r)throw new NotImplementedError(\"seed is not implemented for dropout yet.\");var i=step(add(neg(t),randomUniform(e.shape,0,1,\"float32\")));return i=mul(div(getScalar(1),sub(getScalar(1),t)),i),mul(e,i)})}function hardSigmoid(e){return tidy(function(){var t=add(getScalar(.5),mul(getScalar(.2),e));return clipByValue(t,0,1)})}function inTrainPhase(e,t,n){return void 0===n&&(n=!1),n?e():t()}var VALID_FAN_MODE_VALUES=[\"fanIn\",\"fanOut\",\"fanAvg\"];function checkFanMode(e){checkStringTypeUnionValue(VALID_FAN_MODE_VALUES,\"FanMode\",e);}var VALID_DISTRIBUTION_VALUES=[\"normal\",\"uniform\"];function checkDistribution(e){checkStringTypeUnionValue(VALID_DISTRIBUTION_VALUES,\"Distribution\",e);}var Initializer=function(e){function t(){return null!==e&&e.apply(this,arguments)||this}return __extends$1(t,e),t.prototype.fromConfigUsesCustomObjects=function(){return !1},t.prototype.getConfig=function(){return {}},t}(serialization.Serializable),Zeros=function(e){function t(){return null!==e&&e.apply(this,arguments)||this}return __extends$1(t,e),t.prototype.apply=function(e,t){return zeros(e,t)},t.className=\"Zeros\",t}(Initializer);serialization.registerClass(Zeros);var Ones=function(e){function t(){return null!==e&&e.apply(this,arguments)||this}return __extends$1(t,e),t.prototype.apply=function(e,t){return ones$1(e,t)},t.className=\"Ones\",t}(Initializer);serialization.registerClass(Ones);var Constant=function(e){function t(t){var n=e.call(this)||this;if(\"object\"!=typeof t)throw new ValueError(\"Expected argument of type ConstantConfig but got \"+t);if(void 0===t.value)throw new ValueError(\"config must have value set but got \"+t);return n.value=t.value,n}return __extends$1(t,e),t.prototype.apply=function(e,t){var n=this;return tidy(function(){return mul(scalar(n.value),ones$1(e,t))})},t.prototype.getConfig=function(){return {value:this.value}},t.className=\"Constant\",t}(Initializer);serialization.registerClass(Constant);var RandomUniform=function(e){function t(t){var n=e.call(this)||this;return n.DEFAULT_MINVAL=-.05,n.DEFAULT_MAXVAL=.05,n.minval=t.minval||n.DEFAULT_MINVAL,n.maxval=t.maxval||n.DEFAULT_MAXVAL,n.seed=t.seed,n}return __extends$1(t,e),t.prototype.apply=function(e,t){return randomUniform(e,this.minval,this.maxval,t)},t.prototype.getConfig=function(){return {minval:this.minval,maxval:this.maxval,seed:this.seed}},t.className=\"RandomUniform\",t}(Initializer);serialization.registerClass(RandomUniform);var RandomNormal=function(e){function t(t){var n=e.call(this)||this;return n.DEFAULT_MEAN=0,n.DEFAULT_STDDEV=.05,n.mean=t.mean||n.DEFAULT_MEAN,n.stddev=t.stddev||n.DEFAULT_STDDEV,n.seed=t.seed,n}return __extends$1(t,e),t.prototype.apply=function(e,t){if(\"float32\"!==(t=t||\"float32\")&&\"int32\"!==t)throw new NotImplementedError(\"randomNormal does not support dType \"+t+\".\");return randomNormal$1(e,this.mean,this.stddev,t,this.seed)},t.prototype.getConfig=function(){return {mean:this.mean,stddev:this.stddev,seed:this.seed}},t.className=\"RandomNormal\",t}(Initializer);serialization.registerClass(RandomNormal);var TruncatedNormal=function(e){function t(t){var n=e.call(this)||this;return n.DEFAULT_MEAN=0,n.DEFAULT_STDDEV=.05,n.mean=t.mean||n.DEFAULT_MEAN,n.stddev=t.stddev||n.DEFAULT_STDDEV,n.seed=t.seed,n}return __extends$1(t,e),t.prototype.apply=function(e,t){if(\"float32\"!==(t=t||\"float32\")&&\"int32\"!==t)throw new NotImplementedError(\"truncatedNormal does not support dType \"+t+\".\");return truncatedNormal(e,this.mean,this.stddev,t,this.seed)},t.prototype.getConfig=function(){return {mean:this.mean,stddev:this.stddev,seed:this.seed}},t.className=\"TruncatedNormal\",t}(Initializer);serialization.registerClass(TruncatedNormal);var Identity=function(e){function t(t){var n=e.call(this)||this;return n.gain=null!=t.gain?scalar(t.gain):getScalar(1),n}return __extends$1(t,e),t.prototype.apply=function(e,t){var n=this;return tidy(function(){if(2!==e.length||e[0]!==e[1])throw new ValueError(\"Identity matrix initializer can only be used for 2D square matrices.\");return mul(n.gain,eye(e[0]))})},t.prototype.getConfig=function(){return {gain:this.gain.get()}},t.className=\"Identity\",t}(Initializer);function computeFans(e,t){var n,r;if(void 0===t&&(t=\"channelsLast\"),checkDataFormat(t),2===e.length)n=e[0],r=e[1];else if(-1!==[3,4,5].indexOf(e.length)){if(\"channelsFirst\"===t){var i=arrayProd(e,2);n=e[1]*i,r=e[0]*i;}else if(\"channelsLast\"===t){i=arrayProd(e,0,e.length-2);n=e[e.length-2]*i,r=e[e.length-1]*i;}}else{var a=arrayProd(e);n=Math.sqrt(a),r=Math.sqrt(a);}return [n,r]}serialization.registerClass(Identity);var VarianceScaling=function(e){function t(t){var n=e.call(this)||this;if(t.scale<0)throw new ValueError(\"scale must be a positive float. Got: \"+t.scale);return n.scale=null==t.scale?1:t.scale,n.mode=t.mode,checkFanMode(n.mode),n.distribution=t.distribution,checkDistribution(n.distribution),n.seed=t.seed,n}return __extends$1(t,e),t.prototype.apply=function(e,t){var n=computeFans(e),r=n[0],i=n[1],a=this.scale;if(\"fanIn\"===this.mode?a/=Math.max(1,r):\"fanOut\"===this.mode?a/=Math.max(1,i):a/=Math.max(1,(r+i)/2),\"normal\"===this.distribution){var o=Math.sqrt(a);if(\"float32\"!==(t=t||\"float32\")&&\"int32\"!==t)throw new NotImplementedError(this.getClassName()+\" does not support dType \"+t+\".\");return truncatedNormal(e,0,o,t,this.seed)}var s=Math.sqrt(3*a);return randomUniform(e,-s,s,t)},t.prototype.getConfig=function(){return {scale:this.scale,mode:this.mode,distribution:this.distribution,seed:this.seed}},t.className=\"VarianceScaling\",t}(Initializer);serialization.registerClass(VarianceScaling);var GlorotUniform=function(e){function t(t){return e.call(this,{scale:1,mode:\"fanAvg\",distribution:\"uniform\",seed:null==t?null:t.seed})||this}return __extends$1(t,e),t.prototype.getClassName=function(){return VarianceScaling.className},t.className=\"GlorotUniform\",t}(VarianceScaling);serialization.registerClass(GlorotUniform);var GlorotNormal=function(e){function t(t){return e.call(this,{scale:1,mode:\"fanAvg\",distribution:\"normal\",seed:null==t?null:t.seed})||this}return __extends$1(t,e),t.prototype.getClassName=function(){return VarianceScaling.className},t.className=\"GlorotNormal\",t}(VarianceScaling);serialization.registerClass(GlorotNormal);var HeNormal=function(e){function t(t){return e.call(this,{scale:2,mode:\"fanIn\",distribution:\"normal\",seed:null==t?null:t.seed})||this}return __extends$1(t,e),t.prototype.getClassName=function(){return VarianceScaling.className},t.className=\"HeNormal\",t}(VarianceScaling);serialization.registerClass(HeNormal);var LeCunNormal=function(e){function t(t){return e.call(this,{scale:1,mode:\"fanIn\",distribution:\"normal\",seed:null==t?null:t.seed})||this}return __extends$1(t,e),t.prototype.getClassName=function(){return VarianceScaling.className},t.className=\"LeCunNormal\",t}(VarianceScaling);serialization.registerClass(LeCunNormal);var Orthogonal=function(e){function t(t){var n=e.call(this)||this;if(n.DEFAULT_GAIN=1,n.gain=null==t.gain?n.DEFAULT_GAIN:t.gain,n.seed=t.seed,null!=n.seed)throw new NotImplementedError(\"Random seed is not implemented for Orthogonal Initializer yet.\");return n}return __extends$1(t,e),t.prototype.apply=function(e,t){var n=this;return tidy(function(){if(2!==e.length)throw new NotImplementedError(\"The Orthogonal Initializer does not support non-2D shapes yet.\");e[0]*e[1]>2e3&&console.warn(\"Orthogonal initializer is being called on a matrix with more than 2000 (\"+e[0]*e[1]+\") elements: Slowness may result.\");var t=randomNormal$1(e[0]>e[1]?[e[1],e[0]]:e,0,1,\"float32\"),r=linalg_ops.gramSchmidt(t);return e[0]>e[1]&&(r=r.transpose()),mul(getScalar(n.gain),r)})},t.prototype.getConfig=function(){return {gain:this.gain,seed:this.seed}},t.className=\"Orthogonal\",t}(Initializer);serialization.registerClass(Orthogonal);var INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP={constant:\"Constant\",glorotNormal:\"GlorotNormal\",glorotUniform:\"GlorotUniform\",heNormal:\"HeNormal\",identity:\"Identity\",leCunNormal:\"LeCunNormal\",ones:\"Ones\",orthogonal:\"Orthogonal\",randomNormal:\"RandomNormal\",randomUniform:\"RandomUniform\",truncatedNormal:\"TruncatedNormal\",varianceScaling:\"VarianceScaling\",zeros:\"Zeros\"};function deserializeInitializer(e,t){return void 0===t&&(t={}),deserializeKerasObject(e,serialization.SerializationMap.getMap().classNameMap,t,\"initializer\")}function serializeInitializer(e){return serializeKerasObject(e)}function getInitializer(e){if(\"string\"==typeof e){var t=e in INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP?INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[e]:e;return \"GlorotUniform\"===t?new GlorotUniform:\"GlorotNormal\"===t?new GlorotNormal:\"HeNormal\"===t?new HeNormal:\"LeCunNormal\"===t?new LeCunNormal:deserializeInitializer({className:t,config:{}})}return e instanceof Initializer?e:deserializeInitializer(e)}function zeros$1(){return new Zeros}function ones$1$1(){return new Ones}function constant(e){return new Constant(e)}function randomUniform$1(e){return new RandomUniform(e)}function randomNormal$2(e){return new RandomNormal(e)}function truncatedNormal$1(e){return new TruncatedNormal(e)}function identity(e){return new Identity(e)}function varianceScaling(e){return new VarianceScaling(e)}function glorotUniform(e){return new GlorotUniform(e)}function glorotNormal(e){return new GlorotNormal(e)}function heNormal(e){return new HeNormal(e)}function leCunNormal(e){return new LeCunNormal(e)}function orthogonal(e){return new Orthogonal(e)}var exports_initializers=Object.freeze({zeros:zeros$1,ones:ones$1$1,constant:constant,randomUniform:randomUniform$1,randomNormal:randomNormal$2,truncatedNormal:truncatedNormal$1,identity:identity,varianceScaling:varianceScaling,glorotUniform:glorotUniform,glorotNormal:glorotNormal,heNormal:heNormal,leCunNormal:leCunNormal,orthogonal:orthogonal});function isArrayOfShapes(e){return Array.isArray(e)&&Array.isArray(e[0])}function normalizeShapeList(e){return 0===e.length?[]:Array.isArray(e[0])?e:[e]}function getExactlyOneTensor(e){var t;if(Array.isArray(e)){if(1!==e.length)throw new ValueError(\"Expected Tensor length to be 1; got \"+e.length);t=e[0];}else t=e;return t}function getExactlyOneShape(e){if(Array.isArray(e)&&Array.isArray(e[0])){if(1===e.length)return (e=e)[0];throw new ValueError(\"Expected exactly 1 Shape; got \"+e.length)}return e}function countParamsInWeights(e){for(var t=0,n=0,r=e;n<r.length;n++){var i=r[n];0===i.shape.length?t+=1:t+=i.shape.reduce(function(e,t){return e*t});}return t}var DEFAULT_VARIABLE_NAME_PREFIX=\"Variable\",LayerVariable=function(){function e(e,t,n,r,i){void 0===t&&(t=\"float32\"),void 0===n&&(n=DEFAULT_VARIABLE_NAME_PREFIX),void 0===r&&(r=!0),void 0===i&&(i=null),this.dtype=null==t?\"float32\":t,this.shape=e.shape,this.id=getNextUniqueTensorId(),n=null==n?DEFAULT_VARIABLE_NAME_PREFIX:n,this.originalName=getScopedTensorName(n),this.name=getUniqueTensorName(this.originalName),this.trainable=r,this.constraint=i,this.val=variable(e,this.trainable,this.name,this.dtype);}return e.prototype.read=function(){return this.assertNotDisposed(),this.val},e.prototype.write=function(e){return this.assertNotDisposed(),checkShapesMatch(this.val,e),this.val.id!==e.id&&(this.val.assign(e),null!=this.constraint&&this.val.assign(this.constraint.apply(this.val))),this},e.prototype.dispose=function(){this.assertNotDisposed(),this.val.dispose();},e.prototype.assertNotDisposed=function(){if(this.val.isDisposed)throw new Error(\"LayersVariable \"+this.name+\" is already disposed.\")},e}();function checkShapesMatch(e,t){if(e.shape.toString()!==t.shape.toString())throw new Error(\"Shape mismatch: \"+JSON.stringify(e.shape)+\" vs. \"+JSON.stringify(t.shape))}function batchGetValue(e){return e.map(function(e){return e.read()})}function batchSetValue(e){e.map(function(e){e[0].write(e[1]);});}var InputSpec=function(){return function(e){this.dtype=e.dtype,this.shape=e.shape,null!=e.shape?this.ndim=e.shape.length:this.ndim=e.ndim,this.maxNDim=e.maxNDim,this.minNDim=e.minNDim,this.axes=e.axes||{};}}(),SymbolicTensor=function(){return function(e,t,n,r,i,a,o){this.dtype=e,this.shape=t,this.sourceLayer=n,this.inputs=r,this.callArgs=i,this.outputTensorIndex=o,this.id=getNextUniqueTensorId(),null!=a&&(this.originalName=getScopedTensorName(a),this.name=getUniqueTensorName(this.originalName)),this.rank=t.length;}}(),_nextNodeID=0,Node=function(){function e(e,t){this.callArgs=t,this.id=_nextNodeID++,this.outboundLayer=e.outboundLayer,this.inboundLayers=e.inboundLayers,this.nodeIndices=e.nodeIndices,this.tensorIndices=e.tensorIndices,this.inputTensors=e.inputTensors,this.outputTensors=e.outputTensors,this.inputMasks=e.inputMasks,this.outputMasks=e.outputMasks,this.inputShapes=e.inputShapes,this.outputShapes=e.outputShapes;for(var n=0,r=e.inboundLayers;n<r.length;n++){var i=r[n];null!=i&&i.outboundNodes.push(this);}e.outboundLayer.inboundNodes.push(this);}return e.prototype.getConfig=function(){for(var e=[],t=0,n=this.inboundLayers;t<n.length;t++){var r=n[t];null!=r?e.push(r.name):e.push(null);}return {outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:e,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}},e}(),_nextLayerID=0,Layer=function(e){function t(t){var n=e.call(this)||this;n._callHook=null,n._addedWeightNames=[],n._stateful=!1,n.id=_nextLayerID++,n.activityRegularizer=null,n.inputSpec=null,n.supportsMasking=!1,n._trainableWeights=[],n._nonTrainableWeights=[],n._losses=[],n._updates=[],n._built=!1,n.inboundNodes=[],n.outboundNodes=[];var r=t.name;if(!r){var i=n.getClassName();r=toSnakeCase(i)+\"_\"+getUid(i);}if(n.name=r,n.trainable=null==t.trainable||t.trainable,n.updatable=null==t.updatable||t.updatable,null!=t.inputShape||null!=t.batchInputShape){var a=void 0;if(null!=t.batchInputShape)a=t.batchInputShape;else if(null!=t.inputShape){var o=null;null!=t.batchSize&&(o=t.batchSize),a=[o].concat(t.inputShape);}n.batchInputShape=a;var s=t.dtype;null==s&&(s=t.inputDType),null==s&&(s=\"float32\"),n.dtype=s;}return null!=t.weights?n.initialWeights=t.weights:n.initialWeights=null,n._refCount=null,n}return __extends$1(t,e),t.nodeKey=function(e,t){return e.name+\"_ib-\"+t.toString()},t.prototype.getNodeAtIndex=function(e,t){if(0===this.inboundNodes.length)throw new RuntimeError(\"The layer has never been called and thus has no defined \"+t+\".\");if(this.inboundNodes.length<=e)throw new ValueError(\"Asked to get \"+t+\" at node \"+e+\", but the layer has only \"+this.inboundNodes.length+\" inbound nodes.\");return this.inboundNodes[e]},t.prototype.getInputAt=function(e){return singletonOrArray(this.getNodeAtIndex(e,\"input\").inputTensors)},t.prototype.getOutputAt=function(e){return singletonOrArray(this.getNodeAtIndex(e,\"output\").outputTensors)},Object.defineProperty(t.prototype,\"input\",{get:function(){if(this.inboundNodes.length>1)throw new AttributeError(\"Layer \"+this.name+' has multiple inbound nodes, hence the notion of \"layer input\" is ill-defined. Use `getInputAt(nodeIndex)` instead.');if(0===this.inboundNodes.length)throw new AttributeError(\"Layer \"+this.name+\" is not connected, no input to return.\");return singletonOrArray(this.getNodeAtIndex(0,\"input\").inputTensors)},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,\"output\",{get:function(){if(0===this.inboundNodes.length)throw new AttributeError(\"Layer \"+this.name+\" has no inbound nodes.\");if(this.inboundNodes.length>1)throw new AttributeError(\"Layer \"+this.name+' has multiple inbound nodes, hence the notion of \"layer output\" is ill-defined. Use `getOutputAt(nodeIndex)` instead.');return singletonOrArray(this.getNodeAtIndex(0,\"output\").outputTensors)},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,\"losses\",{get:function(){return this._losses},enumerable:!0,configurable:!0}),t.prototype.calculateLosses=function(){return this.losses.map(function(e){return e()})},Object.defineProperty(t.prototype,\"updates\",{get:function(){return this._updates},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,\"built\",{get:function(){return this._built},set:function(e){this._built=e;},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,\"trainableWeights\",{get:function(){return this.trainable?this._trainableWeights:[]},set:function(e){this._trainableWeights=e;},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,\"nonTrainableWeights\",{get:function(){return this.trainable?this._nonTrainableWeights:this._trainableWeights.concat(this._nonTrainableWeights)},set:function(e){this._nonTrainableWeights=e;},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,\"weights\",{get:function(){return this.trainableWeights.concat(this.nonTrainableWeights)},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,\"stateful\",{get:function(){return this._stateful},enumerable:!0,configurable:!0}),t.prototype.resetStates=function(){if(!this.stateful)throw new Error(\"Cannot call the resetStates() method of a non-stateful Layer object.\")},t.prototype.assertInputCompatibility=function(e){if(e=toList(e),null!=this.inputSpec&&0!==this.inputSpec.length){var t=toList(this.inputSpec);if(e.length!==t.length)throw new ValueError(\"Layer \"+this.name+\" expects \"+t.length+\" inputs, but it received \"+e.length+\" input tensors. Input received: \"+e);for(var n=0;n<e.length;n++){var r=e[n],i=t[n];if(null!=i){var a=r.rank;if(null!=i.ndim&&a!==i.ndim)throw new ValueError(\"Input \"+n+\" is incompatible with layer \"+this.name+\": expected ndim=\"+i.ndim+\", found ndim=\"+a);if(null!=i.maxNDim&&a>i.maxNDim)throw new ValueError(\"Input \"+n+\" is incompatible with layer \"+this.name+\": expected max_ndim=\"+i.maxNDim+\", found ndim=\"+a);if(null!=i.minNDim&&a<i.minNDim)throw new ValueError(\"Input \"+n+\" is incompatible with layer \"+this.name+\": expected min_ndim=\"+i.minNDim+\", found ndim=\"+a+\".\");if(null!=i.dtype&&r.dtype!==i.dtype)throw new ValueError(\"Input \"+n+\" is incompatible with layer \"+this.name+\" : expected dtype=\"+i.dtype+\", found dtype=\"+r.dtype+\".\");if(i.axes){var o=r.shape;for(var s in i.axes){var l=Number(s),u=i.axes[s],c=l>=0?o[l]:o[o.length+l];if(null!=u&&-1===[u,null].indexOf(c))throw new ValueError(\"Input \"+n+\" is incompatible with layer \"+this.name+\": expected axis \"+l+\" of input shape to have value \"+u+\" but got shape \"+o+\".\")}}if(null!=i.shape)for(var p=0;p<i.shape.length;++p){var h=i.shape[p],d=r.shape[p];if(null!=h&&null!=d&&h!==d)throw new ValueError(\"Input \"+n+\" is incompatible with layer \"+this.name+\": expected shape=\"+i.shape+\", found shape=${xShape}.\")}}}}},t.prototype.call=function(e,t){return e},t.prototype.invokeCallHook=function(e,t){null!=this._callHook&&this._callHook(e,t);},t.prototype.setCallHook=function(e){this._callHook=e;},t.prototype.clearCallHook=function(){this._callHook=null;},t.prototype.apply=function(e,t){var n=this;t=t||{},this.assertNotDisposed();for(var r=toList(e),i=!0,a=0,o=r;a<o.length;a++){if(!(o[a]instanceof SymbolicTensor)){i=!1;break}}for(var s=!0,l=0,u=r;l<u.length;l++){if(u[l]instanceof SymbolicTensor){s=!1;break}}if(i===s)throw new ValueError(\"Arguments to apply() must be all SymbolicTensors or all Tensors\");return nameScope(this.name,function(){if(!n.built){n.assertInputCompatibility(e);for(var i=[],a=0,o=toList(e);a<o.length;a++){var l=o[a];i.push(l.shape);}n.build(singletonOrArray(i)),n.built=!0,n.initialWeights&&n.setWeights(n.initialWeights),null===n._refCount&&s&&(n._refCount=1);}if(n.assertInputCompatibility(e),s){for(var u=[],c=0,p=toList(g=n.call(e,t));c<p.length;c++){var h=p[c];-1!==r.indexOf(h)&&(h=h.clone()),u.push(h);}if(g=singletonOrArray(u),null!=n.activityRegularizer)throw new NotImplementedError(\"Layer invocation in the presence of activity regularizer(s) is not supported yet.\");return g}var d=collectInputShape(e),f=n.computeOutputShape(d),g=void 0,m=guessOutputDType(e);if(n.warnOnIncompatibleInputShape(Array.isArray(e)?d[0]:d),g=null!=f&&f.length>0&&Array.isArray(f[0])?f.map(function(r,i){return new SymbolicTensor(m,r,n,toList(e),t,n.name,i)}):new SymbolicTensor(m,f,n,toList(e),t,n.name),n.addInboundNode(e,g,null,null,d,f,t),n._refCount++,null!=n.activityRegularizer)throw new NotImplementedError(\"Layer invocation in the presence of activity regularizer(s) is not supported yet.\");return g})},t.prototype.warnOnIncompatibleInputShape=function(e){if(null!=this.batchInputShape)if(e.length!==this.batchInputShape.length)console.warn(\"The rank of the input tensor provided (shape: \"+JSON.stringify(e)+\") does not match that of the batchInputShape (\"+JSON.stringify(this.batchInputShape)+\") of the layer \"+this.name);else{var t=!1;this.batchInputShape.forEach(function(n,r){null!=n&&null!=e[r]&&e[r]!==n&&(t=!0);}),t&&console.warn(\"The shape of the input tensor (\"+JSON.stringify(e)+\") does not match the expectation of layer \"+this.name+\": \"+JSON.stringify(this.batchInputShape));}},Object.defineProperty(t.prototype,\"outputShape\",{get:function(){if(null==this.inboundNodes||0===this.inboundNodes.length)throw new AttributeError(\"The layer \"+this.name+\" has never been called and thus has no defined output shape.\");for(var e=[],t=0,n=this.inboundNodes;t<n.length;t++){var r=n[t],i=JSON.stringify(r.outputShapes);-1===e.indexOf(i)&&e.push(i);}if(1===e.length){var a=this.inboundNodes[0].outputShapes;return Array.isArray(a)&&Array.isArray(a[0])&&1===a.length?a[0]:a}throw new AttributeError(\"The layer \"+this.name+' has multiple inbound nodes with different output shapes. Hence the notion of \"outut shape\" is ill-defined for the layer.')},enumerable:!0,configurable:!0}),t.prototype.countParams=function(){if(!this.built)throw new RuntimeError(\"You tried to call countParams() on \"+this.name+\", but the layer is not built yet. Build it first by calling build(batchInputShape).\");return countParamsInWeights(this.weights)},t.prototype.build=function(e){this.built=!0;},t.prototype.getWeights=function(e){return void 0===e&&(e=!1),batchGetValue(e?this.trainableWeights:this.weights)},t.prototype.setWeights=function(e){var t=this;tidy(function(){var n=t.weights;if(n.length!==e.length)throw new ValueError('You called setWeights(weights) on layer \"'+t.name+'\" with a weight list of length '+e.length+\", but the layer was expecting \"+n.length+\" weights. Provided weights: \"+e+\"...\");if(0!==n.length){for(var r=[],i=batchGetValue(n),a=0;a<i.length;++a){var o=i[a],s=n[a],l=e[a];if(!util.arraysEqual(o.shape,l.shape))throw new ValueError(\"Layer weight shape \"+o.shape+\" not compatible with provided weight shape \"+l.shape);r.push([s,l]);}batchSetValue(r);}});},t.prototype.addWeight=function(e,t,n,r,i,a,o){if(-1!==this._addedWeightNames.indexOf(e))throw new ValueError(\"Duplicate weight name \"+e+\" for layer \"+this.name);this._addedWeightNames.push(e),null==n&&(n=\"float32\");var s=new LayerVariable(r.apply(t,n),n,e,a,o);return null!=i&&this.addLoss(function(){return i.apply(s.read())}),null==a&&(a=!0),a?this._trainableWeights.push(s):this._nonTrainableWeights.push(s),s},t.prototype.addLoss=function(e){var t;null==e||Array.isArray(e)&&0===e.length||(e=toList(e),void 0!==this._losses&&null!==this._losses&&(t=this.losses).push.apply(t,e));},t.prototype.computeOutputShape=function(e){return e},t.prototype.computeMask=function(e,t){var n=this;if(!this.supportsMasking){if(null!=t){if(!Array.isArray(t))throw new TypeError(\"Layer \"+this.name+\" does not support masking,but was passed an inputMask.\");t.forEach(function(e){if(null!=e)throw new TypeError(\"Layer \"+n.name+\" does not support masking,but was passed an inputMask.\")});}return null}return t},t.prototype.addInboundNode=function(e,t,n,r,i,a,o){void 0===o&&(o=null);var s=toList(e);t=toList(t),n=toList(n),r=toList(r),i=normalizeShapeList(i),a=normalizeShapeList(a);for(var l=[],u=[],c=[],p=0,h=s;p<h.length;p++){var d=h[p];l.push(d.sourceLayer),u.push(d.nodeIndex),c.push(d.tensorIndex);}new Node({outboundLayer:this,inboundLayers:l,nodeIndices:u,tensorIndices:c,inputTensors:s,outputTensors:t,inputMasks:n,outputMasks:r,inputShapes:i,outputShapes:a},o);for(var f=0;f<t.length;f++)t[f].sourceLayer=this,t[f].nodeIndex=this.inboundNodes.length-1,t[f].tensorIndex=f;},t.prototype.getConfig=function(){var e={name:this.name,trainable:this.trainable};return null!=this.batchInputShape&&(e.batchInputShape=this.batchInputShape),null!=this.dtype&&(e.dtype=this.dtype),e},t.prototype.disposeWeights=function(){return this.weights.forEach(function(e){return e.dispose()}),this.weights.length},t.prototype.assertNotDisposed=function(){if(0===this._refCount)throw new Error(\"Layer '\"+this.name+\"' is already disposed.\")},t.prototype.dispose=function(){if(!this.built)throw new Error(\"Cannot dispose Layer \"+this.name+\" because it has not been built yet.\");if(null===this._refCount)throw new Error(\"Cannot dispose Layer \"+this.name+\" because it has not been used yet.\");this.assertNotDisposed();var e=0;return 0==--this._refCount&&(e=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables:e}},t}(serialization.Serializable);function collectInputShape(e){for(var t=[],n=0,r=e=toList(e);n<r.length;n++){var i=r[n];t.push(i.shape);}return singletonOrArray(t)}function guessOutputDType(e){return \"float32\"}function getSourceInputs(e,t,n){if((null==t||null!=n&&n>0)&&(t=e.sourceLayer,n=e.nodeIndex),0===t.inboundNodes.length)return [e];var r=t.inboundNodes[n];if(0===r.inboundLayers.length)return r.inputTensors;for(var i=[],a=0;a<r.inboundLayers.length;a++)for(var o=0,s=getSourceInputs(r.inputTensors[a],r.inboundLayers[a],r.nodeIndices[a]);o<s.length;o++){var l=s[o];-1===i.indexOf(l)&&i.push(l);}return i}var ModelLoggingVerbosity,InputLayer=function(e){function t(t){var n=e.call(this,{dtype:t.dtype,name:null!=t.name?t.name:getUid(\"input\").toString()})||this;if(null==t.batchSize&&(t.batchSize=null),null==t.sparse&&(t.sparse=!1),n.trainable=!1,n.built=!0,n.sparse=t.sparse,null!=t.inputShape&&null!=t.batchInputShape)throw new ValueError(\"Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.\");var r=t.batchInputShape;if(null==r){if(null==t.inputShape)throw new ValueError(\"An InputLayer should be passed either a `batchInputShape` or an `inputShape`.\");r=[t.batchSize].concat(t.inputShape);}else if(null!=t.batchSize)throw new ValueError(\"Cannot specify batchSize if batchInputShape isspecified when creating an InputLayer.\");var i=t.dtype||\"float32\";n.batchInputShape=r,n.dtype=i,n.inputSpec=[{shape:r}];var a=new SymbolicTensor(n.dtype,n.batchInputShape,n,[],{},n.name);return a.nodeIndex=0,a.tensorIndex=0,new Node({outboundLayer:n,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[a],outputTensors:[a],inputMasks:[null],outputMasks:[null],inputShapes:[r],outputShapes:[r]}),n}return __extends$1(t,e),t.prototype.apply=function(e,t){throw new ValueError(\"Cannot pass any input to an InputLayer's apply() method. InputLayer name: \"+this.name)},t.prototype.dispose=function(){return {refCountAfterDispose:this._refCount,numDisposedVariables:0}},t.prototype.getConfig=function(){return {batchInputShape:this.batchInputShape,dtype:this.dtype,sparse:this.sparse,name:this.name}},t.className=\"InputLayer\",t}(Layer);function Input(e){if(null==e.batchShape&&null==e.shape)throw new Error(\"Please provide to Input either a `shape` or a `batchShape` argument. Note that `shape` does not include the batch dimension.\");if(null!=e.batchShape&&null!=e.shape)throw new ValueError(\"Please provide either a `shape` or `batchShape` argument to Input, but not both.\");var t=e.batchShape;null!=e.shape&&null==t&&(t=[null].concat(e.shape));var n=e.dtype;return null==n&&(n=\"float32\"),new InputLayer({batchInputShape:t,name:e.name,dtype:n,sparse:e.sparse}).inboundNodes[0].outputTensors[0]}function resolveScalarsInLogs(e){return __awaiter$1(this,void 0,void 0,function(){var t,n,r,i,a,o,s,l;return __generator$1(this,function(u){switch(u.label){case 0:if(null==e)return [2];for(i in t=[],n=[],r=[],e)\"number\"!=typeof(a=e[i])&&(o=a,t.push(o.data()),n.push(i),r.push(o));return [4,Promise.all(t)];case 1:for(s=u.sent(),l=0;l<s.length;++l)e[n[l]]=s[l][0];return dispose(r),[2]}})})}function disposeTensorsInLogs(e){if(null!=e)for(var t in e){var n=e[t];\"number\"!=typeof n&&n.dispose();}}serialization.registerClass(InputLayer),function(e){e[e.SILENT=0]=\"SILENT\",e[e.VERBOSE=1]=\"VERBOSE\";}(ModelLoggingVerbosity||(ModelLoggingVerbosity={}));var BaseCallback=function(){function e(){this.validationData=null;}return e.prototype.setParams=function(e){this.params=e;},e.prototype.onEpochBegin=function(e,t){return __awaiter$1(this,void 0,void 0,function(){return __generator$1(this,function(e){return [2]})})},e.prototype.onEpochEnd=function(e,t){return __awaiter$1(this,void 0,void 0,function(){return __generator$1(this,function(e){return [2]})})},e.prototype.onBatchBegin=function(e,t){return __awaiter$1(this,void 0,void 0,function(){return __generator$1(this,function(e){return [2]})})},e.prototype.onBatchEnd=function(e,t){return __awaiter$1(this,void 0,void 0,function(){return __generator$1(this,function(e){return [2]})})},e.prototype.onTrainBegin=function(e){return __awaiter$1(this,void 0,void 0,function(){return __generator$1(this,function(e){return [2]})})},e.prototype.onTrainEnd=function(e){return __awaiter$1(this,void 0,void 0,function(){return __generator$1(this,function(e){return [2]})})},e.prototype.setModel=function(e){},e}(),CallbackList=function(){function e(e,t){void 0===t&&(t=10),null==e&&(e=[]),this.callbacks=e,this.queueLength=t;}return e.prototype.append=function(e){this.callbacks.push(e);},e.prototype.setParams=function(e){for(var t=0,n=this.callbacks;t<n.length;t++){n[t].setParams(e);}},e.prototype.setModel=function(e){for(var t=0,n=this.callbacks;t<n.length;t++){n[t].setModel(e);}},e.prototype.onEpochBegin=function(e,t){return __awaiter$1(this,void 0,void 0,function(){var n,r;return __generator$1(this,function(i){switch(i.label){case 0:null==t&&(t={}),n=0,r=this.callbacks,i.label=1;case 1:return n<r.length?[4,r[n].onEpochBegin(e,t)]:[3,4];case 2:i.sent(),i.label=3;case 3:return n++,[3,1];case 4:return [2]}})})},e.prototype.onEpochEnd=function(e,t){return __awaiter$1(this,void 0,void 0,function(){var n,r;return __generator$1(this,function(i){switch(i.label){case 0:null==t&&(t={}),n=0,r=this.callbacks,i.label=1;case 1:return n<r.length?[4,r[n].onEpochEnd(e,t)]:[3,4];case 2:i.sent(),i.label=3;case 3:return n++,[3,1];case 4:return [2]}})})},e.prototype.onBatchBegin=function(e,t){return __awaiter$1(this,void 0,void 0,function(){var n,r;return __generator$1(this,function(i){switch(i.label){case 0:null==t&&(t={}),n=0,r=this.callbacks,i.label=1;case 1:return n<r.length?[4,r[n].onBatchBegin(e,t)]:[3,4];case 2:i.sent(),i.label=3;case 3:return n++,[3,1];case 4:return [2]}})})},e.prototype.onBatchEnd=function(e,t){return __awaiter$1(this,void 0,void 0,function(){var n,r;return __generator$1(this,function(i){switch(i.label){case 0:return null==t&&(t={}),[4,resolveScalarsInLogs(t)];case 1:i.sent(),n=0,r=this.callbacks,i.label=2;case 2:return n<r.length?[4,r[n].onBatchEnd(e,t)]:[3,5];case 3:i.sent(),i.label=4;case 4:return n++,[3,2];case 5:return [2]}})})},e.prototype.onTrainBegin=function(e){return __awaiter$1(this,void 0,void 0,function(){var t,n;return __generator$1(this,function(r){switch(r.label){case 0:null==e&&(e={}),t=0,n=this.callbacks,r.label=1;case 1:return t<n.length?[4,n[t].onTrainBegin(e)]:[3,4];case 2:r.sent(),r.label=3;case 3:return t++,[3,1];case 4:return [2]}})})},e.prototype.onTrainEnd=function(e){return __awaiter$1(this,void 0,void 0,function(){var t,n;return __generator$1(this,function(r){switch(r.label){case 0:null==e&&(e={}),t=0,n=this.callbacks,r.label=1;case 1:return t<n.length?[4,n[t].onTrainEnd(e)]:[3,4];case 2:r.sent(),r.label=3;case 3:return t++,[3,1];case 4:return [2]}})})},e}(),ModelTrainingYielder=function(){function e(e){this.yieldEvery=e,this.batchCount=0,this.batchDurationsMillis=[],this.autoYieldEveryBatches=null,this.batchStartMillis=util.now();}return e.prototype.resolveOneTensorInLogs=function(e){return __awaiter$1(this,void 0,void 0,function(){var t,n,r,i,a;return __generator$1(this,function(o){switch(o.label){case 0:for(n in t=[],e)t.push(n);r=0,o.label=1;case 1:return r<t.length?(i=t[r],\"number\"==typeof(a=e[i])?[3,3]:[4,a.data()]):[3,4];case 2:return o.sent(),[3,4];case 3:return r++,[3,1];case 4:return [2]}})})},e.prototype.maybeYieldOnBatch=function(t){return __awaiter$1(this,void 0,void 0,function(){var n,r;return __generator$1(this,function(i){switch(i.label){case 0:return \"auto\"!==this.yieldEvery?[3,7]:(this.batchCount++,null!=this.autoYieldEveryBatches?[3,3]:[4,this.resolveOneTensorInLogs(t)]);case 1:return i.sent(),n=util.now(),[4,nextFrame()];case 2:return i.sent(),this.batchCount>e.SKIP_FIRST_BATCHES&&(this.batchDurationsMillis.push(n-this.batchStartMillis),this.batchDurationsMillis.length>=e.DECISION_BATCH_COUNT&&(r=this.batchDurationsMillis.reduce(function(e,t){return e+t})/this.batchDurationsMillis.length,this.autoYieldEveryBatches=Math.round(e.THRESHOLD_MILLIS/r),this.autoYieldEveryBatches<1&&(this.autoYieldEveryBatches=1))),this.batchStartMillis=util.now(),this.lastYieldBatchCount=this.batchCount,[3,6];case 3:return this.batchCount-this.lastYieldBatchCount>=this.autoYieldEveryBatches?[4,nextFrame()]:[3,6];case 4:return i.sent(),[4,this.resolveOneTensorInLogs(t)];case 5:i.sent(),this.lastYieldBatchCount=this.batchCount,i.label=6;case 6:return [3,9];case 7:return \"batch\"!==this.yieldEvery?[3,9]:[4,nextFrame()];case 8:i.sent(),i.label=9;case 9:return [2]}})})},e.prototype.maybeYieldOnEpoch=function(){return __awaiter$1(this,void 0,void 0,function(){return __generator$1(this,function(e){switch(e.label){case 0:return \"epoch\"!==this.yieldEvery?[3,2]:[4,nextFrame()];case 1:e.sent(),e.label=2;case 2:return [2]}})})},e.SKIP_FIRST_BATCHES=1,e.DECISION_BATCH_COUNT=2,e.THRESHOLD_MILLIS=16,e}(),BaseLogger=function(e){function t(t){var n=e.call(this)||this;return n.yieldEvery=t||\"auto\",n}return __extends$1(t,e),t.prototype.onTrainBegin=function(e){return __awaiter$1(this,void 0,void 0,function(){return __generator$1(this,function(e){return this.autoYielder=new ModelTrainingYielder(this.yieldEvery),[2]})})},t.prototype.onEpochBegin=function(e){return __awaiter$1(this,void 0,void 0,function(){return __generator$1(this,function(e){return this.seen=0,this.totals={},[2]})})},t.prototype.onBatchEnd=function(e,t){return __awaiter$1(this,void 0,void 0,function(){var e,n,r,i,a=this;return __generator$1(this,function(o){switch(o.label){case 0:return [4,this.autoYielder.maybeYieldOnBatch(t)];case 1:for(i in o.sent(),null==t&&(t={}),e=null==t.size?0:t.size,this.seen+=e,n=function(n){var i=t[n];if(\"number\"==typeof i)r.totals.hasOwnProperty(n)||(r.totals[n]=0),r.totals[n]=r.totals[n]+i*e;else{var o=void 0;n in r.totals?o=r.totals[n]:r.totals[n]=getScalar(0),r.totals[n]=tidy(function(){return add(a.totals[n],mul(i,getScalar(e)))}),null!=o&&o.dispose();}},r=this,t)n(i);return [2]}})})},t.prototype.onEpochEnd=function(e,t){return __awaiter$1(this,void 0,void 0,function(){var e,n,r,i,a,o=this;return __generator$1(this,function(s){switch(s.label){case 0:return [4,this.autoYielder.maybeYieldOnEpoch()];case 1:if(s.sent(),null!=t)for(e=function(e){if(null==n.totals[e])return \"continue\";\"number\"==typeof n.totals[e]?t[e]=n.totals[e]/n.seen:tidy(function(){t[e]=mul(div(getScalar(1),getScalar(o.seen)),o.totals[e]),o.totals[e].dispose(),keep(t[e]);});},n=this,r=0,i=this.params.metrics;r<i.length;r++)a=i[r],e(a);return [2]}})})},t}(BaseCallback),History=function(e){function t(){return null!==e&&e.apply(this,arguments)||this}return __extends$1(t,e),t.prototype.onTrainBegin=function(e){return __awaiter$1(this,void 0,void 0,function(){return __generator$1(this,function(e){return this.epoch=[],this.history={},[2]})})},t.prototype.onEpochEnd=function(e,t){return __awaiter$1(this,void 0,void 0,function(){var n;return __generator$1(this,function(r){for(n in null==t&&(t={}),this.epoch.push(e),t)null==this.history[n]&&(this.history[n]=[]),this.history[n].push(t[n]);return [2]})})},t.prototype.syncData=function(){return __awaiter$1(this,void 0,void 0,function(){var e,t,n,r,i,a,o,s,l;return __generator$1(this,function(u){switch(u.label){case 0:for(r in e=[],t=[],n=[],this.history)for(i=this.history[r],a=0;a<i.length;++a)\"number\"!=typeof i[a]&&(o=i[a],e.push(o.data()),t.push(r),n.push(a));return [4,Promise.all(e)];case 1:for(s=u.sent(),l=0;l<s.length;++l)this.history[t[l]][n[l]].dispose(),this.history[t[l]][n[l]]=s[l][0];return [2]}})})},t}(BaseCallback),CustomCallback=function(e){function t(t){var n=e.call(this)||this;return n.trainBegin=t.onTrainBegin,n.trainEnd=t.onTrainEnd,n.epochBegin=t.onEpochBegin,n.epochEnd=t.onEpochEnd,n.batchBegin=t.onBatchBegin,n.batchEnd=t.onBatchEnd,n}return __extends$1(t,e),t.prototype.onEpochBegin=function(e,t){return __awaiter$1(this,void 0,void 0,function(){return __generator$1(this,function(n){switch(n.label){case 0:return null==this.epochBegin?[3,3]:[4,resolveScalarsInLogs(t)];case 1:return n.sent(),[4,this.epochBegin(e,t)];case 2:n.sent(),n.label=3;case 3:return [2]}})})},t.prototype.onEpochEnd=function(e,t){return __awaiter$1(this,void 0,void 0,function(){return __generator$1(this,function(n){switch(n.label){case 0:return null==this.epochEnd?[3,3]:[4,resolveScalarsInLogs(t)];case 1:return n.sent(),[4,this.epochEnd(e,t)];case 2:n.sent(),n.label=3;case 3:return [2]}})})},t.prototype.onBatchBegin=function(e,t){return __awaiter$1(this,void 0,void 0,function(){return __generator$1(this,function(n){switch(n.label){case 0:return null==this.batchBegin?[3,3]:[4,resolveScalarsInLogs(t)];case 1:return n.sent(),[4,this.batchBegin(e,t)];case 2:n.sent(),n.label=3;case 3:return [2]}})})},t.prototype.onBatchEnd=function(e,t){return __awaiter$1(this,void 0,void 0,function(){return __generator$1(this,function(n){switch(n.label){case 0:return null==this.batchEnd?[3,3]:[4,resolveScalarsInLogs(t)];case 1:return n.sent(),[4,this.batchEnd(e,t)];case 2:n.sent(),n.label=3;case 3:return [2]}})})},t.prototype.onTrainBegin=function(e){return __awaiter$1(this,void 0,void 0,function(){return __generator$1(this,function(t){switch(t.label){case 0:return null==this.trainBegin?[3,3]:[4,resolveScalarsInLogs(e)];case 1:return t.sent(),[4,this.trainBegin(e)];case 2:t.sent(),t.label=3;case 3:return [2]}})})},t.prototype.onTrainEnd=function(e){return __awaiter$1(this,void 0,void 0,function(){return __generator$1(this,function(t){switch(t.label){case 0:return null==this.trainEnd?[3,3]:[4,resolveScalarsInLogs(e)];case 1:return t.sent(),[4,this.trainEnd(e)];case 2:t.sent(),t.label=3;case 3:return [2]}})})},t}(BaseCallback);function standardizeCallbacks(e){return null==e?null:e instanceof BaseCallback?[e]:Array.isArray(e)&&e[0]instanceof BaseCallback?e:toList(e).map(function(e){return new CustomCallback(e)})}var CallbackConstructorRegistry=function(){function e(){}return e.registerCallbackConstructor=function(t,n){util.assert(t>=0&&Number.isInteger(t),\"Verbosity level is expected to be an integer >= 0, but got \"+t),e.checkForDuplicate(n),null==e.constructors[t]&&(e.constructors[t]=[]),e.constructors[t].push(n);},e.checkForDuplicate=function(t){for(var n in e.constructors){e.constructors[+n].forEach(function(e){if(e===t)throw new ValueError(\"Duplicate callback constructor.\")});}},e.clear=function(){e.constructors={};},e.createCallbacks=function(t){var n=[];for(var r in e.constructors){var i=+r;t>=i&&n.push.apply(n,e.constructors[i]);}return n.map(function(e){return new e})},e.constructors={},e}();function configureCallbacks(e,t,n,r,i,a,o,s,l,u){var c=new History,p=[new BaseLogger(t)].concat(CallbackConstructorRegistry.createCallbacks(n));null!=e&&p.push.apply(p,e),p.push(c);var h=new CallbackList(p);return h.setParams({epochs:r,initialEpoch:i,samples:a,steps:o,batchSize:s,verbose:n,doValidation:l,metrics:u}),{callbackList:h,history:c}}function l2Normalize(e,t){return tidy(function(){var n=sum$1(square$1(e),t,!0),r=mul(scalar(epsilon()),onesLike(e)),i=sqrt(maximum(n,r));return div(e,i)})}function meanSquaredError$1(e,t){return tidy(function(){return mean(square$1(sub(t,e)),-1)})}function meanAbsoluteError(e,t){return tidy(function(){return mean(abs(sub(t,e)),-1)})}function meanAbsolutePercentageError(e,t){return tidy(function(){var n=sub(e,t),r=clipByValue(abs(e),epsilon(),Number.MAX_VALUE),i=abs(div(n,r));return mul(getScalar(100),mean(i,-1))})}function meanSquaredLogarithmicError(e,t){return tidy(function(){var n=getScalar(1),r=clipByValue(t,epsilon(),Number.MAX_VALUE),i=log$1(add(n,r)),a=clipByValue(e,epsilon(),Number.MAX_VALUE),o=log$1(add(n,a));return mean(square$1(sub(i,o)),-1)})}function squaredHinge(e,t){return tidy(function(){var n=getScalar(0),r=getScalar(1),i=maximum(n,sub(r,mul(e,t)));return mean(square$1(i),-1)})}function hinge(e,t){return tidy(function(){var n=getScalar(0),r=getScalar(1),i=maximum(n,sub(r,mul(e,t)));return mean(i,-1)})}function categoricalHinge(e,t){return tidy(function(){var n=getScalar(0),r=getScalar(1),i=sum$1(mul(e,t),-1),a=max(mul(sub(r,e),t),-1);return maximum(n,add(r,sub(a,i)))})}function logcosh(e,t){return tidy(function(){var n=getScalar(Math.log(2)),r=sub(t,e),i=sub(add(r,softplus(mul(getScalar(-2),r))),n);return mean(i,-1)})}function categoricalCrossentropy(e,t,n){return void 0===n&&(n=!1),tidy(function(){if(n)t=softmax(t);else{var r=sum$1(t,t.shape.length-1,!0);t=div(t,r);}return t=clipByValue(t,epsilon(),1-epsilon()),neg(sum$1(mul(e.toFloat(),log$1(t)),t.shape.length-1))})}function sparseCategoricalCrossentropy(e,t,n){return void 0===n&&(n=!1),tidy(function(){var r=floor(flatten$1(e)).toInt(),i=t.shape;return categoricalCrossentropy(oneHot(r,i[i.length-1]).reshape(i),t,n)})}function sigmoidCrossEntropyWithLogits(e,t){return tidy(function(){var n=maximum(t,zerosLike(t)),r=mul(t,e),i=log$1(add(getScalar(1),exp(neg(abs(t)))));return add(sub(n,r),i)})}function binaryCrossentropy(e,t){return tidy(function(){var n;return n=clipByValue(t,epsilon(),1-epsilon()),n=log$1(div(n,sub(onesLike(n),n))),mean(sigmoidCrossEntropyWithLogits(e,n),-1)})}function kullbackLeiblerDivergence(e,t){return tidy(function(){var n=clipByValue(e,epsilon(),1),r=clipByValue(t,epsilon(),1);return sum$1(mul(e,log$1(div(n,r))),-1)})}function poisson(e,t){return tidy(function(){var n=log$1(add(getScalar(epsilon()),t));return mean(sub(t,mul(e,n)),-1)})}function cosineProximity(e,t){return tidy(function(){var n=l2Normalize(e,-1),r=l2Normalize(t,-1),i=mul(n,r);return neg(sum$1(i,-1))})}function get(e){var t={meanSquaredError:meanSquaredError$1,meanAbsoluteError:meanAbsoluteError,meanAbsolutePercentageError:meanAbsolutePercentageError,meanSquaredLogarithmicError:meanSquaredLogarithmicError,squaredHinge:squaredHinge,hinge:hinge,categoricalHinge:categoricalHinge,logcosh:logcosh,categoricalCrossentropy:categoricalCrossentropy,sparseCategoricalCrossentropy:sparseCategoricalCrossentropy,binaryCrossentropy:binaryCrossentropy,kullbackLeiblerDivergence:kullbackLeiblerDivergence,poisson:poisson,cosineProximity:cosineProximity};if(\"string\"==typeof e){if(e in t)return t[e];var n=\"Unknown loss \"+e;throw e.toLowerCase().includes(\"softmaxcrossentropy\")&&(n=\"Unknown loss \"+e+'. Use \"categoricalCrossentropy\" as the string name for tf.losses.softmaxCrossEntropy'),new ValueError(n)}return e}function binaryAccuracy(e,t){return tidy(function(){var n=mul(getScalar(.5),onesLike(t)),r=cast$1(greater(t,n),e.dtype);return mean(equal(e,r),-1)})}function categoricalAccuracy(e,t){return tidy(function(){return cast$1(equal(argMax(e,-1),argMax(t,-1)),\"float32\")})}function truePositives(e,t){return tidy(function(){var n=getScalar(1);return logicalAnd(e.equal(n),t.equal(n)).sum().cast(\"float32\")})}function falseNegatives(e,t){return tidy(function(){var n=getScalar(1),r=getScalar(0);return logicalAnd(e.equal(n),t.equal(r)).sum().cast(\"float32\")})}function falsePositives(e,t){return tidy(function(){var n=getScalar(1),r=getScalar(0);return logicalAnd(e.equal(r),t.equal(n)).sum().cast(\"float32\")})}function precision(e,t){return tidy(function(){var n=getScalar(0),r=truePositives(e,t),i=falsePositives(e,t),a=r.add(i);return where(greater(a,n),r.div(a),n).cast(\"float32\")})}function recall(e,t){return tidy(function(){var n=getScalar(0),r=truePositives(e,t),i=falseNegatives(e,t),a=r.add(i);return where(greater(a,n),r.div(a),n).cast(\"float32\")})}function binaryCrossentropy$1(e,t){return binaryCrossentropy(e,t)}function sparseCategoricalAccuracy(e,t){throw new NotImplementedError}var mse$1=meanSquaredError$1,MSE$1=meanSquaredError$1,mae$1=meanAbsoluteError,MAE$1=meanAbsoluteError,mape$1=meanAbsolutePercentageError,MAPE$1=meanAbsolutePercentageError,categoricalCrossentropy$1=categoricalCrossentropy,cosine$1=cosineProximity,sparseCategoricalCrossentropy$1=sparseCategoricalCrossentropy;function get$1(e){var t={binaryAccuracy:binaryAccuracy,categoricalAccuracy:categoricalAccuracy,precision:precision,categoricalCrossentropy:categoricalCrossentropy$1,sparseCategoricalCrossentropy:sparseCategoricalCrossentropy$1,mse:mse$1,MSE:MSE$1,mae:mae$1,MAE:MAE$1,mape:mape$1,MAPE:MAPE$1,cosine:cosine$1};if(\"string\"==typeof e&&e in t)return t[e];if(\"string\"!=typeof e&&null!=e)return e;throw new ValueError(\"Unknown metric \"+e)}function getOptimizer(e){var t={Adagrad:function(){return train.adagrad(.01)},Adadelta:function(){return train.adadelta(1,.95,epsilon())},Adam:function(){return train.adam(.001,.9,.999,epsilon())},Adamax:function(){return train.adamax(.002,.9,.999,epsilon(),0)},RMSProp:function(){return train.rmsprop(.001,.9,0,epsilon())},SGD:function(){return train.sgd(.01)}};if(t.adagrad=t.Adagrad,t.adadelta=t.Adadelta,t.adam=t.Adam,t.adamax=t.Adamax,t.rmsprop=t.RMSProp,t.sgd=t.SGD,e in t)return t[e]();throw new ValueError(\"Unknown Optimizer \"+e)}function printSummary(e,t,n,r){void 0===r&&(r=console.log);var i,a=isModelSequentialLike(e),o=[\"Layer (type)\",\"Output shape\",\"Param #\"];if(a?(t=t||65,n=n||[.45,.85,1]):(t=t||98,n=n||[.33,.55,.67,1]),n[n.length-1]<=1&&(n=n.map(function(e){return Math.floor(t*e)})),!a)for(var s in o.push(\"Receives inputs\"),i=[],e.nodesByDepth)i.push.apply(i,e.nodesByDepth[s]);r(\"_\".repeat(t)),printRow(o,n,r),r(\"=\".repeat(t));for(var l=e.layers,u=0;u<l.length;++u)a?printLayerSummary(l[u],n,r):printLayerSummaryWithConnections(l[u],n,i,r),r((u===l.length-1?\"=\":\"_\").repeat(t));e.checkTrainableWeightsConsistency();var c=countTrainableParams(e),p=countParamsInWeights(e.nonTrainableWeights);r(\"Total params: \"+(c+p)),r(\"Trainable params: \"+c),r(\"Non-trainable params: \"+p),r(\"_\".repeat(t));}function countTrainableParams(e){return null!=e.collectedTrainableWeights?countParamsInWeights(e.collectedTrainableWeights):countParamsInWeights(e.trainableWeights)}function isModelSequentialLike(e){var t=!0,n=[],r=[];for(var i in e.nodesByDepth)n.push(e.nodesByDepth[i]);for(var a=0,o=n;a<o.length;a++){var s=o[a];if(s.length>1||1===s.length&&s[0].inboundLayers.length>1){t=!1;break}r.push.apply(r,s);}if(t)for(var l=0,u=e.layers;l<u.length;l++){for(var c=!1,p=0,h=u[l].inboundNodes;p<h.length;p++){var d=h[p];if(-1!==r.indexOf(d)){if(c){t=!1;break}c=!0;}}if(!t)break}return t}function printRow(e,t,n){void 0===n&&(n=console.log);for(var r=\"\",i=0;i<e.length;++i)i>0&&(r=r.slice(0,r.length-1)+\" \"),r=(r+=e[i]).slice(0,t[i]),r+=\" \".repeat(t[i]-r.length);n(r);}function printLayerSummary(e,t,n){var r;try{r=JSON.stringify(e.outputShape);}catch(e){r=\"multiple\";}printRow([e.name+\" (\"+e.getClassName()+\")\",r,e.countParams().toString()],t,n);}function printLayerSummaryWithConnections(e,t,n,r){var i;try{i=JSON.stringify(e.outputShape);}catch(e){i=\"multiple\";}for(var a=[],o=0,s=e.inboundNodes;o<s.length;o++){var l=s[o];if(!(null!=n&&n.length>0&&-1===n.indexOf(l)))for(var u=0;u<l.inboundLayers.length;++u){var c=l.inboundLayers[u].name,p=l.nodeIndices[u],h=l.tensorIndices[u];a.push(c+\"[\"+p+\"][\"+h+\"]\");}}var d=e.name,f=e.getClassName(),g=0===a.length?\"\":a[0];printRow([d+\" (\"+f+\")\",i,e.countParams().toString(),g],t,r);for(u=1;u<a.length;++u)printRow([\"\",\"\",\"\",a[u]],t,r);}function deserialize(e,t){return void 0===t&&(t={}),deserializeKerasObject(e,serialization.SerializationMap.getMap().classNameMap,t,\"layer\")}function isArrayItemInputOrOutputName(e,t,n){return (\"inboundNodes\"===e||\"outputLayers\"===e||\"inputLayers\"===e)&&0===t&&\"string\"==typeof n}function convertPythonicToTs(e,t){if(null===e)return null;if(\"string\"==typeof e)return toCamelCase(e);if(\"number\"==typeof e||\"boolean\"==typeof e)return e;if(e instanceof Array){for(var n=[],r=e.length,i=0;i<r;++i){var a=e[i];isArrayItemInputOrOutputName(t,i,a)?n.push(a):n.push(convertPythonicToTs(a,t));}return n}for(var o={},s=0,l=Object.keys(e);s<l.length;s++){var u=l[s],c=e[u];if(\"name\"===u&&\"string\"==typeof c)o[u]=c;else{var p=toCamelCase(u);o[p]=convertPythonicToTs(c,p);}}return o}function convertTsToPythonic(e,t){if(null===e||void 0===e)return null;if(\"string\"==typeof e)return toSnakeCase(e);if(\"number\"==typeof e||\"boolean\"==typeof e)return e;if(e instanceof Array){for(var n=[],r=e.length,i=0;i<r;++i){var a=e[i];isArrayItemInputOrOutputName(t,i,a)?n.push(a):n.push(convertTsToPythonic(a,t));}return n}for(var o={},s=0,l=Object.keys(e);s<l.length;s++){var u=l[s],c=e[u],p=toSnakeCase(u);o[p]=\"name\"!==u&&\"className\"!==u||\"string\"!=typeof c?convertTsToPythonic(c,u):c;}return o}var version$2=\"0.8.4\";function preprocessWeightsForLoading(e,t,n,r){if(!n.startsWith(\"2.\"))throw new ValueError(\"Unsupported Keras version in weights being loaded: \"+n);return t}function loadTensor(e,t,n){var r=stringToDType(e);return Tensor.make(t,{values:0===t.length?n:util.flatten(n)},r)}function loadWeightsFromJson(e,t,n){void 0===n&&(n=!1);for(var r=e.keras_version,i=e.backend,a=t.map(function(e){return e.name}),o={},s=0,l=t;s<l.length;s++){null!=(b=l[s]).name&&(null==o[b.name]&&(o[b.name]=[]),o[b.name].push(b));}for(var u=e.weights,c=[],p=0;p<a.length;++p){var h=a[p],d=u[h];null==d&&(d=[]);for(var f=[],g=0;g<d.length;++g){var m=d[g];f.push(new LayerVariable(loadTensor(m.dtype,m.shape,m.value)));}for(var y=0,v=o[h];y<v.length;y++){var b,w=(b=v[y]).weights;if((f=preprocessWeightsForLoading(b,f,r,i)).length!==w.length){if(!n)throw new ValueError(\"Layer #\"+p+' (named \"'+b.name+'\") expects '+w.length+\" weight(s), but the saved weights have \"+f.length+\" element(s).\");console.warn(\"Skipping loading of weights of layer \"+b.name+\" due to mismatch in number of weights: (\"+f.length+\" vs \"+w.length+\").\");}for(var z=0;z<f.length;++z)!n||util.arraysEqual(w[z].shape,f[z].shape)?c.push([w[z],f[z].read()]):console.warn(\"Skipping loading of weights for layer \"+b.name+\" due to mismatch in shape (\"+w[z].shape+\" vs \"+f[z].shape+\")\");}}batchSetValue(c);}function loadWeightsFromNamedTensorMap(e,t,n){void 0===n&&(n=!0);for(var r={},i=0,a=0,o=t;a<o.length;a++)for(var s=0,l=o[a].weights;s<l.length;s++){var u=l[s];if(null!=r[u.originalName])throw new ValueError(\"Duplicate weight name: \"+u.originalName);r[u.originalName]=u,i++;}var c=[];for(var p in e){if(null!=r[p])c.push([r[p],e[p]]);else if(n)throw new ValueError(\"Provided weight data has no target variable: \"+p);delete r[p];}if(n){var h=[];for(var d in r)h.push(d);if(h.length>0)throw new ValueError(h.length+\" of \"+i+\" weights are not set: \"+h)}batchSetValue(c);}var Container=function(e){function t(n){var r=e.call(this,{})||this;if(r.containerNodes=new Set,r.name=n.name,null==r.name){var i=r.getClassName().toLowerCase();r.name=getUid(i);}if(r.supportsMasking=!1,r.trainable=!0,r.updatable=!0,Array.isArray(n.inputs)?r.inputs=n.inputs.slice():r.inputs=[n.inputs],Array.isArray(n.outputs)?r.outputs=n.outputs.slice():r.outputs=[n.outputs],unique(r.inputs).length!==r.inputs.length)throw new ValueError(\"The list of inputs passed to the model is redundant. All inputs should only appear once. Found: \"+r.inputs.map(function(e){return e.name}));unique(r.outputs).length!==r.outputs.length&&console.warn(\"The list of outputs passed to the model is redundant. All outputs should only appear once. Found: \"+r.outputs.map(function(e){return e.name})),r.inputLayers=[],r.inputLayersNodeIndices=[],r.inputLayersTensorIndices=[],r.outputLayers=[],r.outputLayersNodeIndices=[],r.outputLayersTensorIndices=[],r.layers=[];for(var a=0,o=r.outputs;a<o.length;a++){var s=(_=o[a]).sourceLayer,l=_.nodeIndex,u=_.tensorIndex;r.outputLayers.push(s),r.outputLayersNodeIndices.push(l),r.outputLayersTensorIndices.push(u);}for(var c=0,p=r.inputs;c<p.length;c++){s=(_=p[c]).sourceLayer,l=_.nodeIndex,u=_.tensorIndex;assert$1(0===l,\"input layer has >1 nodes\"),assert$1(0===u,\"input layer has >1 tensors\"),r.inputLayers.push(s),r.inputLayersNodeIndices.push(l),r.inputLayersTensorIndices.push(u);}r.inputNames=[],r.outputNames=[],r.feedInputShapes=[],r.feedInputNames=[],r.feedOutputNames=[];for(var h=0;h<r.inputLayers.length;h++){if(!((s=r.inputLayers[h])instanceof InputLayer))throw new TypeError(\"Input layers to a Model must be InputLayer objects. Received inputs: \"+n.inputs+\". Input \"+h+\" (0-based) originates from layer type \"+s.getClassName()+\".\");r.inputNames.push(s.name),r.feedInputShapes.push(s.batchInputShape),r.feedInputNames.push(s.name);}for(var d=0,f=r.outputLayers;d<f.length;d++){s=f[d];r.outputNames.push(s.name);}r.internalInputShapes=r.inputs.map(function(e){return e.shape}),r.internalOutputShapes=r.outputs.map(function(e){return e.shape});for(var g={},m={},y={},v={},b={},w=[],z=function(e,n,i,a,o,s){null!=a&&null!=o&&null!=s||(a=e.sourceLayer,o=e.nodeIndex,s=e.tensorIndex);var l=a.inboundNodes[o];if(-1!==i.indexOf(l))throw new RuntimeError(\"The tensor \"+e.name+' at layer \"'+a.name+'\" is part of a cycle.');if(-1===n.indexOf(l)){r.containerNodes.add(t.nodeKey(a,o)),a.id in b||(b[a.id]=Object.keys(b).length),-1===i.indexOf(l)&&i.push(l);for(var u=l.inboundLayers.length,c=0;c<u;c++){var p=l.inputTensors[c],h=l.inboundLayers[c],d=l.nodeIndices[c],f=l.tensorIndices[c];z(p,n,i,h,d,f);}for(n.push(l);i.indexOf(l)>=0;)i.splice(i.indexOf(l),1);w.push(l);}},S=[],I=[],C=0,N=r.outputs;C<N.length;C++){var _=N[C];z(_,S,I);}for(var A=0,E=w.slice().reverse();A<E.length;A++){m[(K=E[A]).id]=K,K.id in g||(g[K.id]=0);var k=g[K.id],L=null==y[K.outboundLayer.id]?0:y[K.outboundLayer.id];k=Math.max(k,L),y[K.outboundLayer.id]=k,v[K.outboundLayer.id]=K.outboundLayer,g[K.id]=k;for(h=0;h<K.inboundLayers.length;h++){var x=K.inboundLayers[h],T=(l=K.nodeIndices[h],x.inboundNodes[l]),D=null==g[T.id]?0:g[T.id];g[T.id]=Math.max(k+1,D),m[T.id]=T;}}var O={};for(var R in g){(k=g[R])in O||(O[k]=[]),O[k].push(m[R]);}var M={};for(var P in y){(k=y[P])in M||(M[k]=[]),M[k].push(v[P]);}var V=Object.keys(M).map(function(e){return parseInt(e,10)}).sort(reverseNumberCompare);r.layers=[];for(var F=0,B=V;F<B.length;F++){var U=M[k=B[F]];U.sort(function(e,t){var n=b[e.id],r=b[t.id];return n<r?-1:n>r?1:0});for(var j=0,W=U;j<W.length;j++){s=W[j];r.layers.push(s);}}r.layersByDepth=M,V=Object.keys(O).map(function(e){return parseInt(e,10)}).sort(reverseNumberCompare);for(var $=r.inputs.slice(),q=[],G=0,J=V;G<J.length;G++)for(var H=0,Z=O[k=J[G]];H<Z.length;H++){var K;if(null!=(s=(K=Z[H]).outboundLayer)){for(var Y=0,X=K.inputTensors;Y<X.length;Y++){_=X[Y];if(-1===$.indexOf(_))throw new RuntimeError(\"Graph disconnected: cannot obtain value for tensor \"+_+' at layer \"'+s.name+'\". The following previous layers were accessed without issue: '+q)}for(var Q=0,ee=K.outputTensors;Q<ee.length;Q++){_=ee[Q];$.push(_);}q.push(s.name);}}r.nodesByDepth=O;for(var te=r.layers.map(function(e){return e.name}),ne=function(e){var t=te.filter(function(t){return t===e}).length;if(1!==t)throw new RuntimeError('The name \"'+e+'\" is used '+t+\" times in the model. All layer names should be unique. Layer names: \"+JSON.stringify(te))},re=0,ie=te;re<ie.length;re++){ne(ie[re]);}return r.outboundNodes=[],r.inboundNodes=[],new Node({outboundLayer:r,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:r.inputs,outputTensors:r.outputs,inputMasks:r.inputs.map(function(e){return null}),outputMasks:r.outputs.map(function(e){return null}),inputShapes:r.inputs.map(function(e){return e.shape}),outputShapes:r.outputs.map(function(e){return e.shape})}),r.built=!0,r._refCount=1,r}return __extends$1(t,e),t.prototype.assertNotDisposed=function(){if(0===this._refCount)throw new Error(\"Container '\"+this.name+\"' is already disposed.\")},t.prototype.dispose=function(){this.assertNotDisposed();var e={refCountAfterDispose:null,numDisposedVariables:0};if(0==--this._refCount)for(var t=0,n=this.layers;t<n.length;t++){var r=n[t];e.numDisposedVariables+=r.dispose().numDisposedVariables;}return e.refCountAfterDispose=this._refCount,e},Object.defineProperty(t.prototype,\"trainableWeights\",{get:function(){if(this._trainableWeights.length>0)throw new ValueError(\"Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.\");if(!this.trainable)return [];for(var e=[],t=0,n=this.layers;t<n.length;t++){var r=n[t];e=e.concat(r.trainableWeights);}return e},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,\"nonTrainableWeights\",{get:function(){for(var e=[],t=0,n=this.layers;t<n.length;t++){var r=n[t];e.push.apply(e,r.nonTrainableWeights);}if(!this.trainable){for(var i=[],a=0,o=this.layers;a<o.length;a++){r=o[a];i.push.apply(i,r.trainableWeights);}return i.concat(e)}return e},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,\"weights\",{get:function(){return this.trainableWeights.concat(this.nonTrainableWeights)},enumerable:!0,configurable:!0}),t.prototype.loadWeights=function(e,t,n,r){void 0===t&&(t=!1),void 0===n&&(n=!1),void 0===r&&(r=!0),n?loadWeightsFromNamedTensorMap(e,this.layers,r):loadWeightsFromJson(e,this.layers,t);},t.prototype.updatedConfig=function(){var e=this.getConfig();return {className:this.getClassName(),config:e,kerasVersion:\"tfjs-layers \"+version$2,backend:\"TensorFlow.js\"}},t.prototype.toJSON=function(e,t){void 0===t&&(t=!0);var n=convertTsToPythonic(this.updatedConfig());return t?JSON.stringify(n):n},t.prototype.call=function(e,t){var n=this;return tidy(function(){var r;return e=toList(e),r=\"mask\"in t?toList(t.mask):pyListRepeat(null,e.length),n.runInternalGraph(e,r)[0]})},t.prototype.computeMask=function(e,t){var n=this;return tidy(function(){var r;return e=toList(e),r=null==t?pyListRepeat(null,e.length):toList(t),n.runInternalGraph(e,r)[1]})},t.prototype.computeOutputShape=function(e){var t=normalizeShapeList(e);if(t.length!==this.inputLayers.length)throw new ValueError(\"Invalid inputShape argument \"+e+\": model has \"+this.inputLayers.length+\" tensor inputs.\");for(var n={},r=0;r<t.length;r++){var i=this.inputLayers[r],a=t[r];n[I=i.name+\"_0_0\"]=a;}var o=Object.keys(this.nodesByDepth).map(function(e){return parseInt(e,10)}).sort(reverseNumberCompare);if(o.length>1)for(var s=0,l=o;s<l.length;s++)for(var u=l[s],c=0,p=this.nodesByDepth[u];c<p.length;c++){var h=p[c];i=h.outboundLayer;if(-1===this.inputLayers.map(function(e){return e.id}).indexOf(i.id)){for(var d=[],f=0;f<h.inboundLayers.length;f++){var g=h.inboundLayers[f],m=h.nodeIndices[f],y=h.tensorIndices[f],v=n[I=g.name+\"_\"+m+\"_\"+y];d.push(v);}var b=normalizeShapeList(i.computeOutputShape(singletonOrArray(d))),w=i.inboundNodes.indexOf(h);for(f=0;f<b.length;f++){n[I=i.name+\"_\"+w+\"_\"+f]=b[f];}}}var z=[],S=[];for(r=0;r<this.outputLayers.length;r++){i=this.outputLayers[r],w=this.outputLayersNodeIndices[r],y=this.outputLayersTensorIndices[r];var I=i.name+\"_\"+w+\"_\"+y;S.push(I);}for(r=0;r<S.length;r++){var C=S[r];assert$1(C in n),z.push(n[C]);}return singletonOrArray(z)},t.prototype.runInternalGraph=function(e,t){null==t&&(t=pyListRepeat(null,e.length));for(var n={},r=0;r<this.inputs.length;++r){var i=this.inputs[r],a=e[r],o=t[r];n[i.id]=[a,o];}for(var s=0,l=Object.keys(this.nodesByDepth).map(function(e){return parseInt(e,10)}).sort(reverseNumberCompare);s<l.length;s++)for(var u=l[s],c=0,p=this.nodesByDepth[u];c<p.length;c++){for(var h=p[c],d=h.outboundLayer,f=h.inputTensors,g=h.outputTensors,m=new Array,y=0,v=f;y<v.length;y++){(i=v[y]).id in n&&m.push(n[i.id]);}if(m.length===f.length){var b={},w=void 0,z=void 0,S=void 0,I=void 0;if(null!=h.callArgs&&(b=h.callArgs),1===m.length){var C=m[0],N=C[0],_=C[1];null==b.mask&&(b.mask=_),S=toList(d.call(N,b)),I=toList(d.computeMask(N,_)),w=[N],z=[_];}else w=m.map(function(e){return e[0]}),z=m.map(function(e){return e[1]}),null==b.mask&&(b.mask=z),S=toList(d.call(w,b)),I=toList(d.computeMask(w,z));if(d.activityRegularizer)throw new NotImplementedError(\"Model invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.\");for(r=0;r<g.length;++r){i=g[r],a=S[r],o=I[r];n[i.id]=[a,o];}}}for(var A=[],E=[],k=[],L=0,x=this.outputs;L<x.length;L++){assert$1((i=x[L]).id in n,\"Could not compute output \"+i.name+\" : \"+i.id);var T=n[i.id],D=T[0];o=T[1];k.push(D.shape),A.push(D),E.push(o);}return [A,E,k]},t.prototype.buildNodeConversionMap=function(e){for(var n,r={},i=0,a=this.layers;i<a.length;i++){var o=a[i];n=o instanceof t?1:0;for(var s=0;s<o.inboundNodes.length;s++){var l=t.nodeKey(o,s);this.containerNodes.has(l)&&(r[l]=n,n+=1);}}return r},t.prototype.getLayer=function(e,t){if(null!=t){if(this.layers.length<=t)throw new ValueError(\"Was asked to retrieve layer at index \"+t+\", but model only has \"+this.layers.length+\" layer(s).\");return this.layers[t]}if(null==e)throw new ValueError(\"Provide either a layer name or layer index\");for(var n=0,r=this.layers;n<r.length;n++){var i=r[n];if(i.name===e)return i}throw new ValueError(\"No such layer: \"+e)},t.prototype.calculateLosses=function(){var e=this;return tidy(function(){for(var n=[],r=0,i=e.layers;r<i.length;r++)for(var a=i[r],o=0;o<a.inboundNodes.length;++o){var s=t.nodeKey(a,o);e.containerNodes.has(s)&&n.push.apply(n,a.calculateLosses());}return n})},t.prototype.getConfig=function(){for(var e={name:this.name},n=this.buildNodeConversionMap(this.layers),r=[],i=0,a=this.layers;i<a.length;i++){for(var o=(b=a[i]).getClassName(),s=b.getConfig(),l=[],u=0;u<b.inboundNodes.length;u++){var c=b.inboundNodes[u],p=t.nodeKey(b,u),h={};if(this.containerNodes.has(p)){if(c.callArgs)try{JSON.stringify(c.callArgs),h=c.callArgs;}catch(e){console.warn(\"Layer \"+b.name+\" was passed non-serializable keyword arguments: \"+c.callArgs+\". They will not be included in the serialized model (and thus will be missing at deserialization time).\"),h={};}if(c.inboundLayers.length>0){for(var d=[],f=0;f<c.inboundLayers.length;f++){var g=c.inboundLayers[f],m=c.nodeIndices[f],y=c.tensorIndices[f];null==(z=n[t.nodeKey(g,m)])&&(z=0),d.push([g.name,z,y,h]);}l.push(d);}}}r.push({name:b.name,className:o,config:s,inboundNodes:l});}e.layers=r;var v=[];for(f=0;f<this.inputLayers.length;f++){var b=this.inputLayers[f];m=this.inputLayersNodeIndices[f],p=t.nodeKey(b,m);if(this.containerNodes.has(p)){null!==(z=n[p])&&void 0!==z||(z=0);y=this.inputLayersTensorIndices[f];v.push([b.name,z,y]);}}e.inputLayers=v;var w=[];for(f=0;f<this.outputLayers.length;f++){b=this.outputLayers[f],m=this.outputLayersNodeIndices[f],p=t.nodeKey(b,m);if(this.containerNodes.has(p)){var z;null!==(z=n[p])&&void 0!==z||(z=0);y=this.outputLayersTensorIndices[f];w.push([b.name,z,y]);}}return e.outputLayers=w,e},t.fromConfig=function(e,t){var n={},r={};function i(e,t){e.name in r?r[e.name].push(t):r[e.name]=[t];}function a(e,t){for(var r,a=[],o=0,s=t;o<s.length;o++){var l=s[o],u=l[0],c=l[1],p=l[2];if(3===l.length)r={};else{if(4!==l.length)throw new ValueError(\"Improperly formatted model config for layer \"+JSON.stringify(e)+\": \"+JSON.stringify(l));r=l[3];}if(!(u in n))return void i(e,t);var h=n[u];if(h.inboundNodes.length<=c)return void i(e,t);var d=h.inboundNodes[c];a.push(d.outputTensors[p]);}a.length>0&&e.apply(singletonOrArray(a),r);}function o(e){var r=e.name,a=deserialize(e,null!=t.customObjects?t.customObjects:{});n[r]=a;for(var o=0,s=e.inboundNodes;o<s.length;o++){var l=s[o];if(!(l instanceof Array))throw new ValueError(\"Corrupted configuration, expected array for nodeData: \"+l);i(a,l);}}for(var s=t.name,l=t.layers,u=0,c=l;u<c.length;u++){o(d=c[u]);}for(;!isObjectEmpty(r);)for(var p=0,h=l;p<h.length;p++){var d=h[p];if((A=n[d.name]).name in r){var f=r[A.name];delete r[A.name];for(var g=0,m=f;g<m.length;g++){a(A,m[g]);}}}for(var y=[],v=[],b=0,w=t.inputLayers;b<w.length;b++){var z=(d=w[b])[0],S=d[1],I=d[2];assert$1(z in n);var C=(A=n[z]).inboundNodes[S].outputTensors;y.push(C[I]);}for(var N=0,_=t.outputLayers;N<_.length;N++){z=(d=_[N])[0],S=d[1],I=d[2];assert$1(z in n);var A;C=(A=n[z]).inboundNodes[S].outputTensors;v.push(C[I]);}return new e({inputs:y,outputs:v,name:s})},Object.defineProperty(t.prototype,\"stateful\",{get:function(){if(this._stateful)throw new ValueError(\"Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.\");for(var e=0,t=this.layers;e<t.length;e++){if(t[e].stateful)return !0}return !1},enumerable:!0,configurable:!0}),t.prototype.resetStates=function(){var e=this;tidy(function(){e.layers.forEach(function(e){e.stateful&&e.resetStates();});});},t}(Layer);function assertFeedCompatibility(e,t){if(null!=e.shape){if(e.shape.length!==t.shape.length)throw new ValueError(\"The rank of feed (\"+t.shape.length+\") does not match the rank of the key (\"+e.shape.length+\").\");for(var n=0;n<e.shape.length;++n)if(null!=e.shape[n]&&e.shape[n]!==t.shape[n])throw new ValueError(\"The \"+n+\"-th dimension of the feed (\"+t.shape[n]+\") is incompatible with that of the key (\"+e.shape[n]+\").\")}if(null==e.dtype||e.dtype===t.dtype)return t;try{return cast(t,e.dtype)}catch(n){throw new ValueError(\"The dtype of the feed (\"+t.dtype+\") can not be cast to the dtype of the key '\"+e.name+\"' (\"+e.dtype+\").\")}}var FeedDict=function(){function e(t){if(this.id2Value={},t instanceof e)for(var n in t.id2Value)this.id2Value[n]=t.id2Value[n];else{if(null==t)return;for(var r=0,i=t;r<i.length;r++){var a=i[r];this.add(a.key,a.value);}}}return e.prototype.add=function(e,t){if(null!=this.id2Value[e.id])throw new ValueError(\"Duplicate key: name=\"+e.name+\", id=\"+e.id);return this.id2Value[e.id]=assertFeedCompatibility(e,t),this},e.prototype.addFeed=function(e){this.add(e.key,e.value);},e.prototype.hasKey=function(e){return null!=this.id2Value[e.id]},e.prototype.getValue=function(e){if(null==this.id2Value[e.id])throw new ValueError(\"Nonexistent key: \"+JSON.stringify(e));return this.id2Value[e.id]},e}();function execute(e,t,n){for(var r=Array.isArray(e),i=r?e:[e],a=[],o=new FeedDict(t),s=0,l=i;s<l.length;s++){var u=l[s];a.push(executeInternal(u,o,n));}return r?a:a[0]}function executeInternal(e,t,n){if(t.hasKey(e))return t.getValue(e);if(e.sourceLayer instanceof InputLayer)throw new ValueError(\"Missing a feed value for SymbolicTensor from InputLayer '\"+InputLayer.name+\"'\");for(var r=[],i=0,a=e.inputs;i<a.length;i++){var o=executeInternal(a[i],t,n);r.push(o);}var s=e.sourceLayer.apply(r,n);Array.isArray(s)||(s=[s]);for(var l=getNodeOutputs(e),u=Array.isArray(l)?l:[l],c=0;c<u.length;++c)t.add(u[c],s[c]);return 1===s.length?s[0]:s[e.outputTensorIndex]}function getNodeOutputs(e){var t;if(1===e.sourceLayer.inboundNodes.length)t=e.sourceLayer.output;else{for(var n=null,r=0;r<e.sourceLayer.inboundNodes.length;++r)for(var i=0,a=e.sourceLayer.inboundNodes[r].outputTensors;i<a.length;i++){if(a[i].id===e.id){n=r;break}}t=e.sourceLayer.getOutputAt(n);}return t}var DEFAULT_VALIDATION_BATCH_SIZE=32;function standardizeDataIteratorOutput(e,t){if(e.outputs.length>1)throw new NotImplementedError(\"Support for training a model with multiple output tensors with a dataset object is not implemented yet.\");util.assert(Array.isArray(t)&&2===t.length,\"Dataset iterator for fitDataset() is expected to generate an Array of length 2: `[xs, ys]`, but instead generates \"+t);var n=(t=t)[1],r=t[0];if(r instanceof Tensor)return util.assert(1===e.inputs.length,\"Model has multiple \"+e.inputs.length+\" inputs, hence it expects the input dataset to generate a dictionary of tensors  (with keys \"+JSON.stringify(e.inputNames)+\", but received a single tensor.\"),util.assert(r.shape[0]===n.shape[0],\"Mismatch in batch size between x and y tensors (\"+r.shape[0]+\" vs. \"+n.shape[0]+\")\"),[r,n];var i=void 0;r=r;for(var a=[],o=0,s=e.inputNames;o<s.length;o++){var l=s[o];if(null==r[l])throw new ValueError(\"The feature data generated by the dataset lacks the required input key '\"+l+\"'.\");a.push(r[l]),null==i?i=r[l].shape[0]:util.assert(r[l].shape[0]===i,\"Mismatch in batch size between x and y tensors (\"+r[l].shape[0]+\" vs. \"+n.shape[0]+\")\");}return a.concat(n)}function standardizeTensorValidationData(e){if(3===e.length)throw new NotImplementedError(\"Validation with sample weights is not implemented yet.\");return {xs:e[0],ys:e[1]}}function fitDataset(e,t,n){return __awaiter$1(this,void 0,void 0,function(){var r,i,a,o,s,l,u,c,p,h,d,f,g,m,y,v,b,w,z,S,I,C,N,_,A,E;return __generator$1(this,function(k){switch(k.label){case 0:if(util.assert(null!=e.optimizer,\"You must compile a model before training/testing. Use Model.compile(modelCompileConfig).\"),util.assert(null!=n,\"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call.\"),util.assert(null!=n.epochs&&n.epochs>0&&Number.isInteger(n.epochs),\"For fitDataset(), config.epochs is expected to be a positive integer, but got \"+n.epochs),util.assert(null!=n.batchesPerEpoch&&n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),\"For fitDataset(), config.batchesPerEpoch is expected to be a positive integer, but got \"+n.batchesPerEpoch),e.isTraining)throw new Error(\"Cannot start training because another fit() call is ongoing.\");e.isTraining=!0,k.label=1;case 1:return k.trys.push([1,,21,22]),r=null!=n.validationData,i=void 0,a=void 0,o=void 0,r?isDatasetObject(n.validationData)?(util.assert(n.validationBatches>0&&Number.isInteger(n.validationBatches),\"For fitDataset() with dataset-based validation, config.validationBatches is expected to be a positive integer, but got \"+n.validationBatches),[4,n.validationData.iterator()]):[3,3]:[3,4];case 2:return i=k.sent(),[3,4];case 3:s=standardizeTensorValidationData(n.validationData),a=s.xs,o=s.ys,k.label=4;case 4:return l=e.makeTrainFunction(),u=e.getDedupedMetricsNames(),c=void 0,c=r?u.slice().concat(u.map(function(e){return \"val_\"+e})):u.slice(),p=standardizeCallbacks(n.callbacks),h=configureCallbacks(p,n.yieldEvery,n.verbose,n.epochs,null,null,n.batchesPerEpoch,null,r,c),d=h.callbackList,f=h.history,e.history=f,[4,d.onTrainBegin()];case 5:return k.sent(),g=null==n.initialEpoch?0:n.initialEpoch,m={},[4,t.iterator()];case 6:y=k.sent(),k.label=7;case 7:return g<n.epochs?[4,d.onEpochBegin(g)]:[3,18];case 8:k.sent(),v=0,b=0,k.label=9;case 9:return v<n.batchesPerEpoch?[4,y.next()]:[3,16];case 10:if((w=k.sent()).done)return console.warn(\"Your dataset iterator ran out of data; interrupting training. Make sure that your dataset can generate at least `batchesPerEpoch * epochs` batches (in this case, \"+n.batchesPerEpoch*n.epochs+\" batches). You may need to use the repeat() function when building your dataset.\"),[3,16];for(z=standardizeDataIteratorOutput(e,w.value),(S={}).batch=b,S.size=z[0].shape[0],d.onBatchBegin(b,S),I=l(z),dispose(z),E=0;E<u.length;++E)C=u[E],N=I[E],S[C]=N,keep(N);return [4,d.onBatchEnd(b,S)];case 11:return k.sent(),disposeTensorsInLogs(S),b++,++v>=n.batchesPerEpoch&&r?(_=void 0,isDatasetObject(n.validationData)?(A=toList,[4,e.evaluateDataset(i,{batches:n.validationBatches})]):[3,13]):[3,15];case 12:return _=A.apply(void 0,[k.sent()]),[3,14];case 13:_=toList(e.evaluate(a,o,{batchSize:null==n.validationBatchSize?DEFAULT_VALIDATION_BATCH_SIZE:n.validationBatchSize,verbose:0})),k.label=14;case 14:for(E=0;E<e.metricsNames.length;++E)m[\"val_\"+e.metricsNames[E]]=_[E];k.label=15;case 15:return e.stopTraining_?[3,16]:[3,9];case 16:return [4,d.onEpochEnd(g,m)];case 17:return k.sent(),g++,e.stopTraining_?[3,18]:[3,7];case 18:return [4,d.onTrainEnd()];case 19:return k.sent(),[4,e.history.syncData()];case 20:return k.sent(),[2,e.history];case 21:return e.isTraining=!1,[7];case 22:return [2]}})})}function isDatasetObject(e){return \"function\"==typeof e.iterator}function isLazyIteratorObject(e){return \"function\"==typeof e.next}function evaluateDataset(e,t,n){return __awaiter$1(this,void 0,void 0,function(){var r,i,a,o,s,l,u,c,p;return __generator$1(this,function(h){switch(h.label){case 0:if(r=e.testFunction,i=[],n.verbose>0)throw new NotImplementedError(\"Verbose mode is not implemented yet.\");return util.assert(n.batches>0&&Number.isInteger(n.batches),\"Test loop expects `batches` to be a positive integer, but received \"+JSON.stringify(n.batches)),isLazyIteratorObject(t)?(o=t,[3,3]):[3,1];case 1:return [4,t.iterator()];case 2:o=h.sent(),h.label=3;case 3:a=o,s=0,l=function(t){var o,l,u,c,p,h;return __generator$1(this,function(d){switch(d.label){case 0:return [4,a.next()];case 1:if((o=d.sent()).done)return console.warn(\"Your dataset iterator ran out of data during evaluateDataset(). Interrupting evalution. Make sure that your dataset can generate at least `batches` batches (in this case, \"+n.batches+\" batches). You may need to use the repeat() function when building your dataset.\"),[2,\"break\"];if(l=standardizeDataIteratorOutput(e,o.value),u=tidy(function(){return r(l)}),dispose(l),0===t)for(h=0;h<u.length;++h)i.push(getScalar(0));for(c=l[0].shape[0],p=function(e){var n=u[e],r=i[e];i[e]=tidy(function(){return add(i[e],mul(getScalar(c),n))}),t>0&&dispose(r);},h=0;h<u.length;++h)p(h);return dispose(u),s+=c,[2]}})},u=0,h.label=4;case 4:return u<n.batches?[5,l(u)]:[3,7];case 5:if(\"break\"===h.sent())return [3,7];h.label=6;case 6:return ++u,[3,4];case 7:for(c=function(e){var t=i[e];i[e]=tidy(function(){return div(i[e],getScalar(s))}),dispose(t);},p=0;p<i.length;++p)c(p);return [2,singletonOrArray(i)]}})})}function checkBatchSize(e){util.assert(e>0&&Number.isInteger(e),\"batchSize is required to be a positive integer, but got \"+e);}function sliceArrays(e,t,n){return null==e?[null]:Array.isArray(e)?e.map(function(e){return sliceAlongFirstAxis(e,t,n-t)}):sliceAlongFirstAxis(e,t,n-t)}function sliceArraysByIndices(e,t){return tidy(function(){return null==e?null:Array.isArray(e)?e.map(function(e){return sliceArraysByIndices(e,t)}):gather$1(e,\"int32\"===t.dtype?t:t.toInt())})}function makeBatches(e,t){for(var n=[],r=0,i=null;r<e;)(i=r+t)>=e&&(i=e),n.push([r,i]),r=i;return n}function fitLoop(e,t,n,r,i,a,o,s,l,u,c,p,h,d,f,g){return __awaiter$1(this,void 0,void 0,function(){var m,y,v,b,w,z,S,I;return __generator$1(this,function(C){switch(C.label){case 0:if(null==i&&(i=32),null==a&&(a=1),null==c&&(c=!0),null==h&&(h=0),m=!1,null!=l&&null!=u&&(m=!0),null!=f&&(m=!0,null==d))throw new ValueError(\"Can only use `validationSteps` when doing step-wise training, i.e., `stepsPerEpoch` must be set.\");return null!=(y=e.checkNumSamples(n,i,d,\"steps_per_epoch\"))&&(v=range$1(0,y)),null==o&&(o=1),b=configureCallbacks(s,g,o,a,h,y,d,i,m,p),w=b.callbackList,z=b.history,w.setModel(e),e.history=z,[4,w.onTrainBegin()];case 1:C.sent(),e.stopTraining_=!1,S=function(a){var o,s,p,h,f;return __generator$1(this,function(g){switch(g.label){case 0:return [4,w.onEpochBegin(a)];case 1:if(g.sent(),o={},null==d)return [3,2];throw new NotImplementedError(\"stepsPerEpoch mode is not implemented yet.\");case 2:if(\"batch\"===c)throw new NotImplementedError(\"batch shuffling is not implemneted yet\");c&&util.shuffle(v),s=tensor1d(v),p=makeBatches(y,i),h=function(a){var c;return __generator$1(this,function(h){switch(h.label){case 0:return c={},[4,w.onBatchBegin(a,c)];case 1:return h.sent(),tidy(function(){var h=p[a][0],d=p[a][1],f=sliceAlongFirstAxis(s,h,d-h);c.batch=a,c.size=d-h;for(var g=sliceArraysByIndices(n,f),y=t(g),v=0;v<r.length;++v){var b=r[v],w=y[v];c[b]=w,keep(w);}if(a===p.length-1&&m){var z=e.testLoop(l,u,i);for(v=0;v<r.length;++v){b=r[v],w=z[v];keep(w),o[\"val_\"+b]=w;}}}),[4,w.onBatchEnd(a,c)];case 2:return h.sent(),disposeTensorsInLogs(c),e.stopTraining_?[2,\"break\"]:[2]}})},f=0,g.label=3;case 3:return f<p.length?[5,h(f)]:[3,6];case 4:if(\"break\"===g.sent())return [3,6];g.label=5;case 5:return ++f,[3,3];case 6:s.dispose(),g.label=7;case 7:return [4,w.onEpochEnd(a,o)];case 8:return g.sent(),e.stopTraining_?[2,\"break\"]:[2]}})},I=h,C.label=2;case 2:return I<a?[5,S(I)]:[3,5];case 3:if(\"break\"===C.sent())return [3,5];C.label=4;case 4:return ++I,[3,2];case 5:return [4,w.onTrainEnd()];case 6:return C.sent(),[4,e.history.syncData()];case 7:return C.sent(),[2,e.history]}})})}function fitTensors(e,t,n,r){return void 0===r&&(r={}),__awaiter$1(this,void 0,void 0,function(){var i,a,o,s,l,u,c,p,h,d,f,g,m,y,v,b,w,z,S;return __generator$1(this,function(I){switch(I.label){case 0:if(e.isTraining)throw new Error(\"Cannot start training because another fit() call is ongoing.\");e.isTraining=!0,I.label=1;case 1:if(I.trys.push([1,,3,4]),checkBatchSize(i=null==r.batchSize?32:r.batchSize),a=e.standardizeUserData(t,n,!1,i),o=a[0],s=a[1],l=!1,u=void 0,c=void 0,p=void 0,h=!1,null!=r.validationData&&r.validationData.length>0){if(l=!0,2!==r.validationData.length)throw 3===r.validationData.length?new NotImplementedError(\"validationData including sample weights is not supported yet.\"):new ValueError(\"When passing validation data, it must contain 2 (valX, valY) or 3 (valX, valY, valSampleWeight) items; \"+r.validationData+\" is invalid.\");u=r.validationData[0],c=r.validationData[1],d=e.standardizeUserData(u,c,!0,i),u=d[0],c=d[1],p=u.concat(c);}else null!=r.validationSplit&&r.validationSplit>0&&r.validationSplit<1?(l=!0,f=Math.floor(o[0].shape[0]*(1-r.validationSplit)),g=o[0].shape[0],u=sliceArrays(o,f,g),o=sliceArrays(o,0,f),c=sliceArrays(s,f,g),s=sliceArrays(s,0,f),h=!0,p=u.concat(c)):null!=r.validationSteps&&(l=!0);return m=o.concat(s),e.checkTrainableWeightsConsistency(),y=e.makeTrainFunction(),v=e.getDedupedMetricsNames(),b=void 0,w=void 0,l?(e.makeTestFunction(),b=e.testFunction,w=v.slice().concat(v.map(function(e){return \"val_\"+e}))):(b=null,p=[],w=v.slice()),z=standardizeCallbacks(r.callbacks),[4,fitLoop(e,y,m,v,i,r.epochs,r.verbose,z,b,p,r.shuffle,w,r.initialEpoch,null,null,r.yieldEvery)];case 2:return S=I.sent(),h&&(p.forEach(function(e){return e.dispose()}),o.forEach(function(e){return e.dispose()}),s.forEach(function(e){return e.dispose()})),e.isTraining=!1,[2,S];case 3:return e.isTraining=!1,[7];case 4:return [2]}})})}function isDataTensor(e){return e instanceof Tensor}function isDataArray(e){return Array.isArray(e)}function isDataDict(e){return !isDataTensor(e)&&!isDataArray(e)}function standardizeInputData(e,t,n,r,i){if(void 0===r&&(r=!0),void 0===i&&(i=\"\"),null==t||0===t.length){if(null!=e){var a=!1;if(isDataArray(e)&&e.length>0)a=!0;else if(isDataDict(e)){for(var o in e)if(e.hasOwnProperty(o)){a=!0;break}}else a=!0;if(a)throw new ValueError(\"Error when checking model \"+i+\" expected no data, but got \"+e)}return []}if(null==e)return t.map(function(e){return null});var s;if(isDataDict(e)){e=e,s=[];for(var l=0,u=t;l<u.length;l++){var c=u[l];if(null==e[c])throw new ValueError('No data provided for \"'+c+'\". Need data for each key in: '+t);s.push(e[c]);}}else if(isDataArray(e)){if((e=e).length!==t.length)throw new ValueError(\"Error when checking model \"+i+\": the Array of Tensors that you are passing to your model is not the size the model expected. Expected to see \"+t.length+\" Tensor(s), but instead got the following list of Tensor(s): \"+e);s=e;}else{if(e=e,t.length>1)throw new ValueError(\"The model \"+i+\" expects \"+t.length+\" Tensor(s), but only received one Tensor. Found: Tensor with shape \"+e.shape);s=[e];}for(var p=0;p<t.length;++p){1===(h=s[p]).shape.length&&(s[p]=expandDims$1(h,1));}if(null!=n)for(p=0;p<t.length;++p)if(null!=n[p]){var h;if((h=s[p]).shape.length!==n[p].length)throw new ValueError(\"Error when checking \"+i+\": expected \"+t[p]+\" to have \"+n[p].length+\" dimension(s). but got array with shape \"+h.shape);for(var d=0;d<n[p].length;++d)if(0!==d||r){var f=h.shape[d],g=n[p][d];if(null!=g&&g>=0&&f!==g)throw new ValueError(\"Error when checking \"+i+\": expected \"+t[p]+\" to have shape [\"+n[p]+\"], but got array with shape [\"+h.shape+\"].\")}}return s}function checkArrayLengths(e,t,n){var r=unique(e.map(function(e){return e.shape[0]}));r.sort();var i=unique(t.map(function(e){return e.shape[0]}));if(i.sort(),r.length>1)throw new ValueError(\"All input Tensors (x) should have the same number of samples. Got array shapes: \"+JSON.stringify(e.map(function(e){return e.shape})));if(i.length>1)throw new ValueError(\"All target Tensors (y) should have the same number of samples. Got array shapes: \"+JSON.stringify(t.map(function(e){return e.shape})));if(r.length>0&&i.length>0&&!util.arraysEqual(r,i))throw new ValueError(\"Input Tensors should have the same number of samples as target Tensors. Found \"+r[0]+\" input sample(s) and \"+i[0]+\" target sample(s).\")}function checkLossAndTargetCompatibility(e,t,n){for(var r=[meanSquaredError$1,binaryCrossentropy,categoricalCrossentropy],i=0;i<e.length;++i){var a=e[i],o=t[i],s=n[i];if(null!=o){if(o===categoricalCrossentropy&&1===a.shape[a.shape.length-1])throw new ValueError(\"You are passing a target array of shape \"+a.shape+\" while using a loss 'categorical_crossentropy'. 'categorical_crossentropy'expects targets to be binary matrices (1s and 0s) of shape [samples, classes].\");if(-1!==r.indexOf(o))for(var l=a.shape.slice(1),u=s.slice(1),c=0;c<l.length;++c){var p=l[c],h=u[c];if(null!=h&&p!==h)throw new ValueError(\"A target Tensor with shape \"+a.shape+\" was passed for an output of shape \"+s+\", while using a loss function that expects targets to have the same shape as the output.\")}}}}function checkInputData(e,t,n,r,i){var a;if(void 0===r&&(r=!0),void 0===i&&(i=\"\"),Array.isArray(e)){if(e.length!==t.length)throw new ValueError(\"Error when checking model \"+i+\": the Array of Tensors that you are passing to your model is not the size the the model expected. Expected to see \"+t.length+\" Tensor(s), but instead got \"+e.length+\" Tensors(s).\");a=e;}else{if(t.length>1)throw new ValueError(\"The model expects \"+t.length+\" \"+i+\" Tensors, but only received one Tensor. Found: array with shape \"+JSON.stringify(e.shape)+\".\");a=[e];}if(null!=n)for(var o=0;o<t.length;++o)if(null!=n[o]){var s=a[o];if(s.shape.length!==n[o].length)throw new ValueError(\"Error when checking \"+i+\": expected \"+t[o]+\" to have \"+n[o].length+\" dimension(s), but got array with shape \"+JSON.stringify(s.shape));for(var l=0;l<n[o].length;++l)if(0!==l||r){var u=s.shape[l],c=n[o][l];if(null!=c&&c!==u)throw new ValueError(\"Error when checking \"+i+\": expected \"+t[o]+\" to have shape \"+JSON.stringify(n[o])+\" but got array with shape \"+JSON.stringify(s.shape)+\".\")}}}function collectMetrics(e,t){if(null==e||Array.isArray(e)&&0===e.length)return t.map(function(e){return []});if(Array.isArray(e))return t.map(function(t){return e});if(null!=e){for(var n=[],r=0,i=t;r<i.length;r++){var a=i[r],o=e.hasOwnProperty(a)?e[a]:[];Array.isArray(o)||(o=[o]),n.push(o);}return n}throw new TypeError(\"Type of metrics argument not understood. Expected an Array or Object, found: \"+e)}var Model=function(e){function t(t){var n=e.call(this,t)||this;return n.isTraining=!1,n}return __extends$1(t,e),t.prototype.summary=function(e,t,n){if(void 0===n&&(n=console.log),!this.built)throw new ValueError(\"This model has never been called, thus its weights have not been created yet. So no summary can be displayed. Build the model first (e.g., by calling it on some test data).\");printSummary(this,e,t,n);},t.prototype.compile=function(e){var t=this;if(null==e.loss&&(e.loss=[]),this.loss=e.loss,\"string\"==typeof e.optimizer)this.optimizer=getOptimizer(e.optimizer);else{if(!(e.optimizer instanceof Optimizer))throw new ValueError(\"User-defined optimizer must be an instance of tf.Optimizer.\");this.optimizer=e.optimizer;}var n=[];if(Array.isArray(e.loss)||\"string\"==typeof e.loss||\"function\"==typeof e.loss)if(Array.isArray(e.loss)){if(e.loss.length!==this.outputs.length)throw new ValueError(\"When passing an Array as loss, it should have one entry per model output. The model has \"+this.outputs.length+\" output(s), but you passed loss=\"+e.loss+\".\");var r=e.loss;n=r.map(function(e){return get(e)});}else{var i=get(e.loss);this.outputs.map(function(e){n.push(i);});}else{for(var a in e.loss=e.loss,e.loss)if(-1===this.outputNames.indexOf(a))throw new ValueError('Unknown entry in loss dictionary: \"'+a+'\". Only expect the following keys: '+this.outputNames);for(var o in this.outputNames)null==e.loss[o]&&console.warn('Output \"'+o+'\" is missing from loss dictionary. We assume this was done on purpose, and we will not be expecting data to be passed to '+o+\" during training\"),n.push(get(e.loss[o]));}this.lossFunctions=n,this.feedOutputNames=[],this.feedOutputShapes=[],this.feedLossFns=[];for(var s=0;s<this.outputs.length;++s){var l=this.internalOutputShapes[s],u=this.outputNames[s];this.feedOutputNames.push(u),this.feedOutputShapes.push(l),this.feedLossFns.push(this.lossFunctions[s]);}var c=[];this.metrics=e.metrics,this.metricsNames=[\"loss\"],this.metricsTensors=[],nameScope(\"loss\",function(){for(var e=0;e<t.outputs.length;++e)if(-1===c.indexOf(e)){var n=t.lossFunctions[e];t.outputs.length>1&&(t.metricsTensors.push([n,e]),t.metricsNames.push(t.outputNames[e]+\"_loss\"));}});var p=collectMetrics(e.metrics,this.outputNames);nameScope(\"metric\",function(){for(var e=function(e){if(-1!==c.indexOf(e))return \"continue\";!function(n){for(var r,i,a,o=function(n){if(-1!==[\"accuracy\",\"acc\",\"crossentropy\",\"ce\"].indexOf(n)){var o=t.internalOutputShapes[e];1===o[o.length-1]||t.lossFunctions[e]===binaryCrossentropy?-1!==[\"accuracy\",\"acc\"].indexOf(n)?i=binaryAccuracy:-1!==[\"crossentropy\",\"ce\"].indexOf(n)&&(i=binaryCrossentropy$1):t.lossFunctions[e]===sparseCategoricalCrossentropy?-1!==[\"accuracy\",\"acc\"].indexOf(n)?i=sparseCategoricalAccuracy:-1!==[\"crossentropy\",\"ce\"].indexOf(n)&&(i=sparseCategoricalCrossentropy$1):-1!==[\"accuracy\",\"acc\"].indexOf(n)?i=categoricalAccuracy:-1!==[\"crossentropy\",\"ce\"].indexOf(n)&&(i=categoricalCrossentropy$1);var s=void 0;-1!==[\"accuracy\",\"acc\"].indexOf(n)?s=\"acc\":-1!==[\"crossentropy\",\"ce\"].indexOf(n)&&(s=\"ce\"),a=i,r=\"\"+s;}else{var l=get$1(n);a=l,r=\"\"+n;}var u;nameScope(r,function(){u=a;}),function(e,n,r){t.outputNames.length>1&&(n=t.outputNames[e]+\"_\"+n),t.metricsNames.push(n),t.metricsTensors.push([r,e]);}(e,r,u);},s=0,l=n;s<l.length;s++)o(l[s]);}(p[e]);},n=0;n<t.outputs.length;++n)e(n);}),this.collectedTrainableWeights=this.trainableWeights;},t.prototype.checkTrainableWeightsConsistency=function(){null!=this.collectedTrainableWeights&&this.trainableWeights.length!==this.collectedTrainableWeights.length&&console.warn(\"Discrepancy between trainableweights and collected trainable weights. Did you set `model.trainable` without calling `model.compile()` afterwards?\");},t.prototype.evaluate=function(e,t,n){void 0===n&&(n={});var r=null==n.batchSize?32:n.batchSize;checkBatchSize(r);var i=this.standardizeUserData(e,t,!0,r),a=i[0].concat(i[1]);this.makeTestFunction();var o=this.testFunction;return singletonOrArray(this.testLoop(o,a,r,n.verbose,n.steps))},t.prototype.evaluateDataset=function(e,t){return __awaiter$1(this,void 0,void 0,function(){return __generator$1(this,function(n){return this.makeTestFunction(),[2,evaluateDataset(this,e,t)]})})},t.prototype.checkNumSamples=function(e,t,n,r){var i;if(void 0===r&&(r=\"steps\"),null!=n){if(i=null,null!=t)throw new ValueError(\"If \"+r+\" is set, batchSize must be null or undefined.Got batchSize = \"+t)}else{if(null==e)throw new ValueError(\"Either the input data should have a defined shape, or \"+r+\" shoud be specified.\");i=Array.isArray(e)?e[0].shape[0]:e.shape[0];}return i},t.prototype.execute=function(e,t){if(Array.isArray(t)&&0===t.length)throw new ValueError(\"`outputs` is an empty Array, which is not allowed.\");var n=Array.isArray(t),r=n?t:[t],i=this.retrieveSymbolicTensors(r),a=new FeedDict;if(e instanceof Tensor&&(e=[e]),Array.isArray(e)){if(e.length!==this.inputs.length)throw new ValueError(\"The number of inputs provided (\"+e.length+\") does not match the number of inputs of this model (\"+this.inputs.length+\").\");for(var o=0;o<this.inputs.length;++o)a.add(this.inputs[o],e[o]);}else for(var s=0,l=this.inputs;s<l.length;s++){var u=l[s],c=e[u.name];if(null==c)throw new ValueError(\"No value is provided for the model's input \"+u.name);a.add(u,c);}var p=execute(i,a);return n?p:p[0]},t.prototype.retrieveSymbolicTensors=function(e){for(var t=pyListRepeat(null,e.length),n=e.length,r=0,i=this.layers;r<i.length;r++){for(var a=i[r],o=Array.isArray(a.output)?a.output:[a.output],s=o.map(function(e){return e.name}),l=0;l<e.length;++l){var u=s.indexOf(e[l]);if(-1!==u&&(t[l]=o[u],n--),0===n)break}if(0===n)break}if(n>0){var c=[];throw t.forEach(function(t,n){null==t&&c.push(e[n]);}),new ValueError(\"Cannot find SymbolicTensors for output name(s): \"+JSON.stringify(c))}return t},t.prototype.predictLoop=function(e,t,n){var r=this;return void 0===t&&(t=32),void 0===n&&(n=!1),tidy(function(){var i=r.checkNumSamples(e);if(n)throw new NotImplementedError(\"Verbose predictLoop() is not implemented yet.\");for(var a=makeBatches(i,t),o=[],s=function(t){var n=tidy(function(){var n=a[t][0],i=a[t][1],o=sliceArrays(e,n,i),s=[];if(Array.isArray(o))for(var l=0;l<o.length;++l)s.push({key:r.inputs[l],value:o[l]});else s.push({key:r.inputs[0],value:o});var u=new FeedDict(s);return execute(r.outputs,u)});if(0===t)for(var i=0,s=n;i<s.length;i++){var l=s[i];o.push(l);}else for(var u=0;u<n.length;++u)o[u]=concatAlongFirstAxis(o[u],n[u]);},l=0;l<a.length;++l)s(l);return singletonOrArray(o)})},t.prototype.predict=function(e,t){void 0===t&&(t={}),checkInputData(e,this.inputNames,this.feedInputShapes,!1);var n=null==t.batchSize?32:t.batchSize;return checkBatchSize(n),this.predictLoop(e,n)},t.prototype.predictOnBatch=function(e){return checkInputData(e,this.inputNames,this.feedInputShapes,!0),this.predictLoop(e,e.shape[0])},t.prototype.standardizeUserData=function(e,t,n,r){if(void 0===n&&(n=!0),null==this.optimizer)throw new RuntimeError(\"You must compile a model before training/testing. Use Model.compile(modelCompileConfig).\");for(var i=[],a=0;a<this.feedOutputShapes.length;++a){var o=this.feedOutputShapes[a];this.feedLossFns[a]===sparseCategoricalCrossentropy?i.push(o.slice(0,o.length-1).concat([1])):i.push(o);}if(checkArrayLengths(e=standardizeInputData(e,this.feedInputNames,this.feedInputShapes,!1,\"input\"),t=standardizeInputData(t,this.feedOutputNames,i,!1,\"target\"),null),checkLossAndTargetCompatibility(t,this.feedLossFns,this.feedOutputShapes),this.stateful&&null!=r&&r>0&&e[0].shape[0]%r!=0)throw new ValueError(\"In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size \"+r+\". Found: \"+e[0].shape[0]+\" sample(s).\");return [e,t,null]},t.prototype.testLoop=function(e,t,n,r,i){var a=this;return void 0===r&&(r=0),tidy(function(){var o=a.checkNumSamples(t,n,i,\"steps\"),s=[];if(r>0)throw new NotImplementedError(\"Verbose mode is not implemented yet.\");if(null!=i)throw new NotImplementedError(\"steps mode in testLoop() is not implemented yet\");for(var l=makeBatches(o,n),u=tensor1d(range$1(0,o)),c=0;c<l.length;++c){var p=l[c][0],h=l[c][1],d=sliceAlongFirstAxis(u,p,h-p),f=sliceArraysByIndices(t,d),g=e(f);if(0===c)for(var m=0;m<g.length;++m)s.push(getScalar(0));for(m=0;m<g.length;++m){var y=g[m];s[m]=add(s[m],mul(getScalar(h-p),y));}}for(m=0;m<s.length;++m)s[m]=div(s[m],getScalar(o));return s})},t.prototype.getDedupedMetricsNames=function(){for(var e=this.metricsNames,t=[],n=0;n<e.length;++n){var r=e[n],i=r;if(count(e,r)>1)i+=\"_\"+count(e.slice(0,n),r);t.push(i);}return t},t.prototype.makeTrainFunction=function(){var e=this;return function(t){var n=t.slice(0,e.inputs.length),r=t.slice(e.inputs.length,e.inputs.length+e.outputs.length),i=[],a=e.collectedTrainableWeights.map(function(e){return e.read()});return [e.optimizer.minimize(function(){for(var t=[],a=0;a<e.inputs.length;++a)t.push({key:e.inputs[a],value:n[a]});var o,s=new FeedDict(t),l=execute(e.outputs,s,{training:!0});for(a=0;a<e.lossFunctions.length;++a){var u=(0, e.lossFunctions[a])(r[a],l[a]);mean(u),o=0===a?u:add(o,u);}for(a=0;a<e.metricsTensors.length;++a){var c=e.metricsTensors[a][0],p=e.metricsTensors[a][1],h=mean(c(r[p],l[p]));keep(h),i.push(h);}return o=mean(o),e.calculateLosses().forEach(function(e){o=add(o,e);}),o},!0,a)].concat(i)}},t.prototype.makeTestFunction=function(){var e=this;this.testFunction=function(t){return tidy(function(){for(var n,r=[],i=t.slice(0,e.inputs.length),a=t.slice(e.inputs.length,e.inputs.length+e.outputs.length),o=[],s=0;s<e.inputs.length;++s)o.push({key:e.inputs[s],value:i[s]});var l=new FeedDict(o),u=execute(e.outputs,l);for(s=0;s<e.lossFunctions.length;++s){var c=e.lossFunctions[s],p=mean(c(a[s],u[s]));n=0===s?p:add(n,p),r.push(n);}for(s=0;s<e.metricsTensors.length;++s){var h=e.metricsTensors[s][0],d=e.metricsTensors[s][1],f=mean(h(a[d],u[d]));r.push(f);}return r})};},t.prototype.fit=function(e,t,n){return void 0===n&&(n={}),__awaiter$1(this,void 0,void 0,function(){return __generator$1(this,function(r){return [2,fitTensors(this,e,t,n)]})})},t.prototype.fitDataset=function(e,t){return __awaiter$1(this,void 0,void 0,function(){return __generator$1(this,function(n){return [2,fitDataset(this,e,t)]})})},t.prototype.getNamedWeights=function(e){for(var t={},n=null!=e&&e.trainableOnly,r=n?this.trainableWeights:this.weights,i=this.getWeights(n),a=0;a<r.length;++a)n&&!r[a].trainable||(t[r[a].originalName]=i[a]);return t},Object.defineProperty(t.prototype,\"stopTraining\",{set:function(e){this.stopTraining_=e;},enumerable:!0,configurable:!0}),t.prototype.save=function(e,t){return __awaiter$1(this,void 0,void 0,function(){var n,r,i,a,o;return __generator$1(this,function(s){switch(s.label){case 0:if(\"string\"==typeof e){if(0===(n=io.getSaveHandlers(e)).length)throw new ValueError(\"Cannot find any save handlers for URL '\"+e+\"'\");if(n.length>1)throw new ValueError(\"Found more than one (\"+n.length+\") save handlers for URL '\"+e+\"'\");e=n[0];}if(null==e.save)throw new ValueError(\"Model.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.\");return [4,io.encodeWeights(this.getNamedWeights(t))];case 1:return r=s.sent(),i=!1,a=null,o=this.toJSON(a,i),[2,e.save({modelTopology:o,weightData:r.data,weightSpecs:r.specs})]}})})},t.className=\"Model\",t}(Container);function modelFromJSON(e,t){return __awaiter$1(this,void 0,void 0,function(){var n,r,i,a,o,s,l,u,c,p;return __generator$1(this,function(h){switch(h.label){case 0:return \"modelTopology\"in e||(e={modelTopology:e}),null!=(n=(e=e).modelTopology).model_config&&(n=n.model_config),r=convertPythonicToTs(n),i=deserialize(r,t),null==e.weightsManifest?[3,2]:[4,io.loadWeights(e.weightsManifest,e.pathPrefix,i.weights.map(function(e){return e.originalName}))];case 1:for(a=h.sent(),o={},s=0,l=i.weights;s<l.length;s++)u=l[s],o[u.originalName]=a[u.originalName];c=null,p=!0,i.loadWeights(o,c,p),h.label=2;case 2:return [2,i]}})})}function loadModelInternal(e,t){return void 0===t&&(t=!0),__awaiter$1(this,void 0,void 0,function(){var n;return __generator$1(this,function(r){if(\"string\"==typeof e){if(0===(n=io.getLoadHandlers(e)).length)n.push(io.browserHTTPRequest(e));else if(n.length>1)throw new ValueError(\"Found more than one (\"+n.length+\") load handlers for URL '\"+e+\"'\");e=n[0];}return [2,loadModelFromIOHandler(e,void 0,t)]})})}function loadModelFromIOHandler(e,t,n){return void 0===n&&(n=!0),__awaiter$1(this,void 0,void 0,function(){var r,i,a,o,s;return __generator$1(this,function(l){switch(l.label){case 0:if(null==e.load)throw new ValueError(\"Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.\");return [4,e.load()];case 1:if(r=l.sent(),null!=(i=r.modelTopology).model_config&&(i=i.model_config),a=deserialize(convertPythonicToTs(i),t),null!=r.weightData){if(null==r.weightSpecs)throw new ValueError(\"Model artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.\");o=!1,s=!0,a.loadWeights(io.decodeWeights(r.weightData,r.weightSpecs),o,s,n);}return [2,a]}})})}serialization.registerClass(Model);var Sequential=function(e){function t(t){var n=e.call(this,{inputs:[],outputs:[]})||this;if(t=t||{},n.trainable=!0,n._updatable=!0,n.built=!1,n.name=null!=t.name?t.name:getUid(\"sequential_\"),null!=t.layers)for(var r=0,i=t.layers;r<i.length;r++){var a=i[r];n.add(a);}return n}return __extends$1(t,e),t.prototype.checkShape=function(e){if(e.inboundNodes[0].outputTensors[0].shape.some(function(e){return e<0}))throw new ValueError(\"Negative dimension size caused by adding layer \"+e.name+\" with input shape [\"+e.inboundNodes[0].inputTensors[0].shape+\"]\")},t.prototype.add=function(e){var n,r=e instanceof t||e instanceof Model;if(r){if(1!==(n=e).outputs.length)throw new ValueError(\"All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.\");if(1!==n.inputs.length)throw new ValueError(\"All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.\")}if(0===this.outputs.length){if(0===e.inboundNodes.length){if(null==e.batchInputShape)throw new ValueError(\"The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.\");var i=Input({batchShape:e.batchInputShape,dtype:e.dtype,name:e.name+\"_input\"});e.apply(i);}if(r)this.outputs=n.outputs,this.inputs=n.inputs;else{if(1!==e.inboundNodes.length)throw new ValueError(\"A layer added to a Sequential model must not already be connected somewhere else. Model received layer \"+e.name+\" which has \"+e.inboundNodes.length+\" pre-existing inbound connections.\");if(1!==e.inboundNodes[0].outputTensors.length)throw new ValueError(\"All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.\");this.checkShape(e),this.outputs=[e.inboundNodes[0].outputTensors[0]],this.inputs=getSourceInputs(this.outputs[0]);}this.inboundNodes=[],new Node({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:pyListRepeat(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(function(e){return e.shape}),outputShapes:this.outputs[0].shape});}else{var a=e.apply(this.outputs[0]);if(Array.isArray(a))throw new TypeError(\"All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.\");this.checkShape(e),this.outputs=[a],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape];}this.layers.push(e),this.built=!1;},t.prototype.pop=function(){if(0===this.layers.length)throw new TypeError(\"There are no layers in the model.\");if(this.layers.pop(),0===this.layers.length)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{var e=this.layers.length-1;this.layers[e].outboundNodes=[],this.outputs=[this.layers[e].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape];}},t.prototype.call=function(e,t){return null==this.model&&this.build(),this.model.call(e,t)},t.prototype.build=function(e){if(getExactlyOneShape(e),0===this.inputs.length||0===this.outputs.length)throw new TypeError(\"Sequential model cannot be built: model is empty. Add some layers first.\");this.model=new Model({inputs:this.inputs,outputs:this.outputs[0],name:this.name+\"_model\"}),this.model.trainable=this.trainable,this.model.updatable=this.updatable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0;},t.prototype.countParams=function(){return this.built||this.build(),e.prototype.countParams.call(this)},t.prototype.summary=function(t,n,r){void 0===r&&(r=console.log),this.built||this.build(),e.prototype.summary.call(this,t,n,r);},t.prototype.setWeights=function(e){null==this.model&&this.build(),this.model.setWeights(e);},Object.defineProperty(t.prototype,\"updatable\",{get:function(){return this._updatable},set:function(e){this.built&&(this.model.updatable=e),this._updatable=e;},enumerable:!0,configurable:!0}),t.prototype.evaluate=function(e,t,n){if(void 0===n&&(n={}),!this.built)throw new RuntimeError(\"The model needs to be compiled before being used.\");return this.model.evaluate(e,t,n)},t.prototype.evaluateDataset=function(e,t){return __awaiter$1(this,void 0,void 0,function(){return __generator$1(this,function(n){if(!this.built)throw new RuntimeError(\"The model needs to be compiled before being used.\");return [2,this.model.evaluateDataset(e,t)]})})},t.prototype.predict=function(e,t){return void 0===t&&(t={}),null==this.model&&this.build(),this.model.predict(e,t)},t.prototype.predictOnBatch=function(e){return null==this.model&&this.build(),this.model.predictOnBatch(e)},t.prototype.compile=function(e){this.build(),this.model.compile(e),this.optimizer=this.model.optimizer,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames;},t.prototype.fit=function(e,t,n){return void 0===n&&(n={}),__awaiter$1(this,void 0,void 0,function(){return __generator$1(this,function(r){if(!this.built)throw new RuntimeError(\"The model needs to be compiled before being used.\");return [2,this.model.fit(e,t,n)]})})},t.prototype.fitDataset=function(e,t){return __awaiter$1(this,void 0,void 0,function(){return __generator$1(this,function(n){if(!this.built)throw new RuntimeError(\"The model needs to be compiled before being used.\");return [2,this.model.fitDataset(e,t)]})})},t.fromConfig=function(e,n){var r,i={};if(n instanceof Array){if(null==n[0].className||\"Merge\"===n[0].className)throw new ValueError(\"Legacy serialization format not supported yet.\");r=n;}else util.assert(null!=n.layers,\"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field.\"),r=n.layers,delete n.layers,i=n;var a=new e(i);if(!(a instanceof t))throw new NotImplementedError(\"Sequential.fromConfig called on non-Sequential input: \"+a);for(var o=0,s=r;o<s.length;o++){var l=deserialize(s[o]);a.add(l);}return a},Object.defineProperty(t.prototype,\"stopTraining\",{set:function(e){this.model.stopTraining=e;},enumerable:!0,configurable:!0}),t.prototype.getConfig=function(){for(var e=[],t=0,n=this.layers;t<n.length;t++){var r=n[t];e.push({className:r.getClassName(),config:r.getConfig()});}return e},t.className=\"Sequential\",t}(Model);function model(e){return new Model(e)}function sequential(e){return new Sequential(e)}function loadModel(e,t){return void 0===t&&(t=!0),loadModelInternal(e,t)}function input(e){return Input(e)}function registerCallbackConstructor(e,t){CallbackConstructorRegistry.registerCallbackConstructor(e,t);}serialization.registerClass(Sequential);var Activation=function(e){function t(){return null!==e&&e.apply(this,arguments)||this}return __extends$1(t,e),t.prototype.getConfig=function(){return {}},t}(serialization.Serializable),Elu=function(e){function t(){return null!==e&&e.apply(this,arguments)||this}return __extends$1(t,e),t.prototype.apply=function(e,t){return void 0===t&&(t=1),elu$1(e,t)},t.className=\"elu\",t}(Activation);serialization.registerClass(Elu);var Selu=function(e){function t(){return null!==e&&e.apply(this,arguments)||this}return __extends$1(t,e),t.prototype.apply=function(e){return selu(e)},t.className=\"selu\",t}(Activation);serialization.registerClass(Selu);var Relu=function(e){function t(){return null!==e&&e.apply(this,arguments)||this}return __extends$1(t,e),t.prototype.apply=function(e){return relu(e)},t.className=\"relu\",t}(Activation);serialization.registerClass(Relu);var Relu6=function(e){function t(){return null!==e&&e.apply(this,arguments)||this}return __extends$1(t,e),t.prototype.apply=function(e){return tidy(function(){return minimum(getScalar(6),relu(e))})},t.className=\"relu6\",t}(Activation);serialization.registerClass(Relu6);var Linear=function(e){function t(){return null!==e&&e.apply(this,arguments)||this}return __extends$1(t,e),t.prototype.apply=function(e){return e},t.className=\"linear\",t}(Activation);serialization.registerClass(Linear);var Sigmoid=function(e){function t(){return null!==e&&e.apply(this,arguments)||this}return __extends$1(t,e),t.prototype.apply=function(e){return sigmoid(e)},t.className=\"sigmoid\",t}(Activation);serialization.registerClass(Sigmoid);var HardSigmoid=function(e){function t(){return null!==e&&e.apply(this,arguments)||this}return __extends$1(t,e),t.prototype.apply=function(e){return hardSigmoid(e)},t.className=\"hardSigmoid\",t}(Activation);serialization.registerClass(HardSigmoid);var Softplus=function(e){function t(){return null!==e&&e.apply(this,arguments)||this}return __extends$1(t,e),t.prototype.apply=function(e){return softplus(e)},t.className=\"softplus\",t}(Activation);serialization.registerClass(Softplus);var Softsign=function(e){function t(){return null!==e&&e.apply(this,arguments)||this}return __extends$1(t,e),t.prototype.apply=function(e){return softsign(e)},t.className=\"softsign\",t}(Activation);serialization.registerClass(Softsign);var Tanh=function(e){function t(){return null!==e&&e.apply(this,arguments)||this}return __extends$1(t,e),t.prototype.apply=function(e){return tanh$1(e)},t.className=\"tanh\",t}(Activation);serialization.registerClass(Tanh);var Softmax=function(e){function t(){return null!==e&&e.apply(this,arguments)||this}return __extends$1(t,e),t.prototype.apply=function(e,t){return void 0===t&&(t=-1),softmax(e,t)},t.className=\"softmax\",t}(Activation);function serializeActivation(e){return e.getClassName()}function deserializeActivation(e,t){return void 0===t&&(t={}),deserializeKerasObject(e,serialization.SerializationMap.getMap().classNameMap,t,\"activation\")}function getActivation(e){return null==e?deserializeActivation({className:\"linear\",config:{}}):\"string\"==typeof e?deserializeActivation({className:e,config:{}}):e instanceof Activation?e:deserializeActivation(e)}serialization.registerClass(Softmax);var ReLU=function(e){function t(t){var n=e.call(this,null==t?{}:t)||this;return n.supportsMasking=!0,null!=t&&(n.maxValue=t.maxValue),n}return __extends$1(t,e),t.prototype.call=function(e,t){e=getExactlyOneTensor(e);var n=relu(e);return null!=this.maxValue&&(n=clipByValue(n,0,this.maxValue)),n},t.prototype.computeOutputShape=function(e){return e},t.prototype.getConfig=function(){var t={maxValue:this.maxValue},n=e.prototype.getConfig.call(this);return Object.assign(t,n),t},t.className=\"ReLU\",t}(Layer);serialization.registerClass(ReLU);var LeakyReLU=function(e){function t(t){var n=e.call(this,null==t?{}:t)||this;return n.DEFAULT_ALPHA=.3,null==t&&(t={}),n.alpha=null==t.alpha?n.DEFAULT_ALPHA:t.alpha,n}return __extends$1(t,e),t.prototype.call=function(e,t){var n=getExactlyOneTensor(e);return leakyRelu(n,this.alpha)},t.prototype.computeOutputShape=function(e){return e},t.prototype.getConfig=function(){var t={alpha:this.alpha},n=e.prototype.getConfig.call(this);return Object.assign(t,n),t},t.className=\"LeakyReLU\",t}(Layer);serialization.registerClass(LeakyReLU);var ELU$1=function(e){function t(t){var n=e.call(this,null==t?{}:t)||this;if(n.DEFAULT_ALPHA=1,null==t&&(t={}),null!=t.alpha&&t.alpha!==n.DEFAULT_ALPHA)throw new NotImplementedError(\"Non-default alpha value (\"+t.alpha+\") is not supported by the ELU layer yet.\");return n.alpha=null==t.alpha?n.DEFAULT_ALPHA:t.alpha,n}return __extends$1(t,e),t.prototype.call=function(e,t){var n=getExactlyOneTensor(e);return elu(n)},t.prototype.computeOutputShape=function(e){return e},t.prototype.getConfig=function(){var t={alpha:this.alpha},n=e.prototype.getConfig.call(this);return Object.assign(t,n),t},t.className=\"ELU\",t}(Layer);serialization.registerClass(ELU$1);var ThresholdedReLU=function(e){function t(t){var n=e.call(this,null==t?{}:t)||this;return n.DEFAULT_THETA=1,null==t&&(t={}),n.theta=null==t.theta?n.DEFAULT_THETA:t.theta,n.thetaTensor=getScalar(n.theta),n}return __extends$1(t,e),t.prototype.call=function(e,t){var n=getExactlyOneTensor(e);return n.mul(cast$1(n.greater(this.thetaTensor),\"float32\"))},t.prototype.computeOutputShape=function(e){return e},t.prototype.getConfig=function(){var t={theta:this.theta},n=e.prototype.getConfig.call(this);return Object.assign(t,n),t},t.className=\"ThresholdedReLU\",t}(Layer);serialization.registerClass(ThresholdedReLU);var Softmax$1=function(e){function t(t){var n=e.call(this,null==t?{}:t)||this;return n.DEFAULT_AXIS=1,null==t&&(t={}),n.softmax=(new Softmax).apply,n.axis=null==t.axis?n.DEFAULT_AXIS:t.axis,n}return __extends$1(t,e),t.prototype.call=function(e,t){var n=getExactlyOneTensor(e);return this.softmax(n,this.axis)},t.prototype.computeOutputShape=function(e){return e},t.prototype.getConfig=function(){var t={axis:this.axis},n=e.prototype.getConfig.call(this);return Object.assign(t,n),t},t.className=\"Softmax\",t}(Layer);serialization.registerClass(Softmax$1);var Regularizer=function(e){function t(){return null!==e&&e.apply(this,arguments)||this}return __extends$1(t,e),t}(serialization.Serializable),L1L2=function(e){function t(t){var n=e.call(this)||this,r=null==t||null==t.l1?.01:t.l1,i=null==t||null==t.l2?.01:t.l2;return n.hasL1=0!==r,n.hasL2=0!==i,n.l1=getScalar(r),n.l2=getScalar(i),n}return __extends$1(t,e),t.prototype.apply=function(e){var t=this;return tidy(function(){var n=zeros([1]);return t.hasL1&&(n=add(n,sum$1(mul(t.l1,abs(e))))),t.hasL2&&(n=add(n,sum$1(mul(t.l2,square$1(e))))),n.asScalar()})},t.prototype.getConfig=function(){return {l1:this.l1.dataSync()[0],l2:this.l2.dataSync()[0]}},t.fromConfig=function(e,t){return new e({l1:t.l1,l2:t.l2})},t.className=\"L1L2\",t}(Regularizer);function l1(e){return new L1L2({l1:null!=e?e.l1:null,l2:0})}function l2(e){return new L1L2({l2:null!=e?e.l2:null,l1:0})}serialization.registerClass(L1L2);var REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP={l1l2:\"L1L2\"};function serializeRegularizer(e){return serializeKerasObject(e)}function deserializeRegularizer(e,t){return void 0===t&&(t={}),deserializeKerasObject(e,serialization.SerializationMap.getMap().classNameMap,t,\"regularizer\")}function getRegularizer(e){return null==e?null:\"string\"==typeof e?deserializeRegularizer({className:e in REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP?REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[e]:e,config:{}}):e instanceof Regularizer?e:deserializeRegularizer(e)}function normalizeArray(e,t,n){if(\"number\"==typeof e)return pyListRepeat(e,t);if(e.length!==t)throw new ValueError(\"The \"+n+\" argument must be a tuple of \"+t+\" integers. Received: \"+e.length+\" elements.\");for(var r=0;r<t;++r){var i=e[r];if(!isInteger(i))throw new ValueError(\"The \"+n+\" argument must be a tuple of \"+t+\" integers. Received: \"+JSON.stringify(e)+\" including a non-integer number \"+i)}return e}function convOutputLength(e,t,n,r,i){return void 0===i&&(i=1),null==e?e:(a=\"same\"===n?e:e-(t+(t-1)*(i-1))+1,Math.floor((a+r-1)/r));var a;}function deconvLength(e,t,n,r){if(null==e)return null;if(\"valid\"===r)e=e*t+max$1([n-t,0]);else{if(\"same\"!==r)throw new ValueError(\"Unsupport padding mode: \"+r+\".\");e*=t;}return e}function preprocessConv2DInput(e,t){return tidy(function(){return checkDataFormat(t),\"channelsFirst\"===t?transpose(e,[0,2,3,1]):e})}function conv1dWithBias(e,t,n,r,i,a,o){return void 0===r&&(r=1),void 0===i&&(i=\"valid\"),void 0===o&&(o=1),tidy(function(){if(null==a&&(a=imageDataFormat()),checkDataFormat(a),3!==e.shape.length)throw new ValueError(\"The input of a conv1dWithBias operation should be 3, but is \"+e.shape.length+\" instead.\");if(3!==t.shape.length)throw new ValueError(\"The kernel for a conv1dWithBias operation should be 3, but is \"+t.shape.length+\" instead\");if(null!=n&&1!==n.shape.length)throw new ValueError(\"The bias for a conv1dWithBias operation should be 1, but is \"+t.shape.length+\" instead\");if(\"channelsFirst\"===a&&(e=transpose(e,[0,2,1])),\"causal\"===i)throw new NotImplementedError(\"The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.\");var s=conv1d(e,t,r,\"same\"===i?\"same\":\"valid\",\"NWC\",o);return null!=n&&(s=biasAdd(s,n)),s})}function conv2dWithBias(e,t,n,r,i,a,o){return void 0===r&&(r=[1,1]),void 0===i&&(i=\"valid\"),tidy(function(){if(null==a&&(a=imageDataFormat()),checkDataFormat(a),3!==e.rank&&4!==e.rank)throw new ValueError(\"conv2dWithBias expects input to be of rank 3 or 4, but received \"+e.rank+\".\");if(3!==t.rank&&4!==t.rank)throw new ValueError(\"conv2dWithBias expects kernel to be of rank 3 or 4, but received \"+e.rank+\".\");var s=preprocessConv2DInput(e,a);if(\"causal\"===i)throw new NotImplementedError(\"The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.\");return s=conv2d(s,t,r,\"same\"===i?\"same\":\"valid\",\"NHWC\",o),null!=n&&(s=biasAdd(s,n)),\"channelsFirst\"===a&&(s=transpose(s,[0,3,1,2])),s})}var BaseConv=function(e){function t(n,r){var i=e.call(this,r)||this;if(i.bias=null,i.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",i.DEFAULT_BIAS_INITIALIZER=\"zeros\",t.verifyConfig(r),i.rank=n,1!==i.rank&&2!==i.rank)throw new NotImplementedError(\"Convolution layer for rank other than 1 or 2 (\"+i.rank+\") is not implemented yet.\");if(i.kernelSize=normalizeArray(r.kernelSize,n,\"kernelSize\"),i.strides=normalizeArray(null==r.strides?1:r.strides,n,\"strides\"),i.padding=null==r.padding?\"valid\":r.padding,checkPaddingMode(i.padding),i.dataFormat=null==r.dataFormat?\"channelsLast\":r.dataFormat,checkDataFormat(i.dataFormat),i.activation=getActivation(r.activation),i.useBias=null==r.useBias||r.useBias,i.biasInitializer=getInitializer(r.biasInitializer||i.DEFAULT_BIAS_INITIALIZER),i.biasConstraint=getConstraint(r.biasConstraint),i.biasRegularizer=getRegularizer(r.biasRegularizer),i.activityRegularizer=getRegularizer(r.activityRegularizer),i.dilationRate=normalizeArray(null==r.dilationRate?1:r.dilationRate,n,\"dilationRate\"),1===i.rank&&Array.isArray(i.dilationRate)&&1!==i.dilationRate.length)throw new ValueError(\"dilationRate must be a number or an array of a single number for 1D convolution, but received \"+JSON.stringify(i.dilationRate));if(2===i.rank)if(\"number\"==typeof i.dilationRate)i.dilationRate=[i.dilationRate,i.dilationRate];else if(2!==i.dilationRate.length)throw new ValueError(\"dilationRate must be a number or array of two numbers for 2D convolution, but received \"+JSON.stringify(i.dilationRate));return i}return __extends$1(t,e),t.verifyConfig=function(e){if(assert$1(\"kernelSize\"in e,\"required key 'kernelSize' not in config\"),\"number\"!=typeof e.kernelSize&&!checkArrayTypeAndLength(e.kernelSize,\"number\",1,2))throw new ValueError(\"BaseConv expects config.kernelSize to be number or number[] with length 1 or 2, but received \"+JSON.stringify(e.kernelSize)+\".\")},t.prototype.getConfig=function(){var t={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:serializeActivation(this.activation),useBias:this.useBias,biasInitializer:serializeInitializer(this.biasInitializer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),biasConstraint:serializeConstraint(this.biasConstraint)},n=e.prototype.getConfig.call(this);return Object.assign(t,n),t},t}(Layer),Conv=function(e){function t(n,r){var i=e.call(this,n,r)||this;return i.kernel=null,t.verifyConfig(r),i.filters=r.filters,i.kernelInitializer=getInitializer(r.kernelInitializer||i.DEFAULT_KERNEL_INITIALIZER),i.kernelConstraint=getConstraint(r.kernelConstraint),i.kernelRegularizer=getRegularizer(r.kernelRegularizer),i}return __extends$1(t,e),t.prototype.build=function(e){e=getExactlyOneShape(e);var t=\"channelsFirst\"===this.dataFormat?1:e.length-1;if(null==e[t])throw new ValueError(\"The channel dimension of the input should be defined. Found \"+e[t]);var n,r=e[t],i=this.kernelSize.concat([r,this.filters]);this.kernel=this.addWeight(\"kernel\",i,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight(\"bias\",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:(n={},n[t]=r,n)}],this.built=!0;},t.prototype.call=function(e,t){var n=this;return tidy(function(){var t;e=getExactlyOneTensor(e);var r=null==n.bias?null:n.bias.read();if(1===n.rank)t=conv1dWithBias(e,n.kernel.read(),r,n.strides[0],n.padding,n.dataFormat,n.dilationRate[0]);else if(2===n.rank)t=conv2dWithBias(e,n.kernel.read(),r,n.strides,n.padding,n.dataFormat,n.dilationRate);else if(3===n.rank)throw new NotImplementedError(\"3D convolution is not implemented yet.\");return null!=n.activation&&(t=n.activation.apply(t)),t})},t.prototype.computeOutputShape=function(e){e=getExactlyOneShape(e);for(var t=[],n=\"channelsLast\"===this.dataFormat?e.slice(1,e.length-1):e.slice(2),r=0;r<n.length;++r){var i=convOutputLength(n[r],this.kernelSize[r],this.padding,this.strides[r],\"number\"==typeof this.dilationRate?this.dilationRate:this.dilationRate[r]);t.push(i);}var a=[e[0]];return \"channelsLast\"===this.dataFormat?(a=a.concat(t)).push(this.filters):(a.push(this.filters),a=a.concat(t)),a},t.prototype.getConfig=function(){var t={filters:this.filters,kernelInitializer:serializeInitializer(this.kernelInitializer),kernelRegularizer:serializeRegularizer(this.kernelRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint)},n=e.prototype.getConfig.call(this);return Object.assign(t,n),t},t.verifyConfig=function(e){if(!(\"filters\"in e)||\"number\"!=typeof e.filters||e.filters<1)throw new ValueError(\"Convolution layer expected config.filters to be a 'number' > 0 but got \"+JSON.stringify(e.filters))},t}(BaseConv),Conv2D=function(e){function t(n){var r=e.call(this,2,n)||this;return t.verifyConfig(n),r}return __extends$1(t,e),t.prototype.getConfig=function(){var t=e.prototype.getConfig.call(this);return delete t.rank,t},t.verifyConfig=function(e){if(\"number\"!=typeof e.kernelSize&&!checkArrayTypeAndLength(e.kernelSize,\"number\",1,2))throw new ValueError(\"Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received \"+JSON.stringify(e.kernelSize)+\".\")},t.className=\"Conv2D\",t}(Conv);serialization.registerClass(Conv2D);var Conv2DTranspose=function(e){function t(t){var n=e.call(this,t)||this;if(n.inputSpec=[new InputSpec({ndim:4})],\"same\"!==n.padding&&\"valid\"!==n.padding)throw new ValueError(\"Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode \"+n.padding);return n}return __extends$1(t,e),t.prototype.build=function(e){if(4!==(e=getExactlyOneShape(e)).length)throw new ValueError(\"Input should have rank 4; Received input shape: \"+JSON.stringify(e));var t=\"channelsFirst\"===this.dataFormat?1:e.length-1;if(null==e[t])throw new ValueError(\"The channel dimension of the inputs should be defined. Found `None`.\");var n,r=e[t],i=this.kernelSize.concat([this.filters,r]);this.kernel=this.addWeight(\"kernel\",i,\"float32\",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight(\"bias\",[this.filters],\"float32\",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new InputSpec({ndim:4,axes:(n={},n[t]=r,n)})],this.built=!0;},t.prototype.call=function(e,t){var n=this;return tidy(function(){var t=getExactlyOneTensor(e);if(4!==t.shape.length)throw new ValueError(\"Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-\"+t.shape.length);var r,i,a=t.shape,o=a[0];\"channelsFirst\"===n.dataFormat?(r=2,i=3):(r=1,i=2);var s=a[r],l=a[i],u=n.kernelSize[0],c=n.kernelSize[1],p=n.strides[0],h=n.strides[1],d=[o,deconvLength(s,p,u,n.padding),deconvLength(l,h,c,n.padding),n.filters];\"channelsLast\"!==n.dataFormat&&(t=transpose(t,[0,2,3,1]));var f=conv2dTranspose(t,n.kernel.read(),d,n.strides,n.padding);return \"channelsLast\"!==n.dataFormat&&(f=transpose(f,[0,3,1,2])),null!=n.bias&&(f=biasAdd(f,n.bias.read(),n.dataFormat)),null!=n.activation&&(f=n.activation.apply(f)),f})},t.prototype.computeOutputShape=function(e){var t,n,r,i=(e=getExactlyOneShape(e)).slice();\"channelsFirst\"===this.dataFormat?(t=1,n=2,r=3):(t=3,n=1,r=2);var a=this.kernelSize[0],o=this.kernelSize[1],s=this.strides[0],l=this.strides[1];return i[t]=this.filters,i[n]=deconvLength(i[n],s,a,this.padding),i[r]=deconvLength(i[r],l,o,this.padding),i},t.prototype.getConfig=function(){var t=e.prototype.getConfig.call(this);return delete t.dilationRate,t},t.className=\"Conv2DTranspose\",t}(Conv2D);serialization.registerClass(Conv2DTranspose);var SeparableConv=function(e){function t(t,n){var r=e.call(this,t,n)||this;if(r.DEFAULT_DEPTHWISE_INITIALIZER=\"glorotUniform\",r.DEFAULT_POINTWISE_INITIALIZER=\"glorotUniform\",r.depthwiseKernel=null,r.pointwiseKernel=null,null==n.filters)throw new ValueError(\"The `filters` configuration field is required by SeparableConv, but is unspecified.\");if(null!=n.kernelInitializer||null!=n.kernelRegularizer||null!=n.kernelConstraint)throw new ValueError(\"Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.\");if(null!=n.padding&&\"same\"!==n.padding&&\"valid\"!==n.padding)throw new ValueError(\"SeparableConv\"+r.rank+\"D supports only padding modes: 'same' and 'valid', but received \"+JSON.stringify(n.padding));return r.depthMultiplier=null==n.depthMultiplier?1:n.depthMultiplier,r.depthwiseInitializer=getInitializer(n.depthwiseInitializer||r.DEFAULT_DEPTHWISE_INITIALIZER),r.depthwiseRegularizer=getRegularizer(n.depthwiseRegularizer),r.depthwiseConstraint=getConstraint(n.depthwiseConstraint),r.pointwiseInitializer=getInitializer(n.depthwiseInitializer||r.DEFAULT_POINTWISE_INITIALIZER),r.pointwiseRegularizer=getRegularizer(n.pointwiseRegularizer),r.pointwiseConstraint=getConstraint(n.pointwiseConstraint),r}return __extends$1(t,e),t.prototype.build=function(e){if((e=getExactlyOneShape(e)).length<this.rank+2)throw new ValueError(\"Inputs to SeparableConv\"+this.rank+\"D should have rank \"+(this.rank+2)+\", but received input shape: \"+JSON.stringify(e));var t=\"channelsFirst\"===this.dataFormat?1:e.length-1;if(null==e[t]||e[t]<0)throw new ValueError(\"The channel dimension of the inputs should be defined, but found \"+JSON.stringify(e[t]));for(var n=e[t],r=this.kernelSize.concat([n,this.depthMultiplier]),i=[],a=0;a<this.rank;++a)i.push(1);i.push(n*this.depthMultiplier,this.filters);var o;this.depthwiseKernel=this.addWeight(\"depthwise_kernel\",r,\"float32\",this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight(\"pointwise_kernel\",i,\"float32\",this.pointwiseInitializer,this.pointwiseRegularizer,!0,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight(\"bias\",[this.filters],\"float32\",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.inputSpec=[new InputSpec({ndim:this.rank+2,axes:(o={},o[t]=n,o)})],this.built=!0;},t.prototype.call=function(e,t){var n=this;return tidy(function(){var t;if(e=getExactlyOneTensor(e),1===n.rank)throw new NotImplementedError(\"1D separable convolution is not implemented yet.\");return 2===n.rank&&(\"channelsFirst\"===n.dataFormat&&(e=transpose(e,[0,2,3,1])),t=separableConv2d(e,n.depthwiseKernel.read(),n.pointwiseKernel.read(),n.strides,n.padding,n.dilationRate,\"NHWC\")),n.useBias&&(t=biasAdd(t,n.bias.read(),n.dataFormat)),null!=n.activation&&(t=n.activation.apply(t)),\"channelsFirst\"===n.dataFormat&&(t=transpose(t,[0,3,1,2])),t})},t.prototype.getConfig=function(){var t=e.prototype.getConfig.call(this);return delete t.rank,delete t.kernelInitializer,delete t.kernelRegularizer,delete t.kernelConstraint,t.depthwiseInitializer=serializeInitializer(this.depthwiseInitializer),t.pointwiseInitializer=serializeInitializer(this.pointwiseInitializer),t.depthwiseRegularizer=serializeRegularizer(this.depthwiseRegularizer),t.pointwiseRegularizer=serializeRegularizer(this.pointwiseRegularizer),t.depthwiseConstraint=serializeConstraint(this.depthwiseConstraint),t.pointwiseConstraint=serializeConstraint(this.pointwiseConstraint),t},t.className=\"SeparableConv\",t}(Conv),SeparableConv2D=function(e){function t(t){return e.call(this,2,t)||this}return __extends$1(t,e),t.className=\"SeparableConv2D\",t}(SeparableConv);serialization.registerClass(SeparableConv2D);var Conv1D=function(e){function t(n){var r=e.call(this,1,n)||this;return t.verifyConfig(n),r.inputSpec=[{ndim:3}],r}return __extends$1(t,e),t.prototype.getConfig=function(){var t=e.prototype.getConfig.call(this);return delete t.rank,delete t.dataFormat,t},t.verifyConfig=function(e){if(\"number\"!=typeof e.kernelSize&&!checkArrayTypeAndLength(e.kernelSize,\"number\",1,1))throw new ValueError(\"Conv1D expects config.kernelSize to be number or number[] with length 1, but received \"+JSON.stringify(e.kernelSize)+\".\")},t.className=\"Conv1D\",t}(Conv);serialization.registerClass(Conv1D);var Cropping2D=function(e){function t(t){var n=e.call(this,t)||this;return \"number\"==typeof t.cropping?n.cropping=[[t.cropping,t.cropping],[t.cropping,t.cropping]]:\"number\"==typeof t.cropping[0]?n.cropping=[[t.cropping[0],t.cropping[0]],[t.cropping[1],t.cropping[1]]]:n.cropping=t.cropping,n.dataFormat=void 0===t.dataFormat?\"channelsLast\":t.dataFormat,n.inputSpec=[{ndim:4}],n}return __extends$1(t,e),t.prototype.computeOutputShape=function(e){return \"channelsFirst\"===this.dataFormat?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]},t.prototype.call=function(e,t){var n=this;return tidy(function(){return e=getExactlyOneTensor(e),\"channelsLast\"===n.dataFormat?sliceAlongAxis(sliceAlongAxis(e,n.cropping[0][0],e.shape[1]-n.cropping[0][0]-n.cropping[0][1],2),n.cropping[1][0],e.shape[2]-n.cropping[1][1]-n.cropping[1][0],3):sliceAlongAxis(sliceAlongAxis(e,n.cropping[0][0],e.shape[2]-n.cropping[0][0]-n.cropping[0][1],3),n.cropping[1][0],e.shape[3]-n.cropping[1][1]-n.cropping[1][0],4)})},t.prototype.getConfig=function(){var t={cropping:this.cropping,dataFormat:this.dataFormat},n=e.prototype.getConfig.call(this);return Object.assign(t,n),t},t.className=\"Cropping2D\",t}(Layer);serialization.registerClass(Cropping2D);var UpSampling2D=function(e){function t(t){var n=e.call(this,t)||this;return n.DEFAULT_SIZE=[2,2],n.inputSpec=[{ndim:4}],n.size=null==t.size?n.DEFAULT_SIZE:t.size,n.dataFormat=null==t.dataFormat?\"channelsLast\":t.dataFormat,n}return __extends$1(t,e),t.prototype.computeOutputShape=function(e){if(\"channelsFirst\"===this.dataFormat){var t=null==e[2]?null:this.size[0]*e[2],n=null==e[3]?null:this.size[1]*e[3];return [e[0],e[1],t,n]}t=null==e[1]?null:this.size[0]*e[1],n=null==e[2]?null:this.size[1]*e[2];return [e[0],t,n,e[3]]},t.prototype.call=function(e,t){var n=this;return tidy(function(){var t=getExactlyOneTensor(e),r=t.shape;if(\"channelsFirst\"===n.dataFormat){t=transpose(t,[0,2,3,1]);var i=n.size[0]*r[2],a=n.size[1]*r[3],o=t.resizeNearestNeighbor([i,a]);return transpose(o,[0,3,1,2])}i=n.size[0]*r[1],a=n.size[1]*r[2];return t.resizeNearestNeighbor([i,a])})},t.prototype.getConfig=function(){var t={size:this.size,dataFormat:this.dataFormat},n=e.prototype.getConfig.call(this);return Object.assign(t,n),t},t.className=\"UpSampling2D\",t}(Layer);function depthwiseConv2d$1(e,t,n,r,i,a){return void 0===n&&(n=[1,1]),void 0===r&&(r=\"valid\"),tidy(function(){null==i&&(i=imageDataFormat()),checkDataFormat(i);var o=preprocessConv2DInput(e,i);if(4!==e.rank)throw new ValueError(\"Input for depthwiseConv2d is required to be 4-D, but is instead \"+e.rank+\"-D\");if(4!==t.rank)throw new ValueError(\"depthwiseKernel is required to be 4-D, but is instead \"+t.rank+\"-D\");return o=depthwiseConv2d(o,t,n,\"same\"===r?\"same\":\"valid\",\"NHWC\",a),\"channelsFirst\"===i&&(o=transpose(o,[0,3,1,2])),o})}serialization.registerClass(UpSampling2D);var DepthwiseConv2D=function(e){function t(t){var n=e.call(this,2,t)||this;return n.depthwiseKernel=null,n.depthMultiplier=null==t.depthMultiplier?1:t.depthMultiplier,n.depthwiseInitializer=getInitializer(t.depthwiseInitializer||n.DEFAULT_KERNEL_INITIALIZER),n.depthwiseConstraint=getConstraint(t.depthwiseConstraint),n.depthwiseRegularizer=getRegularizer(t.depthwiseRegularizer),n}return __extends$1(t,e),t.prototype.build=function(e){if((e=getExactlyOneShape(e)).length<4)throw new ValueError(\"Inputs to DepthwiseConv2D should have rank 4. Received input shape: \"+JSON.stringify(e)+\".\");var t=\"channelsFirst\"===this.dataFormat?1:3;if(null==e[t]||e[t]<0)throw new ValueError(\"The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (\"+e[t]+\").\");var n=e[t],r=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight(\"depthwise_kernel\",r,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight(\"bias\",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0;},t.prototype.call=function(e,t){var n=this;return tidy(function(){var t=depthwiseConv2d$1(e=getExactlyOneTensor(e),n.depthwiseKernel.read(),n.strides,n.padding,n.dataFormat,null);return n.useBias&&(t=biasAdd(t,n.bias.read(),n.dataFormat)),null!=n.activation&&(t=n.activation.apply(t)),t})},t.prototype.computeOutputShape=function(e){e=getExactlyOneShape(e);var t=\"channelsFirst\"===this.dataFormat?e[2]:e[1],n=\"channelsFirst\"===this.dataFormat?e[3]:e[2],r=\"channelsFirst\"===this.dataFormat?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,i=convOutputLength(t,this.kernelSize[0],this.padding,this.strides[0]),a=convOutputLength(n,this.kernelSize[1],this.padding,this.strides[1]);return \"channelsFirst\"===this.dataFormat?[e[0],r,i,a]:[e[0],i,a,r]},t.prototype.getConfig=function(){var t=e.prototype.getConfig.call(this);return t.depthMultiplier=this.depthMultiplier,t.depthwiseInitializer=serializeInitializer(this.depthwiseInitializer),t.depthwiseRegularizer=serializeRegularizer(this.depthwiseRegularizer),t.depthwiseConstraint=serializeConstraint(this.depthwiseRegularizer),t},t.className=\"DepthwiseConv2D\",t}(BaseConv);serialization.registerClass(DepthwiseConv2D);var Dropout=function(e){function t(t){var n=e.call(this,t)||this;if(n.rate=Math.max(Math.min(t.rate,1),0),n.rateScalar=getScalar(n.rate),n.noiseShape=t.noiseShape,n.seed=t.seed,null!=n.seed)throw new NotImplementedError(\"Non-default seed is not implemented in Dropout layer yet: \"+n.seed);return n.supportsMasking=!0,n}return __extends$1(t,e),t.prototype.getNoiseShape=function(e){if(null==this.noiseShape)return this.noiseShape;for(var t=e.shape,n=[],r=0;r<this.noiseShape.length;++r)n.push(null==this.noiseShape[r]?t[r]:this.noiseShape[r]);return n},t.prototype.call=function(e,t){var n=this;return tidy(function(){n.invokeCallHook(e,t);var r=getExactlyOneTensor(e);if(null!=n.noiseShape&&!util.arraysEqual(r.shape,n.noiseShape))throw new NotImplementedError(\"Non-default noise shape is not implemented in Dropout layer yet: \"+JSON.stringify(n.noiseShape));if(0<n.rate&&n.rate<1){var i=null!=t.training&&t.training,a=n.getNoiseShape(r);return inTrainPhase(function(){return dropout(r,n.rateScalar,a,n.seed)},function(){return r},i)}return e})},t.prototype.getConfig=function(){var t={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},n=e.prototype.getConfig.call(this);return Object.assign(t,n),t},t.className=\"Dropout\",t}(Layer);serialization.registerClass(Dropout);var Dense=function(e){function t(t){var n=e.call(this,t)||this;if(n.activation=null,n.useBias=!0,n.kernel=null,n.bias=null,n.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",n.DEFAULT_BIAS_INITIALIZER=\"zeros\",null==t.batchInputShape&&null==t.inputShape&&null!=t.inputDim){var r=null;null!=t.batchSize&&(r=t.batchSize),n.batchInputShape=[r,t.inputDim];}return n.units=t.units,n.activation=getActivation(t.activation),null!=t.useBias&&(n.useBias=t.useBias),n.kernelInitializer=getInitializer(t.kernelInitializer||n.DEFAULT_KERNEL_INITIALIZER),n.biasInitializer=getInitializer(t.biasInitializer||n.DEFAULT_BIAS_INITIALIZER),n.kernelConstraint=getConstraint(t.kernelConstraint),n.biasConstraint=getConstraint(t.biasConstraint),n.kernelRegularizer=getRegularizer(t.kernelRegularizer),n.biasRegularizer=getRegularizer(t.biasRegularizer),n.activityRegularizer=getRegularizer(t.activityRegularizer),n.inputSpec=[{minNDim:2}],n}return __extends$1(t,e),t.prototype.build=function(e){var t,n=(e=getExactlyOneShape(e))[e.length-1];null==this.kernel&&(this.kernel=this.addWeight(\"kernel\",[n,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight(\"bias\",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:(t={},t[-1]=n,t)}],this.built=!0;},t.prototype.computeOutputShape=function(e){var t=(e=getExactlyOneShape(e)).slice();return t[t.length-1]=this.units,t},t.prototype.call=function(e,t){var n=this;return tidy(function(){n.invokeCallHook(e,t);var r=dot$1(getExactlyOneTensor(e),n.kernel.read());return null!=n.bias&&(r=biasAdd(r,n.bias.read())),null!=n.activation&&(r=n.activation.apply(r)),r})},t.prototype.getConfig=function(){var t={units:this.units,activation:serializeActivation(this.activation),useBias:this.useBias,kernelInitializer:serializeInitializer(this.kernelInitializer),biasInitializer:serializeInitializer(this.biasInitializer),kernelRegularizer:serializeRegularizer(this.kernelRegularizer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint),biasConstraint:serializeConstraint(this.biasConstraint)},n=e.prototype.getConfig.call(this);return Object.assign(t,n),t},t.className=\"Dense\",t}(Layer);serialization.registerClass(Dense);var Flatten=function(e){function t(t){var n=e.call(this,t||{})||this;return n.inputSpec=[{minNDim:3}],n}return __extends$1(t,e),t.prototype.computeOutputShape=function(e){for(var t=0,n=(e=getExactlyOneShape(e)).slice(1);t<n.length;t++){if(null==n[t])throw new ValueError('The shape of the input to \"Flatten\" is not fully defined (got '+e.slice(1)+'). Make sure to pass a complete \"input_shape\" or \"batch_input_shape\" argument to the first layer in your model.')}return [e[0],arrayProd(e,1)]},t.prototype.call=function(e,t){var n=this;return tidy(function(){return n.invokeCallHook(e,t),batchFlatten(getExactlyOneTensor(e))})},t.className=\"Flatten\",t}(Layer);serialization.registerClass(Flatten);var Activation$1=function(e){function t(t){var n=e.call(this,t)||this;return n.supportsMasking=!0,n.activation=getActivation(t.activation),n}return __extends$1(t,e),t.prototype.call=function(e,t){var n=this;return tidy(function(){n.invokeCallHook(e,t);var r=getExactlyOneTensor(e);return n.activation.apply(r)})},t.prototype.getConfig=function(){var t={activation:serializeActivation(this.activation)},n=e.prototype.getConfig.call(this);return Object.assign(t,n),t},t.className=\"Activation\",t}(Layer);serialization.registerClass(Activation$1);var RepeatVector=function(e){function t(t){var n=e.call(this,t)||this;return n.n=t.n,n.inputSpec=[{ndim:2}],n}return __extends$1(t,e),t.prototype.computeOutputShape=function(e){return [e[0],this.n,e[1]]},t.prototype.call=function(e,t){var n=this;return tidy(function(){return repeat(e=getExactlyOneTensor(e),n.n)})},t.prototype.getConfig=function(){var t={n:this.n},n=e.prototype.getConfig.call(this);return Object.assign(t,n),t},t.className=\"RepeatVector\",t}(Layer);serialization.registerClass(RepeatVector);var Reshape=function(e){function t(t){var n=e.call(this,t)||this;n.targetShape=t.targetShape;for(var r=0;r<n.targetShape.length;++r)n.isUnknown(n.targetShape[r])&&(n.targetShape[r]=null);return n}return __extends$1(t,e),t.prototype.isUnknown=function(e){return e<0||null==e},t.prototype.fixUnknownDimension=function(e,t){for(var n=\"Total size of new array must be unchanged.\",r=t.slice(),i=1,a=null,o=0;o<r.length;++o){var s=r[o];if(this.isUnknown(s)){if(null!==a)throw new ValueError(\"Can only specifiy one unknown dimension.\");a=o;}else i*=s;}var l=arrayProd(e);if(null!==a){if(0===i||l%i!=0)throw new ValueError(n);r[a]=l/i;}else if(l!==i)throw new ValueError(n);return r},t.prototype.computeOutputShape=function(e){for(var t=!1,n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))},t.prototype.call=function(e,t){var n=this;return tidy(function(){n.invokeCallHook(e,t);var r=getExactlyOneTensor(e),i=r.shape,a=i.slice(0,1).concat(n.fixUnknownDimension(i.slice(1),n.targetShape));return r.reshape(a)})},t.prototype.getConfig=function(){var t={targetShape:this.targetShape},n=e.prototype.getConfig.call(this);return Object.assign(t,n),t},t.className=\"Reshape\",t}(Layer);serialization.registerClass(Reshape);var Permute=function(e){function t(t){var n=e.call(this,t)||this;if(null==t.dims)throw new Error(\"Required configuration field `dims` is missing during Permute constructor call.\");if(!Array.isArray(t.dims))throw new Error(\"Permute constructor requires `dims` to be an Array, but received \"+t.dims+\" instead.\");var r=range$1(1,t.dims.length+1);if(!util.arraysEqual(t.dims.slice().sort(),r))throw new Error(\"Invalid permutation `dims`: \"+JSON.stringify(t.dims)+\" `dims` must contain consecutive integers starting from 1.\");return n.dims=t.dims,n.dimsIncludingBatch=[0].concat(n.dims),n.inputSpec=[new InputSpec({ndim:n.dims.length+1})],n}return __extends$1(t,e),t.prototype.computeOutputShape=function(e){var t=(e=getExactlyOneShape(e)).slice();return this.dims.forEach(function(n,r){t[r+1]=e[n];}),t},t.prototype.call=function(e,t){return transpose(getExactlyOneTensor(e),this.dimsIncludingBatch)},t.prototype.getConfig=function(){var t={dims:this.dims},n=e.prototype.getConfig.call(this);return Object.assign(t,n),t},t.className=\"Permute\",t}(Layer);serialization.registerClass(Permute);var Embedding=function(e){function t(t){var n=e.call(this,t)||this;if(n.embeddings=null,n.DEFAULT_EMBEDDINGS_INITIALIZER=\"randomUniform\",null==t.batchInputShape&&null==t.inputShape){var r=null;null!=t.batchSize&&(r=t.batchSize),null==t.inputLength?n.batchInputShape=[r,null]:n.batchInputShape=[r].concat(toList(t.inputLength));}return n.inputDim=t.inputDim,n.outputDim=t.outputDim,n.embeddingsInitializer=getInitializer(t.embeddingsInitializer||n.DEFAULT_EMBEDDINGS_INITIALIZER),n.embeddingsRegularizer=getRegularizer(t.embeddingsRegularizer),n.activityRegularizer=getRegularizer(t.activityRegularizer),n.embeddingsConstraint=getConstraint(t.embeddingsConstraint),n.maskZero=t.maskZero,n.inputLength=t.inputLength,n}return __extends$1(t,e),t.prototype.build=function(e){this.embeddings=this.addWeight(\"embeddings\",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0;},t.prototype.warnOnIncompatibleInputShape=function(e){},t.prototype.computeMask=function(e,t){throw new NotImplementedError(\"computeMask has not been implemented for Embedding yet\")},t.prototype.computeOutputShape=function(e){if(e=getExactlyOneShape(e),null==this.inputLength)return e.concat([this.outputDim]);var t=toList(this.inputLength);if(t.length!==e.length-1)throw new ValueError('\"inputLength\" is '+this.inputLength+\", but received input shape has shape \"+e);for(var n=0,r=0;r<t.length;++r){var i=t[r],a=e[r+1];if(null!=i&&null!=a&&i!==a)throw new ValueError('\"inputLength\" is '+this.inputLength+\", but received input shape has shape \"+e);null==i&&(t[n]=a),n++;}return [e[0]].concat(t,[this.outputDim])},t.prototype.call=function(e,t){var n=this;return tidy(function(){n.invokeCallHook(e,t);var r=getExactlyOneTensor(e);return \"int32\"!==r.dtype&&(r=cast$1(r,\"int32\")),gather$1(n.embeddings.read(),r.as1D()).reshape(getExactlyOneShape(n.computeOutputShape(r.shape)))})},t.prototype.getConfig=function(){var t={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:serializeInitializer(this.embeddingsInitializer),embeddingsRegularizer:serializeRegularizer(this.embeddingsRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),embeddingsConstraint:serializeConstraint(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},n=e.prototype.getConfig.call(this);return Object.assign(t,n),t},t.className=\"Embedding\",t}(Layer);serialization.registerClass(Embedding);var Merge=function(e){function t(t){var n=e.call(this,t||{})||this;return n.supportsMasking=!0,n}return __extends$1(t,e),t.prototype.mergeFunction=function(e){throw new NotImplementedError},t.prototype.computeElementwiseOpOutputShape=function(e,t){if(null==e||null==t)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(0===t.length)return e;for(var n=e.slice(0,e.length-t.length),r=0;r<t.length;++r){var i=e[e.length-t.length+r],a=t[r];if(null==i||null==a||i<0||a<0)n.push(null);else if(1===i)n.push(a);else if(1===a)n.push(i);else{if(i!==a)throw new ValueError(\"Operands could not be broadcast together with shapes \"+JSON.stringify(e)+\" \"+JSON.stringify(t));n.push(i);}}return n},t.prototype.build=function(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[getExactlyOneShape(e)]),(e=e).length<2)throw new ValueError(\"A merge layer should be called on an Array of at least 2 inputs. Got \"+e.length+\" input(s).\");for(var t=[],n=0,r=e;n<r.length;n++){null!=(o=r[n])&&null!==o[0]&&t.push(o[0]);}if((t=unique(t)).length>1)throw new ValueError(\"Can not merge tensors with different batch sizes. Got tensors with shapes: \"+JSON.stringify(e)+\".\");for(var i=null==e[0]?null:e[0].slice(1),a=1;a<e.length;++a){var o=null==e[a]?null:e[a].slice(1);i=this.computeElementwiseOpOutputShape(i,o);}var s=e.map(function(e){return e.length});-1===e.indexOf(null)&&1===unique(s).length?this.reshapeRequired=!1:this.reshapeRequired=!0;},t.prototype.call=function(e,t){var n=this;return tidy(function(){if(e=e,n.reshapeRequired){var t=[],r=e.map(function(e){return e.rank});if(-1===r.indexOf(null)){for(var i=max$1(r),a=0,o=e;a<o.length;a++){for(var s=(h=o[a]).rank,l=0;l<i-s;++l)h=expandDims$1(h,1);t.push(h);}return n.mergeFunction(t)}for(var u=!1,c=0,p=e;c<p.length;c++){var h;if(null==(s=(h=p[c]).rank)){var d=h.shape,f=d[0],g=d.slice(1).concat([f]),m=h.reshape([f].concat(arrayProd(d.slice(1))));m=(m=transpose(m,[1,0])).reshape(g),t.push(m),u=!0;}else if(s>1){var y=range$1(1,s).concat([0]);t.push(transpose(h,y)),u=!0;}else t.push(h);}var v=n.mergeFunction(t),b=v.rank;if(u)if(null==b){var w=v.shape;g=[f=w[w.length-1]].concat(w.slice(0,w.length-1));v=transpose(v.reshape([-1,f]),[1,0]).reshape(g);}else if(b>1){y=[b-1].concat(range$1(0,b-1));v=transpose(v,y);}return v}return n.mergeFunction(e)})},t.prototype.computeOutputShape=function(e){var t;t=null==(e=e)[0]?null:e[0].slice(1);for(var n=1;n<e.length;++n){var r=null==e[n]?null:e[n].slice(1);t=this.computeElementwiseOpOutputShape(t,r);}for(var i=[],a=0,o=e;a<o.length;a++){null!=(r=o[a])&&null!==r[0]&&i.push(r[0]);}return t=1===(i=unique(i)).length?i.concat(t):[null].concat(t)},t.prototype.computeMask=function(e,t){throw new NotImplementedError(\"computeMask has not been implemented for Merge yet\")},t}(Layer),Add=function(e){function t(t){return e.call(this,t)||this}return __extends$1(t,e),t.prototype.mergeFunction=function(e){return tidy(function(){for(var t=e[0].clone(),n=1;n<e.length;++n)t=add(t,e[n]);return t})},t.className=\"Add\",t}(Merge);serialization.registerClass(Add);var Multiply=function(e){function t(t){return e.call(this,t)||this}return __extends$1(t,e),t.prototype.mergeFunction=function(e){return tidy(function(){for(var t=e[0].clone(),n=1;n<e.length;++n)t=mul(t,e[n]);return t})},t.className=\"Multiply\",t}(Merge);serialization.registerClass(Multiply);var Average=function(e){function t(t){return e.call(this,t)||this}return __extends$1(t,e),t.prototype.mergeFunction=function(e){return tidy(function(){for(var t=e[0].clone(),n=1;n<e.length;++n)t=add(t,e[n]);return mul(getScalar(1/e.length),t)})},t.className=\"Average\",t}(Merge);serialization.registerClass(Average);var Maximum=function(e){function t(t){return e.call(this,t)||this}return __extends$1(t,e),t.prototype.mergeFunction=function(e){return tidy(function(){for(var t=e[0],n=1;n<e.length;++n)t=maximum(t,e[n]);return t})},t.className=\"Maximum\",t}(Merge);serialization.registerClass(Maximum);var Minimum=function(e){function t(t){return e.call(this,t)||this}return __extends$1(t,e),t.prototype.mergeFunction=function(e){return tidy(function(){for(var t=e[0],n=1;n<e.length;++n)t=minimum(t,e[n]);return t})},t.className=\"Minimum\",t}(Merge);serialization.registerClass(Minimum);var Concatenate=function(e){function t(t){var n=e.call(this,t)||this;return n.DEFAULT_AXIS=-1,null==t&&(t={}),n.axis=null==t.axis?n.DEFAULT_AXIS:t.axis,n.supportsMasking=!0,n.reshapeRequired=!1,n}return __extends$1(t,e),t.prototype.build=function(e){if(!Array.isArray(e)||!Array.isArray(e[0])||1===e.length)throw new ValueError(\"A `Concatenate` layer should be called on a list of at least 2 inputs\");for(var t=!0,n=0,r=e=e;n<r.length;n++){if(null!=(c=r[n])){t=!1;break}}if(!t){for(var i=[],a=0;a<e.length;++a){var o=e[a].slice();o.splice(this.axis,1);for(var s=!1,l=0,u=i;l<u.length;l++){var c=u[l];if(util.arraysEqual(c,o)){s=!0;break}}s||i.push(o);}if(i.length>1)throw new ValueError(\"A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: \"+JSON.stringify(e))}},t.prototype.mergeFunction=function(e){var t=this;return tidy(function(){return concatenate(e,t.axis)})},t.prototype.computeOutputShape=function(e){if(!Array.isArray(e)||!Array.isArray(e[0]))throw new ValueError(\"A `Concatenate` layer should be called on a list of inputs.\");for(var t=e,n=t[0].slice(),r=this.axis<0?n.length+this.axis:this.axis,i=0,a=t.slice(1);i<a.length;i++){var o=a[i];if(null==n[r]||null==o[r]){n[r]=null;break}n[r]+=o[r];}return n},t.prototype.computeMask=function(e,t){throw new NotImplementedError(\"computeMask has not been implemented for Concatenate yet\")},t.prototype.getConfig=function(){var t={axis:this.axis},n=e.prototype.getConfig.call(this);return Object.assign(t,n),t},t.className=\"Concatenate\",t}(Merge);function interpretAxis(e,t){for(;e<0;)e+=t;return e}function batchDot(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new NotImplementedError(\"batchDot is not implemented for tensors of 4D or higher rank yet\");if(util.assert(e.shape.length>=2,\"batchDot requires the rank of x to be >= 2, but got \"+e.shape.length),util.assert(e.shape.length>=2,\"batchDot requires the rank of y to be >= 2, but got \"+t.shape.length),\"number\"==typeof n&&(n=[n,n]),\"complex64\"===e.dtype||\"complex64\"===t.dtype)throw new NotImplementedError(\"batchDot is not implemented for complex64-type Tensors yet.\");var r=e.shape.length,i=t.shape.length;null==n&&(n=[r-1,i-2]);var a=n;return tidy(function(){var n,o;if(r>i){n=r-i;for(var s=[],l=0;l<n;++l)s.push(1);t=t.reshape(t.shape.concat(s));}else if(i>r){n=i-r;for(s=[],l=0;l<n;++l)s.push(1);e=e.reshape(e.shape.concat(s));}else n=0;if(2===e.shape.length&&2===t.shape.length)o=a[0]===a[1]?e.mulStrict(t).sum(a[0]):e.transpose([1,0]).mulStrict(t).sum(a[1]);else{var u=a[0]!==e.shape.length-1||null,c=a[1]===t.shape.length-1||null;o=e.matMul(t,u,c);}if(n>0){var p=void 0,h=[];for(l=p=r>i?r+i-3:r-1;l<p+n;++l)h.push(l);o=o.squeeze(h);}return 1===o.shape.length&&(o=o.expandDims(1)),o})}serialization.registerClass(Concatenate);var Dot=function(e){function t(t){var n=e.call(this,t)||this;return n.axes=t.axes,n.normalize=null!=t.normalize&&t.normalize,n.supportsMasking=!0,n.reshapeRequired=!1,n}return __extends$1(t,e),t.prototype.build=function(e){util.assert(Array.isArray(e)&&2===e.length&&Array.isArray(e[0])&&Array.isArray(e[1]),\"A `Dot` layer should be called on a list of exactly 2 inputs.\");var t=e[0],n=e[1];if(t.length>3||n.length>3)throw new NotImplementedError(\"Dot layer does not support tensors of 4D or higher rank yet.\");var r=this.interpretAxes(t,n);if(t[r[0]]!==n[r[1]])throw new ValueError(\"Dimension incompatibility: \"+t[r[0]]+\" !== \"+n[r[1]])},t.prototype.mergeFunction=function(e){if(2!==e.length)throw new ValueError(\"A `Dot` layer must be called on exactly 2 inputs, but received \"+e.length+\" input(s).\");var t,n=e[0],r=e[1];return t=Array.isArray(this.axes)?this.axes.map(function(t,n){return interpretAxis(t,e[n].shape.length)}):[interpretAxis(this.axes,n.shape.length),interpretAxis(this.axes,r.shape.length)],this.normalize&&(n=l2Normalize(n,t[0]),r=l2Normalize(r,t[1])),batchDot(n,r,t)},t.prototype.interpretAxes=function(e,t){return Array.isArray(this.axes)?this.axes:[interpretAxis(this.axes,e.length),interpretAxis(this.axes,t.length)]},t.prototype.computeOutputShape=function(e){util.assert(Array.isArray(e)&&2===e.length&&Array.isArray(e[0])&&Array.isArray(e[1]),\"A `Dot` layer should be called on a list of exactly 2 inputs.\");var t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new NotImplementedError(\"Dot layer does not support tensors of 4D or higher rank yet.\");var r=this.interpretAxes(t,n);t.splice(r[0],1),n.splice(r[1],1),n.splice(0,1);var i=t.concat(n);return 1===i.length&&i.push(1),i},t.prototype.computeMask=function(e,t){throw new NotImplementedError(\"computeMask has not been implemented for Dot yet\")},t.prototype.getConfig=function(){var t={axes:this.axes,normalize:this.normalize},n=e.prototype.getConfig.call(this);return Object.assign(t,n),t},t.className=\"Dot\",t}(Merge);function batchNormalization$1(e,t,n,r,i,a){var o;if(void 0===a&&(a=.001),2===e.rank)o=batchNormalization2d(e,t,n,a,i,r);else if(3===e.rank)o=batchNormalization3d(e,t,n,a,i,r);else{if(4!==e.rank)throw new NotImplementedError(\"batchNormalization is not implemented for array of rank \"+e.rank+\" yet\");o=batchNormalization4d(e,t,n,a,i,r);}return o}function regularNormalizeBatchInTraining(e,t,n,r,i){return void 0===i&&(i=.001),tidy(function(){var a=moments(e,r),o=a.mean,s=a.variance;return [batchNormalization$1(e,o,s,n,t,i),o,s]})}function broadcastNormalizeBatchInTraining(e,t,n,r,i){return void 0===i&&(i=.001),tidy(function(){for(var a=moments(e,r),o=a.mean,s=a.variance,l=[],u=0,c=range$1(0,e.rank);u<c.length;u++){var p=c[u];-1!==r.indexOf(p)?l.push(1):l.push(e.shape[p]);}var h=o.reshape(l),d=s.reshape(l),f=null==t?null:t.reshape(l),g=null==n?null:n.reshape(l);return [batchNormalization$1(e,h,d,g,f,i),o,s]})}function normalizeBatchInTraining(e,t,n,r,i){return void 0===i&&(i=.001),util.arraysEqual(r.slice().sort(),range$1(0,e.rank-1))?regularNormalizeBatchInTraining(e,t,n,r,i):broadcastNormalizeBatchInTraining(e,t,n,r,i)}serialization.registerClass(Dot);var BatchNormalization=function(e){function t(t){var n=this;return null==t&&(t={}),(n=e.call(this,t)||this).supportsMasking=!0,n.axis=null==t.axis?-1:t.axis,n.momentum=null==t.momentum?.99:t.momentum,n.epsilon=null==t.epsilon?.001:t.epsilon,n.center=null==t.center||t.center,n.scale=null==t.scale||t.scale,n.betaInitializer=getInitializer(t.betaInitializer||\"zeros\"),n.gammaInitializer=getInitializer(t.gammaInitializer||\"ones\"),n.movingMeanInitializer=getInitializer(t.movingMeanInitializer||\"zeros\"),n.movingVarianceInitializer=getInitializer(t.movingVarianceInitializer||\"ones\"),n.betaConstraint=getConstraint(t.betaConstraint),n.gammaConstraint=getConstraint(t.gammaConstraint),n.betaRegularizer=getRegularizer(t.betaRegularizer),n.gammaRegularizer=getRegularizer(t.gammaRegularizer),n.stepCount=0,n}return __extends$1(t,e),t.prototype.build=function(e){e=getExactlyOneShape(e);var t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(null==n)throw new ValueError(\"Axis \"+t+\" of input tensor should have a defined dimension but the layer received an input with shape \"+JSON.stringify(e)+\".\");this.inputSpec=[new InputSpec({ndim:e.length,axes:(r={},r[t]=n,r)})];var r,i=[n];this.scale&&(this.gamma=this.addWeight(\"gamma\",i,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight(\"beta\",i,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight(\"moving_mean\",i,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight(\"moving_variance\",i,null,this.movingVarianceInitializer,null,!1),this.built=!0;},t.prototype.call=function(e,t){var n=this;return tidy(function(){var r=null!=t.training&&t.training,i=getExactlyOneTensor(e),a=i.shape,o=a.length,s=range$1(0,o),l=n.axis>=0?n.axis:n.axis+o;s.splice(l,1);var u=pyListRepeat(1,o);u[l]=a[l];var c=s.slice();c.sort();var p=!util.arraysEqual(c,range$1(0,o).slice(0,o-1));if(!r)return function(){if(p){var e=n.movingMean.read().reshape(u),t=n.movingVariance.read().reshape(u),r=n.center?n.beta.read().reshape(u):null,a=n.scale?n.gamma.read().reshape(u):null;return batchNormalization$1(i,e,t,r,a,n.epsilon)}return batchNormalization$1(i,n.movingMean.read(),n.movingVariance.read(),null==n.beta?null:n.beta.read(),null==n.gamma?null:n.gamma.read(),n.epsilon)}();var h=normalizeBatchInTraining(i,n.gamma.read(),n.beta.read(),s,n.epsilon),d=h[0],f=h[1],g=h[2],m=arrayProd(s.map(function(e){return i.shape[e]})),y=g.mul(getScalar(m/(m-(1+n.epsilon))));return function(){n.stepCount++;var e=movingAverage(n.movingMean.read(),f,n.momentum,n.stepCount);n.movingMean.write(e);var t=movingAverage(n.movingVariance.read(),y,n.momentum,n.stepCount);n.movingVariance.write(t);}(),d})},t.prototype.getConfig=function(){var t={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:serializeInitializer(this.betaInitializer),gammaInitializer:serializeInitializer(this.gammaInitializer),movingMeanInitializer:serializeInitializer(this.movingMeanInitializer),movingVarianceInitializer:serializeInitializer(this.movingVarianceInitializer),betaRegularizer:serializeRegularizer(this.betaRegularizer),gammaRegularizer:serializeRegularizer(this.gammaRegularizer),betaConstraint:serializeConstraint(this.betaConstraint),gammaConstraint:serializeConstraint(this.gammaConstraint)},n=e.prototype.getConfig.call(this);return Object.assign(t,n),t},t.className=\"BatchNormalization\",t}(Layer);function spatial2dPadding(e,t,n){return tidy(function(){if(4!==e.rank)throw new ValueError(\"temporalPadding expects input tensor to be 4-D, but received a \"+e.rank+\"-D tensor.\");if(null==t&&(t=[[1,1],[1,1]]),2!==t.length||2!==t[0].length||2!==t[1].length)throw new ValueError(\"spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.\");if(null==n&&(n=imageDataFormat()),\"channelsLast\"!==n&&\"channelsFirst\"!==n)throw new ValueError(\"Unknown data format: \"+n+\". Supported data formats are 'channelsLast' and 'channelsFirst.\");var r;return r=\"channelsFirst\"===n?[[0,0],[0,0],t[0],t[1]]:[[0,0],t[0],t[1],[0,0]],pad(e,r)})}serialization.registerClass(BatchNormalization);var ZeroPadding2D=function(e){function t(t){var n=this;if(null==t&&(t={}),(n=e.call(this,t)||this).dataFormat=null==t.dataFormat?imageDataFormat():t.dataFormat,null==t.padding)n.padding=[[1,1],[1,1]];else if(\"number\"==typeof t.padding)n.padding=[[t.padding,t.padding],[t.padding,t.padding]];else{if(t.padding=t.padding,2!==t.padding.length)throw new ValueError(\"ZeroPadding2D expects padding to be a length-2 array, but received a length-\"+t.padding.length+\" array.\");var r=void 0,i=void 0;if(\"number\"==typeof t.padding[0])r=[t.padding[0],t.padding[0]],i=[t.padding[1],t.padding[1]];else{if(t.padding=t.padding,2!==t.padding[0].length)throw new ValueError(\"ZeroPadding2D expects height padding to be a length-2 array, but received a length-\"+t.padding[0].length+\" array.\");if(r=t.padding[0],2!==t.padding[1].length)throw new ValueError(\"ZeroPadding2D expects width padding to be a length-2 array, but received a length-\"+t.padding[1].length+\" array.\");i=t.padding[1];}n.padding=[r,i];}return n.inputSpec=[new InputSpec({ndim:4})],n}return __extends$1(t,e),t.prototype.computeOutputShape=function(e){var t,n;return e=getExactlyOneShape(e),\"channelsFirst\"===this.dataFormat?(t=null!=e[2]&&e[2]>=0?e[2]+this.padding[0][0]+this.padding[0][1]:null,n=null!=e[3]&&e[3]>=0?e[3]+this.padding[1][0]+this.padding[1][1]:null,[e[0],e[1],t,n]):(t=null!=e[1]&&e[1]>=0?e[1]+this.padding[0][0]+this.padding[0][1]:null,n=null!=e[2]&&e[2]>=0?e[2]+this.padding[1][0]+this.padding[1][1]:null,[e[0],t,n,e[3]])},t.prototype.call=function(e,t){var n=this;return tidy(function(){return spatial2dPadding(getExactlyOneTensor(e),n.padding,n.dataFormat)})},t.prototype.getConfig=function(){var t={padding:this.padding,dataFormat:this.dataFormat},n=e.prototype.getConfig.call(this);return Object.assign(t,n),t},t.className=\"ZeroPadding2D\",t}(Layer);function pool2d(e,t,n,r,i,a){return tidy(function(){var o;checkDataFormat(i),checkPoolMode(a),checkPaddingMode(r),null==n&&(n=[1,1]),null==r&&(r=\"valid\"),null==i&&(i=imageDataFormat()),null==a&&(a=\"max\"),e=preprocessConv2DInput(e,i);var s=\"same\"===r?\"same\":\"valid\";return o=\"max\"===a?maxPool(e,t,n,s):avgPool(e,t,n,s),\"channelsFirst\"===i&&(o=transpose(o,[0,3,1,2])),o})}serialization.registerClass(ZeroPadding2D);var Pooling1D=function(e){function t(t){var n=this;if(null==t.poolSize&&(t.poolSize=2),n=e.call(this,t)||this,\"number\"==typeof t.poolSize)n.poolSize=[t.poolSize];else{if(!Array.isArray(t.poolSize)||1!==t.poolSize.length||\"number\"!=typeof t.poolSize[0])throw new ValueError(\"poolSize for 1D convolutional layer must be a number or an Array of a single number, but received \"+JSON.stringify(t.poolSize));n.poolSize=t.poolSize;}if(null==t.strides)n.strides=n.poolSize;else if(\"number\"==typeof t.strides)n.strides=[t.strides];else{if(!Array.isArray(t.strides)||1!==t.strides.length||\"number\"!=typeof t.strides[0])throw new ValueError(\"strides for 1D convolutional layer must be a number or an Array of a single number, but received \"+JSON.stringify(t.strides));n.strides=t.strides;}return n.padding=null==t.padding?\"valid\":t.padding,checkPaddingMode(n.padding),n.inputSpec=[new InputSpec({ndim:3})],n}return __extends$1(t,e),t.prototype.computeOutputShape=function(e){var t=convOutputLength((e=getExactlyOneShape(e))[1],this.poolSize[0],this.padding,this.strides[0]);return [e[0],t,e[2]]},t.prototype.call=function(e,t){var n=this;return tidy(function(){n.invokeCallHook(e,t),e=expandDims$1(getExactlyOneTensor(e),2);var r=n.poolingFunction(getExactlyOneTensor(e),[n.poolSize[0],1],[n.strides[0],1],n.padding,\"channelsLast\");return squeeze(r,[2])})},t.prototype.getConfig=function(){var t={poolSize:this.poolSize,padding:this.padding,strides:this.strides},n=e.prototype.getConfig.call(this);return Object.assign(t,n),t},t}(Layer),MaxPooling1D=function(e){function t(t){return e.call(this,t)||this}return __extends$1(t,e),t.prototype.poolingFunction=function(e,t,n,r,i){return checkDataFormat(i),checkPaddingMode(r),pool2d(e,t,n,r,i,\"max\")},t.className=\"MaxPooling1D\",t}(Pooling1D);serialization.registerClass(MaxPooling1D);var AveragePooling1D=function(e){function t(t){return e.call(this,t)||this}return __extends$1(t,e),t.prototype.poolingFunction=function(e,t,n,r,i){return checkDataFormat(i),checkPaddingMode(r),pool2d(e,t,n,r,i,\"avg\")},t.className=\"AveragePooling1D\",t}(Pooling1D);serialization.registerClass(AveragePooling1D);var Pooling2D=function(e){function t(t){var n=this;if(null==t.poolSize&&(t.poolSize=[2,2]),(n=e.call(this,t)||this).poolSize=Array.isArray(t.poolSize)?t.poolSize:[t.poolSize,t.poolSize],null==t.strides)n.strides=n.poolSize;else if(Array.isArray(t.strides)){if(2!==t.strides.length)throw new ValueError(\"If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length \"+t.strides.length+\".\");n.strides=t.strides;}else n.strides=[t.strides,t.strides];return n.padding=null==t.padding?\"valid\":t.padding,n.dataFormat=null==t.dataFormat?\"channelsLast\":t.dataFormat,checkDataFormat(n.dataFormat),checkPaddingMode(n.padding),n.inputSpec=[new InputSpec({ndim:4})],n}return __extends$1(t,e),t.prototype.computeOutputShape=function(e){e=getExactlyOneShape(e);var t=\"channelsFirst\"===this.dataFormat?e[2]:e[1],n=\"channelsFirst\"===this.dataFormat?e[3]:e[2];return t=convOutputLength(t,this.poolSize[0],this.padding,this.strides[0]),n=convOutputLength(n,this.poolSize[1],this.padding,this.strides[1]),\"channelsFirst\"===this.dataFormat?[e[0],e[1],t,n]:[e[0],t,n,e[3]]},t.prototype.call=function(e,t){var n=this;return tidy(function(){return n.invokeCallHook(e,t),n.poolingFunction(getExactlyOneTensor(e),n.poolSize,n.strides,n.padding,n.dataFormat)})},t.prototype.getConfig=function(){var t={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},n=e.prototype.getConfig.call(this);return Object.assign(t,n),t},t}(Layer),MaxPooling2D=function(e){function t(t){return e.call(this,t)||this}return __extends$1(t,e),t.prototype.poolingFunction=function(e,t,n,r,i){return checkDataFormat(i),checkPaddingMode(r),pool2d(e,t,n,r,i,\"max\")},t.className=\"MaxPooling2D\",t}(Pooling2D);serialization.registerClass(MaxPooling2D);var AveragePooling2D=function(e){function t(t){return e.call(this,t)||this}return __extends$1(t,e),t.prototype.poolingFunction=function(e,t,n,r,i){return checkDataFormat(i),checkPaddingMode(r),pool2d(e,t,n,r,i,\"avg\")},t.className=\"AveragePooling2D\",t}(Pooling2D);serialization.registerClass(AveragePooling2D);var GlobalPooling1D=function(e){function t(t){var n=e.call(this,t)||this;return n.inputSpec=[new InputSpec({ndim:3})],n}return __extends$1(t,e),t.prototype.computeOutputShape=function(e){return [e[0],e[2]]},t.prototype.call=function(e,t){throw new NotImplementedError},t}(Layer),GlobalAveragePooling1D=function(e){function t(t){return e.call(this,t)||this}return __extends$1(t,e),t.prototype.call=function(e,t){return tidy(function(){var t=getExactlyOneTensor(e);return mean(t,1)})},t.className=\"GlobalAveragePooling1D\",t}(GlobalPooling1D);serialization.registerClass(GlobalAveragePooling1D);var GlobalMaxPooling1D=function(e){function t(t){return e.call(this,t)||this}return __extends$1(t,e),t.prototype.call=function(e,t){return tidy(function(){var t=getExactlyOneTensor(e);return max(t,1)})},t.className=\"GlobalMaxPooling1D\",t}(GlobalPooling1D);serialization.registerClass(GlobalMaxPooling1D);var GlobalPooling2D=function(e){function t(t){var n=e.call(this,t)||this;return n.dataFormat=null==t.dataFormat?\"channelsLast\":t.dataFormat,checkDataFormat(n.dataFormat),n.inputSpec=[new InputSpec({ndim:4})],n}return __extends$1(t,e),t.prototype.computeOutputShape=function(e){return e=e,\"channelsLast\"===this.dataFormat?[e[0],e[3]]:[e[0],e[1]]},t.prototype.call=function(e,t){throw new NotImplementedError},t.prototype.getConfig=function(){var t={dataFormat:this.dataFormat},n=e.prototype.getConfig.call(this);return Object.assign(t,n),t},t}(Layer),GlobalAveragePooling2D=function(e){function t(){return null!==e&&e.apply(this,arguments)||this}return __extends$1(t,e),t.prototype.call=function(e,t){var n=this;return tidy(function(){var t=getExactlyOneTensor(e);return \"channelsLast\"===n.dataFormat?mean(t,[1,2]):mean(t,[2,3])})},t.className=\"GlobalAveragePooling2D\",t}(GlobalPooling2D);serialization.registerClass(GlobalAveragePooling2D);var GlobalMaxPooling2D=function(e){function t(){return null!==e&&e.apply(this,arguments)||this}return __extends$1(t,e),t.prototype.call=function(e,t){var n=this;return tidy(function(){var t=getExactlyOneTensor(e);return \"channelsLast\"===n.dataFormat?max(t,[1,2]):max(t,[2,3])})},t.className=\"GlobalMaxPooling2D\",t}(GlobalPooling2D);function standardizeArgs(e,t,n,r){if(Array.isArray(e)){if(null!=t||null!=n)throw new ValueError(\"When inputs is an array, neither initialState or constants should be provided\");null!=r&&(n=e.slice(e.length-r,e.length),e=e.slice(0,e.length-r)),e.length>1&&(t=e.slice(1,e.length)),e=e[0];}function i(e){return null==e||Array.isArray(e)?e:[e]}return {inputs:e,initialState:t=i(t),constants:n=i(n)}}function rnn(e,t,n,r,i,a,o,s){void 0===r&&(r=!1),void 0===o&&(o=!1),void 0===s&&(s=!1);var l=t.shape.length;if(l<3)throw new ValueError(\"Input should be at least 3D, but is \"+l+\"D.\");var u,c,p=[1,0].concat(range$1(2,l));if(t=transpose(t,p),null!=i)throw new NotImplementedError(\"The rnn() function of the deeplearn.js backend does not support masking yet.\");if(null!=a)throw new NotImplementedError(\"The rnn() functoin of the deeplearn.js backend does not support constants yet.\");o&&console.warn(\"Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend.\"),r&&(t=reverse(t,0));for(var h=n,d=t.shape[0],f=function(n){var r=sliceAlongFirstAxis(t,n,1);r=r.reshape(r.shape.slice(1));var i=tidy(function(){return e(r,h)});if(c=i[0],s)if(0===n)u=c.expandDims(1);else{var a=concat([u,c.expandDims(1)],1);u.dispose(),u=a;}h=i[1];},g=0;g<d;++g)f(g);return [c,u,h]}serialization.registerClass(GlobalMaxPooling2D);var RNN=function(e){function t(t){var n,r=e.call(this,t)||this;if(null==t.cell)throw new ValueError(\"cell property is missing for the constructor of RNN.\");if(null==(n=Array.isArray(t.cell)?new StackedRNNCells({cells:t.cell}):t.cell).stateSize)throw new ValueError(\"The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).\");return r.cell=n,r.returnSequences=null!=t.returnSequences&&t.returnSequences,r.returnState=null!=t.returnState&&t.returnState,r.goBackwards=null!=t.goBackwards&&t.goBackwards,r._stateful=null!=t.stateful&&t.stateful,r.unroll=null!=t.unroll&&t.unroll,r.supportsMasking=!0,r.inputSpec=[new InputSpec({ndim:3})],r.stateSpec=null,r.states=null,r.numConstants=null,r.keptStates=[],r}return __extends$1(t,e),t.prototype.getStates=function(){return null==this.states?range$1(0,Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1).map(function(e){return null}):this.states},t.prototype.setStates=function(e){this.states=e;},t.prototype.computeOutputShape=function(e){isArrayOfShapes(e)&&(e=e[0]),e=e;var t=this.cell.stateSize;Array.isArray(t)||(t=[t]);var n,r=t[0];if(n=this.returnSequences?[e[0],e[1],r]:[e[0],r],this.returnState){for(var i=[],a=0,o=t;a<o.length;a++){var s=o[a];i.push([e[0],s]);}return [n].concat(i)}return n},t.prototype.computeMask=function(e,t){throw new NotImplementedError(\"computeMask has not been implemented for RNN yet\")},t.prototype.build=function(e){if(null!=this.numConstants)throw new NotImplementedError(\"Constants support is not implemented in RNN yet.\");isArrayOfShapes(e)&&(e=e[0]),e=e;var t=this.stateful?e[0]:null,n=e[e.length-1];this.inputSpec[0]=new InputSpec({shape:[t,null,n]});var r,i=[e[0]].concat(e.slice(2));if(this.cell.build(i),r=Array.isArray(this.cell.stateSize)?this.cell.stateSize:[this.cell.stateSize],null!=this.stateSpec){if(!util.arraysEqual(this.stateSpec.map(function(e){return e.shape[e.shape.length-1]}),r))throw new ValueError(\"An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=\"+this.stateSpec+\"; However cell.stateSize is \"+this.cell.stateSize)}else this.stateSpec=r.map(function(e){return new InputSpec({shape:[null,e]})});this.stateful&&this.resetStates();},t.prototype.resetStates=function(e,t){var n=this;void 0===t&&(t=!1),tidy(function(){if(!n.stateful)throw new AttributeError(\"Cannot call resetStates() on an RNN Layer that is not stateful.\");var r=n.inputSpec[0].shape[0];if(null==r)throw new ValueError(\"If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \\n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.\");if(null==n.states)Array.isArray(n.cell.stateSize)?n.states=n.cell.stateSize.map(function(e){return zeros([r,e])}):n.states=[zeros([r,n.cell.stateSize])];else if(null==e)dispose(n.states),null!=n.keptStates&&(dispose(n.keptStates),n.keptStates=[]),Array.isArray(n.cell.stateSize)?n.states=n.cell.stateSize.map(function(e){return zeros([r,e])}):n.states[0]=zeros([r,n.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==n.states.length)throw new ValueError(\"Layer \"+n.name+\" expects \"+n.states.length+\" state(s), but it received \"+e.length+\" state value(s). Input received: \"+e);!0===t?n.keptStates.push(n.states.slice()):dispose(n.states);for(var i=0;i<n.states.length;++i){var a=e[i],o=Array.isArray(n.cell.stateSize)?n.cell.stateSize[i]:n.cell.stateSize,s=[r,o];if(!util.arraysEqual(a.shape,s))throw new ValueError(\"State \"+i+\" is incompatible with layer \"+n.name+\": expected shape=\"+s+\", received shape=\"+a.shape);n.states[i]=a;}}n.states.forEach(function(e){return keep(e)});});},t.prototype.apply=function(t,n){var r=null==n?null:n.initialState,i=null==n?null:n.constants;null==n&&(n={});var a=standardizeArgs(t,r,i,this.numConstants);t=a.inputs,r=a.initialState,i=a.constants;var o=[],s=[];if(null!=r){n.initialState=r,o=o.concat(r),this.stateSpec=[];for(var l=0,u=r;l<u.length;l++){var c=u[l];this.stateSpec.push(new InputSpec({shape:c.shape}));}s=s.concat(this.stateSpec);}if(null!=i&&(n.constants=i,o=o.concat(i),this.numConstants=i.length),o[0]instanceof SymbolicTensor){var p=[t].concat(o),h=this.inputSpec.concat(s),d=this.inputSpec;this.inputSpec=h;var f=e.prototype.apply.call(this,p,n);return this.inputSpec=d,f}return e.prototype.apply.call(this,t,n)},t.prototype.call=function(e,t){var n=this;return tidy(function(){var r=null==t?null:t.mask,i=null==t?null:t.training,a=null==t?null:t.initialState;if(e=getExactlyOneTensor(e),null==a&&(a=n.stateful?n.states:n.getInitialState(e)),null!=r)throw new NotImplementedError(\"Masking is not implemented for RNN yet\");var o=Array.isArray(n.cell.stateSize)?n.cell.stateSize.length:1;if(a.length!==o)throw new ValueError(\"RNN Layer has \"+o+\" state(s) but was passed \"+a.length+\" initial state(s).\");n.unroll&&console.warn(\"Ignoring unroll = true for RNN layer, due to imperative backend.\");var s={training:i},l=rnn(function(e,t){var r=n.cell.call([e].concat(t),s);return [r[0],r.slice(1)]},e,a,n.goBackwards,null,null,n.unroll,n.returnSequences),u=l[0],c=l[1],p=l[2];n.stateful&&n.resetStates(p,i);var h=n.returnSequences?c:u;return n.returnState?[h].concat(p):h})},t.prototype.getInitialState=function(e){var t=this;return tidy(function(){var n=zeros(e.shape);return n=expandDims$1(n=sum$1(n,[1,2])),Array.isArray(t.cell.stateSize)?t.cell.stateSize.map(function(e){return e>1?tile$1(n,[1,e]):n}):t.cell.stateSize>1?[tile$1(n,[1,t.cell.stateSize])]:[n]})},Object.defineProperty(t.prototype,\"trainableWeights\",{get:function(){return this.trainable?this.cell.trainableWeights:[]},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,\"nonTrainableWeights\",{get:function(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights},enumerable:!0,configurable:!0}),t.prototype.getConfig=function(){var t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};null!=this.numConstants&&(t.numConstants=this.numConstants);var n=this.cell.getConfig();t.cell={className:this.cell.getClassName(),config:n};var r=e.prototype.getConfig.call(this);return Object.assign(t,r),t},t.className=\"RNN\",t}(Layer);serialization.registerClass(RNN);var RNNCell=function(e){function t(){return null!==e&&e.apply(this,arguments)||this}return __extends$1(t,e),t}(Layer),SimpleRNNCell=function(e){function t(t){var n=e.call(this,t)||this;return n.DEFAULT_ACTIVATION=\"tanh\",n.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",n.DEFAULT_RECURRENT_INITIALIZER=\"orthogonal\",n.DEFAULT_BIAS_INITIALIZER=\"zeros\",n.units=t.units,n.activation=getActivation(null==t.activation?n.DEFAULT_ACTIVATION:t.activation),n.useBias=null==t.useBias||t.useBias,n.kernelInitializer=getInitializer(t.kernelInitializer||n.DEFAULT_KERNEL_INITIALIZER),n.recurrentInitializer=getInitializer(t.recurrentInitializer||n.DEFAULT_RECURRENT_INITIALIZER),n.biasInitializer=getInitializer(t.biasInitializer||n.DEFAULT_BIAS_INITIALIZER),n.kernelRegularizer=getRegularizer(t.kernelRegularizer),n.recurrentRegularizer=getRegularizer(t.recurrentRegularizer),n.biasRegularizer=getRegularizer(t.biasRegularizer),n.kernelConstraint=getConstraint(t.kernelConstraint),n.recurrentConstraint=getConstraint(t.recurrentConstraint),n.biasConstraint=getConstraint(t.biasConstraint),n.dropout=min$1([1,max$1([0,null==t.dropout?0:t.dropout])]),n.recurrentDropout=min$1([1,max$1([0,null==t.recurrentDropout?0:t.recurrentDropout])]),n.stateSize=n.units,n.dropoutMask=null,n.recurrentDropoutMask=null,n}return __extends$1(t,e),t.prototype.build=function(e){e=getExactlyOneShape(e),this.kernel=this.addWeight(\"kernel\",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight(\"recurrent_kernel\",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight(\"bias\",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0;},t.prototype.call=function(e,t){var n=this;return tidy(function(){if(2!==(e=e).length)throw new ValueError(\"SimpleRNNCell expects 2 input Tensors, got \"+e.length+\".\");var r=e[1];e=e[0];var i,a=null!=t.training&&t.training;0<n.dropout&&n.dropout<1&&null==n.dropoutMask&&(n.dropoutMask=generateDropoutMask(function(){return onesLike(e)},n.dropout,a)),0<n.recurrentDropout&&n.recurrentDropout<1&&null==n.recurrentDropoutMask&&(n.recurrentDropoutMask=generateDropoutMask(function(){return onesLike(r)},n.recurrentDropout,a));var o=n.dropoutMask,s=n.recurrentDropoutMask;i=dot$1(null!=o?mul(e,o):e,n.kernel.read()),null!=n.bias&&(i=biasAdd(i,n.bias.read())),null!=s&&(r=mul(r,s));var l=add(i,dot$1(r,n.recurrentKernel.read()));return null!=n.activation&&(l=n.activation.apply(l)),[l,l]})},t.prototype.getConfig=function(){var t={units:this.units,activation:serializeActivation(this.activation),useBias:this.useBias,kernelInitializer:serializeInitializer(this.kernelInitializer),recurrentInitializer:serializeInitializer(this.recurrentInitializer),biasInitializer:serializeInitializer(this.biasInitializer),kernelRegularizer:serializeRegularizer(this.kernelRegularizer),recurrentRegularizer:serializeRegularizer(this.recurrentRegularizer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint),recurrentConstraint:serializeConstraint(this.recurrentConstraint),biasConstraint:serializeConstraint(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout},n=e.prototype.getConfig.call(this);return Object.assign(t,n),t},t.className=\"SimpleRNNCell\",t}(RNNCell);serialization.registerClass(SimpleRNNCell);var SimpleRNN=function(e){function t(t){return t.cell=new SimpleRNNCell(t),e.call(this,t)||this}return __extends$1(t,e),t.prototype.call=function(t,n){var r=this;return tidy(function(){null!=r.cell.dropoutMask&&(dispose(r.cell.dropoutMask),r.cell.dropoutMask=null),null!=r.cell.recurrentDropoutMask&&(dispose(r.cell.recurrentDropoutMask),r.cell.recurrentDropoutMask=null);var i=null==n?null:n.mask,a=null==n?null:n.training,o=null==n?null:n.initialState;return e.prototype.call.call(r,t,{mask:i,training:a,initialState:o})})},Object.defineProperty(t.prototype,\"units\",{get:function(){return this.cell.units},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,\"activation\",{get:function(){return this.cell.activation},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,\"useBias\",{get:function(){return this.cell.useBias},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,\"kernelInitializer\",{get:function(){return this.cell.kernelInitializer},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,\"recurrentInitializer\",{get:function(){return this.cell.recurrentInitializer},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,\"biasInitializer\",{get:function(){return this.cell.biasInitializer},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,\"kernelRegularizer\",{get:function(){return this.cell.kernelRegularizer},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,\"recurrentRegularizer\",{get:function(){return this.cell.recurrentRegularizer},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,\"biasRegularizer\",{get:function(){return this.cell.biasRegularizer},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,\"kernelConstraint\",{get:function(){return this.cell.kernelConstraint},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,\"recurrentConstraint\",{get:function(){return this.cell.recurrentConstraint},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,\"biasConstraint\",{get:function(){return this.cell.biasConstraint},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,\"dropout\",{get:function(){return this.cell.dropout},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,\"recurrentDropout\",{get:function(){return this.cell.recurrentDropout},enumerable:!0,configurable:!0}),t.prototype.getConfig=function(){var t={units:this.units,activation:serializeActivation(this.activation),useBias:this.useBias,kernelInitializer:serializeInitializer(this.kernelInitializer),recurrentInitializer:serializeInitializer(this.recurrentInitializer),biasInitializer:serializeInitializer(this.biasInitializer),kernelRegularizer:serializeRegularizer(this.kernelRegularizer),recurrentRegularizer:serializeRegularizer(this.recurrentRegularizer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint),recurrentConstraint:serializeConstraint(this.recurrentConstraint),biasConstraint:serializeConstraint(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout},n=e.prototype.getConfig.call(this);return delete n.cell,Object.assign(t,n),t},t.className=\"SimpleRNN\",t}(RNN);serialization.registerClass(SimpleRNN);var GRUCell=function(e){function t(t){var n=e.call(this,t)||this;return n.DEFAULT_ACTIVATION=\"tanh\",n.DEFAULT_RECURRENT_ACTIVATION=\"hardSigmoid\",n.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",n.DEFAULT_RECURRENT_INITIALIZER=\"orthogonal\",n.DEFAULT_BIAS_INITIALIZER=\"zeros\",n.units=t.units,n.activation=getActivation(void 0===t.activation?n.DEFAULT_ACTIVATION:t.activation),n.recurrentActivation=getActivation(void 0===t.recurrentActivation?n.DEFAULT_RECURRENT_ACTIVATION:t.recurrentActivation),n.useBias=null==t.useBias||t.useBias,n.kernelInitializer=getInitializer(t.kernelInitializer||n.DEFAULT_KERNEL_INITIALIZER),n.recurrentInitializer=getInitializer(t.recurrentInitializer||n.DEFAULT_RECURRENT_INITIALIZER),n.biasInitializer=getInitializer(t.biasInitializer||n.DEFAULT_BIAS_INITIALIZER),n.kernelRegularizer=getRegularizer(t.kernelRegularizer),n.recurrentRegularizer=getRegularizer(t.recurrentRegularizer),n.biasRegularizer=getRegularizer(t.biasRegularizer),n.kernelConstraint=getConstraint(t.kernelConstraint),n.recurrentConstraint=getConstraint(t.recurrentConstraint),n.biasConstraint=getConstraint(t.biasConstraint),n.dropout=min$1([1,max$1([0,null==t.dropout?0:t.dropout])]),n.recurrentDropout=min$1([1,max$1([0,null==t.recurrentDropout?0:t.recurrentDropout])]),n.implementation=t.implementation,n.stateSize=n.units,n.dropoutMask=null,n.recurrentDropoutMask=null,n}return __extends$1(t,e),t.prototype.build=function(e){var t=(e=getExactlyOneShape(e))[e.length-1];this.kernel=this.addWeight(\"kernel\",[t,3*this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight(\"recurrent_kernel\",[this.units,3*this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight(\"bias\",[3*this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0;},t.prototype.call=function(e,t){var n=this;return tidy(function(){if(2!==(e=e).length)throw new ValueError(\"GRUCell expects 2 input Tensors (inputs, h, c), got \"+e.length+\".\");var r=null!=t.training&&t.training,i=e[1];e=e[0],0<n.dropout&&n.dropout<1&&null==n.dropoutMask&&(n.dropoutMask=generateDropoutMask(function(){return onesLike(e)},n.dropout,r,3)),0<n.recurrentDropout&&n.recurrentDropout<1&&null==n.recurrentDropoutMask&&(n.recurrentDropoutMask=generateDropoutMask(function(){return onesLike(i)},n.recurrentDropout,r,3));var a,o,s,l=n.dropoutMask,u=n.recurrentDropoutMask;0<n.dropout&&n.dropout<1&&(e=mul(e,l[0]));var c=dot$1(e,n.kernel.read());n.useBias&&(c=biasAdd(c,n.bias.read())),0<n.recurrentDropout&&n.recurrentDropout<1&&(i=mul(i,u[0]));var p=n.recurrentKernel.read(),h=split$1(p,[2*n.units,n.units],p.rank-1),d=h[0],f=h[1],g=dot$1(i,d),m=split$1(c,3,c.rank-1),y=m[0],v=m[1],b=m[2],w=split$1(g,2,g.rank-1),z=w[0],S=w[1];a=n.recurrentActivation.apply(add(y,z)),o=n.recurrentActivation.apply(add(v,S));var I=dot$1(mul(o,i),f);s=n.activation.apply(add(b,I));var C=add(mul(a,i),mul(add(getScalar(1),neg(a)),s));return [C,C]})},t.prototype.getConfig=function(){var t={units:this.units,activation:serializeActivation(this.activation),recurrentActivation:serializeActivation(this.recurrentActivation),useBias:this.useBias,kernelInitializer:serializeInitializer(this.kernelInitializer),recurrentInitializer:serializeInitializer(this.recurrentInitializer),biasInitializer:serializeInitializer(this.biasInitializer),kernelRegularizer:serializeRegularizer(this.kernelRegularizer),recurrentRegularizer:serializeRegularizer(this.recurrentRegularizer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint),recurrentConstraint:serializeConstraint(this.recurrentConstraint),biasConstraint:serializeConstraint(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation},n=e.prototype.getConfig.call(this);return Object.assign(t,n),t},t.className=\"GRUCell\",t}(RNNCell);serialization.registerClass(GRUCell);var GRU=function(e){function t(t){return 0===t.implementation&&console.warn(\"`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call.\"),t.cell=new GRUCell(t),e.call(this,t)||this}return __extends$1(t,e),t.prototype.call=function(t,n){var r=this;return tidy(function(){null!=r.cell.dropoutMask&&(dispose(r.cell.dropoutMask),r.cell.dropoutMask=null),null!=r.cell.recurrentDropoutMask&&(dispose(r.cell.recurrentDropoutMask),r.cell.recurrentDropoutMask=null);var i=null==n?null:n.mask,a=null==n?null:n.training,o=null==n?null:n.initialState;return e.prototype.call.call(r,t,{mask:i,training:a,initialState:o})})},Object.defineProperty(t.prototype,\"units\",{get:function(){return this.cell.units},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,\"activation\",{get:function(){return this.cell.activation},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,\"recurrentActivation\",{get:function(){return this.cell.recurrentActivation},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,\"useBias\",{get:function(){return this.cell.useBias},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,\"kernelInitializer\",{get:function(){return this.cell.kernelInitializer},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,\"recurrentInitializer\",{get:function(){return this.cell.recurrentInitializer},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,\"biasInitializer\",{get:function(){return this.cell.biasInitializer},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,\"kernelRegularizer\",{get:function(){return this.cell.kernelRegularizer},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,\"recurrentRegularizer\",{get:function(){return this.cell.recurrentRegularizer},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,\"biasRegularizer\",{get:function(){return this.cell.biasRegularizer},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,\"kernelConstraint\",{get:function(){return this.cell.kernelConstraint},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,\"recurrentConstraint\",{get:function(){return this.cell.recurrentConstraint},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,\"biasConstraint\",{get:function(){return this.cell.biasConstraint},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,\"dropout\",{get:function(){return this.cell.dropout},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,\"recurrentDropout\",{get:function(){return this.cell.recurrentDropout},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,\"implementation\",{get:function(){return this.cell.implementation},enumerable:!0,configurable:!0}),t.prototype.getConfig=function(){var t={units:this.units,activation:serializeActivation(this.activation),recurrentActivation:serializeActivation(this.recurrentActivation),useBias:this.useBias,kernelInitializer:serializeInitializer(this.kernelInitializer),recurrentInitializer:serializeInitializer(this.recurrentInitializer),biasInitializer:serializeInitializer(this.biasInitializer),kernelRegularizer:serializeRegularizer(this.kernelRegularizer),recurrentRegularizer:serializeRegularizer(this.recurrentRegularizer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint),recurrentConstraint:serializeConstraint(this.recurrentConstraint),biasConstraint:serializeConstraint(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation},n=e.prototype.getConfig.call(this);return delete n.cell,Object.assign(t,n),t},t.fromConfig=function(e,t){return 0===t.implmentation&&(t.implementation=1),new e(t)},t.className=\"GRU\",t}(RNN);serialization.registerClass(GRU);var LSTMCell=function(e){function t(t){var n=e.call(this,t)||this;return n.DEFAULT_ACTIVATION=\"tanh\",n.DEFAULT_RECURRENT_ACTIVATION=\"hardSigmoid\",n.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",n.DEFAULT_RECURRENT_INITIALIZER=\"orthogonal\",n.DEFAULT_BIAS_INITIALIZER=\"zeros\",n.units=t.units,n.activation=getActivation(void 0===t.activation?n.DEFAULT_ACTIVATION:t.activation),n.recurrentActivation=getActivation(void 0===t.recurrentActivation?n.DEFAULT_RECURRENT_ACTIVATION:t.recurrentActivation),n.useBias=null==t.useBias||t.useBias,n.kernelInitializer=getInitializer(t.kernelInitializer||n.DEFAULT_KERNEL_INITIALIZER),n.recurrentInitializer=getInitializer(t.recurrentInitializer||n.DEFAULT_RECURRENT_INITIALIZER),n.biasInitializer=getInitializer(t.biasInitializer||n.DEFAULT_BIAS_INITIALIZER),n.unitForgetBias=t.unitForgetBias,n.kernelRegularizer=getRegularizer(t.kernelRegularizer),n.recurrentRegularizer=getRegularizer(t.recurrentRegularizer),n.biasRegularizer=getRegularizer(t.biasRegularizer),n.kernelConstraint=getConstraint(t.kernelConstraint),n.recurrentConstraint=getConstraint(t.recurrentConstraint),n.biasConstraint=getConstraint(t.biasConstraint),n.dropout=min$1([1,max$1([0,null==t.dropout?0:t.dropout])]),n.recurrentDropout=min$1([1,max$1([0,null==t.recurrentDropout?0:t.recurrentDropout])]),n.implementation=t.implementation,n.stateSize=[n.units,n.units],n.dropoutMask=null,n.recurrentDropoutMask=null,n}return __extends$1(t,e),t.prototype.build=function(e){var t,n,r=(e=getExactlyOneShape(e))[e.length-1];if(this.kernel=this.addWeight(\"kernel\",[r,4*this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight(\"recurrent_kernel\",[this.units,4*this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){if(this.unitForgetBias){var i=this.biasInitializer,a=this.units;t=new((n=function(e){function t(){return null!==e&&e.apply(this,arguments)||this}return __extends$1(t,e),t.prototype.apply=function(e,t){var n=i.apply([a]),r=(new Ones).apply([a]),o=i.apply([2*a]);return concatAlongFirstAxis(concatAlongFirstAxis(n,r),o)},t}(Initializer)).className=\"CustomInit\",n);}else t=this.biasInitializer;this.bias=this.addWeight(\"bias\",[4*this.units],null,t,this.biasRegularizer,!0,this.biasConstraint);}else this.bias=null;this.built=!0;},t.prototype.call=function(e,t){var n=this;return tidy(function(){var r=null!=t.training&&t.training;if(3!==(e=e).length)throw new ValueError(\"LSTMCell expects 3 input Tensors (inputs, h, c), got \"+e.length+\".\");var i=e[1],a=e[2];e=e[0],0<n.dropout&&n.dropout<1&&null==n.dropoutMask&&(n.dropoutMask=generateDropoutMask(function(){return onesLike(e)},n.dropout,r,4)),0<n.recurrentDropout&&n.recurrentDropout<1&&null==n.recurrentDropoutMask&&(n.recurrentDropoutMask=generateDropoutMask(function(){return onesLike(i)},n.recurrentDropout,r,4));var o,s,l,u,c=n.dropoutMask,p=n.recurrentDropoutMask;0<n.dropout&&n.dropout<1&&(e=mul(e,c[0]));var h=dot$1(e,n.kernel.read());0<n.recurrentDropout&&n.recurrentDropout<1&&(i=mul(i,p[0])),h=add(h,dot$1(i,n.recurrentKernel.read())),n.useBias&&(h=biasAdd(h,n.bias.read()));var d=split$1(h,4,h.rank-1),f=d[0],g=d[1],m=d[2],y=d[3];o=n.recurrentActivation.apply(f),s=n.recurrentActivation.apply(g),l=add(mul(s,a),mul(o,n.activation.apply(m))),u=n.recurrentActivation.apply(y);var v=mul(u,n.activation.apply(l));return [v,v,l]})},t.prototype.getConfig=function(){var t={units:this.units,activation:serializeActivation(this.activation),recurrentActivation:serializeActivation(this.recurrentActivation),useBias:this.useBias,kernelInitializer:serializeInitializer(this.kernelInitializer),recurrentInitializer:serializeInitializer(this.recurrentInitializer),biasInitializer:serializeInitializer(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:serializeRegularizer(this.kernelRegularizer),recurrentRegularizer:serializeRegularizer(this.recurrentRegularizer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint),recurrentConstraint:serializeConstraint(this.recurrentConstraint),biasConstraint:serializeConstraint(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation},n=e.prototype.getConfig.call(this);return Object.assign(t,n),t},t.className=\"LSTMCell\",t}(RNNCell);serialization.registerClass(LSTMCell);var LSTM=function(e){function t(t){return 0===t.implementation&&console.warn(\"`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call.\"),t.cell=new LSTMCell(t),e.call(this,t)||this}return __extends$1(t,e),t.prototype.call=function(t,n){var r=this;return tidy(function(){null!=r.cell.dropoutMask&&(dispose(r.cell.dropoutMask),r.cell.dropoutMask=null),null!=r.cell.recurrentDropoutMask&&(dispose(r.cell.recurrentDropoutMask),r.cell.recurrentDropoutMask=null);var i=null==n?null:n.mask,a=null==n?null:n.training,o=null==n?null:n.initialState;return e.prototype.call.call(r,t,{mask:i,training:a,initialState:o})})},Object.defineProperty(t.prototype,\"units\",{get:function(){return this.cell.units},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,\"activation\",{get:function(){return this.cell.activation},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,\"recurrentActivation\",{get:function(){return this.cell.recurrentActivation},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,\"useBias\",{get:function(){return this.cell.useBias},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,\"kernelInitializer\",{get:function(){return this.cell.kernelInitializer},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,\"recurrentInitializer\",{get:function(){return this.cell.recurrentInitializer},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,\"biasInitializer\",{get:function(){return this.cell.biasInitializer},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,\"unitForgetBias\",{get:function(){return this.cell.unitForgetBias},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,\"kernelRegularizer\",{get:function(){return this.cell.kernelRegularizer},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,\"recurrentRegularizer\",{get:function(){return this.cell.recurrentRegularizer},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,\"biasRegularizer\",{get:function(){return this.cell.biasRegularizer},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,\"kernelConstraint\",{get:function(){return this.cell.kernelConstraint},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,\"recurrentConstraint\",{get:function(){return this.cell.recurrentConstraint},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,\"biasConstraint\",{get:function(){return this.cell.biasConstraint},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,\"dropout\",{get:function(){return this.cell.dropout},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,\"recurrentDropout\",{get:function(){return this.cell.recurrentDropout},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,\"implementation\",{get:function(){return this.cell.implementation},enumerable:!0,configurable:!0}),t.prototype.getConfig=function(){var t={units:this.units,activation:serializeActivation(this.activation),recurrentActivation:serializeActivation(this.recurrentActivation),useBias:this.useBias,kernelInitializer:serializeInitializer(this.kernelInitializer),recurrentInitializer:serializeInitializer(this.recurrentInitializer),biasInitializer:serializeInitializer(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:serializeRegularizer(this.kernelRegularizer),recurrentRegularizer:serializeRegularizer(this.recurrentRegularizer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint),recurrentConstraint:serializeConstraint(this.recurrentConstraint),biasConstraint:serializeConstraint(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation},n=e.prototype.getConfig.call(this);return delete n.cell,Object.assign(t,n),t},t.fromConfig=function(e,t){return 0===t.implmentation&&(t.implementation=1),new e(t)},t.className=\"LSTM\",t}(RNN);serialization.registerClass(LSTM);var StackedRNNCells=function(e){function t(t){var n=e.call(this,t)||this;return n.cells=t.cells,n}return __extends$1(t,e),Object.defineProperty(t.prototype,\"stateSize\",{get:function(){for(var e=[],t=0,n=this.cells.slice().reverse();t<n.length;t++){var r=n[t];Array.isArray(r.stateSize)?e.push.apply(e,r.stateSize):e.push(r.stateSize);}return e},enumerable:!0,configurable:!0}),t.prototype.call=function(e,t){var n=this;return tidy(function(){for(var r=(e=e).slice(1),i=[],a=0,o=n.cells.slice().reverse();a<o.length;a++){var s=o[a];Array.isArray(s.stateSize)?i.push(r.splice(0,s.stateSize.length)):i.push(r.splice(0,1));}i.reverse();for(var l,u=[],c=0;c<n.cells.length;++c){s=n.cells[c];r=i[c],l=0===c?[e[0]].concat(r):[l[0]].concat(r),l=s.call(l,t),u.push(l.slice(1));}r=[];for(var p=0,h=u.slice().reverse();p<h.length;p++){var d=h[p];r.push.apply(r,d);}return [l[0]].concat(r)})},t.prototype.build=function(e){var t;isArrayOfShapes(e)&&(e=e[0]),e=e;for(var n=0,r=this.cells;n<r.length;n++){var i=r[n];i.build(e),t=Array.isArray(i.stateSize)?i.stateSize[0]:i.stateSize,e=[e[0],t];}this.built=!0;},t.prototype.getConfig=function(){for(var t=[],n=0,r=this.cells;n<r.length;n++){var i=r[n];t.push({className:this.getClassName(),config:i.getConfig()});}var a={cells:t},o=e.prototype.getConfig.call(this);return Object.assign(a,o),a},t.fromConfig=function(e,t,n){void 0===n&&(n={});for(var r=[],i=0,a=t.cells;i<a.length;i++){var o=a[i];r.push(deserialize(o,n));}return new e({cells:r})},Object.defineProperty(t.prototype,\"trainableWeights\",{get:function(){if(!this.trainable)return [];for(var e=[],t=0,n=this.cells;t<n.length;t++){var r=n[t];e.push.apply(e,r.trainableWeights);}return e},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,\"nonTrainableWeights\",{get:function(){for(var e=[],t=0,n=this.cells;t<n.length;t++){var r=n[t];e.push.apply(e,r.nonTrainableWeights);}if(!this.trainable){for(var i=[],a=0,o=this.cells;a<o.length;a++){r=o[a];i.push.apply(i,r.trainableWeights);}return i.concat(e)}return e},enumerable:!0,configurable:!0}),t.prototype.getWeights=function(){for(var e=[],t=0,n=this.cells;t<n.length;t++){var r=n[t];e.push.apply(e,r.weights);}return batchGetValue(e)},t.prototype.setWeights=function(e){for(var t=[],n=0,r=this.cells;n<r.length;n++)for(var i=r[n],a=i.weights.length,o=e.splice(a),s=0;s<i.weights.length;++s)t.push([i.weights[s],o[s]]);batchSetValue(t);},t.className=\"StackedRNNCells\",t}(RNNCell);function generateDropoutMask(e,t,n,r){function i(){return dropout(e(),getScalar(t))}if(void 0===n&&(n=null),void 0===r&&(r=1),r>1){for(var a=[],o=0;o<r;o++)a.push(inTrainPhase(i,e,n));return a.forEach(function(e){return keep(e)}),a}return keep(inTrainPhase(i,e,n))}serialization.registerClass(StackedRNNCells);var Wrapper=function(e){function t(t){var n=e.call(this,t)||this;return n.layer=t.layer,n}return __extends$1(t,e),t.prototype.build=function(e){this.built=!0;},Object.defineProperty(t.prototype,\"trainable\",{get:function(){return null!=this.layer&&this.layer.trainable},set:function(e){null!=this.layer&&(this.layer.trainable=e);},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,\"trainableWeights\",{get:function(){return this.layer.trainableWeights},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,\"nonTrainableWeights\",{get:function(){return this.layer.nonTrainableWeights},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,\"updates\",{get:function(){return this.layer._updates},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,\"losses\",{get:function(){return this.layer.losses},enumerable:!0,configurable:!0}),t.prototype.getWeights=function(){return this.layer.getWeights()},t.prototype.setWeights=function(e){this.layer.setWeights(e);},t.prototype.getConfig=function(){var t={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},n=e.prototype.getConfig.call(this);return Object.assign(t,n),t},t.fromConfig=function(e,t,n){void 0===n&&(n={});var r=deserialize(t.layer,n);delete t.layer;var i={layer:r};return Object.assign(i,t),new e(i)},t}(Layer),TimeDistributed=function(e){function t(t){var n=e.call(this,t)||this;return n.supportsMasking=!0,n}return __extends$1(t,e),t.prototype.build=function(t){if((t=getExactlyOneShape(t)).length<3)throw new ValueError(\"TimeDistributed layer expects an input shape >= 3D, but received input shape \"+JSON.stringify(t));this.inputSpec=[{shape:t}];var n=[t[0]].concat(t.slice(2));this.layer.built||(this.layer.build(n),this.layer.built=!0),e.prototype.build.call(this,t);},t.prototype.computeOutputShape=function(e){var t=[(e=getExactlyOneShape(e))[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),r=e[1];return [n[0],r].concat(n.slice(1))},t.prototype.call=function(e,t){var n=this;return tidy(function(){return rnn(function(e,r){return [getExactlyOneTensor(n.layer.call(e,t)),[]]},e=getExactlyOneTensor(e),[],!1,null,null,!1,!0)[1]})},t.className=\"TimeDistributed\",t}(Wrapper);serialization.registerClass(TimeDistributed);var VALID_BIDIRECTIONAL_MERGE_MODES=[\"sum\",\"mul\",\"concat\",\"ave\"];function checkBidirectionalMergeMode(e){checkStringTypeUnionValue(VALID_BIDIRECTIONAL_MERGE_MODES,\"BidirectionalMergeMode\",e);}var Bidirectional=function(e){function t(t){var n=e.call(this,t)||this,r=t.layer.getConfig();if(n.forwardLayer=deserialize({className:t.layer.getClassName(),config:r}),r.goBackwards=!0!==r.goBackwards,n.backwardLayer=deserialize({className:t.layer.getClassName(),config:r}),n.forwardLayer.name=\"forward_\"+n.forwardLayer.name,n.backwardLayer.name=\"backward_\"+n.backwardLayer.name,checkBidirectionalMergeMode(t.mergeMode),n.mergeMode=t.mergeMode,t.weights)throw new NotImplementedError(\"weights support is not implemented for Bidirectional layer yet.\");return n._stateful=t.layer.stateful,n.returnSequences=t.layer.returnSequences,n.returnState=t.layer.returnState,n.supportsMasking=!0,n._trainable=!0,n.inputSpec=t.layer.inputSpec,n.numConstants=null,n}return __extends$1(t,e),Object.defineProperty(t.prototype,\"trainable\",{get:function(){return this._trainable},set:function(e){this._trainable=e,null!=this.forwardLayer&&(this.forwardLayer.trainable=e),null!=this.backwardLayer&&(this.backwardLayer.trainable=e);},enumerable:!0,configurable:!0}),t.prototype.getWeights=function(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())},t.prototype.setWeights=function(e){var t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n));},t.prototype.computeOutputShape=function(e){var t,n,r,i=this.forwardLayer.computeOutputShape(e);return Array.isArray(i)&&Array.isArray(i[0])||(i=[i]),i=i,this.returnState?(r=i.slice(1),t=i[0]):t=i[0],t=t,\"concat\"===this.mergeMode?(t[t.length-1]*=2,n=[t]):n=null==this.mergeMode?[t,t.slice()]:[t],this.returnState?null==this.mergeMode?n.concat(r).concat(r.slice()):[t].concat(r).concat(r.slice()):singletonOrArray(n)},t.prototype.apply=function(t,n){var r=null==n?null:n.initialState,i=null==n?null:n.constants;null==n&&(n={});var a=standardizeArgs(t,r,i,this.numConstants);if(t=a.inputs,r=a.initialState,i=a.constants,Array.isArray(t)&&(r=t.slice(1),t=t[0]),(null==r||0===r.length)&&null==i)return e.prototype.apply.call(this,t,n);var o=[],s=[];if(null!=r){var l=r.length;if(l%2>0)throw new ValueError(\"When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.\");n.initialState=r,o.push.apply(o,r);var u=r.map(function(e){return new InputSpec({shape:e.shape})});this.forwardLayer.stateSpec=u.slice(0,l/2),this.backwardLayer.stateSpec=u.slice(l/2),s.push.apply(s,u);}if(null!=i)throw new NotImplementedError(\"Support for constants in Bidirectional layers is not implemented yet.\");for(var c=o[0]instanceof SymbolicTensor,p=0,h=o;p<h.length;p++){if(h[p]instanceof SymbolicTensor!==c)throw new ValueError(\"The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors\")}if(c){var 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r,n=null,o=[],s=0;t<a;)(r=e[t++])<128?o[s++]=r:r>191&&r<224?o[s++]=(31&r)<<6|63&e[t++]:r>239&&r<365?(r=((7&r)<<18|(63&e[t++])<<12|(63&e[t++])<<6|63&e[t++])-65536,o[s++]=55296+(r>>10),o[s++]=56320+(1023&r)):o[s++]=(15&r)<<12|(63&e[t++])<<6|63&e[t++],s>8191&&((n||(n=[])).push(String.fromCharCode.apply(String,o)),s=0);return n?(s&&n.push(String.fromCharCode.apply(String,o.slice(0,s))),n.join(\"\")):String.fromCharCode.apply(String,o.slice(0,s))},a.write=function(e,t,a){for(var r,n,o=a,s=0;s<e.length;++s)(r=e.charCodeAt(s))<128?t[a++]=r:r<2048?(t[a++]=r>>6|192,t[a++]=63&r|128):55296==(64512&r)&&56320==(64512&(n=e.charCodeAt(s+1)))?(r=65536+((1023&r)<<10)+(1023&n),++s,t[a++]=r>>18|240,t[a++]=r>>12&63|128,t[a++]=r>>6&63|128,t[a++]=63&r|128):(t[a++]=r>>12|224,t[a++]=r>>6&63|128,t[a++]=63&r|128);return a-o};}),pool_1=pool$1;function pool$1(e,t,a){var r=a||8192,n=r>>>1,o=null,s=r;return function(a){if(a<1||a>n)return e(a);s+a>r&&(o=e(r),s=0);var i=t.call(o,s,s+=a);return 7&s&&(s=1+(7|s)),i}}var longbits=LongBits;function LongBits(e,t){this.lo=e>>>0,this.hi=t>>>0;}var zero=LongBits.zero=new LongBits(0,0);zero.toNumber=function(){return 0},zero.zzEncode=zero.zzDecode=function(){return this},zero.length=function(){return 1};var zeroHash=LongBits.zeroHash=\"\\0\\0\\0\\0\\0\\0\\0\\0\";LongBits.fromNumber=function(e){if(0===e)return zero;var t=e<0;t&&(e=-e);var a=e>>>0,r=(e-a)/4294967296>>>0;return t&&(r=~r>>>0,a=~a>>>0,++a>4294967295&&(a=0,++r>4294967295&&(r=0))),new LongBits(a,r)},LongBits.from=function(e){if(\"number\"==typeof e)return LongBits.fromNumber(e);if(minimal.isString(e)){if(!minimal.Long)return LongBits.fromNumber(parseInt(e,10));e=minimal.Long.fromString(e);}return e.low||e.high?new LongBits(e.low>>>0,e.high>>>0):zero},LongBits.prototype.toNumber=function(e){if(!e&&this.hi>>>31){var t=1+~this.lo>>>0,a=~this.hi>>>0;return t||(a=a+1>>>0),-(t+4294967296*a)}return this.lo+4294967296*this.hi},LongBits.prototype.toLong=function(e){return minimal.Long?new minimal.Long(0|this.lo,0|this.hi,Boolean(e)):{low:0|this.lo,high:0|this.hi,unsigned:Boolean(e)}};var charCodeAt=String.prototype.charCodeAt;LongBits.fromHash=function(e){return e===zeroHash?zero:new LongBits((charCodeAt.call(e,0)|charCodeAt.call(e,1)<<8|charCodeAt.call(e,2)<<16|charCodeAt.call(e,3)<<24)>>>0,(charCodeAt.call(e,4)|charCodeAt.call(e,5)<<8|charCodeAt.call(e,6)<<16|charCodeAt.call(e,7)<<24)>>>0)},LongBits.prototype.toHash=function(){return String.fromCharCode(255&this.lo,this.lo>>>8&255,this.lo>>>16&255,this.lo>>>24,255&this.hi,this.hi>>>8&255,this.hi>>>16&255,this.hi>>>24)},LongBits.prototype.zzEncode=function(){var e=this.hi>>31;return this.hi=((this.hi<<1|this.lo>>>31)^e)>>>0,this.lo=(this.lo<<1^e)>>>0,this},LongBits.prototype.zzDecode=function(){var e=-(1&this.lo);return this.lo=((this.lo>>>1|this.hi<<31)^e)>>>0,this.hi=(this.hi>>>1^e)>>>0,this},LongBits.prototype.length=function(){var e=this.lo,t=(this.lo>>>28|this.hi<<4)>>>0,a=this.hi>>>24;return 0===a?0===t?e<16384?e<128?1:2:e<2097152?3:4:t<16384?t<128?5:6:t<2097152?7:8:a<128?9:10};var minimal=createCommonjsModule$1(function(e,t){var a=t;function r(e,t,a){for(var r=Object.keys(t),n=0;n<r.length;++n)void 0!==e[r[n]]&&a||(e[r[n]]=t[r[n]]);return e}function n(e){function t(e,a){if(!(this instanceof t))return new t(e,a);Object.defineProperty(this,\"message\",{get:function(){return e}}),Error.captureStackTrace?Error.captureStackTrace(this,t):Object.defineProperty(this,\"stack\",{value:(new Error).stack||\"\"}),a&&r(this,a);}return (t.prototype=Object.create(Error.prototype)).constructor=t,Object.defineProperty(t.prototype,\"name\",{get:function(){return e}}),t.prototype.toString=function(){return this.name+\": 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e?a.Buffer?a._Buffer_allocUnsafe(e):new a.Array(e):a.Buffer?a._Buffer_from(e):\"undefined\"==typeof Uint8Array?e:new Uint8Array(e)},a.Array=\"undefined\"!=typeof Uint8Array?Uint8Array:Array,a.Long=commonjsGlobal$1.dcodeIO&&commonjsGlobal$1.dcodeIO.Long||a.inquire(\"long\"),a.key2Re=/^true|false|0|1$/,a.key32Re=/^-?(?:0|[1-9][0-9]*)$/,a.key64Re=/^(?:[\\\\x00-\\\\xff]{8}|-?(?:0|[1-9][0-9]*))$/,a.longToHash=function(e){return e?a.LongBits.from(e).toHash():a.LongBits.zeroHash},a.longFromHash=function(e,t){var r=a.LongBits.fromHash(e);return a.Long?a.Long.fromBits(r.lo,r.hi,t):r.toNumber(Boolean(t))},a.merge=r,a.lcFirst=function(e){return e.charAt(0).toLowerCase()+e.substring(1)},a.newError=n,a.ProtocolError=n(\"ProtocolError\"),a.oneOfGetter=function(e){for(var t={},a=0;a<e.length;++a)t[e[a]]=1;return function(){for(var e=Object.keys(this),a=e.length-1;a>-1;--a)if(1===t[e[a]]&&void 0!==this[e[a]]&&null!==this[e[a]])return e[a]}},a.oneOfSetter=function(e){return function(t){for(var a=0;a<e.length;++a)e[a]!==t&&delete this[e[a]];}},a.toJSONOptions={longs:String,enums:String,bytes:String,json:!0},a._configure=function(){var e=a.Buffer;e?(a._Buffer_from=e.from!==Uint8Array.from&&e.from||function(t,a){return new e(t,a)},a._Buffer_allocUnsafe=e.allocUnsafe||function(t){return new e(t)}):a._Buffer_from=a._Buffer_allocUnsafe=null;};}),writer=Writer,BufferWriter,LongBits$1=minimal.LongBits,base64=minimal.base64,utf8=minimal.utf8;function Op(e,t,a){this.fn=e,this.len=t,this.next=void 0,this.val=a;}function noop$1(){}function State(e){this.head=e.head,this.tail=e.tail,this.len=e.len,this.next=e.states;}function Writer(){this.len=0,this.head=new Op(noop$1,0,0),this.tail=this.head,this.states=null;}function writeByte(e,t,a){t[a]=255&e;}function writeVarint32(e,t,a){for(;e>127;)t[a++]=127&e|128,e>>>=7;t[a]=e;}function VarintOp(e,t){this.len=e,this.next=void 0,this.val=t;}function writeVarint64(e,t,a){for(;e.hi;)t[a++]=127&e.lo|128,e.lo=(e.lo>>>7|e.hi<<25)>>>0,e.hi>>>=7;for(;e.lo>127;)t[a++]=127&e.lo|128,e.lo=e.lo>>>7;t[a++]=e.lo;}function writeFixed32(e,t,a){t[a]=255&e,t[a+1]=e>>>8&255,t[a+2]=e>>>16&255,t[a+3]=e>>>24;}Writer.create=minimal.Buffer?function(){return (Writer.create=function(){return new BufferWriter})()}:function(){return new Writer},Writer.alloc=function(e){return new minimal.Array(e)},minimal.Array!==Array&&(Writer.alloc=minimal.pool(Writer.alloc,minimal.Array.prototype.subarray)),Writer.prototype._push=function(e,t,a){return this.tail=this.tail.next=new Op(e,t,a),this.len+=t,this},VarintOp.prototype=Object.create(Op.prototype),VarintOp.prototype.fn=writeVarint32,Writer.prototype.uint32=function(e){return this.len+=(this.tail=this.tail.next=new VarintOp((e>>>=0)<128?1:e<16384?2:e<2097152?3:e<268435456?4:5,e)).len,this},Writer.prototype.int32=function(e){return e<0?this._push(writeVarint64,10,LongBits$1.fromNumber(e)):this.uint32(e)},Writer.prototype.sint32=function(e){return this.uint32((e<<1^e>>31)>>>0)},Writer.prototype.uint64=function(e){var t=LongBits$1.from(e);return this._push(writeVarint64,t.length(),t)},Writer.prototype.int64=Writer.prototype.uint64,Writer.prototype.sint64=function(e){var t=LongBits$1.from(e).zzEncode();return this._push(writeVarint64,t.length(),t)},Writer.prototype.bool=function(e){return this._push(writeByte,1,e?1:0)},Writer.prototype.fixed32=function(e){return this._push(writeFixed32,4,e>>>0)},Writer.prototype.sfixed32=Writer.prototype.fixed32,Writer.prototype.fixed64=function(e){var t=LongBits$1.from(e);return this._push(writeFixed32,4,t.lo)._push(writeFixed32,4,t.hi)},Writer.prototype.sfixed64=Writer.prototype.fixed64,Writer.prototype.float=function(e){return this._push(minimal.float.writeFloatLE,4,e)},Writer.prototype.double=function(e){return this._push(minimal.float.writeDoubleLE,8,e)};var writeBytes=minimal.Array.prototype.set?function(e,t,a){t.set(e,a);}:function(e,t,a){for(var r=0;r<e.length;++r)t[a+r]=e[r];};Writer.prototype.bytes=function(e){var t=e.length>>>0;if(!t)return this._push(writeByte,1,0);if(minimal.isString(e)){var a=Writer.alloc(t=base64.length(e));base64.decode(e,a,0),e=a;}return this.uint32(t)._push(writeBytes,t,e)},Writer.prototype.string=function(e){var t=utf8.length(e);return t?this.uint32(t)._push(utf8.write,t,e):this._push(writeByte,1,0)},Writer.prototype.fork=function(){return this.states=new State(this),this.head=this.tail=new Op(noop$1,0,0),this.len=0,this},Writer.prototype.reset=function(){return this.states?(this.head=this.states.head,this.tail=this.states.tail,this.len=this.states.len,this.states=this.states.next):(this.head=this.tail=new Op(noop$1,0,0),this.len=0),this},Writer.prototype.ldelim=function(){var e=this.head,t=this.tail,a=this.len;return this.reset().uint32(a),a&&(this.tail.next=e.next,this.tail=t,this.len+=a),this},Writer.prototype.finish=function(){for(var e=this.head.next,t=this.constructor.alloc(this.len),a=0;e;)e.fn(e.val,t,a),a+=e.len,e=e.next;return t},Writer._configure=function(e){BufferWriter=e;};var writer_buffer=BufferWriter$1;(BufferWriter$1.prototype=Object.create(writer.prototype)).constructor=BufferWriter$1;var Buffer$1=minimal.Buffer;function BufferWriter$1(){writer.call(this);}BufferWriter$1.alloc=function(e){return (BufferWriter$1.alloc=minimal._Buffer_allocUnsafe)(e)};var writeBytesBuffer=Buffer$1&&Buffer$1.prototype instanceof Uint8Array&&\"set\"===Buffer$1.prototype.set.name?function(e,t,a){t.set(e,a);}:function(e,t,a){if(e.copy)e.copy(t,a,0,e.length);else for(var r=0;r<e.length;)t[a++]=e[r++];};function writeStringBuffer(e,t,a){e.length<40?minimal.utf8.write(e,t,a):t.utf8Write(e,a);}BufferWriter$1.prototype.bytes=function(e){minimal.isString(e)&&(e=minimal._Buffer_from(e,\"base64\"));var t=e.length>>>0;return this.uint32(t),t&&this._push(writeBytesBuffer,t,e),this},BufferWriter$1.prototype.string=function(e){var t=Buffer$1.byteLength(e);return this.uint32(t),t&&this._push(writeStringBuffer,t,e),this};var reader=Reader,BufferReader,LongBits$2=minimal.LongBits,utf8$1=minimal.utf8;function indexOutOfRange(e,t){return RangeError(\"index out of range: \"+e.pos+\" + \"+(t||1)+\" > \"+e.len)}function Reader(e){this.buf=e,this.pos=0,this.len=e.length;}var create_array=\"undefined\"!=typeof Uint8Array?function(e){if(e instanceof Uint8Array||Array.isArray(e))return new Reader(e);throw Error(\"illegal buffer\")}:function(e){if(Array.isArray(e))return new Reader(e);throw Error(\"illegal buffer\")};function readLongVarint(){var e=new LongBits$2(0,0),t=0;if(!(this.len-this.pos>4)){for(;t<3;++t){if(this.pos>=this.len)throw indexOutOfRange(this);if(e.lo=(e.lo|(127&this.buf[this.pos])<<7*t)>>>0,this.buf[this.pos++]<128)return e}return e.lo=(e.lo|(127&this.buf[this.pos++])<<7*t)>>>0,e}for(;t<4;++t)if(e.lo=(e.lo|(127&this.buf[this.pos])<<7*t)>>>0,this.buf[this.pos++]<128)return e;if(e.lo=(e.lo|(127&this.buf[this.pos])<<28)>>>0,e.hi=(e.hi|(127&this.buf[this.pos])>>4)>>>0,this.buf[this.pos++]<128)return e;if(t=0,this.len-this.pos>4){for(;t<5;++t)if(e.hi=(e.hi|(127&this.buf[this.pos])<<7*t+3)>>>0,this.buf[this.pos++]<128)return e}else for(;t<5;++t){if(this.pos>=this.len)throw indexOutOfRange(this);if(e.hi=(e.hi|(127&this.buf[this.pos])<<7*t+3)>>>0,this.buf[this.pos++]<128)return e}throw Error(\"invalid varint encoding\")}function readFixed32_end(e,t){return (e[t-4]|e[t-3]<<8|e[t-2]<<16|e[t-1]<<24)>>>0}function readFixed64(){if(this.pos+8>this.len)throw indexOutOfRange(this,8);return new LongBits$2(readFixed32_end(this.buf,this.pos+=4),readFixed32_end(this.buf,this.pos+=4))}Reader.create=minimal.Buffer?function(e){return (Reader.create=function(e){return minimal.Buffer.isBuffer(e)?new BufferReader(e):create_array(e)})(e)}:create_array,Reader.prototype._slice=minimal.Array.prototype.subarray||minimal.Array.prototype.slice,Reader.prototype.uint32=function(){var e=4294967295;return function(){if(e=(127&this.buf[this.pos])>>>0,this.buf[this.pos++]<128)return e;if(e=(e|(127&this.buf[this.pos])<<7)>>>0,this.buf[this.pos++]<128)return e;if(e=(e|(127&this.buf[this.pos])<<14)>>>0,this.buf[this.pos++]<128)return e;if(e=(e|(127&this.buf[this.pos])<<21)>>>0,this.buf[this.pos++]<128)return e;if(e=(e|(15&this.buf[this.pos])<<28)>>>0,this.buf[this.pos++]<128)return e;if((this.pos+=5)>this.len)throw this.pos=this.len,indexOutOfRange(this,10);return e}}(),Reader.prototype.int32=function(){return 0|this.uint32()},Reader.prototype.sint32=function(){var e=this.uint32();return e>>>1^-(1&e)|0},Reader.prototype.bool=function(){return 0!==this.uint32()},Reader.prototype.fixed32=function(){if(this.pos+4>this.len)throw indexOutOfRange(this,4);return readFixed32_end(this.buf,this.pos+=4)},Reader.prototype.sfixed32=function(){if(this.pos+4>this.len)throw indexOutOfRange(this,4);return 0|readFixed32_end(this.buf,this.pos+=4)},Reader.prototype.float=function(){if(this.pos+4>this.len)throw indexOutOfRange(this,4);var e=minimal.float.readFloatLE(this.buf,this.pos);return this.pos+=4,e},Reader.prototype.double=function(){if(this.pos+8>this.len)throw indexOutOfRange(this,4);var e=minimal.float.readDoubleLE(this.buf,this.pos);return this.pos+=8,e},Reader.prototype.bytes=function(){var e=this.uint32(),t=this.pos,a=this.pos+e;if(a>this.len)throw indexOutOfRange(this,e);return this.pos+=e,Array.isArray(this.buf)?this.buf.slice(t,a):t===a?new this.buf.constructor(0):this._slice.call(this.buf,t,a)},Reader.prototype.string=function(){var e=this.bytes();return utf8$1.read(e,0,e.length)},Reader.prototype.skip=function(e){if(\"number\"==typeof e){if(this.pos+e>this.len)throw indexOutOfRange(this,e);this.pos+=e;}else do{if(this.pos>=this.len)throw indexOutOfRange(this)}while(128&this.buf[this.pos++]);return this},Reader.prototype.skipType=function(e){switch(e){case 0:this.skip();break;case 1:this.skip(8);break;case 2:this.skip(this.uint32());break;case 3:for(;;){if(4==(e=7&this.uint32()))break;this.skipType(e);}break;case 5:this.skip(4);break;default:throw Error(\"invalid wire type \"+e+\" at offset \"+this.pos)}return this},Reader._configure=function(e){BufferReader=e;var t=minimal.Long?\"toLong\":\"toNumber\";minimal.merge(Reader.prototype,{int64:function(){return readLongVarint.call(this)[t](!1)},uint64:function(){return readLongVarint.call(this)[t](!0)},sint64:function(){return readLongVarint.call(this).zzDecode()[t](!1)},fixed64:function(){return readFixed64.call(this)[t](!0)},sfixed64:function(){return readFixed64.call(this)[t](!1)}});};var reader_buffer=BufferReader$1;function BufferReader$1(e){reader.call(this,e);}(BufferReader$1.prototype=Object.create(reader.prototype)).constructor=BufferReader$1,minimal.Buffer&&(BufferReader$1.prototype._slice=minimal.Buffer.prototype.slice),BufferReader$1.prototype.string=function(){var e=this.uint32();return this.buf.utf8Slice(this.pos,this.pos=Math.min(this.pos+e,this.len))};var service=Service;function Service(e,t,a){if(\"function\"!=typeof e)throw TypeError(\"rpcImpl must be a function\");minimal.EventEmitter.call(this),this.rpcImpl=e,this.requestDelimited=Boolean(t),this.responseDelimited=Boolean(a);}(Service.prototype=Object.create(minimal.EventEmitter.prototype)).constructor=Service,Service.prototype.rpcCall=function e(t,a,r,n,o){if(!n)throw TypeError(\"request must be specified\");var s=this;if(!o)return minimal.asPromise(e,s,t,a,r,n);if(s.rpcImpl)try{return s.rpcImpl(t,a[s.requestDelimited?\"encodeDelimited\":\"encode\"](n).finish(),function(e,a){if(e)return s.emit(\"error\",e,t),o(e);if(null!==a){if(!(a instanceof r))try{a=r[s.responseDelimited?\"decodeDelimited\":\"decode\"](a);}catch(e){return s.emit(\"error\",e,t),o(e)}return s.emit(\"data\",a,t),o(null,a)}s.end(!0);})}catch(e){return s.emit(\"error\",e,t),void setTimeout(function(){o(e);},0)}else setTimeout(function(){o(Error(\"already ended\"));},0);},Service.prototype.end=function(e){return this.rpcImpl&&(e||this.rpcImpl(null,null,null),this.rpcImpl=null,this.emit(\"end\").off()),this};var rpc_1=createCommonjsModule$1(function(e,t){t.Service=service;}),roots={},indexMinimal=createCommonjsModule$1(function(e,t){var a=t;function r(){a.Reader._configure(a.BufferReader),a.util._configure();}a.build=\"minimal\",a.Writer=writer,a.BufferWriter=writer_buffer,a.Reader=reader,a.BufferReader=reader_buffer,a.util=minimal,a.rpc=rpc_1,a.roots=roots,a.configure=r,a.Writer._configure(a.BufferWriter),r();}),minimal$1=indexMinimal,minimal_1=minimal$1.roots,minimal_2=minimal$1.Reader,minimal_3=minimal$1.util,$Reader=minimal$1.Reader,$util=minimal$1.util,$root=minimal$1.roots.default||(minimal$1.roots.default={});$root.tensorflow=function(){var e,t,a={};return a.Any=function(){function e(e){if(e)for(var t=Object.keys(e),a=0;a<t.length;++a)null!=e[t[a]]&&(this[t[a]]=e[t[a]]);}return e.prototype.typeUrl=\"\",e.prototype.value=$util.newBuffer([]),e.decode=function(e,t){e instanceof $Reader||(e=$Reader.create(e));for(var a=void 0===t?e.len:e.pos+t,r=new $root.tensorflow.Any;e.pos<a;){var n=e.uint32();switch(n>>>3){case 1:r.typeUrl=e.string();break;case 2:r.value=e.bytes();break;default:e.skipType(7&n);}}return r},e}(),a.DataType=(e={},(t=Object.create(e))[e[0]=\"DT_INVALID\"]=0,t[e[1]=\"DT_FLOAT\"]=1,t[e[2]=\"DT_DOUBLE\"]=2,t[e[3]=\"DT_INT32\"]=3,t[e[4]=\"DT_UINT8\"]=4,t[e[5]=\"DT_INT16\"]=5,t[e[6]=\"DT_INT8\"]=6,t[e[7]=\"DT_STRING\"]=7,t[e[8]=\"DT_COMPLEX64\"]=8,t[e[9]=\"DT_INT64\"]=9,t[e[10]=\"DT_BOOL\"]=10,t[e[11]=\"DT_QINT8\"]=11,t[e[12]=\"DT_QUINT8\"]=12,t[e[13]=\"DT_QINT32\"]=13,t[e[14]=\"DT_BFLOAT16\"]=14,t[e[101]=\"DT_FLOAT_REF\"]=101,t[e[102]=\"DT_DOUBLE_REF\"]=102,t[e[103]=\"DT_INT32_REF\"]=103,t[e[104]=\"DT_UINT8_REF\"]=104,t[e[105]=\"DT_INT16_REF\"]=105,t[e[106]=\"DT_INT8_REF\"]=106,t[e[107]=\"DT_STRING_REF\"]=107,t[e[108]=\"DT_COMPLEX64_REF\"]=108,t[e[109]=\"DT_INT64_REF\"]=109,t[e[110]=\"DT_BOOL_REF\"]=110,t[e[111]=\"DT_QINT8_REF\"]=111,t[e[112]=\"DT_QUINT8_REF\"]=112,t[e[113]=\"DT_QINT32_REF\"]=113,t[e[114]=\"DT_BFLOAT16_REF\"]=114,t),a.TensorShape=function(){function e(e){if(this.dim=[],e)for(var t=Object.keys(e),a=0;a<t.length;++a)null!=e[t[a]]&&(this[t[a]]=e[t[a]]);}return 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2:r.tensorShape=$root.tensorflow.TensorShape.decode(e,e.uint32());break;case 3:r.versionNumber=e.int32();break;case 4:r.tensorContent=e.bytes();break;case 5:if(r.floatVal&&r.floatVal.length||(r.floatVal=[]),2==(7&n))for(var o=e.uint32()+e.pos;e.pos<o;)r.floatVal.push(e.float());else r.floatVal.push(e.float());break;case 6:if(r.doubleVal&&r.doubleVal.length||(r.doubleVal=[]),2==(7&n))for(o=e.uint32()+e.pos;e.pos<o;)r.doubleVal.push(e.double());else r.doubleVal.push(e.double());break;case 7:if(r.intVal&&r.intVal.length||(r.intVal=[]),2==(7&n))for(o=e.uint32()+e.pos;e.pos<o;)r.intVal.push(e.int32());else r.intVal.push(e.int32());break;case 8:r.stringVal&&r.stringVal.length||(r.stringVal=[]),r.stringVal.push(e.bytes());break;case 9:if(r.scomplexVal&&r.scomplexVal.length||(r.scomplexVal=[]),2==(7&n))for(o=e.uint32()+e.pos;e.pos<o;)r.scomplexVal.push(e.float());else r.scomplexVal.push(e.float());break;case 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9:r.placeholder=e.string();break;case 10:r.func=$root.tensorflow.NameAttrList.decode(e,e.uint32());break;default:e.skipType(7&n);}}return r},e.ListValue=function(){function e(e){if(this.s=[],this.i=[],this.f=[],this.b=[],this.type=[],this.shape=[],this.tensor=[],this.func=[],e)for(var t=Object.keys(e),a=0;a<t.length;++a)null!=e[t[a]]&&(this[t[a]]=e[t[a]]);}return e.prototype.s=$util.emptyArray,e.prototype.i=$util.emptyArray,e.prototype.f=$util.emptyArray,e.prototype.b=$util.emptyArray,e.prototype.type=$util.emptyArray,e.prototype.shape=$util.emptyArray,e.prototype.tensor=$util.emptyArray,e.prototype.func=$util.emptyArray,e.decode=function(e,t){e instanceof $Reader||(e=$Reader.create(e));for(var a=void 0===t?e.len:e.pos+t,r=new $root.tensorflow.AttrValue.ListValue;e.pos<a;){var n=e.uint32();switch(n>>>3){case 2:r.s&&r.s.length||(r.s=[]),r.s.push(e.bytes());break;case 3:if(r.i&&r.i.length||(r.i=[]),2==(7&n))for(var o=e.uint32()+e.pos;e.pos<o;)r.i.push(e.int64());else 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3:n.input&&n.input.length||(n.input=[]),n.input.push(e.string());break;case 4:n.device=e.string();break;case 5:e.skip().pos++,n.attr===$util.emptyObject&&(n.attr={}),a=e.string(),e.pos++,n.attr[a]=$root.tensorflow.AttrValue.decode(e,e.uint32());break;default:e.skipType(7&o);}}return n},e}(),a.VersionDef=function(){function e(e){if(this.badConsumers=[],e)for(var t=Object.keys(e),a=0;a<t.length;++a)null!=e[t[a]]&&(this[t[a]]=e[t[a]]);}return e.prototype.producer=0,e.prototype.minConsumer=0,e.prototype.badConsumers=$util.emptyArray,e.decode=function(e,t){e instanceof $Reader||(e=$Reader.create(e));for(var a=void 0===t?e.len:e.pos+t,r=new $root.tensorflow.VersionDef;e.pos<a;){var n=e.uint32();switch(n>>>3){case 1:r.producer=e.int32();break;case 2:r.minConsumer=e.int32();break;case 3:if(r.badConsumers&&r.badConsumers.length||(r.badConsumers=[]),2==(7&n))for(var o=e.uint32()+e.pos;e.pos<o;)r.badConsumers.push(e.int32());else r.badConsumers.push(e.int32());break;default:e.skipType(7&n);}}return r},e}(),a.GraphDef=function(){function e(e){if(this.node=[],e)for(var t=Object.keys(e),a=0;a<t.length;++a)null!=e[t[a]]&&(this[t[a]]=e[t[a]]);}return e.prototype.node=$util.emptyArray,e.prototype.versions=null,e.prototype.library=null,e.decode=function(e,t){e instanceof $Reader||(e=$Reader.create(e));for(var a=void 0===t?e.len:e.pos+t,r=new $root.tensorflow.GraphDef;e.pos<a;){var n=e.uint32();switch(n>>>3){case 1:r.node&&r.node.length||(r.node=[]),r.node.push($root.tensorflow.NodeDef.decode(e,e.uint32()));break;case 4:r.versions=$root.tensorflow.VersionDef.decode(e,e.uint32());break;case 2:r.library=$root.tensorflow.FunctionDefLibrary.decode(e,e.uint32());break;default:e.skipType(7&n);}}return r},e}(),a.CollectionDef=function(){function e(e){if(e)for(var t=Object.keys(e),a=0;a<t.length;++a)null!=e[t[a]]&&(this[t[a]]=e[t[a]]);}var t;return e.prototype.nodeList=null,e.prototype.bytesList=null,e.prototype.int64List=null,e.prototype.floatList=null,e.prototype.anyList=null,Object.defineProperty(e.prototype,\"kind\",{get:$util.oneOfGetter(t=[\"nodeList\",\"bytesList\",\"int64List\",\"floatList\",\"anyList\"]),set:$util.oneOfSetter(t)}),e.decode=function(e,t){e instanceof $Reader||(e=$Reader.create(e));for(var a=void 0===t?e.len:e.pos+t,r=new $root.tensorflow.CollectionDef;e.pos<a;){var n=e.uint32();switch(n>>>3){case 1:r.nodeList=$root.tensorflow.CollectionDef.NodeList.decode(e,e.uint32());break;case 2:r.bytesList=$root.tensorflow.CollectionDef.BytesList.decode(e,e.uint32());break;case 3:r.int64List=$root.tensorflow.CollectionDef.Int64List.decode(e,e.uint32());break;case 4:r.floatList=$root.tensorflow.CollectionDef.FloatList.decode(e,e.uint32());break;case 5:r.anyList=$root.tensorflow.CollectionDef.AnyList.decode(e,e.uint32());break;default:e.skipType(7&n);}}return r},e.NodeList=function(){function e(e){if(this.value=[],e)for(var t=Object.keys(e),a=0;a<t.length;++a)null!=e[t[a]]&&(this[t[a]]=e[t[a]]);}return e.prototype.value=$util.emptyArray,e.decode=function(e,t){e instanceof $Reader||(e=$Reader.create(e));for(var a=void 0===t?e.len:e.pos+t,r=new $root.tensorflow.CollectionDef.NodeList;e.pos<a;){var n=e.uint32();switch(n>>>3){case 1:r.value&&r.value.length||(r.value=[]),r.value.push(e.string());break;default:e.skipType(7&n);}}return r},e}(),e.BytesList=function(){function e(e){if(this.value=[],e)for(var t=Object.keys(e),a=0;a<t.length;++a)null!=e[t[a]]&&(this[t[a]]=e[t[a]]);}return e.prototype.value=$util.emptyArray,e.decode=function(e,t){e instanceof $Reader||(e=$Reader.create(e));for(var a=void 0===t?e.len:e.pos+t,r=new $root.tensorflow.CollectionDef.BytesList;e.pos<a;){var n=e.uint32();switch(n>>>3){case 1:r.value&&r.value.length||(r.value=[]),r.value.push(e.bytes());break;default:e.skipType(7&n);}}return r},e}(),e.Int64List=function(){function e(e){if(this.value=[],e)for(var t=Object.keys(e),a=0;a<t.length;++a)null!=e[t[a]]&&(this[t[a]]=e[t[a]]);}return e.prototype.value=$util.emptyArray,e.decode=function(e,t){e instanceof $Reader||(e=$Reader.create(e));for(var a=void 0===t?e.len:e.pos+t,r=new $root.tensorflow.CollectionDef.Int64List;e.pos<a;){var n=e.uint32();switch(n>>>3){case 1:if(r.value&&r.value.length||(r.value=[]),2==(7&n))for(var o=e.uint32()+e.pos;e.pos<o;)r.value.push(e.int64());else r.value.push(e.int64());break;default:e.skipType(7&n);}}return r},e}(),e.FloatList=function(){function e(e){if(this.value=[],e)for(var t=Object.keys(e),a=0;a<t.length;++a)null!=e[t[a]]&&(this[t[a]]=e[t[a]]);}return e.prototype.value=$util.emptyArray,e.decode=function(e,t){e instanceof $Reader||(e=$Reader.create(e));for(var a=void 0===t?e.len:e.pos+t,r=new $root.tensorflow.CollectionDef.FloatList;e.pos<a;){var n=e.uint32();switch(n>>>3){case 1:if(r.value&&r.value.length||(r.value=[]),2==(7&n))for(var 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e.prototype.filenameTensorName=\"\",e.prototype.saveTensorName=\"\",e.prototype.restoreOpName=\"\",e.prototype.maxToKeep=0,e.prototype.sharded=!1,e.prototype.keepCheckpointEveryNHours=0,e.prototype.version=0,e.decode=function(e,t){e instanceof $Reader||(e=$Reader.create(e));for(var a=void 0===t?e.len:e.pos+t,r=new $root.tensorflow.SaverDef;e.pos<a;){var n=e.uint32();switch(n>>>3){case 1:r.filenameTensorName=e.string();break;case 2:r.saveTensorName=e.string();break;case 3:r.restoreOpName=e.string();break;case 4:r.maxToKeep=e.int32();break;case 5:r.sharded=e.bool();break;case 6:r.keepCheckpointEveryNHours=e.float();break;case 7:r.version=e.int32();break;default:e.skipType(7&n);}}return r},e.CheckpointFormatVersion=(t={},(a=Object.create(t))[t[0]=\"LEGACY\"]=0,a[t[1]=\"V1\"]=1,a[t[2]=\"V2\"]=2,a),e}(),a.TensorInfo=function(){function e(e){if(e)for(var t=Object.keys(e),a=0;a<t.length;++a)null!=e[t[a]]&&(this[t[a]]=e[t[a]]);}var t;return 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e(){var e=[arithmetic,basicMath,control,convolution,creation,dynamic,evaluation,logical,image$1,graph,matrices,normalization,reduction,sliceJoin,transformation],t=[].concat.apply([],e.map(function(e){return e.json}));this.opMappers=t.reduce(function(e,t){return e[t.tfOpName]=t,e},{});}return Object.defineProperty(e,\"Instance\",{get:function(){return this._instance||(this._instance=new this)},enumerable:!0,configurable:!0}),e.prototype.isControlFlow=function(e){return CONTROL_FLOW_OPS.some(function(t){return t===e.op})},e.prototype.isDynamicShape=function(e){return DYNAMIC_SHAPE_OPS.some(function(t){return t===e.op})},e.prototype.transformGraph=function(e){var t=this,a=!1,r=!1,n=[],o=[],s=e.node.reduce(function(e,s){return e[s.name]=t.mapNode(s),t.isControlFlow(s)&&(a=!0),t.isDynamicShape(s)&&(r=!0),\"Placeholder\"===s.op&&n.push(e[s.name]),\"Const\"===s.op&&o.push(e[s.name]),e},{}),i=[],p=[];return Object.keys(s).forEach(function(e){var t=s[e];t.inputNames.forEach(function(e){var a=getNodeNameAndIndex(e)[0];t.inputs.push(s[a]),s[a].children.push(t);}),0===t.inputs.length&&i.push(t);}),Object.keys(s).forEach(function(e){var t=s[e];0===t.children.length&&p.push(t);}),{nodes:s,inputs:i,outputs:p,weights:o,placeholders:n,withControlFlow:a,withDynamicShape:r}},e.prototype.mapNode=function(e){var t=this,a=this.opMappers[e.op];if(void 0===a)throw new Error(\"Tensorflow Op is not supported: \"+e.op);var r={name:e.name,op:a.dlOpName,category:a.category,inputNames:(e.input||[]).map(function(e){return e.startsWith(\"^\")?e.substr(1):e}),inputs:[],children:[],params:{}};return a.params&&(r.params=a.params.reduce(function(a,r){var n=r.tfInputIndex,o=r.tfInputParamLength,s=r.type,i=void 0;if(void 0===n)switch(r.type){case\"string\":void 0===(i=t.getStringParam(e.attr,r.tfParamName,r.defaultValue))&&r.tfParamNameDeprecated&&(i=t.getStringParam(e.attr,r.tfParamNameDeprecated,r.defaultValue));break;case\"number\":void 0===(i=t.getNumberParam(e.attr,r.tfParamName,r.defaultValue))&&r.tfParamNameDeprecated&&(i=t.getNumberParam(e.attr,r.tfParamNameDeprecated,r.defaultValue));break;case\"number[]\":void 0===(i=t.getNumericArrayParam(e.attr,r.tfParamName,r.defaultValue))&&r.tfParamNameDeprecated&&(i=t.getNumericArrayParam(e.attr,r.tfParamNameDeprecated,r.defaultValue));break;case\"bool\":void 0===(i=t.getBoolParam(e.attr,r.tfParamName,r.defaultValue))&&r.tfParamNameDeprecated&&(i=t.getBoolParam(e.attr,r.tfParamNameDeprecated,r.defaultValue));break;case\"shape\":void 0===(i=t.getTensorShapeParam(e.attr,r.tfParamName,r.defaultValue))&&r.tfParamNameDeprecated&&(i=t.getTensorShapeParam(e.attr,r.tfParamNameDeprecated,r.defaultValue));break;case\"dtype\":void 0===(i=t.getDtypeParam(e.attr,r.tfParamName,r.defaultValue))&&r.tfParamNameDeprecated&&(i=t.getDtypeParam(e.attr,r.tfParamNameDeprecated,r.defaultValue));break;case\"tensor\":case\"tensors\":break;default:throw new Error(\"Unsupported param type: \"+r.type+\" for op: \"+e.op)}return a[r.dlParamName]={value:i,inputIndex:n,type:s,inputParamLength:o},a},{})),r},e.prototype.getStringParam=function(e,t,a,r){void 0===r&&(r=!1);var n=e[t];if(void 0!==n){var o=String.fromCharCode.apply(null,n.s);return r?o:o.toLowerCase()}return a},e.prototype.getBoolParam=function(e,t,a){var r=e[t];return r?r.b:a},e.prototype.getNumberParam=function(e,t,a){var r=e[t],n=r?r[r.value]:a;return \"number\"==typeof n?n:n.toInt()},e.prototype.getDtypeParam=function(e,t,a){var r=e[t];if(r&&r.type)switch(r.type){case compiled_api_1.DataType.DT_FLOAT:return \"float32\";case compiled_api_1.DataType.DT_INT32:return \"int32\";case compiled_api_1.DataType.DT_BOOL:return \"bool\";default:return a}return a},e.prototype.getTensorShapeParam=function(e,t,a){var r=e[t];return r&&r.shape?r.shape.dim.map(function(e){return \"number\"==typeof e.size?e.size:e.size.toInt()}):a},e.prototype.getNumericArrayParam=function(e,t,a){var r=e[t];return r?(r.list.f&&r.list.f.length?r.list.f:r.list.i).map(function(e){return \"number\"==typeof e?e:e.toInt()}):a},e}(),executeOp=function(e,t,a){switch(e.op){case\"add\":return [add(getParamValue(\"a\",e,t,a),getParamValue(\"b\",e,t,a))];case\"addN\":return [addN(getParamValue(\"tensors\",e,t,a))];case\"mod\":return [mod(getParamValue(\"a\",e,t,a),getParamValue(\"b\",e,t,a))];case\"mul\":return [mul(getParamValue(\"a\",e,t,a),getParamValue(\"b\",e,t,a))];case\"div\":return [div(getParamValue(\"a\",e,t,a),getParamValue(\"b\",e,t,a))];case\"floorDiv\":return [floorDiv(getParamValue(\"a\",e,t,a),getParamValue(\"b\",e,t,a))];case\"sub\":return [sub(getParamValue(\"a\",e,t,a),getParamValue(\"b\",e,t,a))];case\"minimum\":return [minimum(getParamValue(\"a\",e,t,a),getParamValue(\"b\",e,t,a))];case\"maximum\":return [maximum(getParamValue(\"a\",e,t,a),getParamValue(\"b\",e,t,a))];case\"pow\":return [pow(getParamValue(\"a\",e,t,a),getParamValue(\"b\",e,t,a))];case\"squaredDifference\":return [squaredDifference(getParamValue(\"a\",e,t,a),getParamValue(\"b\",e,t,a))];default:throw TypeError(\"Node type \"+e.op+\" is not implemented\")}},executeOp$1=function(e,t,a){switch(e.op){case\"abs\":return [abs(getParamValue(\"x\",e,t,a))];case\"acos\":return [acos(getParamValue(\"x\",e,t,a))];case\"acosh\":return [acosh(getParamValue(\"x\",e,t,a))];case\"asin\":return [asin(getParamValue(\"x\",e,t,a))];case\"asinh\":return [asinh(getParamValue(\"x\",e,t,a))];case\"atan\":return [atan(getParamValue(\"x\",e,t,a))];case\"atanh\":return [atanh(getParamValue(\"x\",e,t,a))];case\"ceil\":return [ceil(getParamValue(\"x\",e,t,a))];case\"cos\":return [cos(getParamValue(\"x\",e,t,a))];case\"cosh\":return [cosh(getParamValue(\"x\",e,t,a))];case\"elu\":return [elu(getParamValue(\"x\",e,t,a))];case\"erf\":return [erf(getParamValue(\"x\",e,t,a))];case\"exp\":return [exp(getParamValue(\"x\",e,t,a))];case\"expm1\":return [expm1(getParamValue(\"x\",e,t,a))];case\"floor\":return [floor(getParamValue(\"x\",e,t,a))];case\"log\":return [log$1(getParamValue(\"x\",e,t,a))];case\"log1p\":return [log1p(getParamValue(\"x\",e,t,a))];case\"neg\":return [neg(getParamValue(\"x\",e,t,a))];case\"reciprocal\":return [reciprocal(getParamValue(\"x\",e,t,a))];case\"relu\":return [relu(getParamValue(\"x\",e,t,a))];case\"round\":return [round(getParamValue(\"x\",e,t,a))];case\"selu\":return [selu(getParamValue(\"x\",e,t,a))];case\"sigmoid\":return [sigmoid(getParamValue(\"x\",e,t,a))];case\"sin\":return [sin(getParamValue(\"x\",e,t,a))];case\"sign\":return [sign(getParamValue(\"x\",e,t,a))];case\"sinh\":return [sinh(getParamValue(\"x\",e,t,a))];case\"softplus\":return [softplus(getParamValue(\"x\",e,t,a))];case\"sqrt\":return [sqrt(getParamValue(\"x\",e,t,a))];case\"square\":return [square(getParamValue(\"x\",e,t,a))];case\"tanh\":return [tanh$1(getParamValue(\"x\",e,t,a))];case\"tan\":return [tan(getParamValue(\"x\",e,t,a))];case\"clipByValue\":return [clipByValue(getParamValue(\"x\",e,t,a),getParamValue(\"clipValueMin\",e,t,a),getParamValue(\"clipValueMax\",e,t,a))];case\"rsqrt\":return [div(scalar(1,\"float32\"),sqrt(getTensor(e.inputNames[0],t,a)))];case\"prod\":return [prod(getParamValue(\"x\",e,t,a),getParamValue(\"axes\",e,t,a))];default:throw TypeError(\"Node type \"+e.op+\" is not implemented\")}},TensorArray=function(){function e(t,a,r,n,o,s,i){this.name=t,this.dtype=a,this.maxSize=r,this.elementShape=n,this.identicalElementShapes=o,this.dynamicSize=s,this.clearAfterRead=i,this.tensors=[],this.closed_=!1,this.id=e.nextId++;}return Object.defineProperty(e.prototype,\"closed\",{get:function(){return this.closed_},enumerable:!0,configurable:!0}),e.prototype.clearAndClose=function(){this.tensors.forEach(function(e){return e.tensor.dispose()}),this.tensors=[],this.closed_=!0;},e.prototype.size=function(){return this.tensors.length},e.prototype.read=function(e){if(this.closed_)throw new Error(\"TensorArray \"+this.name+\" has already been closed.\");if(e<0||e>=this.tensors.length)throw new Error(\"Tried to read from index \"+e+\", but array size is: \"+this.tensors.length);var t=this.tensors[e];if(t.cleared)throw new Error(\"TensorArray \"+this.name+\": Could not read index \"+e+\" twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).\");return this.clearAfterRead&&(t.cleared=!0),t.read=!0,t.tensor},e.prototype.readMany=function(e){var t=this;return e.map(function(e){return t.read(e)})},e.prototype.write=function(e,t){if(this.closed_)throw new Error(\"TensorArray \"+this.name+\" has already been closed.\");if(e<0||!this.dynamicSize&&e>=this.maxSize)throw new Error(\"Tried to write to index \"+e+\", but array is not resizeable and size is: \"+this.maxSize);var a=this.tensors[e]||{};if(t.dtype!==this.dtype)throw new Error(\"TensorArray \"+this.name+\": Could not write to TensorArray index \"+e+\",\\n          because the value dtype is \"+t.dtype+\", but TensorArray dtype is \"+this.dtype+\".\");if(0===this.size()&&0===this.elementShape.length&&(this.elementShape=t.shape),this.assertShapesMatch(this.elementShape,t.shape,\"TensorArray \"+this.name+\": Could not write to TensorArray index \"+e+\".\"),a&&a.read)throw new Error(\"TensorArray \"+this.name+\": Could not write to TensorArray index \"+e+\", because it has already been read.\");if(a&&a.written)throw new Error(\"TensorArray \"+this.name+\": Could not write to TensorArray index \"+e+\", because it has already been written.\");a.tensor=t,a.written=!0,this.tensors[e]=a;},e.prototype.writeMany=function(e,t){var a=this;if(e.length!==t.length)throw new Error(\"TensorArray \"+this.name+\": could not write multiple tensors,because the index size: \"+e.length+\" is not the same as tensors size: \"+t.length+\".\");e.forEach(function(e,r){return a.write(e,t[r])});},e.prototype.gather=function(e,t){if(t&&t!==this.dtype)throw new Error(\"TensorArray dtype is \"+this.dtype+\" but gather requested dtype \"+t);if(!e){e=[];for(var a=0;a<this.size();a++)e.push(a);}if(0===e.length)return tensor([],[0].concat(this.elementShape));var r=this.readMany(e);return this.assertShapesMatch(this.elementShape,r[0].shape,\"TensorArray shape mismatch: \"),stack(r,0)},e.prototype.concat=function(e){if(e&&e!==this.dtype)throw new Error(\"TensorArray dtype is \"+this.dtype+\" but concat requested dtype \"+e);if(0===this.size())return tensor([],[0].concat(this.elementShape));for(var t=[],a=0;a<this.size();a++)t.push(a);var r=this.readMany(t);return this.assertShapesMatch(this.elementShape,r[0].shape,\"TensorArray shape mismatch: tensor array shape (\"+this.elementShape+\") vs first tensor shape (\"+r[0].shape+\")\"),concat(r,0)},e.prototype.scatter=function(e,t){if(t.dtype!==this.dtype)throw new Error(\"TensorArray dtype is \"+this.dtype+\" but tensor has dtype \"+t.dtype);if(e.length!==t.shape[0])throw new Error(\"Expected len(indices) == tensor.shape[0], but saw: \"+e.length+\" vs. \"+t.shape[0]);var a=Math.max.apply(Math,e);if(!this.dynamicSize&&a>=this.maxSize)throw new Error(\"Max index must be < array size (\"+a+\"  vs. \"+this.maxSize+\")\");this.writeMany(e,unstack(t,0));},e.prototype.split=function(e,t){var a=this;if(t.dtype!==this.dtype)throw new Error(\"TensorArray dtype is \"+this.dtype+\" but tensor has dtype \"+t.dtype);var r=0,n=e.map(function(e){return r+=e});if(r!==t.shape[0])throw new Error(\"Expected sum of lengths to be equal to\\n          tensor.shape[0], but sum of lengths is\\n        \"+r+\", and tensor's shape is: \"+t.shape);if(!this.dynamicSize&&e.length!==this.maxSize)throw new Error(\"TensorArray's size is not equal to the size of lengths (\"+this.maxSize+\" vs. \"+e.length+\"), and the TensorArray is not marked as dynamically resizeable\");var o=0===r?0:t.size/r,s=[];tidy(function(){t=t.reshape([1,r,o]);for(var i=0;i<e.length;++i){var p=[0,0===i?0:n[i-1],0],u=[1,e[i],o];s[i]=slice(t,p,u).reshape(a.elementShape);}return s});for(var i=[],p=0;p<e.length;p++)i[p]=p;this.writeMany(i,s);},e.prototype.assertShapesMatch=function(e,t,a){void 0===a&&(a=\"\"),util.assert(this.arraysEqual(e,t),a+\" Shapes \"+e+\" and \"+t+\" must match\");},e.prototype.arraysEqual=function(e,t){if(e.length!==t.length)return !1;for(var a=0;a<e.length;a++)if(-1!==e[a]&&-1!==t[a]&&e[a]!==t[a])return !1;return !0},e.nextId=0,e}();function executeOp$2(e,t,a){return __awaiter$2(this,void 0,void 0,function(){var r,n,o,s,i,p,u,m,l,d,c,f,y,h,g,N,P,b,x,I,v,O,w,V,T,k,$,S,_,A,D,j,E,C,R;return __generator$2(this,function(L){switch(L.label){case 0:switch(e.op){case\"loopCond\":return [3,1];case\"switch\":return [3,2];case\"merge\":return [3,4];case\"enter\":return [3,5];case\"exit\":return [3,6];case\"nextIteration\":return [3,7];case\"tensorArray\":return [3,8];case\"tensorArrayWrite\":return [3,9];case\"tensorArrayRead\":return [3,10];case\"tensorArrayGather\":return [3,11];case\"tensorArrayScatter\":return [3,12];case\"tensorArrayConcat\":return [3,13];case\"tensorArraySplit\":return [3,14];case\"tensorArraySize\":return [3,15];case\"tensorArrayClose\":return [3,16]}return [3,17];case 1:return [2,[getParamValue(\"pred\",e,t,a).clone()]];case 2:return r=getParamValue(\"pred\",e,t,a),n=getParamValue(\"data\",e,t,a),[4,r.data()];case 3:return [2,L.sent()[0]?[void 0,n.clone()]:[n.clone(),void 0]];case 4:return [2,(o=e.inputNames.find(function(e){return void 0!==getTensor(e,t,a)}))?[getTensor(o,t,a).clone()]:void 0];case 5:return s=getParamValue(\"frameName\",e,t,a),i=getParamValue(\"tensor\",e,t,a),a.enterFrame(s),[2,[i.clone()]];case 6:return p=getParamValue(\"tensor\",e,t,a),a.exitFrame(),[2,[p.clone()]];case 7:return u=getParamValue(\"tensor\",e,t,a),a.nextIteration(),[2,[u.clone()]];case 8:return m=getParamValue(\"size\",e,t,a),l=getParamValue(\"dtype\",e,t,a),d=getParamValue(\"elementShape\",e,t,a),c=getParamValue(\"dynamicSize\",e,t,a),f=getParamValue(\"clearAfterRead\",e,t,a),y=getParamValue(\"identicalElementShapes\",e,t,a),h=getParamValue(\"name\",e,t,a),g=new TensorArray(h,l,m,d,y,c,f),a.addTensorArray(g),[2,[scalar(g.id),scalar(1)]];case 9:return N=getParamValue(\"tensorArrayId\",e,t,a),P=getParamValue(\"index\",e,t,a),b=getParamValue(\"tensor\",e,t,a),a.getTensorArray(N).write(P,b),[2,[scalar(1)]];case 10:return x=getParamValue(\"tensorArrayId\",e,t,a),I=getParamValue(\"index\",e,t,a),[2,[a.getTensorArray(x).read(I)]];case 11:return v=getParamValue(\"tensorArrayId\",e,t,a),O=getParamValue(\"indices\",e,t,a),w=getParamValue(\"dtype\",e,t,a),[2,[a.getTensorArray(v).gather(O,w)]];case 12:return V=getParamValue(\"tensorArrayId\",e,t,a),T=getParamValue(\"indices\",e,t,a),k=getParamValue(\"tensor\",e,t,a),a.getTensorArray(V).scatter(T,k),[2,[scalar(1)]];case 13:return $=getParamValue(\"tensorArrayId\",e,t,a),S=a.getTensorArray($),_=getParamValue(\"dtype\",e,t,a),[2,[S.concat(_)]];case 14:return A=getParamValue(\"tensorArrayId\",e,t,a),D=getParamValue(\"tensor\",e,t,a),j=getParamValue(\"lengths\",e,t,a),a.getTensorArray(A).split(j,D),[2,[scalar(1)]];case 15:return E=getParamValue(\"tensorArrayId\",e,t,a),C=a.getTensorArray(E),[2,[scalar(C.size(),\"int32\")]];case 16:return R=getParamValue(\"tensorArrayId\",e,t,a),a.getTensorArray(R).clearAndClose(),[2,[]];case 17:throw TypeError(\"Node type \"+e.op+\" is not implemented\")}})})}var executeOp$3=function(e,t,a){switch(e.op){case\"conv1d\":var r=getParamValue(\"stride\",e,t,a),n=getParamValue(\"pad\",e,t,a),o=getParamValue(\"dataFormat\",e,t,a).toUpperCase(),s=getParamValue(\"dilation\",e,t,a);return [conv1d(getParamValue(\"x\",e,t,a),getParamValue(\"filter\",e,t,a),r,n,o,s)];case\"conv2d\":r=getParamValue(\"strides\",e,t,a),n=getParamValue(\"pad\",e,t,a),o=getParamValue(\"dataFormat\",e,t,a).toUpperCase();var i=getParamValue(\"dilations\",e,t,a);return [conv2d(getParamValue(\"x\",e,t,a),getParamValue(\"filter\",e,t,a),[r[1],r[2]],n,o,[i[0],i[1]])];case\"conv2dTranspose\":var p=getParamValue(\"outputShape\",e,t,a);r=getParamValue(\"strides\",e,t,a),n=getParamValue(\"pad\",e,t,a);return [conv2dTranspose(getParamValue(\"x\",e,t,a),getParamValue(\"filter\",e,t,a),p,[r[1],r[2]],n)];case\"depthwiseConv2d\":r=getParamValue(\"strides\",e,t,a),n=getParamValue(\"pad\",e,t,a),i=getParamValue(\"dilations\",e,t,a),o=getParamValue(\"dataFormat\",e,t,a).toUpperCase();return [depthwiseConv2d(getParamValue(\"input\",e,t,a),getParamValue(\"filter\",e,t,a),[r[1],r[2]],n,o,[i[0],i[1]])];case\"avgPool\":r=getParamValue(\"strides\",e,t,a),n=getParamValue(\"pad\",e,t,a);var u=getParamValue(\"kernelSize\",e,t,a);return [avgPool(getParamValue(\"x\",e,t,a),[u[1],u[2]],[r[1],r[2]],n)];case\"maxPool\":r=getParamValue(\"strides\",e,t,a),n=getParamValue(\"pad\",e,t,a),u=getParamValue(\"kernelSize\",e,t,a);return [maxPool(getParamValue(\"x\",e,t,a),[u[1],u[2]],[r[1],r[2]],n)];default:throw TypeError(\"Node type \"+e.op+\" is not implemented\")}},executeOp$4=function(e,t,a){switch(e.op){case\"fill\":var r=getParamValue(\"shape\",e,t,a),n=getParamValue(\"dtype\",e,t,a),o=getParamValue(\"value\",e,t,a);return [fill(r,o,n)];case\"linspace\":var s=getParamValue(\"start\",e,t,a),i=getParamValue(\"stop\",e,t,a),p=getParamValue(\"num\",e,t,a);return [linspace(s,i,p)];case\"oneHot\":var u=getParamValue(\"indices\",e,t,a),m=getParamValue(\"depth\",e,t,a),l=getParamValue(\"onValue\",e,t,a),d=getParamValue(\"offValue\",e,t,a);return [oneHot(u,m,l,d)];case\"ones\":return [ones$1(getParamValue(\"shape\",e,t,a),getParamValue(\"dtype\",e,t,a))];case\"onesLike\":return [onesLike(getParamValue(\"x\",e,t,a))];case\"randomUniform\":return [randomUniform(getParamValue(\"shape\",e,t,a),getParamValue(\"minval\",e,t,a),getParamValue(\"maxval\",e,t,a),getParamValue(\"dtype\",e,t,a))];case\"range\":s=getParamValue(\"start\",e,t,a);var c=getParamValue(\"stop\",e,t,a),f=getParamValue(\"step\",e,t,a);return [range(s,c,f,getParamValue(\"dtype\",e,t,a))];case\"truncatedNormal\":r=getParamValue(\"shape\",e,t,a);var y=getParamValue(\"mean\",e,t,a),h=getParamValue(\"stdDev\",e,t,a),g=getParamValue(\"seed\",e,t,a);return [truncatedNormal(r,y,h,getParamValue(\"dtype\",e,t,a),g)];case\"zeros\":return [zeros(getParamValue(\"shape\",e,t,a),getParamValue(\"dtype\",e,t,a))];case\"zerosLike\":return [zerosLike(getParamValue(\"x\",e,t,a))];default:throw TypeError(\"Node type \"+e.op+\" is not implemented\")}};function executeOp$5(e,t,a){return __awaiter$2(this,void 0,void 0,function(){var r,n,o,s,i;return __generator$2(this,function(p){switch(p.label){case 0:switch(e.op){case\"nonMaxSuppression\":return [3,1];case\"whereAsync\":return [3,3]}return [3,5];case 1:return r=getParamValue(\"boxes\",e,t,a),n=getParamValue(\"scores\",e,t,a),o=getParamValue(\"maxOutputSize\",e,t,a),s=getParamValue(\"iouThreshold\",e,t,a),i=getParamValue(\"scoreThreshold\",e,t,a),[4,image_ops.nonMaxSuppressionAsync(r,n,o,s,i)];case 2:return [2,[p.sent()]];case 3:return [4,whereAsync(getParamValue(\"condition\",e,t,a))];case 4:return [2,[p.sent()]];case 5:throw TypeError(\"Node type \"+e.op+\" is not implemented\")}})})}var executeOp$6=function(e,t,a){switch(e.op){case\"topK\":var r=getParamValue(\"x\",e,t,a),n=getParamValue(\"k\",e,t,a),o=getParamValue(\"sorted\",e,t,a),s=topk(r,n,o);return [s.values,s.indices];default:throw TypeError(\"Node type \"+e.op+\" is not implemented\")}},executeOp$7=function(e,t,a){switch(e.op){case\"const\":return t[e.name];case\"placeholder\":var r=getParamValue(\"default\",e,t,a);return [getTensor(e.name,t,a)||r];case\"identity\":case\"stopGradient\":case\"fakeQuantWithMinMaxVars\":return [getParamValue(\"x\",e,t,a).clone()];case\"snapshot\":return [getParamValue(\"x\",e,t,a).clone()];case\"shape\":return [tensor1d(getParamValue(\"x\",e,t,a).shape,\"int32\")];case\"shapeN\":return getParamValue(\"x\",e,t,a).map(function(e){return tensor1d(e.shape)});case\"size\":return [scalar(getParamValue(\"x\",e,t,a).size,\"int32\")];case\"rank\":return [scalar(getParamValue(\"x\",e,t,a).rank,\"int32\")];case\"noop\":return [];case\"print\":var n=getParamValue(\"x\",e,t,a),o=getParamValue(\"data\",e,t,a),s=getParamValue(\"message\",e,t,a),i=getParamValue(\"summarize\",e,t,a);console.warn(\"The graph has a tf.print() operation,usually used for debugging, which slows down performance.\"),console.log(s);for(var p=0;p<o.length;p++)console.log(Array.prototype.slice.call(o[0].dataSync()).slice(0,i));return [n];default:throw TypeError(\"Node type \"+e.op+\" is not implemented\")}},executeOp$8=function(e,t,a){switch(e.op){case\"resizeBilinear\":var r=getParamValue(\"images\",e,t,a),n=getParamValue(\"size\",e,t,a),o=getParamValue(\"alignCorners\",e,t,a);return [image_ops.resizeBilinear(r,[n[0],n[1]],o)];case\"resizeNearestNeighbor\":r=getParamValue(\"images\",e,t,a),n=getParamValue(\"size\",e,t,a),o=getParamValue(\"alignCorners\",e,t,a);return [image_ops.resizeNearestNeighbor(r,[n[0],n[1]],o)];case\"cropAndResize\":var s=getParamValue(\"image\",e,t,a),i=getParamValue(\"boxes\",e,t,a),p=getParamValue(\"boxInd\",e,t,a),u=getParamValue(\"cropSize\",e,t,a),m=getParamValue(\"method\",e,t,a),l=getParamValue(\"extrapolationValue\",e,t,a);return [image_ops.cropAndResize(s,i,p,u,m,l)];default:throw TypeError(\"Node type \"+e.op+\" is not implemented\")}},executeOp$9=function(e,t,a){switch(e.op){case\"equal\":return [equal(getParamValue(\"a\",e,t,a),getParamValue(\"b\",e,t,a))];case\"notEqual\":return [notEqual(getParamValue(\"a\",e,t,a),getParamValue(\"b\",e,t,a))];case\"greater\":return [greater(getParamValue(\"a\",e,t,a),getParamValue(\"b\",e,t,a))];case\"greaterEqual\":return [greaterEqual(getParamValue(\"a\",e,t,a),getParamValue(\"b\",e,t,a))];case\"less\":return [less(getParamValue(\"a\",e,t,a),getParamValue(\"b\",e,t,a))];case\"lessEqual\":return [lessEqual(getParamValue(\"a\",e,t,a),getParamValue(\"b\",e,t,a))];case\"logicalAnd\":return [logicalAnd(getParamValue(\"a\",e,t,a),getParamValue(\"b\",e,t,a))];case\"logicalNot\":return [logicalNot(getParamValue(\"a\",e,t,a))];case\"logicalOr\":return [logicalOr(getParamValue(\"a\",e,t,a),getParamValue(\"b\",e,t,a))];case\"where\":return [where(getParamValue(\"condition\",e,t,a),getParamValue(\"a\",e,t,a),getParamValue(\"b\",e,t,a))];default:throw TypeError(\"Node type \"+e.op+\" is not implemented\")}},executeOp$10=function(e,t,a){switch(e.op){case\"matMul\":return [matMul(getParamValue(\"a\",e,t,a),getParamValue(\"b\",e,t,a),getParamValue(\"transposeA\",e,t,a),getParamValue(\"transposeB\",e,t,a))];case\"transpose\":return [transpose(getParamValue(\"x\",e,t,a),getParamValue(\"perm\",e,t,a))];default:throw TypeError(\"Node type \"+e.op+\" is not implemented\")}},executeOp$11=function(e,t,a){switch(e.op){case\"batchNormalization\":return [batchNormalization(getParamValue(\"x\",e,t,a),getParamValue(\"mean\",e,t,a),getParamValue(\"variance\",e,t,a),getParamValue(\"epsilon\",e,t,a),getParamValue(\"scale\",e,t,a),getParamValue(\"offset\",e,t,a))];case\"localResponseNormalization\":return [localResponseNormalization(getParamValue(\"x\",e,t,a),getParamValue(\"radius\",e,t,a),getParamValue(\"bias\",e,t,a),getParamValue(\"alpha\",e,t,a),getParamValue(\"beta\",e,t,a))];case\"softmax\":return [softmax(getParamValue(\"x\",e,t,a))];default:throw TypeError(\"Node type \"+e.op+\" is not implemented\")}},executeOp$12=function(e,t,a){switch(e.op){case\"max\":var r=getParamValue(\"axis\",e,t,a),n=getParamValue(\"keepDims\",e,t,a);return [max(getParamValue(\"x\",e,t,a),r,n)];case\"mean\":r=getParamValue(\"axis\",e,t,a),n=getParamValue(\"keepDims\",e,t,a);return [mean(getParamValue(\"x\",e,t,a),r,n)];case\"min\":r=getParamValue(\"axis\",e,t,a),n=getParamValue(\"keepDims\",e,t,a);return [min(getParamValue(\"x\",e,t,a),r,n)];case\"sum\":r=getParamValue(\"axis\",e,t,a),n=getParamValue(\"keepDims\",e,t,a);return [sum$1(getParamValue(\"x\",e,t,a),r,n)];case\"all\":r=getParamValue(\"axis\",e,t,a),n=getParamValue(\"keepDims\",e,t,a);return [all(getParamValue(\"x\",e,t,a),r,n)];case\"any\":r=getParamValue(\"axis\",e,t,a),n=getParamValue(\"keepDims\",e,t,a);return [any(getParamValue(\"x\",e,t,a),r,n)];case\"argMax\":r=getParamValue(\"axis\",e,t,a);return [argMax(getParamValue(\"x\",e,t,a),r)];case\"argMin\":r=getParamValue(\"axis\",e,t,a);return [argMin(getParamValue(\"x\",e,t,a),r)];case\"prod\":r=getParamValue(\"axis\",e,t,a),n=getParamValue(\"keepDims\",e,t,a);return [prod(getParamValue(\"x\",e,t,a),r,n)];default:throw TypeError(\"Node type \"+e.op+\" is not implemented\")}},executeOp$13=function(e,t,a){switch(e.op){case\"concat\":var r=getParamValue(\"axis\",e,t,a),n=getParamValue(\"tensors\",e,t,a);return [concat(n,r)];case\"gather\":r=getParamValue(\"axis\",e,t,a);var o=getParamValue(\"x\",e,t,a),s=getParamValue(\"indices\",e,t,a);return [gather(o,s,r)];case\"reverse\":r=getParamValue(\"axis\",e,t,a),o=getParamValue(\"x\",e,t,a);return [reverse(o,r)];case\"slice\":var i=getParamValue(\"begin\",e,t,a),p=getParamValue(\"size\",e,t,a);return [slice(getParamValue(\"x\",e,t,a),i,p)];case\"stridedSlice\":i=getParamValue(\"begin\",e,t,a);var u=getParamValue(\"end\",e,t,a),m=getParamValue(\"strides\",e,t,a),l=getParamValue(\"beginMask\",e,t,a),d=getParamValue(\"endMask\",e,t,a),c=getParamValue(\"ellipsisMask\",e,t,a),f=getParamValue(\"newAxisMask\",e,t,a),y=getParamValue(\"shrinkAxisMask\",e,t,a),h=getParamValue(\"x\",e,t,a);if(1===i.length&&h.shape.length>1)for(var g=1;g<h.shape.length;g++)i.push(0),u.push(h.shape[g]),m.push(m[0]);return [stridedSlice(h,i,u,m,l,d,c,f,y)];case\"stack\":return tidy(function(){var r=getParamValue(\"axis\",e,t,a),n=getParamValue(\"tensors\",e,t,a),o=n[0].shape,s=n[0].squeeze().shape,i=n.map(function(e){var t=util.arraysEqual(e.shape,o);if(!t&&!util.arraysEqual(e.squeeze().shape,s))throw new Error(\"the input tensors shape does not match\");return t?e:e.reshape(o)});return [stack(i,r)]});case\"unstack\":return tidy(function(){var r=getParamValue(\"axis\",e,t,a),n=getParamValue(\"tensor\",e,t,a);return unstack(n,r)});case\"tile\":var N=getParamValue(\"reps\",e,t,a);return [tile(getParamValue(\"x\",e,t,a),N)];case\"split\":r=getParamValue(\"axis\",e,t,a);var P=getParamValue(\"numOrSizeSplits\",e,t,a);return split$1(getParamValue(\"x\",e,t,a),P,r);case\"scatterNd\":s=getParamValue(\"indices\",e,t,a);var b=getParamValue(\"values\",e,t,a),x=getParamValue(\"shape\",e,t,a);return [scatterND(s,b,x)];case\"gatherNd\":var I=getParamValue(\"x\",e,t,a);s=getParamValue(\"indices\",e,t,a);return [gatherND(I,s)];case\"sparseToDense\":s=getParamValue(\"sparseIndices\",e,t,a),x=getParamValue(\"outputShape\",e,t,a);var v=getParamValue(\"sparseValues\",e,t,a),O=getParamValue(\"defaultValue\",e,t,a);return [sparseToDense(s,v,x,O)];default:throw TypeError(\"Node type \"+e.op+\" is not implemented\")}},executeOp$14=function(e,t,a){switch(e.op){case\"cast\":return [cast(getParamValue(\"x\",e,t,a),getParamValue(\"dtype\",e,t,a))];case\"expandDims\":var r=getParamValue(\"axis\",e,t,a);return [expandDims(getParamValue(\"x\",e,t,a),r)];case\"squeeze\":r=getParamValue(\"axis\",e,t,a);return [squeeze(getParamValue(\"x\",e,t,a),r)];case\"reshape\":return [reshape(getParamValue(\"x\",e,t,a),getParamValue(\"shape\",e,t,a))];case\"pad\":return [pad(getParamValue(\"x\",e,t,a),split$1$1(getParamValue(\"padding\",e,t,a),2),getParamValue(\"constantValue\",e,t,a))];case\"spaceToBatchND\":var n=getParamValue(\"blockShape\",e,t,a),o=split$1$1(getParamValue(\"paddings\",e,t,a),2);return [spaceToBatchND(getParamValue(\"x\",e,t,a),n,o)];case\"batchToSpaceND\":n=getParamValue(\"blockShape\",e,t,a);var s=split$1$1(getParamValue(\"crops\",e,t,a),2);return [batchToSpaceND(getParamValue(\"x\",e,t,a),n,s)];default:throw TypeError(\"Node type \"+e.op+\" is not implemented\")}};function executeOp$15(e,t,a){switch(e.category){case\"arithmetic\":return executeOp(e,t,a);case\"basic_math\":return executeOp$1(e,t,a);case\"control\":return executeOp$2(e,t,a);case\"convolution\":return executeOp$3(e,t,a);case\"creation\":return executeOp$4(e,t,a);case\"dynamic\":return executeOp$5(e,t,a);case\"evaluation\":return executeOp$6(e,t,a);case\"image\":return executeOp$8(e,t,a);case\"graph\":return executeOp$7(e,t,a);case\"logical\":return executeOp$9(e,t,a);case\"matrices\":return executeOp$10(e,t,a);case\"normalization\":return executeOp$11(e,t,a);case\"reduction\":return executeOp$12(e,t,a);case\"slice_join\":return executeOp$13(e,t,a);case\"transformation\":return executeOp$14(e,t,a);default:throw TypeError(\"Node type \"+e.op+\" is not implemented\")}}var ExecutionContext=function(){function e(e,t){this.weightMap=e,this.tensorArrayMap=t,this.rootContext={id:0,frameName:\"\",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds();}return e.prototype.newFrame=function(e,t){return {id:e,frameName:t,iterationId:0}},Object.defineProperty(e.prototype,\"currentContext\",{get:function(){return this.contexts},set:function(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds());},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,\"currentContextId\",{get:function(){return this._currentContextIds[0]},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,\"currentContextIds\",{get:function(){return this._currentContextIds},enumerable:!0,configurable:!0}),e.prototype.generateCurrentContextIds=function(){for(var e=[],t=0;t<this.contexts.length-1;t++){var a=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(a));}e.push(\"\"),this._currentContextIds=e;},e.prototype.contextIdforContexts=function(e){return e?e.map(function(e){return 0===e.id&&0===e.iterationId?\"\":e.frameName+\"-\"+e.iterationId}).join(\"/\"):\"\"},e.prototype.enterFrame=function(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)));},e.prototype.exitFrame=function(){if(!(this.contexts&&this.contexts.length>1))throw new Error(\"Cannot exit frame, the context is empty\");this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();},e.prototype.nextIteration=function(){if(!(this.contexts&&this.contexts.length>0))throw new Error(\"Cannot increase frame iteration, the context is empty\");this.contexts=this.contexts.slice(),this.lastId++;var e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts));},e.prototype.getWeight=function(e){return this.weightMap[e]},e.prototype.addTensorArray=function(e){this.tensorArrayMap[e.id]=e;},e.prototype.getTensorArray=function(e){return this.tensorArrayMap[e]},e}(),GraphExecutor=function(){function e(e){this.graph=e,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=\",\",this.placeholders=e.placeholders,this._outputs=e.outputs,this.compile();}return Object.defineProperty(e.prototype,\"weightMap\",{get:function(){return this._weightMap},set:function(e){var t=Object.keys(e).map(function(t){return e[t].map(function(e){return e.id})});this.weightIds=[].concat.apply([],t),this._weightMap=e;},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,\"inputs\",{get:function(){return this.placeholders.map(function(e){return {name:e.name,shape:e.params.shape?e.params.shape.value:void 0,dtype:e.params.dtype?e.params.dtype.value:void 0}})},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,\"outputs\",{get:function(){return this._outputs.map(function(e){return {name:e.name,shape:e.params.shape?e.params.shape.value:void 0,dtype:e.params.dtype?e.params.dtype.value:void 0}})},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,\"inputNodes\",{get:function(){return this.placeholders.map(function(e){return e.name})},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,\"outputNodes\",{get:function(){return this.outputs.map(function(e){return e.name})},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,\"isControlFlowModel\",{get:function(){return this.graph.withControlFlow},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,\"isDynamicShapeModel\",{get:function(){return this.graph.withDynamicShape},enumerable:!0,configurable:!0}),e.prototype.compile=function(e){if(!this.graph.withControlFlow&&!this.graph.withDynamicShape){var t=[],a=e||this.graph.placeholders,r=a.map(function(e){return e.name}).sort().join(this.SEPERATOR);if(!this.compiledMap.get(r)){for(var n=a.concat(this.graph.weights),o={};n.length>0;){var s=n.pop();o[s.name]=!0,t.push(s),s.children.forEach(function(e){!o[e.name]&&e.inputNames.every(function(e){var t=getNodeNameAndIndex(e)[0];return o[t]})&&n.push(e);});}this.compiledMap.set(r,t);}}},e.prototype.execute=function(e,t,a){var r=this;void 0===t&&(t=!0);var n=Object.keys(e).sort();this.checkInput(e,t),this.checkInputShapeAndType(e,t),this.compile(n.map(function(e){return r.graph.nodes[e]}));var o=this.calculateOutputs(a);this.checkOutput(this.compiledMap.get(n.join(this.SEPERATOR)),o);var s={};return tidy(function(){for(var t=new ExecutionContext(r._weightMap,s),a=__assign$2({},r.weightMap,e),i=r.getFrozenTensorIds(a),p={},u=r.compiledMap.get(n.join(r.SEPERATOR)),m=0;m<u.length;m++){var l=u[m];if(a[l.name]||(a[l.name]=executeOp$15(l,a,t),r.checkTensorForDisposal(l.name,l,a,t,i,p)),o.every(function(e){return !!a[e]}))break}return r.findOutputs(a,t,o)})},e.prototype.getFrozenTensorIds=function(e){var t=Object.keys(e).map(function(t){return e[t]}).map(function(e){return e.map(function(e){return e.id})});return new(Set.bind.apply(Set,[void 0].concat(t)))},e.prototype.checkTensorForDisposal=function(e,t,a,r,n,o){\"control\"!==t.category&&(a[e].forEach(function(e){null!=e&&(o[e.id]=(o[e.id]||0)+t.children.length);}),t.inputs.forEach(function(e){if(\"control\"!==e.category){var t=getTensorsForCurrentContenxt(e.name,a,r);null!=t&&t.forEach(function(e){if(e&&!n.has(e.id)){var t=o[e.id];1===t?(e.dispose(),delete o[e.id]):null!=t&&o[e.id]--;}});}}));},e.prototype.executeAsync=function(e,t){return __awaiter$2(this,void 0,void 0,function(){var a,r,n,o,s,i,p,u=this;return __generator$2(this,function(m){switch(m.label){case 0:return this.checkInput(e,!1),this.checkInputShapeAndType(e,!1),a={},r=new ExecutionContext(this._weightMap,a),[4,this.executeWithControlFlow(e,r)];case 1:return n=m.sent(),o=this.findOutputs(n,r,t),s=Object.keys(o).map(function(e){return o[e].id}),i=Object.keys(e).map(function(t){return e[t].map(function(e){return e.id})}),p=[].concat.apply([],i),Object.keys(n).forEach(function(e){n[e].forEach(function(e){e&&-1===s.indexOf(e.id)&&-1===p.indexOf(e.id)&&-1===u.weightIds.indexOf(e.id)&&e.dispose();});}),[2,o]}})})},e.prototype.executeWithControlFlow=function(e,t){return __awaiter$2(this,void 0,void 0,function(){var a,r,n,o,s,i,p,u,m=this;return __generator$2(this,function(l){switch(l.label){case 0:a=Object.keys(e),r=a.map(function(e){return m.graph.nodes[e]}),n=r.concat(this.graph.weights).map(function(e){return {node:e,contexts:t.currentContext}}),o=__assign$2({},this.weightMap,e),s={},i=this.getFrozenTensorIds(o),p={},l.label=1;case 1:return n.length>0?(u=this.processStack(r,n,t,o,p,i,s),[4,Promise.all(u)]):[3,3];case 2:return l.sent(),[3,1];case 3:return [2,o]}})})},e.prototype.processStack=function(e,t,a,r,n,o,s){for(var i=this,p=[],u=function(){var u=t.pop();a.currentContext=u.contexts;var l=\"\";if(\"enter\"===u.node.op&&getParamValue(\"isConstant\",u.node,r,a)&&(l=getNodeNameAndIndex(u.node.name,a)[0]),-1===e.indexOf(u.node)){var d=executeOp$15(u.node,r,a);l||(l=getNodeNameAndIndex(u.node.name,a)[0]);var c=a.currentContext;d instanceof Promise?p.push(d.then(function(e){return r[l]=e,a.currentContext=c,i.checkTensorForDisposal(l,u.node,r,a,o,s),i.processChildNodes(u.node,t,a,r,n),e})):(r[l]=d,m.checkTensorForDisposal(l,u.node,r,a,o,s),m.processChildNodes(u.node,t,a,r,n));}else m.processChildNodes(u.node,t,a,r,n);},m=this;t.length>0;)u();return p},e.prototype.processChildNodes=function(e,t,a,r,n){e.children.forEach(function(e){var o=getNodeNameAndIndex(e.name,a)[0];n[o]||(\"merge\"===e.op?e.inputNames.some(function(e){return !!getTensor(e,r,a)})&&(n[o]=!0,t.push({contexts:a.currentContext,node:e})):e.inputNames.every(function(e){return !!getTensor(e,r,a)})&&(n[o]=!0,t.push({contexts:a.currentContext,node:e})));});},e.prototype.calculateOutputs=function(e){return !e||e instanceof Array||(e=[e]),e||this.graph.outputs.map(function(e){return e.name})},e.prototype.findOutputs=function(e,t,a){return this.calculateOutputs(a).reduce(function(a,r){return a[r]=getTensor(r,e,t),a},{})},e.prototype.dispose=function(){var e=this;Object.keys(this.weightMap).forEach(function(t){return e.weightMap[t].forEach(function(e){return e.dispose()})});},e.prototype.checkInputShapeAndType=function(e,t){void 0===t&&(t=!0),this.placeholders.forEach(function(a){var r=e[a.name];if(t||r){var n=r[0];if(a.params.shape&&a.params.shape.value){var o=a.params.shape.value,s=o.length===n.shape.length&&n.shape.every(function(e,t){return -1===o[t]||o[t]===e});util.assert(s,\"The shape of dict['\"+a.name+\"'] provided in model.execute(dict) must be [\"+o+\"], but was [\"+n.shape+\"]\");}a.params.dtype&&a.params.dtype.value&&util.assert(n.dtype===a.params.dtype.value,\"The dtype of dict['\"+a.name+\"'] provided in model.execute(dict) must be \"+a.params.dtype.value+\", but was \"+n.dtype);}});},e.prototype.checkInput=function(e,t){var a=this;void 0===t&&(t=!0);var r=Object.keys(e),n=[],o=[];this.inputNodes.forEach(function(e){-1===r.indexOf(e)&&n.push(e);}),r.forEach(function(e){-1===a.inputNodes.indexOf(e)&&o.push(e);});var s=o.filter(function(e){return !a.graph.nodes[e]});if(n.length>0&&t)throw new Error(\"The dict provided in model.execute(dict) has the keys [\"+r+\"], but is missing the required keys: [\"+n+\"].\");if(o.length>0&&t)throw new Error(\"The dict provided in model.execute(dict) has unused keys: [\"+o+\"]. Please provide only the following keys: [\"+this.inputNodes+\"].\");if(s.length>0)throw new Error(\"The dict provided in model.execute(dict) has keys: [\"+s+\"] not part of model graph.\")},e.prototype.checkOutput=function(e,t){var a=e.map(function(e){return e.name}),r=[];if(t.forEach(function(e){-1===a.indexOf(e)&&r.push(e);}),r.length>0)throw new Error(\"The following outputs are not be generated by the execution: [\"+r+\"].\")},e}(),FrozenModel=function(){function e(e,t,a){this.modelUrl=e,this.weightManifestUrl=t,this.requestOption=a,this.version=\"n/a\";}return Object.defineProperty(e.prototype,\"modelVersion\",{get:function(){return this.version},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,\"inputNodes\",{get:function(){return this.executor.inputNodes},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,\"outputNodes\",{get:function(){return this.executor.outputNodes},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,\"inputs\",{get:function(){return this.executor.inputs},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,\"outputs\",{get:function(){return this.executor.outputs},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,\"weights\",{get:function(){return this.executor.weightMap},enumerable:!0,configurable:!0}),e.prototype.findIOHandler=function(){var e=[this.modelUrl,this.weightManifestUrl];if(this.requestOption)this.handler=io.browserHTTPRequest(e,this.requestOption);else{var t=io.getLoadHandlers(e);if(0===t.length)t.push(io.browserHTTPRequest(e,this.requestOption));else if(t.length>1)throw new Error(\"Found more than one (\"+t.length+\") load handlers for URL '\"+[e]+\"'\");this.handler=t[0];}},e.prototype.load=function(){return __awaiter$2(this,void 0,void 0,function(){var e,t,a;return __generator$2(this,function(r){switch(r.label){case 0:if(this.findIOHandler(),null==this.handler.load)throw new Error(\"Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.\");return [4,this.handler.load()];case 1:return e=r.sent(),t=compiled_api_1.GraphDef.decode(new Uint8Array(e.modelTopology)),this.version=t.versions.producer+\".\"+t.versions.minConsumer,a=io.decodeWeights(e.weightData,e.weightSpecs),this.executor=new GraphExecutor(OperationMapper.Instance.transformGraph(t)),this.executor.weightMap=this.convertTensorMapToTensorsMap(a),[2,!0]}})})},e.prototype.predict=function(e,t){return this.execute_(e,!0,this.outputNodes)},e.prototype.constructTensorMap=function(e){var t=e instanceof Tensor?[e]:e;if(t.length!==this.inputNodes.length)throw new Error(\"Input tensor count mismatch,the frozen model has \"+this.inputNodes.length+\" placeholders, while there are \"+t.length+\" input tensors.\");return this.inputNodes.reduce(function(e,a,r){return e[a]=t[r],e},{})},e.prototype.execute=function(e,t){return this.execute_(e,!1,t)},e.prototype.execute_=function(e,t,a){if(void 0===t&&(t=!0),a=a||this.outputNodes,(e instanceof Tensor||Array.isArray(e))&&(e=this.constructTensorMap(e)),this.executor.isControlFlowModel||this.executor.isDynamicShapeModel)throw new Error(\"The model contains control flow or dynamic shape ops, please use executeAsync method\");var r=this.executor.execute(this.convertTensorMapToTensorsMap(e),t,a),n=Object.keys(r);return Array.isArray(a)&&a.length>1?a.map(function(e){return r[e]}):r[n[0]]},e.prototype.executeAsync=function(e,t){return __awaiter$2(this,void 0,void 0,function(){var a,r;return __generator$2(this,function(n){switch(n.label){case 0:if(!this.executor.isControlFlowModel&&!this.executor.isDynamicShapeModel)throw new Error(\"The model does not contain control flow or dynamic shape ops, please use execute method for better performance.\");return t=t||this.outputNodes,(e instanceof Tensor||Array.isArray(e))&&(e=this.constructTensorMap(e)),[4,this.executor.executeAsync(this.convertTensorMapToTensorsMap(e),t)];case 1:return a=n.sent(),r=Object.keys(a),[2,Array.isArray(t)&&t.length>1?t.map(function(e){return a[e]}):a[r[0]]]}})})},e.prototype.convertTensorMapToTensorsMap=function(e){return Object.keys(e).reduce(function(t,a){return t[a]=[e[a]],t},{})},e.prototype.dispose=function(){this.executor.dispose();},e}();function loadFrozenModel(e,t,a){return __awaiter$2(this,void 0,void 0,function(){var r;return __generator$2(this,function(n){switch(n.label){case 0:return [4,(r=new FrozenModel(e,t,a)).load()];case 1:return n.sent(),[2,r]}})})}var version$3=\"0.6.6\";\n\n            // @tensorflow/tfjs Copyright 2018 Google\n            var version$4=\"0.13.4\",version$1$1={\"tfjs-core\":version$1,\"tfjs-layers\":version$2,\"tfjs-converter\":version$3,tfjs:version$4};\n\n            var tensorflow = /*#__PURE__*/Object.freeze({\n                        version: version$1$1,\n                        setBackend: setBackend,\n                        getBackend: getBackend,\n                        disposeVariables: disposeVariables,\n                        memory: memory,\n                        version_core: version$1,\n                        nextFrame: nextFrame,\n                        environment: environment,\n                        io: io,\n                        math: math,\n                        serialization: serialization,\n                        test_util: test_util,\n                        util: util,\n                        webgl: webgl,\n                        AdadeltaOptimizer: AdadeltaOptimizer,\n                        AdagradOptimizer: AdagradOptimizer,\n                        AdamOptimizer: AdamOptimizer,\n                        AdamaxOptimizer: AdamaxOptimizer,\n                        MomentumOptimizer: MomentumOptimizer,\n                        Optimizer: Optimizer,\n                        RMSPropOptimizer: RMSPropOptimizer,\n                        SGDOptimizer: SGDOptimizer,\n                        Tensor: Tensor,\n                        TensorBuffer: TensorBuffer,\n                        variable: variable,\n                        Variable: Variable,\n                        get Rank () { return Rank; },\n                        get Reduction () { return Reduction; },\n                        ENV: ENV,\n                        Environment: Environment,\n                        KernelBackend: KernelBackend,\n                        DataStorage: DataStorage,\n                        image: image_ops,\n                        linalg: linalg_ops,\n                        losses: loss_ops,\n                        spectral: spectral_ops,\n                        op: op,\n                        batchNormalization2d: batchNormalization2d,\n                        batchNormalization3d: batchNormalization3d,\n                        batchNormalization4d: batchNormalization4d,\n                        batchNormalization: batchNormalization,\n                        complex: complex,\n                        real: real,\n                        imag: imag,\n                        concat: concat,\n                        concat1d: concat1d,\n                        concat2d: concat2d,\n                        concat3d: concat3d,\n                        concat4d: concat4d,\n                        split: split$1,\n                        conv1d: conv1d,\n                        conv2d: conv2d,\n                        conv2dDerFilter: conv2dDerFilter,\n                        depthwiseConv2d: depthwiseConv2d,\n                        separableConv2d: separableConv2d,\n                        conv2dTranspose: conv2dTranspose,\n                        matMul: matMul,\n                        dot: dot,\n                        outerProduct: outerProduct,\n                        reverse: reverse,\n                        reverse1d: reverse1d,\n                        reverse2d: reverse2d,\n                        reverse3d: reverse3d,\n                        reverse4d: reverse4d,\n                        maxPool: maxPool,\n                        avgPool: avgPool,\n                        pool: pool,\n                        slice: slice,\n                        slice1d: slice1d,\n                        slice2d: slice2d,\n                        slice3d: slice3d,\n                        slice4d: slice4d,\n                        abs: abs,\n                        acos: acos,\n                        acosh: acosh,\n                        asin: asin,\n                        asinh: asinh,\n                        atan: atan,\n                        atanh: atanh,\n                        ceil: ceil,\n                        clipByValue: clipByValue,\n                        cos: cos,\n                        cosh: cosh,\n                        erf: erf,\n                        exp: exp,\n                        expm1: expm1,\n                        floor: floor,\n                        log: log$1,\n                        log1p: log1p,\n                        logSigmoid: logSigmoid,\n                        neg: neg,\n                        reciprocal: reciprocal,\n                        round: round,\n                        rsqrt: rsqrt,\n                        sigmoid: sigmoid,\n                        sign: sign,\n                        sin: sin,\n                        sinh: sinh,\n                        softplus: softplus,\n                        sqrt: sqrt,\n                        square: square,\n                        step: step,\n                        tan: tan,\n                        tanh: tanh$1,\n                        all: all,\n                        any: any,\n                        argMax: argMax,\n                        argMin: argMin,\n                        logSumExp: logSumExp,\n                        max: max,\n                        mean: mean,\n                        min: min,\n                        moments: moments,\n                        sum: sum$1,\n                        prod: prod,\n                        equal: equal,\n                        equalStrict: equalStrict,\n                        greater: greater,\n                        greaterEqual: greaterEqual,\n                        greaterEqualStrict: greaterEqualStrict,\n                        greaterStrict: greaterStrict,\n                        less: less,\n                        lessEqual: lessEqual,\n                        lessEqualStrict: lessEqualStrict,\n                        lessStrict: lessStrict,\n                        notEqual: notEqual,\n                        notEqualStrict: notEqualStrict,\n                        add: add,\n                        addN: addN,\n                        addStrict: addStrict,\n                        atan2: atan2,\n                        div: div,\n                        divStrict: divStrict,\n                        floorDiv: floorDiv,\n                        maximum: maximum,\n                        maximumStrict: maximumStrict,\n                        minimum: minimum,\n                        minimumStrict: minimumStrict,\n                        mod: mod,\n                        modStrict: modStrict,\n                        mul: mul,\n                        mulStrict: mulStrict,\n                        pow: pow,\n                        powStrict: powStrict,\n                        squaredDifference: squaredDifference,\n                        squaredDifferenceStrict: squaredDifferenceStrict,\n                        sub: sub,\n                        subStrict: subStrict,\n                        elu: elu,\n                        leakyRelu: leakyRelu,\n                        prelu: prelu,\n                        relu: relu,\n                        selu: selu,\n                        logicalAnd: logicalAnd,\n                        logicalNot: logicalNot,\n                        logicalOr: logicalOr,\n                        logicalXor: logicalXor,\n                        where: where,\n                        whereAsync: whereAsync,\n                        buffer: buffer,\n                        toPixels: toPixels,\n                        print: print,\n                        batchToSpaceND: batchToSpaceND,\n                        cast: cast,\n                        clone: clone,\n                        cumsum: cumsum,\n                        depthToSpace: depthToSpace,\n                        expandDims: expandDims,\n                        eye: eye,\n                        fromPixels: fromPixels,\n                        multinomial: multinomial,\n                        oneHot: oneHot,\n                        pad: pad,\n                        pad1d: pad1d,\n                        pad2d: pad2d,\n                        pad3d: pad3d,\n                        pad4d: pad4d,\n                        rand: rand,\n                        randomNormal: randomNormal,\n                        randomUniform: randomUniform,\n                        reshape: reshape,\n                        spaceToBatchND: spaceToBatchND,\n                        squeeze: squeeze,\n                        stack: stack,\n                        tile: tile,\n                        truncatedNormal: truncatedNormal,\n                        unstack: unstack,\n                        fill: fill,\n                        linspace: linspace,\n                        ones: ones$1,\n                        range: range,\n                        scalar: scalar,\n                        tensor: tensor,\n                        tensor1d: tensor1d,\n                        tensor2d: tensor2d,\n                        tensor3d: tensor3d,\n                        tensor4d: tensor4d,\n                        tensor5d: tensor5d,\n                        tensor6d: tensor6d,\n                        zeros: zeros,\n                        onesLike: onesLike,\n                        zerosLike: zerosLike,\n                        transpose: transpose,\n                        softmax: softmax,\n                        localResponseNormalization: localResponseNormalization,\n                        norm: norm,\n                        gather: gather,\n                        unsortedSegmentSum: unsortedSegmentSum,\n                        basicLSTMCell: basicLSTMCell,\n                        multiRNNCell: multiRNNCell,\n                        movingAverage: movingAverage,\n                        stridedSlice: stridedSlice,\n                        topk: topk,\n                        scatterND: scatterND,\n                        fft: fft,\n                        ifft: ifft,\n                        rfft: rfft,\n                        sparseToDense: sparseToDense,\n                        gatherND: gatherND,\n                        train: train,\n                        tidy: tidy,\n                        keep: keep,\n                        dispose: dispose,\n                        time: time,\n                        profile: profile,\n                        customGrad: customGrad,\n                        grad: grad,\n                        grads: grads,\n                        valueAndGrad: valueAndGrad,\n                        valueAndGrads: valueAndGrads,\n                        variableGrads: variableGrads,\n                        constraints: exports_constraints,\n                        initializers: exports_initializers,\n                        layers: exports_layers,\n                        models: exports_models,\n                        metrics: exports_metrics,\n                        regularizers: exports_regularizers,\n                        CallbackList: CallbackList,\n                        CustomCallback: CustomCallback,\n                        History: History,\n                        Callback: Callback,\n                        InputSpec: InputSpec,\n                        SymbolicTensor: SymbolicTensor,\n                        Model: Model,\n                        input: input,\n                        loadModel: loadModel,\n                        model: model,\n                        registerCallbackConstructor: registerCallbackConstructor,\n                        sequential: sequential,\n                        RNN: RNN,\n                        Sequential: Sequential,\n                        LayerVariable: LayerVariable,\n                        version_layers: version$2,\n                        FrozenModel: FrozenModel,\n                        loadFrozenModel: loadFrozenModel,\n                        version_converter: version$3\n            });\n\n            // import '@tensorflow/tfjs-node';\r\n            /* fix for rollup */\r\n            /* istanbul ignore next */\r\n            const tf = (undefined) ? undefined : tensorflow;\r\n            /**\r\n             * Base class for tensorscript models\r\n             * @interface TensorScriptModelInterface\r\n             * @property {Object} settings - tensorflow model hyperparameters\r\n             * @property {Object} model - tensorflow model\r\n             * @property {Object} tf - tensorflow / tensorflow-node / tensorflow-node-gpu\r\n             * @property {Function} reshape - static reshape array function\r\n             * @property {Function} getInputShape - static TensorScriptModelInterface\r\n             */\r\n            class TensorScriptModelInterface {\r\n              /**\r\n               * @param {Object} options - tensorflow model hyperparameters\r\n               * @param {Object} customTF - custom, overridale tensorflow / tensorflow-node / tensorflow-node-gpu\r\n               * @param {{model:Object,tf:Object,}} properties - extra instance properties\r\n               */\r\n              constructor(options = {}, properties = {}) {\r\n                /** @type {Object} */\r\n                this.settings = options;\r\n                /** @type {Object} */\r\n                this.model = properties.model;\r\n                /** @type {Object} */\r\n                this.tf = properties.tf || tf;\r\n                /** @type {Function} */\r\n                this.reshape = TensorScriptModelInterface.reshape;\r\n                /** @type {Function} */\r\n                this.getInputShape = TensorScriptModelInterface.getInputShape;\r\n                return this;\r\n              }\r\n              /**\r\n               * Reshapes an array\r\n               * @function\r\n               * @example \r\n               * const array = [ 0, 1, 1, 0, ];\r\n               * const shape = [2,2];\r\n               * TensorScriptModelInterface.reshape(array,shape) // => \r\n               * [\r\n               *   [ 0, 1, ],\r\n               *   [ 1, 0, ],\r\n               * ];\r\n               * @param {Array<number>} array - input array \r\n               * @param {Array<number>} shape - shape array \r\n               * @return {Array<Array<number>>} returns a matrix with the defined shape\r\n               */\r\n              /* istanbul ignore next */\r\n              static reshape(array, shape) {\r\n                const flatArray = flatten$2(array);\r\n               \r\n\r\n                function product (arr) {\r\n                  return arr.reduce((prev, curr) => prev * curr);\r\n                }\r\n              \r\n                if (!Array.isArray(array) || !Array.isArray(shape)) {\r\n                  throw new TypeError('Array expected');\r\n                }\r\n              \r\n                if (shape.length === 0) {\r\n                  throw new DimensionError(0, product(size(array)), '!=');\r\n                }\r\n                let newArray;\r\n                let totalSize = 1;\r\n                const rows = shape[ 0 ];\r\n                for (let sizeIndex = 0; sizeIndex < shape.length; sizeIndex++) {\r\n                  totalSize *= shape[sizeIndex];\r\n                }\r\n              \r\n                if (flatArray.length !== totalSize) {\r\n                  throw new DimensionError(\r\n                    product(shape),\r\n                    product(size(array)),\r\n                    '!='\r\n                  );\r\n                }\r\n              \r\n                try {\r\n                  newArray = _reshape(flatArray, shape);\r\n                } catch (e) {\r\n                  if (e instanceof DimensionError) {\r\n                    throw new DimensionError(\r\n                      product(shape),\r\n                      product(size(array)),\r\n                      '!='\r\n                    );\r\n                  }\r\n                  throw e;\r\n                }\r\n                if (newArray.length !== rows) throw new SyntaxError(`specified shape (${shape}) is compatible with input array or length (${array.length})`);\r\n\r\n                // console.log({ newArray ,});\r\n                return newArray;\r\n              }\r\n              /**\r\n               * Returns the shape of an input matrix\r\n               * @function\r\n               * @example \r\n               * const input = [\r\n               *   [ 0, 1, ],\r\n               *   [ 1, 0, ],\r\n               * ];\r\n               * TensorScriptModelInterface.getInputShape(input) // => [2,2]\r\n               * @see {https://stackoverflow.com/questions/10237615/get-size-of-dimensions-in-array}\r\n               * @param {Array<Array<number>>} matrix - input matrix \r\n               * @return {Array<number>} returns the shape of a matrix (e.g. [2,2])\r\n               */\r\n              static getInputShape(matrix=[]) {\r\n                if (Array.isArray(matrix) === false || !matrix[ 0 ] || !matrix[ 0 ].length || Array.isArray(matrix[ 0 ]) === false) throw new TypeError('input must be a matrix');\r\n                const dim = [];\r\n                const x_dimensions = matrix[ 0 ].length;\r\n                let vectors = matrix;\r\n                matrix.forEach(vector => {\r\n                  if (vector.length !== x_dimensions) throw new SyntaxError('input must have the same length in each row');\r\n                });\r\n                for (;;) {\r\n                  dim.push(vectors.length);\r\n                  if (Array.isArray(vectors[0])) {\r\n                    vectors = vectors[0];\r\n                  } else {\r\n                    break;\r\n                  }\r\n                }\r\n                return dim;\r\n              }\r\n              /**\r\n               * Asynchronously trains tensorflow model, must be implemented by tensorscript class\r\n               * @abstract \r\n               * @param {Array<Array<number>>} x_matrix - independent variables\r\n               * @param {Array<Array<number>>} y_matrix - dependent variables\r\n               * @return {Object} returns trained tensorflow model \r\n               */\r\n              train(x_matrix, y_matrix) {\r\n                throw new ReferenceError('train method is not implemented');\r\n              }\r\n              /**\r\n               * Predicts new dependent variables\r\n               * @abstract \r\n               * @param {Array<Array<number>>|Array<number>} matrix - new test independent variables\r\n               * @return {{data: Promise}} returns tensorflow prediction \r\n               */\r\n              calculate(matrix) {\r\n                throw new ReferenceError('calculate method is not implemented');\r\n              }\r\n              /**\r\n               * Loads a saved tensoflow / keras model\r\n               * @param {Object} options - tensorflow load model options\r\n               * @return {Object} tensorflow model\r\n               */\r\n              async loadModel(options) {\r\n                this.model = await this.tf.loadModel(options);\r\n                return this.model;\r\n              }\r\n              /**\r\n               * Returns prediction values from tensorflow model\r\n               * @param {Array<Array<number>>|Array<number>} input_matrix - new test independent variables \r\n               * @param {Boolean} [options.json=true] - return object instead of typed array\r\n               * @param {Boolean} [options.probability=true] - return real values instead of integers\r\n               * @return {Array<number>|Array<Array<number>>} predicted model values\r\n               */\r\n              async predict(input_matrix, options = {}) {\r\n                if (!input_matrix || Array.isArray(input_matrix)===false) throw new Error('invalid input matrix');\r\n                const x_matrix = (Array.isArray(input_matrix[ 0 ]))\r\n                  ? input_matrix\r\n                  : [\r\n                    input_matrix,\r\n                  ];\r\n                const config = Object.assign({\r\n                  json: true,\r\n                  probability: true,\r\n                }, options);\r\n                return this.calculate(x_matrix)\r\n                  .data()\r\n                  .then(predictions => {\r\n                    if (config.json === false) {\r\n                      return predictions;\r\n                    } else {\r\n                      const shape = [x_matrix.length, this.yShape[ 1 ], ];\r\n                      const predictionValues = (options.probability === false) ? Array.from(predictions).map(Math.round) : Array.from(predictions);\r\n                      return this.reshape(predictionValues, shape);\r\n                    }\r\n                  })\r\n                  .catch(e => {\r\n                    throw e; \r\n                  });\r\n              }\r\n            }\r\n\r\n            /**\r\n             * Calculate the size of a multi dimensional array.\r\n             * This function checks the size of the first entry, it does not validate\r\n             * whether all dimensions match. (use function `validate` for that) (from math.js)\r\n             * @param {Array} x\r\n             * @see {https://github.com/josdejong/mathjs/blob/develop/src/utils/array.js}\r\n             * @ignore\r\n             * @return {Number[]} size\r\n             */\r\n            /* istanbul ignore next */\r\n            function size (x) {\r\n              let s = [];\r\n\r\n              while (Array.isArray(x)) {\r\n                s.push(x.length);\r\n                x = x[0];\r\n              }\r\n\r\n              return s;\r\n            }\r\n            /**\r\n             * Iteratively re-shape a multi dimensional array to fit the specified dimensions (from math.js)\r\n             * @param {Array} array           Array to be reshaped\r\n             * @param {Array.<number>} sizes  List of sizes for each dimension\r\n             * @returns {Array}               Array whose data has been formatted to fit the\r\n             *                                specified dimensions\r\n             * @ignore\r\n             * @see {https://github.com/josdejong/mathjs/blob/develop/src/utils/array.js}\r\n             */\r\n            /* istanbul ignore next */\r\n            function _reshape(array, sizes) {\r\n              // testing if there are enough elements for the requested shape\r\n              var tmpArray = array;\r\n              var tmpArray2;\r\n              // for each dimensions starting by the last one and ignoring the first one\r\n              for (var sizeIndex = sizes.length - 1; sizeIndex > 0; sizeIndex--) {\r\n                var size = sizes[sizeIndex];\r\n                tmpArray2 = [];\r\n\r\n                // aggregate the elements of the current tmpArray in elements of the requested size\r\n                var length = tmpArray.length / size;\r\n                for (var i = 0; i < length; i++) {\r\n                  tmpArray2.push(tmpArray.slice(i * size, (i + 1) * size));\r\n                }\r\n                // set it as the new tmpArray for the next loop turn or for return\r\n                tmpArray = tmpArray2;\r\n              }\r\n              return tmpArray;\r\n            }\r\n\r\n            /**\r\n             * Create a range error with the message:\r\n             *     'Dimension mismatch (<actual size> != <expected size>)' (from math.js)\r\n             * @param {number | number[]} actual        The actual size\r\n             * @param {number | number[]} expected      The expected size\r\n             * @param {string} [relation='!=']          Optional relation between actual\r\n             *                                          and expected size: '!=', '<', etc.\r\n             * @extends RangeError\r\n             * @ignore\r\n             * @see {https://github.com/josdejong/mathjs/blob/develop/src/utils/array.js}\r\n             */\r\n            /* istanbul ignore next */\r\n            class DimensionError extends RangeError {\r\n              constructor(actual, expected, relation) {\r\n                /* istanbul ignore next */\r\n                const message = 'Dimension mismatch (' + (Array.isArray(actual) ? ('[' + actual.join(', ') + ']') : actual) + ' ' + ('!=') + ' ' + (Array.isArray(expected) ? ('[' + expected.join(', ') + ']') : expected) +  ')';\r\n                super(message);\r\n              \r\n                this.actual = actual;\r\n                this.expected = expected;\r\n                this.relation = relation;\r\n                // this.stack = (new Error()).stack\r\n                this.message = message;\r\n                this.name = 'DimensionError';\r\n                this.isDimensionError = true;\r\n              }\r\n            }\r\n\r\n            /**\r\n             * Flatten a multi dimensional array, put all elements in a one dimensional\r\n             * array\r\n             * @param {Array} array   A multi dimensional array\r\n             * @ignore\r\n             * @see {https://github.com/josdejong/mathjs/blob/develop/src/utils/array.js}\r\n             * @return {Array}        The flattened array (1 dimensional)\r\n             */\r\n            /* istanbul ignore next */\r\n            function flatten$2 (array) {\r\n              /* istanbul ignore next */\r\n              if (!Array.isArray(array)) {\r\n                // if not an array, return as is\r\n                /* istanbul ignore next */\r\n                return array;\r\n              }\r\n              let flat = [];\r\n              \r\n              /* istanbul ignore next */\r\n              array.forEach(function callback (value) {\r\n                if (Array.isArray(value)) {\r\n                  value.forEach(callback); // traverse through sub-arrays recursively\r\n                } else {\r\n                  flat.push(value);\r\n                }\r\n              });\r\n\r\n              return flat;\r\n            }\n\n            /**\r\n             * Deep Learning with Tensorflow\r\n             * @class BaseNeuralNetwork\r\n             * @implements {TensorScriptModelInterface}\r\n             */\r\n            class BaseNeuralNetwork extends TensorScriptModelInterface {\r\n              /**\r\n               * @param {{layers:Array<Object>,compile:Object,fit:Object}} options - neural network configuration and tensorflow model hyperparameters\r\n               * @param {{model:Object,tf:Object,}} properties - extra instance properties\r\n               */\r\n              constructor(options = {}, properties) {\r\n                const config = Object.assign({\r\n                  layers: [],\r\n                  compile: {\r\n                    loss: 'meanSquaredError',\r\n                    optimizer: 'adam',\r\n                  },\r\n                  fit: {\r\n                    epochs: 100,\r\n                    batchSize: 5,\r\n                  },\r\n                }, options);\r\n                super(config, properties);\r\n                return this;\r\n              }\r\n              /**\r\n               * Adds dense layers to tensorflow model\r\n               * @abstract \r\n               * @param {Array<Array<number>>} x_matrix - independent variables\r\n               * @param {Array<Array<number>>} y_matrix - dependent variables\r\n               * @param {Array<Object>} layers - model dense layer parameters\r\n               */\r\n              generateLayers(x_matrix, y_matrix, layers) {\r\n                throw new ReferenceError('generateLayers method is not implemented');\r\n              }\r\n              /**\r\n               * Asynchronously trains tensorflow model\r\n               * @override\r\n               * @param {Array<Array<number>>} x_matrix - independent variables\r\n               * @param {Array<Array<number>>} y_matrix - dependent variables\r\n               * @param {Array<Object>} layers - array of model dense layer parameters\r\n               * @param {Array<Array<number>>} x_text - validation data independent variables\r\n               * @param {Array<Array<number>>} y_text - validation data dependent variables\r\n               * @return {Object} returns trained tensorflow model \r\n               */\r\n              async train(x_matrix, y_matrix, layers, x_test, y_test) {\r\n                const xShape = this.getInputShape(x_matrix);\r\n                const yShape = this.getInputShape(y_matrix);\r\n                const xs = this.tf.tensor(x_matrix, xShape);\r\n                const ys = this.tf.tensor(y_matrix, yShape);\r\n                this.xShape = xShape;\r\n                this.yShape = yShape;\r\n                this.model = this.tf.sequential();\r\n                this.generateLayers.call(this, x_matrix, y_matrix, layers || this.layers, x_test, y_test);\r\n                this.model.compile(this.settings.compile);\r\n                await this.model.fit(xs, ys, this.settings.fit);\r\n                xs.dispose();\r\n                ys.dispose();\r\n                return this.model;\r\n              }\r\n              /**\r\n               * Predicts new dependent variables\r\n               * @override\r\n               * @param {Array<Array<number>>|Array<number>} matrix - new test independent variables\r\n               * @param {Object} options - model prediction options\r\n               * @return {{data: Promise}} returns tensorflow prediction \r\n               */\r\n              calculate(input_matrix, options) {\r\n                if (!input_matrix || Array.isArray(input_matrix)===false) throw new Error('invalid input matrix');\r\n                const predictionInput = (Array.isArray(input_matrix[ 0 ]))\r\n                  ? input_matrix\r\n                  : [\r\n                    input_matrix,\r\n                  ];\r\n                const predictionTensor = this.tf.tensor(predictionInput);\r\n                const prediction = this.model.predict(predictionTensor, options);\r\n                predictionTensor.dispose();\r\n                return prediction;\r\n              }\r\n            }\n\n            /**\r\n             * Deep Learning Regression with Tensorflow\r\n             * @class DeepLearningRegression\r\n             * @implements {BaseNeuralNetwork}\r\n             */\r\n            class DeepLearningRegression extends BaseNeuralNetwork {\r\n              /**\r\n               * @param {{layers:Array<Object>,compile:Object,fit:Object,layerPreference:String}} options - neural network configuration and tensorflow model hyperparameters\r\n               * @param {{model:Object,tf:Object,}} properties - extra instance properties\r\n               */\r\n              constructor(options = {}, properties) {\r\n                const config = Object.assign({\r\n                  layers: [],\r\n                  layerPreference:'deep',\r\n                  compile: {\r\n                    loss: 'meanSquaredError',\r\n                    optimizer: 'adam',\r\n                  },\r\n                  fit: {\r\n                    epochs: 100,\r\n                    batchSize: 5,\r\n                  },\r\n                }, options);\r\n                super(config, properties);\r\n                return this;\r\n              }\r\n              /**\r\n               * Adds dense layers to tensorflow regression model\r\n               * @override \r\n               * @param {Array<Array<number>>} x_matrix - independent variables\r\n               * @param {Array<Array<number>>} y_matrix - dependent variables\r\n               * @param {Array<Object>} layers - model dense layer parameters\r\n               */\r\n              generateLayers(x_matrix, y_matrix, layers) {\r\n                const xShape = this.getInputShape(x_matrix);\r\n                const yShape = this.getInputShape(y_matrix);\r\n                const denseLayers = [];\r\n                if (layers) {\r\n                  denseLayers.push(...layers);\r\n                } else if(this.settings.layerPreference==='deep') {\r\n                  denseLayers.push({ units: xShape[ 1 ], inputShape: [xShape[1],], kernelInitializer: 'randomNormal', activation: 'relu', });\r\n                  denseLayers.push({ units: parseInt(Math.ceil(xShape[ 1 ] / 2), 10), kernelInitializer: 'randomNormal', activation: 'relu', });\r\n                  denseLayers.push({ units: yShape[ 1 ], kernelInitializer: 'randomNormal', });\r\n                } else {\r\n                  denseLayers.push({ units: (xShape[ 1 ] * 2), inputShape: [xShape[1],], kernelInitializer: 'randomNormal', activation: 'relu', });\r\n                  denseLayers.push({ units: yShape[ 1 ], kernelInitializer: 'randomNormal', });\r\n                }\r\n                this.layers = denseLayers;\r\n                denseLayers.forEach(layer => {\r\n                  this.model.add(this.tf.layers.dense(layer));\r\n                });\r\n              }\r\n            }\n\n            /**\r\n             * Deep Learning Classification with Tensorflow\r\n             * @class DeepLearningClassification\r\n             * @implements {BaseNeuralNetwork}\r\n             */\r\n            class DeepLearningClassification extends BaseNeuralNetwork{\r\n              /**\r\n               * @param {{layers:Array<Object>,compile:Object,fit:Object}} options - neural network configuration and tensorflow model hyperparameters\r\n               * @param {{model:Object,tf:Object,}} properties - extra instance properties\r\n               */\r\n              constructor(options = {}, properties) {\r\n                const config = Object.assign({\r\n                  layers: [],\r\n                  compile: {\r\n                    loss: 'categoricalCrossentropy',\r\n                    optimizer: 'adam',\r\n                  },\r\n                  fit: {\r\n                    epochs: 100,\r\n                    batchSize: 5,\r\n                  },\r\n                }, options);\r\n                super(config, properties);\r\n                return this;\r\n              }\r\n              /**\r\n               * Adds dense layers to tensorflow classification model\r\n               * @override \r\n               * @param {Array<Array<number>>} x_matrix - independent variables\r\n               * @param {Array<Array<number>>} y_matrix - dependent variables\r\n               * @param {Array<Object>} layers - model dense layer parameters\r\n               */\r\n              generateLayers(x_matrix, y_matrix, layers) {\r\n                const xShape = this.getInputShape(x_matrix);\r\n                const yShape = this.getInputShape(y_matrix);\r\n                this.yShape = yShape;\r\n                this.xShape = xShape;\r\n                const denseLayers = [];\r\n                if (layers) {\r\n                  denseLayers.push(...layers);\r\n                } else {\r\n                  denseLayers.push({ units: (xShape[ 1 ] * 2), inputDim: xShape[1],  activation: 'relu', });\r\n                  denseLayers.push({ units: yShape[ 1 ], activation: 'softmax', });\r\n                }\r\n                this.layers = denseLayers;\r\n                denseLayers.forEach(layer => {\r\n                  this.model.add(this.tf.layers.dense(layer));\r\n                });\r\n              }\r\n            }\n\n            /**\r\n             * Logistic Regression Classification with Tensorflow\r\n             * @class LogisticRegression\r\n             * @implements {BaseNeuralNetwork}\r\n             */\r\n            class LogisticRegression extends BaseNeuralNetwork {\r\n              /**\r\n               * @param {{layers:Array<Object>,compile:Object,fit:Object}} options - neural network configuration and tensorflow model hyperparameters\r\n               * @param {{model:Object,tf:Object,}} properties - extra instance properties\r\n               */\r\n              constructor(options = {}, properties) {\r\n                const config = Object.assign({\r\n                  layers: [],\r\n                  type:'simple',\r\n                  compile: {\r\n                    loss: 'meanSquaredError',\r\n                    optimizer: 'rmsprop',\r\n                  },\r\n                  fit: {\r\n                    epochs: 100,\r\n                    batchSize: 5,\r\n                  },\r\n                }, options);\r\n                super(config, properties);\r\n                return this;\r\n              }\r\n              /**\r\n               * Adds dense layers to tensorflow classification model\r\n               * @override \r\n               * @param {Array<Array<number>>} x_matrix - independent variables\r\n               * @param {Array<Array<number>>} y_matrix - dependent variables\r\n               * @param {Array<Object>} layers - model dense layer parameters\r\n               * @param {Array<Array<number>>} x_test - validation data independent variables\r\n               * @param {Array<Array<number>>} y_test - validation data dependent variables\r\n               */\r\n              generateLayers(x_matrix, y_matrix, layers, x_test, y_test) {\r\n                const xShape = this.getInputShape(x_matrix);\r\n                const yShape = this.getInputShape(y_matrix);\r\n                this.yShape = yShape;\r\n                this.xShape = xShape;\r\n                const denseLayers = [];\r\n                if (layers) {\r\n                  denseLayers.push(...layers);\r\n                } else if (this.settings.type==='class') { \r\n                  denseLayers.push({ units: 1, inputDim:  xShape[ 1 ], activation: 'sigmoid', });\r\n                  this.settings.compile.loss = 'binaryCrossentropy';\r\n                } else if (this.settings.type === 'l1l2') { \r\n                  const kernelRegularizer = this.tf.regularizers.l1l2({ l1: 0.01, l2: 0.01, });\r\n                  denseLayers.push({ units: 1, inputDim:  xShape[ 1 ], activation: 'sigmoid', kernelRegularizer, });\r\n                  this.settings.compile.loss = 'binaryCrossentropy';\r\n                } else {\r\n                  denseLayers.push({ units: 1, inputShape: [xShape[1], ], });\r\n                }\r\n                this.layers = denseLayers;\r\n                denseLayers.forEach(layer => {\r\n                  this.model.add(this.tf.layers.dense(layer));\r\n                });\r\n                /* istanbul ignore next */\r\n                if (x_test && y_test) {\r\n                  this.settings.fit.validationData = [x_test, y_test];\r\n                }\r\n              }\r\n            }\n\n            /**\r\n             * Mulitple Linear Regression with Tensorflow\r\n             * @class MultipleLinearRegression\r\n             * @implements {BaseNeuralNetwork}\r\n             */\r\n            class MultipleLinearRegression extends BaseNeuralNetwork {\r\n              /**\r\n               * @param {{layers:Array<Object>,compile:Object,fit:Object}} options - neural network configuration and tensorflow model hyperparameters\r\n               * @param {{model:Object,tf:Object,}} properties - extra instance properties\r\n               */\r\n              /* istanbul ignore next */\r\n              constructor(options = {}, properties = {}) {\r\n                const config = Object.assign({\r\n                  layers: [],\r\n                  compile: {\r\n                    loss: 'meanSquaredError',\r\n                    optimizer: 'sgd',\r\n                  },\r\n                  fit: {\r\n                    epochs: 500,\r\n                    batchSize: 5,\r\n                  },\r\n                }, options);\r\n                super(config, properties);\r\n                return this;\r\n              }\r\n              /**\r\n               * Adds dense layers to tensorflow regression model\r\n               * @override \r\n               * @param {Array<Array<number>>} x_matrix - independent variables\r\n               * @param {Array<Array<number>>} y_matrix - dependent variables\r\n               * @param {Array<Object>} layers - model dense layer parameters\r\n               */\r\n              generateLayers(x_matrix, y_matrix, layers) {\r\n                const xShape = this.getInputShape(x_matrix);\r\n                const yShape = this.getInputShape(y_matrix);\r\n                const denseLayers = [];\r\n                if (layers) {\r\n                  denseLayers.push(...layers);\r\n                } else {\r\n                  denseLayers.push({ units: yShape[1], inputShape: [xShape[1],], });\r\n                }\r\n                this.layers = denseLayers;\r\n                denseLayers.forEach(layer => {\r\n                  this.model.add(this.tf.layers.dense(layer));\r\n                });\r\n              }\r\n            }\n\n            /**\n             * lodash (Custom Build) <https://lodash.com/>\n             * Build: `lodash modularize exports=\"npm\" -o ./`\n             * Copyright jQuery Foundation and other contributors <https://jquery.org/>\n             * Released under MIT license <https://lodash.com/license>\n             * Based on Underscore.js 1.8.3 <http://underscorejs.org/LICENSE>\n             * Copyright Jeremy Ashkenas, DocumentCloud and Investigative Reporters & Editors\n             */\n\n            /** Used as references for various `Number` constants. */\n            var INFINITY = 1 / 0,\n                MAX_SAFE_INTEGER = 9007199254740991,\n                MAX_INTEGER = 1.7976931348623157e+308,\n                NAN = 0 / 0;\n\n            /** `Object#toString` result references. */\n            var funcTag = '[object Function]',\n                genTag = '[object GeneratorFunction]',\n                symbolTag = '[object Symbol]';\n\n            /** Used to match leading and trailing whitespace. */\n            var reTrim = /^\\s+|\\s+$/g;\n\n            /** Used to detect bad signed hexadecimal string values. */\n            var reIsBadHex = /^[-+]0x[0-9a-f]+$/i;\n\n            /** Used to detect binary string values. */\n            var reIsBinary = /^0b[01]+$/i;\n\n            /** Used to detect octal string values. */\n            var reIsOctal = /^0o[0-7]+$/i;\n\n            /** Used to detect unsigned integer values. */\n            var reIsUint = /^(?:0|[1-9]\\d*)$/;\n\n            /** Built-in method references without a dependency on `root`. */\n            var freeParseInt = parseInt;\n\n            /** Used for built-in method references. */\n            var objectProto = Object.prototype;\n\n            /**\n             * Used to resolve the\n             * [`toStringTag`](http://ecma-international.org/ecma-262/7.0/#sec-object.prototype.tostring)\n             * of values.\n             */\n            var objectToString = objectProto.toString;\n\n            /* Built-in method references for those with the same name as other `lodash` methods. */\n            var nativeCeil = Math.ceil,\n                nativeMax = Math.max;\n\n            /**\n             * The base implementation of `_.range` and `_.rangeRight` which doesn't\n             * coerce arguments.\n             *\n             * @private\n             * @param {number} start The start of the range.\n             * @param {number} end The end of the range.\n             * @param {number} step The value to increment or decrement by.\n             * @param {boolean} [fromRight] Specify iterating from right to left.\n             * @returns {Array} Returns the range of numbers.\n             */\n            function baseRange(start, end, step, fromRight) {\n              var index = -1,\n                  length = nativeMax(nativeCeil((end - start) / (step || 1)), 0),\n                  result = Array(length);\n\n              while (length--) {\n                result[fromRight ? length : ++index] = start;\n                start += step;\n              }\n              return result;\n            }\n\n            /**\n             * Creates a `_.range` or `_.rangeRight` function.\n             *\n             * @private\n             * @param {boolean} [fromRight] Specify iterating from right to left.\n             * @returns {Function} Returns the new range function.\n             */\n            function createRange(fromRight) {\n              return function(start, end, step) {\n                if (step && typeof step != 'number' && isIterateeCall(start, end, step)) {\n                  end = step = undefined;\n                }\n                // Ensure the sign of `-0` is preserved.\n                start = toFinite(start);\n                if (end === undefined) {\n                  end = start;\n                  start = 0;\n                } else {\n                  end = toFinite(end);\n                }\n                step = step === undefined ? (start < end ? 1 : -1) : toFinite(step);\n                return baseRange(start, end, step, fromRight);\n              };\n            }\n\n            /**\n             * Checks if `value` is a valid array-like index.\n             *\n             * @private\n             * @param {*} value The value to check.\n             * @param {number} [length=MAX_SAFE_INTEGER] The upper bounds of a valid index.\n             * @returns {boolean} Returns `true` if `value` is a valid index, else `false`.\n             */\n            function isIndex(value, length) {\n              length = length == null ? MAX_SAFE_INTEGER : length;\n              return !!length &&\n                (typeof value == 'number' || reIsUint.test(value)) &&\n                (value > -1 && value % 1 == 0 && value < length);\n            }\n\n            /**\n             * Checks if the given arguments are from an iteratee call.\n             *\n             * @private\n             * @param {*} value The potential iteratee value argument.\n             * @param {*} index The potential iteratee index or key argument.\n             * @param {*} object The potential iteratee object argument.\n             * @returns {boolean} Returns `true` if the arguments are from an iteratee call,\n             *  else `false`.\n             */\n            function isIterateeCall(value, index, object) {\n              if (!isObject(object)) {\n                return false;\n              }\n              var type = typeof index;\n              if (type == 'number'\n                    ? (isArrayLike(object) && isIndex(index, object.length))\n                    : (type == 'string' && index in object)\n                  ) {\n                return eq(object[index], value);\n              }\n              return false;\n            }\n\n            /**\n             * Performs a\n             * [`SameValueZero`](http://ecma-international.org/ecma-262/7.0/#sec-samevaluezero)\n             * comparison between two values to determine if they are equivalent.\n             *\n             * @static\n             * @memberOf _\n             * @since 4.0.0\n             * @category Lang\n             * @param {*} value The value to compare.\n             * @param {*} other The other value to compare.\n             * @returns {boolean} Returns `true` if the values are equivalent, else `false`.\n             * @example\n             *\n             * var object = { 'a': 1 };\n             * var other = { 'a': 1 };\n             *\n             * _.eq(object, object);\n             * // => true\n             *\n             * _.eq(object, other);\n             * // => false\n             *\n             * _.eq('a', 'a');\n             * // => true\n             *\n             * _.eq('a', Object('a'));\n             * // => false\n             *\n             * _.eq(NaN, NaN);\n             * // => true\n             */\n            function eq(value, other) {\n              return value === other || (value !== value && other !== other);\n            }\n\n            /**\n             * Checks if `value` is array-like. A value is considered array-like if it's\n             * not a function and has a `value.length` that's an integer greater than or\n             * equal to `0` and less than or equal to `Number.MAX_SAFE_INTEGER`.\n             *\n             * @static\n             * @memberOf _\n             * @since 4.0.0\n             * @category Lang\n             * @param {*} value The value to check.\n             * @returns {boolean} Returns `true` if `value` is array-like, else `false`.\n             * @example\n             *\n             * _.isArrayLike([1, 2, 3]);\n             * // => true\n             *\n             * _.isArrayLike(document.body.children);\n             * // => true\n             *\n             * _.isArrayLike('abc');\n             * // => true\n             *\n             * _.isArrayLike(_.noop);\n             * // => false\n             */\n            function isArrayLike(value) {\n              return value != null && isLength(value.length) && !isFunction$1(value);\n            }\n\n            /**\n             * Checks if `value` is classified as a `Function` object.\n             *\n             * @static\n             * @memberOf _\n             * @since 0.1.0\n             * @category Lang\n             * @param {*} value The value to check.\n             * @returns {boolean} Returns `true` if `value` is a function, else `false`.\n             * @example\n             *\n             * _.isFunction(_);\n             * // => true\n             *\n             * _.isFunction(/abc/);\n             * // => false\n             */\n            function isFunction$1(value) {\n              // The use of `Object#toString` avoids issues with the `typeof` operator\n              // in Safari 8-9 which returns 'object' for typed array and other constructors.\n              var tag = isObject(value) ? objectToString.call(value) : '';\n              return tag == funcTag || tag == genTag;\n            }\n\n            /**\n             * Checks if `value` is a valid array-like length.\n             *\n             * **Note:** This method is loosely based on\n             * [`ToLength`](http://ecma-international.org/ecma-262/7.0/#sec-tolength).\n             *\n             * @static\n             * @memberOf _\n             * @since 4.0.0\n             * @category Lang\n             * @param {*} value The value to check.\n             * @returns {boolean} Returns `true` if `value` is a valid length, else `false`.\n             * @example\n             *\n             * _.isLength(3);\n             * // => true\n             *\n             * _.isLength(Number.MIN_VALUE);\n             * // => false\n             *\n             * _.isLength(Infinity);\n             * // => false\n             *\n             * _.isLength('3');\n             * // => false\n             */\n            function isLength(value) {\n              return typeof value == 'number' &&\n                value > -1 && value % 1 == 0 && value <= MAX_SAFE_INTEGER;\n            }\n\n            /**\n             * Checks if `value` is the\n             * [language type](http://www.ecma-international.org/ecma-262/7.0/#sec-ecmascript-language-types)\n             * of `Object`. (e.g. arrays, functions, objects, regexes, `new Number(0)`, and `new String('')`)\n             *\n             * @static\n             * @memberOf _\n             * @since 0.1.0\n             * @category Lang\n             * @param {*} value The value to check.\n             * @returns {boolean} Returns `true` if `value` is an object, else `false`.\n             * @example\n             *\n             * _.isObject({});\n             * // => true\n             *\n             * _.isObject([1, 2, 3]);\n             * // => true\n             *\n             * _.isObject(_.noop);\n             * // => true\n             *\n             * _.isObject(null);\n             * // => false\n             */\n            function isObject(value) {\n              var type = typeof value;\n              return !!value && (type == 'object' || type == 'function');\n            }\n\n            /**\n             * Checks if `value` is object-like. A value is object-like if it's not `null`\n             * and has a `typeof` result of \"object\".\n             *\n             * @static\n             * @memberOf _\n             * @since 4.0.0\n             * @category Lang\n             * @param {*} value The value to check.\n             * @returns {boolean} Returns `true` if `value` is object-like, else `false`.\n             * @example\n             *\n             * _.isObjectLike({});\n             * // => true\n             *\n             * _.isObjectLike([1, 2, 3]);\n             * // => true\n             *\n             * _.isObjectLike(_.noop);\n             * // => false\n             *\n             * _.isObjectLike(null);\n             * // => false\n             */\n            function isObjectLike(value) {\n              return !!value && typeof value == 'object';\n            }\n\n            /**\n             * Checks if `value` is classified as a `Symbol` primitive or object.\n             *\n             * @static\n             * @memberOf _\n             * @since 4.0.0\n             * @category Lang\n             * @param {*} value The value to check.\n             * @returns {boolean} Returns `true` if `value` is a symbol, else `false`.\n             * @example\n             *\n             * _.isSymbol(Symbol.iterator);\n             * // => true\n             *\n             * _.isSymbol('abc');\n             * // => false\n             */\n            function isSymbol(value) {\n              return typeof value == 'symbol' ||\n                (isObjectLike(value) && objectToString.call(value) == symbolTag);\n            }\n\n            /**\n             * Converts `value` to a finite number.\n             *\n             * @static\n             * @memberOf _\n             * @since 4.12.0\n             * @category Lang\n             * @param {*} value The value to convert.\n             * @returns {number} Returns the converted number.\n             * @example\n             *\n             * _.toFinite(3.2);\n             * // => 3.2\n             *\n             * _.toFinite(Number.MIN_VALUE);\n             * // => 5e-324\n             *\n             * _.toFinite(Infinity);\n             * // => 1.7976931348623157e+308\n             *\n             * _.toFinite('3.2');\n             * // => 3.2\n             */\n            function toFinite(value) {\n              if (!value) {\n                return value === 0 ? value : 0;\n              }\n              value = toNumber(value);\n              if (value === INFINITY || value === -INFINITY) {\n                var sign = (value < 0 ? -1 : 1);\n                return sign * MAX_INTEGER;\n              }\n              return value === value ? value : 0;\n            }\n\n            /**\n             * Converts `value` to a number.\n             *\n             * @static\n             * @memberOf _\n             * @since 4.0.0\n             * @category Lang\n             * @param {*} value The value to process.\n             * @returns {number} Returns the number.\n             * @example\n             *\n             * _.toNumber(3.2);\n             * // => 3.2\n             *\n             * _.toNumber(Number.MIN_VALUE);\n             * // => 5e-324\n             *\n             * _.toNumber(Infinity);\n             * // => Infinity\n             *\n             * _.toNumber('3.2');\n             * // => 3.2\n             */\n            function toNumber(value) {\n              if (typeof value == 'number') {\n                return value;\n              }\n              if (isSymbol(value)) {\n                return NAN;\n              }\n              if (isObject(value)) {\n                var other = typeof value.valueOf == 'function' ? value.valueOf() : value;\n                value = isObject(other) ? (other + '') : other;\n              }\n              if (typeof value != 'string') {\n                return value === 0 ? value : +value;\n              }\n              value = value.replace(reTrim, '');\n              var isBinary = reIsBinary.test(value);\n              return (isBinary || reIsOctal.test(value))\n                ? freeParseInt(value.slice(2), isBinary ? 2 : 8)\n                : (reIsBadHex.test(value) ? NAN : +value);\n            }\n\n            /**\n             * Creates an array of numbers (positive and/or negative) progressing from\n             * `start` up to, but not including, `end`. A step of `-1` is used if a negative\n             * `start` is specified without an `end` or `step`. If `end` is not specified,\n             * it's set to `start` with `start` then set to `0`.\n             *\n             * **Note:** JavaScript follows the IEEE-754 standard for resolving\n             * floating-point values which can produce unexpected results.\n             *\n             * @static\n             * @since 0.1.0\n             * @memberOf _\n             * @category Util\n             * @param {number} [start=0] The start of the range.\n             * @param {number} end The end of the range.\n             * @param {number} [step=1] The value to increment or decrement by.\n             * @returns {Array} Returns the range of numbers.\n             * @see _.inRange, _.rangeRight\n             * @example\n             *\n             * _.range(4);\n             * // => [0, 1, 2, 3]\n             *\n             * _.range(-4);\n             * // => [0, -1, -2, -3]\n             *\n             * _.range(1, 5);\n             * // => [1, 2, 3, 4]\n             *\n             * _.range(0, 20, 5);\n             * // => [0, 5, 10, 15]\n             *\n             * _.range(0, -4, -1);\n             * // => [0, -1, -2, -3]\n             *\n             * _.range(1, 4, 0);\n             * // => [1, 1, 1]\n             *\n             * _.range(0);\n             * // => []\n             */\n            var range$2 = createRange();\n\n            var lodash_range = range$2;\n\n            /**\r\n             * Long Short Term Memory Time Series with Tensorflow\r\n             * @class LSTMTimeSeries\r\n             * @implements {BaseNeuralNetwork}\r\n             */\r\n            class LSTMTimeSeries extends BaseNeuralNetwork {\r\n              /**\r\n               * Creates dataset data\r\n               * @example\r\n               * LSTMTimeSeries.createDataset([ [ 1, ], [ 2, ], [ 3, ], [ 4, ], [ 5, ], [ 6, ], [ 7, ], [ 8, ], [ 9, ], [ 10, ], ], 3) // => \r\n                  //  [ \r\n                  //    [ \r\n                  //      [ [ 1 ], [ 2 ], [ 3 ] ],\r\n                  //      [ [ 2 ], [ 3 ], [ 4 ] ],\r\n                  //      [ [ 3 ], [ 4 ], [ 5 ] ],\r\n                  //      [ [ 4 ], [ 5 ], [ 6 ] ],\r\n                  //      [ [ 5 ], [ 6 ], [ 7 ] ],\r\n                  //      [ [ 6 ], [ 7 ], [ 8 ] ], \r\n                  //   ], //x_matrix\r\n                  //   [ [ 4 ], [ 5 ], [ 6 ], [ 7 ], [ 8 ], [ 9 ] ] //y_matrix\r\n                  // ]\r\n               * @param {Array<Array<number>} dataset - array of values\r\n               * @param {Number} look_back - number of values in each feature \r\n               * @return {[Array<Array<number>>,Array<number>]} returns x matrix and y matrix for model trainning\r\n               */\r\n              /* istanbul ignore next */\r\n              static createDataset(dataset=[], look_back = 1) { \r\n                const dataX = [];\r\n                const dataY = [];\r\n                for (let index in lodash_range(dataset.length - look_back - 1)) {\r\n                  let i = parseInt(index);\r\n                  let a = dataset.slice(i, i + look_back);\r\n                  dataX.push(a);\r\n                  dataY.push(dataset[ i + look_back ]);\r\n                }\r\n                return [dataX, dataY, ];\r\n              }\r\n              /**\r\n               * Reshape input to be [samples, time steps, features]\r\n               * @example\r\n               * LSTMTimeSeries.getTimeseriesShape([ \r\n                  [ [ 1 ], [ 2 ], [ 3 ] ],\r\n                  [ [ 2 ], [ 3 ], [ 4 ] ],\r\n                  [ [ 3 ], [ 4 ], [ 5 ] ],\r\n                  [ [ 4 ], [ 5 ], [ 6 ] ],\r\n                  [ [ 5 ], [ 6 ], [ 7 ] ],\r\n                  [ [ 6 ], [ 7 ], [ 8 ] ], \r\n                ]) //=> [6, 1, 3,]\r\n               * @param {Array<Array<number>} x_timeseries - dataset array of values\r\n               * @return {Array<Array<number>>} returns proper timeseries forecasting shape\r\n               */\r\n              static getTimeseriesShape(x_timeseries) {\r\n                const time_steps = this.settings.timeSteps;\r\n                const xShape = this.getInputShape(x_timeseries);\r\n                const _samples = xShape[ 0 ];\r\n                const _timeSteps = time_steps;\r\n                const _features = xShape[ 1 ];\r\n                const newShape = (this.settings.mulitpleTimeSteps || this.settings.stateful)\r\n                  ? [_samples,  _features, _timeSteps, ]\r\n                  : [ _samples, _timeSteps, _features, ];\r\n                // console.log({newShape})\r\n                return newShape;\r\n              }\r\n              /**\r\n               * Returns data for predicting values\r\n               * @param timeseries \r\n               * @param look_back \r\n               */\r\n              static getTimeseriesDataSet(timeseries, look_back) {\r\n                const lookBack = look_back || this.settings.lookBack;\r\n                const matrices = LSTMTimeSeries.createDataset.call(this, timeseries, lookBack);\r\n                const x_matrix = matrices[ 0 ];\r\n                const y_matrix = matrices[ 1 ];\r\n                // const timeseriesShape = LSTMTimeSeries.getTimeseriesShape.call(this,x_matrix);\r\n                const x_matrix_timeseries = BaseNeuralNetwork.reshape(x_matrix, [x_matrix.length, lookBack, ]);\r\n                const xShape = BaseNeuralNetwork.getInputShape(x_matrix_timeseries);\r\n                const yShape = BaseNeuralNetwork.getInputShape(y_matrix);\r\n                return {\r\n                  yShape,\r\n                  xShape,\r\n                  y_matrix,\r\n                  x_matrix:x_matrix_timeseries,\r\n                };\r\n              }\r\n              /**\r\n               * @param {{layers:Array<Object>,compile:Object,fit:Object}} options - neural network configuration and tensorflow model hyperparameters\r\n               * @param {{model:Object,tf:Object,}} properties - extra instance properties\r\n               */\r\n              constructor(options = {}, properties) {\r\n                const config = Object.assign({\r\n                  layers: [],\r\n                  type: 'simple',\r\n                  stateful:false,\r\n                  stacked: false,\r\n                  mulitpleTimeSteps:false,\r\n                  lookBack:1,\r\n                  batchSize:1,\r\n                  timeSteps:1,\r\n                  learningRate:0.1,\r\n                  compile: {\r\n                    loss: 'meanSquaredError',\r\n                    optimizer: 'adam',\r\n                  },\r\n                  fit: {\r\n                    epochs: 100,\r\n                    batchSize: 1,\r\n                  },\r\n                }, options);\r\n                super(config, properties);\r\n                this.createDataset = LSTMTimeSeries.createDataset;\r\n                this.getTimeseriesDataSet = LSTMTimeSeries.getTimeseriesDataSet;\r\n                this.getTimeseriesShape = LSTMTimeSeries.getTimeseriesShape;\r\n                return this;\r\n              }\r\n              /**\r\n               * Adds dense layers to tensorflow classification model\r\n               * @override \r\n               * @param {Array<Array<number>>} x_matrix - independent variables\r\n               * @param {Array<Array<number>>} y_matrix - dependent variables\r\n               * @param {Array<Object>} layers - model dense layer parameters\r\n               * @param {Array<Array<number>>} x_test - validation data independent variables\r\n               * @param {Array<Array<number>>} y_test - validation data dependent variables\r\n               */\r\n              generateLayers(x_matrix, y_matrix, layers) {\r\n                const xShape = this.getInputShape(x_matrix);\r\n                const yShape = this.getInputShape(y_matrix);\r\n                this.yShape = yShape;\r\n                this.xShape = xShape;\r\n                // const sgdoptimizer = this.tf.train.sgd(this.settings.learningRate);\r\n                const lstmLayers = [];\r\n                const rnnLayers = [];\r\n                const denseLayers = [];\r\n                /* istanbul ignore next */\r\n                if (layers) {\r\n                  if(layers.lstmLayers && layers.lstmLayers.length) lstmLayers.push(...layers.lstmLayers);\r\n                  if(layers.rnnLayers && layers.rnnLayers.length) rnnLayers.push(...layers.rnnLayers);\r\n                  if(layers.denseLayers && layers.denseLayers.length) denseLayers.push(...layers.denseLayers);\r\n                } else if (this.settings.stateful) {\r\n                  const batchInputShape = [this.settings.fit.batchSize, this.settings.lookBack, 1, ];\r\n                  rnnLayers.push({ units: 4, batchInputShape:batchInputShape,  returnSequences:true, });\r\n                  rnnLayers.push({ units: 4, batchInputShape:batchInputShape,  });\r\n                  denseLayers.push({ units: yShape[1], });\r\n                // } else if(this.settings.stacked) {\r\n                //   lstmLayers.push({ units: 4, inputShape: [ 1, this.settings.lookBack ], });\r\n                //   // model.add(LSTM(4, batch_input_shape=(batch_size, look_back, 1), stateful=True, return_sequences=True))\r\n                //   // model.add(LSTM(4, batch_input_shape=(batch_size, look_back, 1), stateful=True))\r\n                //   denseLayers.push({ units: yShape[1], });\r\n                } else {\r\n                  const inputShape= [1, this.settings.lookBack, ];\r\n                  // console.log('default timeseries', { inputShape, xShape, yShape ,  });\r\n                  lstmLayers.push({ units:4, inputShape,  });\r\n                  denseLayers.push({ units: yShape[1], });\r\n                }\r\n                // console.log('lstmLayers',lstmLayers)\r\n                // console.log('denseLayers',denseLayers)\r\n                if (lstmLayers.length) {\r\n                  lstmLayers.forEach(layer => {\r\n                    this.model.add(this.tf.layers.lstm(layer));\r\n                  });\r\n                }\r\n                if (rnnLayers.length) {\r\n                  /* istanbul ignore next */\r\n                  rnnLayers.forEach(layer => {\r\n                    this.model.add(this.tf.layers.simpleRNN(layer));\r\n                  });\r\n                }\r\n                if (denseLayers.length) {\r\n                  denseLayers.forEach(layer => {\r\n                    this.model.add(this.tf.layers.dense(layer));\r\n                  });\r\n                }\r\n                this.layers = {\r\n                  lstmLayers,\r\n                  rnnLayers,\r\n                  denseLayers,\r\n                };\r\n                // this.settings.compile.optimizer = sgdoptimizer;\r\n              }\r\n              async train(x_timeseries, y_timeseries, layers, x_test, y_test) {\r\n                let yShape;\r\n                let x_matrix;\r\n                let y_matrix;\r\n                const look_back = this.settings.lookBack;\r\n                if (y_timeseries) {\r\n                  x_matrix = x_timeseries;\r\n                  y_matrix = y_timeseries;\r\n                } else {\r\n                  const matrices = this.createDataset(x_timeseries, look_back);\r\n                  x_matrix = matrices[ 0 ];\r\n                  y_matrix = matrices[ 1 ];\r\n                  yShape = this.getInputShape(y_matrix);\r\n                }\r\n                //_samples, _timeSteps, _features\r\n                const timeseriesShape = this.getTimeseriesShape(x_matrix);\r\n                const x_matrix_timeseries = BaseNeuralNetwork.reshape(x_matrix, timeseriesShape);\r\n                const xs = this.tf.tensor(x_matrix_timeseries, timeseriesShape);\r\n                const ys = this.tf.tensor(y_matrix, yShape);\r\n                this.xShape = timeseriesShape;\r\n                this.yShape = yShape;\r\n                this.model = this.tf.sequential();\r\n                this.generateLayers.call(this, x_matrix_timeseries, y_matrix, layers || this.layers, x_test, y_test);\r\n                this.model.compile(this.settings.compile);\r\n                if (this.settings.stateful) {\r\n                  this.settings.fit.shuffle = false;\r\n                }\r\n                await this.model.fit(xs, ys, this.settings.fit);\r\n                // this.model.summary();\r\n                xs.dispose();\r\n                ys.dispose();\r\n                return this.model;\r\n              }\r\n              calculate(x_matrix) {\r\n                const timeseriesShape = this.getTimeseriesShape(x_matrix);\r\n                const input_matrix = BaseNeuralNetwork.reshape(x_matrix, timeseriesShape);\r\n                return super.calculate(input_matrix);\r\n              }\r\n              async predict(input_matrix, options) {\r\n                if (this.settings.stateful && input_matrix.length > 1) {\r\n                  return Promise.all(input_matrix.map(input=>super.predict([input, ], options))) ;\r\n                } else {\r\n                  return super.predict(input_matrix, options);\r\n                }\r\n              }\r\n            }\n\n            /**\r\n             * Long Short Term Memory Multivariate Time Series with Tensorflow\r\n             * @class LSTMMultivariateTimeSeries\r\n             * @extends {LSTMTimeSeries}\r\n             */\r\n            class LSTMMultivariateTimeSeries extends LSTMTimeSeries {\r\n              /**\r\n               * Creates dataset data\r\n               * @example\r\n               * const ds = [\r\n              [10, 20, 30, 40, 50, 60, 70, 80, 90,],\r\n              [11, 21, 31, 41, 51, 61, 71, 81, 91,],\r\n              [12, 22, 32, 42, 52, 62, 72, 82, 92,],\r\n              [13, 23, 33, 43, 53, 63, 73, 83, 93,],\r\n              [14, 24, 34, 44, 54, 64, 74, 84, 94,],\r\n              [15, 25, 35, 45, 55, 65, 75, 85, 95,],\r\n              [16, 26, 36, 46, 56, 66, 76, 86, 96,],\r\n              [17, 27, 37, 47, 57, 67, 77, 87, 97,],\r\n              [18, 28, 38, 48, 58, 68, 78, 88, 98,],\r\n              [19, 29, 39, 49, 59, 69, 79, 89, 99,],\r\n            ];\r\n               * LSTMMultivariateTimeSeries.createDataset(ds,1) // => \r\n                  //  [ \r\n                  //   [ \r\n                  //    [ 20, 30, 40, 50, 60, 70, 80, 90 ],\r\n                  //    [ 21, 31, 41, 51, 61, 71, 81, 91 ],\r\n                  //    [ 22, 32, 42, 52, 62, 72, 82, 92 ],\r\n                  //    [ 23, 33, 43, 53, 63, 73, 83, 93 ],\r\n                  //    [ 24, 34, 44, 54, 64, 74, 84, 94 ],\r\n                  //    [ 25, 35, 45, 55, 65, 75, 85, 95 ],\r\n                  //    [ 26, 36, 46, 56, 66, 76, 86, 96 ],\r\n                  //    [ 27, 37, 47, 57, 67, 77, 87, 97 ],\r\n                  //    [ 28, 38, 48, 58, 68, 78, 88, 98 ]    \r\n                  //   ], //x_matrix\r\n                  //   [ 11, 12, 13, 14, 15, 16, 17, 18, 19 ], //y_matrix\r\n                  //   8 //features\r\n                  // ]\r\n               * @param {Array<Array<number>} dataset - array of values\r\n               * @param {Number} look_back - number of values in each feature \r\n               * @override \r\n               * @return {[Array<Array<number>>,Array<number>]} returns x matrix and y matrix for model trainning\r\n               */\r\n              /* istanbul ignore next */\r\n              static createDataset(dataset = [], look_back = 1) { \r\n                const features = (this.settings && this.settings.features) ? this.settings.features : dataset[ 0 ].length - 1;\r\n                const n_in = look_back || this.settings.lookback; //1; //lookbacks\r\n                const n_out = (this.settings && this.settings.outputs) ? this.settings.outputs : 1; //1;\r\n                const series = LSTMMultivariateTimeSeries.seriesToSupervised(dataset, n_in, n_out);\r\n                const dropped = LSTMMultivariateTimeSeries.getDropableColumns(features, n_in, n_out);\r\n                const droppedColumns = LSTMMultivariateTimeSeries.drop(series, dropped);\r\n                const original_dropped_c_columns = [ 0, droppedColumns[ 0 ].length - 1, ];\r\n                \r\n                // console.log({ series, dropped_c_columns,original_dropped_c_columns, dropped, });\r\n                const y = pivotVector(droppedColumns)[ droppedColumns[0].length - 1 ];\r\n                const x = LSTMMultivariateTimeSeries.drop(\r\n                  droppedColumns,\r\n                  original_dropped_c_columns,\r\n                  // [ 0, droppedColumns[ 0 ].length - 1, ]\r\n                );\r\n                return [ x, y, features, ];\r\n              }\r\n              /**\r\n               * Drops columns by array index\r\n               * @example\r\n            const data = [ [ 10, 20, 30, 40, 50, 60, 70, 80, 90, 11, 21, 31, 41, 51, 61, 71, 81, 91 ],\r\n                 [ 11, 21, 31, 41, 51, 61, 71, 81, 91, 12, 22, 32, 42, 52, 62, 72, 82, 92 ],\r\n                 [ 12, 22, 32, 42, 52, 62, 72, 82, 92, 13, 23, 33, 43, 53, 63, 73, 83, 93 ],\r\n                 [ 13, 23, 33, 43, 53, 63, 73, 83, 93, 14, 24, 34, 44, 54, 64, 74, 84, 94 ],\r\n                 [ 14, 24, 34, 44, 54, 64, 74, 84, 94, 15, 25, 35, 45, 55, 65, 75, 85, 95 ],\r\n                 [ 15, 25, 35, 45, 55, 65, 75, 85, 95, 16, 26, 36, 46, 56, 66, 76, 86, 96 ],\r\n                 [ 16, 26, 36, 46, 56, 66, 76, 86, 96, 17, 27, 37, 47, 57, 67, 77, 87, 97 ],\r\n                 [ 17, 27, 37, 47, 57, 67, 77, 87, 97, 18, 28, 38, 48, 58, 68, 78, 88, 98 ],\r\n                 [ 18, 28, 38, 48, 58, 68, 78, 88, 98, 19, 29, 39, 49, 59, 69, 79, 89, 99 ] ];\r\n            const n_in = 1; //lookbacks\r\n            const n_out = 1;\r\n            const dropColumns = getDropableColumns(8, n_in, n_out); // =>[ 10, 11, 12, 13, 14, 15, 16, 17 ]\r\n            const newdata = drop(data,dropColumns); //=> [ \r\n                // [ 10, 20, 30, 40, 50, 60, 70, 80, 90, 11 ],\r\n                // [ 11, 21, 31, 41, 51, 61, 71, 81, 91, 12 ],\r\n                // [ 12, 22, 32, 42, 52, 62, 72, 82, 92, 13 ],\r\n                // [ 13, 23, 33, 43, 53, 63, 73, 83, 93, 14 ],\r\n                // [ 14, 24, 34, 44, 54, 64, 74, 84, 94, 15 ],\r\n                // [ 15, 25, 35, 45, 55, 65, 75, 85, 95, 16 ],\r\n                // [ 16, 26, 36, 46, 56, 66, 76, 86, 96, 17 ],\r\n                // [ 17, 27, 37, 47, 57, 67, 77, 87, 97, 18 ],\r\n                // [ 18, 28, 38, 48, 58, 68, 78, 88, 98, 19 ] \r\n                //]\r\n              * @param {Array<Array<number>>} data - data set to drop columns \r\n              * @param {Array<number>} columns - array of column indexes\r\n               * @returns {Array<Array<number>>} matrix with dropped columns\r\n               */\r\n              static drop(data, columns) {\r\n                return data.reduce((cols, row, i) => { \r\n                  cols[ i ] = [];\r\n                  row.forEach((col, idx) => {\r\n                    if (columns.indexOf(idx)===-1) {\r\n                      cols[ i ].push(col);\r\n                    }\r\n                  });\r\n                  return cols;\r\n                }, []);\r\n              }\r\n              /**\r\n               * Converts data set to supervised labels for forecasting, the first column must be the dependent variable\r\n               * @example \r\n               const ds = [\r\n                [10, 20, 30, 40, 50, 60, 70, 80, 90,],\r\n                [11, 21, 31, 41, 51, 61, 71, 81, 91,],\r\n                [12, 22, 32, 42, 52, 62, 72, 82, 92,],\r\n                [13, 23, 33, 43, 53, 63, 73, 83, 93,],\r\n                [14, 24, 34, 44, 54, 64, 74, 84, 94,],\r\n                [15, 25, 35, 45, 55, 65, 75, 85, 95,],\r\n                [16, 26, 36, 46, 56, 66, 76, 86, 96,],\r\n                [17, 27, 37, 47, 57, 67, 77, 87, 97,],\r\n                [18, 28, 38, 48, 58, 68, 78, 88, 98,],\r\n                [19, 29, 39, 49, 59, 69, 79, 89, 99,],\r\n              ]; \r\n              const n_in = 1; //lookbacks\r\n              const n_out = 1;\r\n              const series = seriesToSupervised(ds, n_in, n_out); //=> [ \r\n                // [ 10, 20, 30, 40, 50, 60, 70, 80, 90, 11, 21, 31, 41, 51, 61, 71, 81, 91 ],\r\n                // [ 11, 21, 31, 41, 51, 61, 71, 81, 91, 12, 22, 32, 42, 52, 62, 72, 82, 92 ],\r\n                // [ 12, 22, 32, 42, 52, 62, 72, 82, 92, 13, 23, 33, 43, 53, 63, 73, 83, 93 ],\r\n                // [ 13, 23, 33, 43, 53, 63, 73, 83, 93, 14, 24, 34, 44, 54, 64, 74, 84, 94 ],\r\n                // [ 14, 24, 34, 44, 54, 64, 74, 84, 94, 15, 25, 35, 45, 55, 65, 75, 85, 95 ],\r\n                // [ 15, 25, 35, 45, 55, 65, 75, 85, 95, 16, 26, 36, 46, 56, 66, 76, 86, 96 ],\r\n                // [ 16, 26, 36, 46, 56, 66, 76, 86, 96, 17, 27, 37, 47, 57, 67, 77, 87, 97 ],\r\n                // [ 17, 27, 37, 47, 57, 67, 77, 87, 97, 18, 28, 38, 48, 58, 68, 78, 88, 98 ],\r\n                // [ 18, 28, 38, 48, 58, 68, 78, 88, 98, 19, 29, 39, 49, 59, 69, 79, 89, 99 ] \r\n                //];\r\n               * \r\n               * @param {Array<Array<number>>} data - data set \r\n               * @param {number} n_in - look backs \r\n               * @param {number} n_out - future iterations (only 1 supported) \r\n               * @todo support multiple future iterations\r\n               * @returns {Array<Array<number>>} multivariate dataset for time series\r\n               */\r\n              static seriesToSupervised(data, n_in = 1, n_out = 1) {\r\n                if (n_out !== 1) throw new RangeError('Only 1 future iteration supported');\r\n                if (data && Array.isArray(data) && Array.isArray(data[0]) && data[ 0 ].length < 2) throw new RangeError('Must include at least two columns, the first column must be the dependent variable, followed by independent variables');\r\n                // let n_vars = data[ 0 ].length;\r\n                let cols = [];\r\n                // let names = [];\r\n                // input sequence (t-n, ... t-1)\r\n                for (let x in data) {\r\n                  x = parseInt(x);\r\n                  let maxIndex = x + n_in + n_out;\r\n                  if (maxIndex > data.length) break;\r\n                  cols[ x ] = [];\r\n                  // console.log({ x,maxIndex });\r\n                  for (let i in lodash_range(n_in)) {\r\n                    i = parseInt(i);\r\n                    cols[ x ].push(...data[x+i]);\r\n                    // console.log({ i, cols, });\r\n                  }\r\n                  for (let j in lodash_range(n_out)){\r\n                    j = parseInt(j);\r\n                    cols[ x ].push(...data[ x+j+n_in ]);\r\n                    // console.log({ j, cols, });\r\n                  }\r\n                }\r\n                return cols;\r\n              }\r\n              /**\r\n               * Calculates which columns to drop by index\r\n               * @todo support multiple iterations in the future, also only one output variable supported in column features * lookbacks -1\r\n               * @example\r\n            const ds = [\r\n              [10, 20, 30, 40, 50, 60, 70, 80, 90,],\r\n              [11, 21, 31, 41, 51, 61, 71, 81, 91,],\r\n              [12, 22, 32, 42, 52, 62, 72, 82, 92,],\r\n              [13, 23, 33, 43, 53, 63, 73, 83, 93,],\r\n              [14, 24, 34, 44, 54, 64, 74, 84, 94,],\r\n              [15, 25, 35, 45, 55, 65, 75, 85, 95,],\r\n              [16, 26, 36, 46, 56, 66, 76, 86, 96,],\r\n              [17, 27, 37, 47, 57, 67, 77, 87, 97,],\r\n              [18, 28, 38, 48, 58, 68, 78, 88, 98,],\r\n              [19, 29, 39, 49, 59, 69, 79, 89, 99,],\r\n            ];\r\n            const n_in = 1; //lookbacks\r\n            const n_out = 1;\r\n            const dropped = getDropableColumns(8, n_in, n_out); //=> [ 10, 11, 12, 13, 14, 15, 16, 17 ]\r\n               * @param {number} features - number of independent variables\r\n               * @param {number} n_in - look backs \r\n               * @param {number} n_out - future iterations (currently only 1 supported)\r\n               * @returns {Array<number>} array indexes to drop\r\n               */\r\n              static getDropableColumns(features, n_in, n_out) {\r\n                if (n_out !== 1) throw new RangeError('Only 1 future iteration supported');\r\n                const cols = features + 1;\r\n                const total_cols = cols * n_in + cols * n_out;\r\n                // console.log({ cols, total_cols });\r\n                return lodash_range(total_cols - cols +1, total_cols);\r\n              }\r\n              /**\r\n               * Reshape input to be [samples, time steps, features]\r\n               * @example\r\n               * @override \r\n               * LSTMTimeSeries.getTimeseriesShape([ \r\n                  [ [ 1 ], [ 2 ], [ 3 ] ],\r\n                  [ [ 2 ], [ 3 ], [ 4 ] ],\r\n                  [ [ 3 ], [ 4 ], [ 5 ] ],\r\n                  [ [ 4 ], [ 5 ], [ 6 ] ],\r\n                  [ [ 5 ], [ 6 ], [ 7 ] ],\r\n                  [ [ 6 ], [ 7 ], [ 8 ] ], \r\n                ]) //=> [6, 1, 3,]\r\n               * @param {Array<Array<number>} x_timeseries - dataset array of values\r\n               * @return {Array<Array<number>>} returns proper timeseries forecasting shape\r\n               */\r\n              static getTimeseriesShape(x_timeseries) {\r\n                const time_steps = (this.settings && this.settings.lookback) ? this.settings.lookback : 1;\r\n                const xShape = this.getInputShape(x_timeseries);\r\n                const _samples = xShape[ 0 ];\r\n                const _timeSteps = time_steps;\r\n                const _features = (this.settings && this.settings.features) ? this.settings.features : xShape[ 1 ];\r\n                // reshape input to be 3D [samples, timesteps, features]\r\n                // train_X = train_X.reshape((train_X.shape[0], n_hours, n_features))\r\n                const newShape = [ _samples, _timeSteps, _features, ];\r\n                // console.log({newShape})\r\n                return newShape;\r\n              }\r\n              /**\r\n               * Returns data for predicting values\r\n               * @param timeseries \r\n               * @param look_back \r\n               * @override \r\n               */\r\n              static getTimeseriesDataSet(timeseries, look_back) {\r\n                const lookBack = look_back || this.settings.lookback;\r\n                const matrices = LSTMMultivariateTimeSeries.createDataset.call(this, timeseries, lookBack);\r\n                const x_matrix = matrices[ 0 ];\r\n                const y_matrix_m = LSTMMultivariateTimeSeries.reshape(matrices[ 1 ], [ matrices[ 1 ].length, 1 ]);\r\n                const timeseriesShape = LSTMMultivariateTimeSeries.getTimeseriesShape.call(this,x_matrix);\r\n                // const timeseriesShape = LSTMMultivariateTimeSeries.getTimeseriesShape(x_matrix);\r\n                const x_matrix_timeseries = LSTMMultivariateTimeSeries.reshape(x_matrix, timeseriesShape);\r\n                // const x_matrix_timeseries = LSTMMultivariateTimeSeries.reshape(x_matrix, [x_matrix.length, lookBack, ]);\r\n                const xShape = LSTMMultivariateTimeSeries.getInputShape(x_matrix_timeseries);\r\n                const y_matrix = pivotVector(y_matrix_m)[ y_matrix_m[0].length - 1 ];\r\n\r\n                const yShape = LSTMMultivariateTimeSeries.getInputShape(y_matrix_m);\r\n                return {\r\n                  yShape,\r\n                  xShape,\r\n                  y_matrix,\r\n                  x_matrix: x_matrix_timeseries,\r\n                };\r\n              }\r\n              /**\r\n               * @param {{layers:Array<Object>,compile:Object,fit:Object}} options - neural network configuration and tensorflow model hyperparameters\r\n               * @param {{model:Object,tf:Object,}} properties - extra instance properties\r\n               */\r\n              constructor(options = {}, properties) {\r\n                const config = Object.assign({\r\n                  layers: [],\r\n                  stateful: true,\r\n                  mulitpleTimeSteps:false,\r\n                  lookback: 1,\r\n                  features: undefined,\r\n                  outputs:1,\r\n                  learningRate: 0.1,\r\n                  compile: {\r\n                    loss: 'meanSquaredError',\r\n                    optimizer: 'adam',\r\n                  },\r\n                  fit: {\r\n                    epochs: 100,\r\n                    batchSize: 1,\r\n                    shuffle: false,\r\n                  },\r\n                }, options);\r\n                super(config, properties);\r\n                this.createDataset = LSTMMultivariateTimeSeries.createDataset;\r\n                this.seriesToSupervised = LSTMMultivariateTimeSeries.seriesToSupervised;\r\n                this.getDropableColumns = LSTMMultivariateTimeSeries.getDropableColumns;\r\n                this.drop = LSTMMultivariateTimeSeries.drop;\r\n                this.getTimeseriesShape = LSTMMultivariateTimeSeries.getTimeseriesShape;\r\n                this.getTimeseriesDataSet = LSTMMultivariateTimeSeries.getTimeseriesDataSet;\r\n                return this;\r\n              }\r\n              /**\r\n               * Adds dense layers to tensorflow classification model\r\n               * @override \r\n               * @param {Array<Array<number>>} x_matrix - independent variables\r\n               * @param {Array<Array<number>>} y_matrix - dependent variables\r\n               * @param {Array<Object>} layers - model dense layer parameters\r\n               * @param {Array<Array<number>>} x_test - validation data independent variables\r\n               * @param {Array<Array<number>>} y_test - validation data dependent variables\r\n               */\r\n              generateLayers(x_matrix, y_matrix, layers) {\r\n                const xShape = this.getInputShape(x_matrix);\r\n                const yShape = this.getInputShape(y_matrix);\r\n                this.yShape = yShape;\r\n                this.xShape = xShape;\r\n                const lstmLayers = [];\r\n                const denseLayers = [];\r\n                if (layers) {\r\n                  if(layers.lstmLayers && layers.lstmLayers.length) lstmLayers.push(...layers.lstmLayers);\r\n                  if(layers.denseLayers && layers.denseLayers.length) denseLayers.push(...layers.denseLayers);\r\n               \r\n                } else {\r\n                  const inputShape = [ xShape[ 1 ], xShape[ 2 ], ];\r\n                  // console.log('default timeseries', { inputShape, xShape, yShape, });\r\n                  // model.add(LSTM(50, input_shape=(train_X.shape[1], train_X.shape[2])))\r\n                  // model.add(Dense(1))\r\n\r\n                  lstmLayers.push({ units: 10, inputShape,  });\r\n                  denseLayers.push({ units: yShape[1], });\r\n                }\r\n                // console.log('lstmLayers',lstmLayers)\r\n                // console.log('denseLayers',denseLayers)\r\n                if (lstmLayers.length) {\r\n                  lstmLayers.forEach(layer => {\r\n                    this.model.add(this.tf.layers.lstm(layer));\r\n                  });\r\n                }\r\n                if (denseLayers.length) {\r\n                  denseLayers.forEach(layer => {\r\n                    this.model.add(this.tf.layers.dense(layer));\r\n                  });\r\n                }\r\n                this.layers = {\r\n                  lstmLayers,\r\n                  denseLayers,\r\n                };\r\n                // this.settings.compile.optimizer = sgdoptimizer;\r\n              }\r\n              /**\r\n               * @override \r\n               * @param x_timeseries \r\n               * @param y_timeseries \r\n               * @param layers \r\n               * @param x_test \r\n               * @param y_test \r\n               */\r\n              async train(x_timeseries, y_timeseries, layers, x_test, y_test) {\r\n                let xShape;\r\n                let yShape;\r\n                let x_matrix;\r\n                let y_matrix;\r\n                const look_back = this.settings.lookback;\r\n                if (y_timeseries) {\r\n                  x_matrix = x_timeseries;\r\n                  y_matrix = y_timeseries;\r\n                  xShape = this.getInputShape(x_matrix);\r\n                  yShape = this.getInputShape(y_matrix);\r\n                } else {\r\n                  const matrices = this.createDataset(x_timeseries, look_back);\r\n                  // console.log({matrices})\r\n                  x_matrix = matrices[ 0 ];\r\n                  y_matrix = this.reshape(matrices[ 1 ], [ matrices[ 1 ].length, 1 ]);\r\n                  xShape = this.getInputShape(x_matrix);\r\n                  yShape = this.getInputShape(y_matrix);\r\n                }\r\n                //_samples, _timeSteps, _features\r\n                const timeseriesShape = this.getTimeseriesShape(x_matrix);\r\n                // console.log({\r\n                //   timeseriesShape, yShape, xShape,\r\n                //   // y_matrix, x_matrix,\r\n                // });\r\n                const x_matrix_timeseries = LSTMMultivariateTimeSeries.reshape(x_matrix, timeseriesShape);\r\n                // console.log('x_matrix_timeseries',JSON.stringify(x_matrix_timeseries));\r\n                // console.log('x_matrix',JSON.stringify(x_matrix));\r\n                // console.log('y_matrix',JSON.stringify(y_matrix));\r\n                const xs = this.tf.tensor(x_matrix_timeseries, timeseriesShape);\r\n                const ys = this.tf.tensor(y_matrix, yShape);\r\n                this.xShape = timeseriesShape;\r\n                this.yShape = yShape;\r\n                this.model = this.tf.sequential();\r\n                this.generateLayers.call(this, x_matrix_timeseries, y_matrix, layers || this.layers, x_test, y_test);\r\n                this.model.compile(this.settings.compile);\r\n                if (x_test && y_test) {\r\n                  this.settings.fit.validation_data = [ x_test, y_test, ];\r\n                }\r\n                await this.model.fit(xs, ys, this.settings.fit);\r\n                // this.model.summary();\r\n                xs.dispose();\r\n                ys.dispose();\r\n                return this.model;\r\n              }\r\n            }\r\n\r\n            /**\r\n             * Returns an array of vectors as an array of arrays (from modelscript)\r\n             * @example\r\n            const vectors = [ [1,2,3], [1,2,3], [3,3,4], [3,3,3] ];\r\n            const arrays = pivotVector(vectors); // => [ [1,2,3,3], [2,2,3,3], [3,3,4,3] ];\r\n             * @memberOf util\r\n             * @param {Array[]} vectors \r\n             * @returns {Array[]}\r\n             * @ignore\r\n             * @see {https://github.com/repetere/modelscript/blob/master/src/util.js}\r\n             */\r\n            /* istanbul ignore next */\r\n            function pivotVector(vectors=[]) {\r\n              /* istanbul ignore next */\r\n              return vectors.reduce((result, val, index/*, arr*/) => {\r\n                val.forEach((vecVal, i) => {\r\n                  (index === 0)\r\n                    ? (result.push([vecVal,]))\r\n                    : (result[ i ].push(vecVal));\r\n                });\r\n                return result;\r\n              }, []);\r\n            }\n\n            exports.TensorScriptModelInterface = TensorScriptModelInterface;\n            exports.BaseNeuralNetwork = BaseNeuralNetwork;\n            exports.DeepLearningRegression = DeepLearningRegression;\n            exports.DeepLearningClassification = DeepLearningClassification;\n            exports.LogisticRegression = LogisticRegression;\n            exports.MultipleLinearRegression = MultipleLinearRegression;\n            exports.LSTMTimeSeries = LSTMTimeSeries;\n            exports.LSTMMultivariateTimeSeries = LSTMMultivariateTimeSeries;\n\n            Object.defineProperty(exports, '__esModule', { value: true });\n\n})));\n"]}