Quant_Code/5.课程代码/4.Stock-Prediction-Models/使用文档/Stock-Prediction-Models-master/stock-forecasting-js/js/tf-expand.js

! function(e, t) {
    "object" == typeof exports && "undefined" != typeof module ? t(exports) : "function" == typeof define && define.amd ? define(["exports"], t) : t(e.tf = e.tf || {})
}(this, function(exports) {
    "use strict";

    function isMobile() {
        var e = navigator.userAgent || navigator.vendor || window.opera;
        return /(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(e) || /1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i.test(e.substr(0, 4))
    }

    function doc(e) {
        return function() {
            for (var e = [], t = 0; t < arguments.length; t++) e[t] = arguments[t]
        }
    }

    function assertArgumentIsTensor(e, t, r) {
        assert(e instanceof Tensor, "Argument '" + t + "' passed to '" + r + "' must be a Tensor, but got " + typeof e + ".")
    }

    function assertArgumentsAreTensors(e, t) {
        for (var r in e) ! function(r) {
            var n = e[r];
            Array.isArray(n) ? n.forEach(function(e, n) {
                assertArgumentIsTensor(e, r + "[" + n + "]", t)
            }) : assertArgumentIsTensor(n, r, t)
        }(r)
    }

    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 randUniform(e, t) {
        return Math.random() * (t - e) + e
    }

    function distSquared(e, t) {
        for (var r = 0, n = 0; n < e.length; n++) {
            var a = Number(e[n]) - Number(t[n]);
            r += a * a
        }
        return r
    }

    function assert(e, t) {
        if (!e) throw new Error(t)
    }

    function assertShapesMatch(e, t, r) {
        void 0 === r && (r = ""), assert(arraysEqual(e, t), r + " Shapes " + e + " and " + t + " must match")
    }

    function assertTypesMatch(e, t) {
        assert(e.dtype === t.dtype, " The dtypes of the first(" + e.dtype + ") and second(" + t.dtype + ") input must match")
    }

    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 inferShape(e) {
        if (isTypedArray(e)) return [e.length];
        if (!Array.isArray(e)) return [];
        for (var t = []; e instanceof Array;) t.push(e.length), e = e[0];
        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.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, a) {
            var o = 0,
                i = function() {
                    if (e()) n();
                    else {
                        var s = t(++o);
                        null != r && o >= r ? a() : setTimeout(i, s)
                    }
                };
            i()
        })
    }

    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 || "")
    }

    function inferFromImplicitShape(e, t) {
        for (var r = 1, n = -1, a = 0; a < e.length; ++a)
            if (e[a] > 0) r *= e[a];
            else if (-1 === e[a]) {
            if (-1 !== n) throw Error("Shapes can only have 1 implicit size. Found - 1 at dim " + n + " and dim " + a);
            n = a
        } else if (e[a] <= 0) throw Error("Shapes can not be <= 0. Found " + e[a] + " at dim " + a);
        if (-1 === n) {
            if (t > 0 && t !== r) throw Error("Size(" + t + ") must match the product of shape " + e);
            return e
        }
        if (t % r != 0) throw Error("The implicit shape can't be a fractional number. Got " + t + " / " + r);
        var o = e.slice();
        return o[n] = t / r, o
    }

    function squeezeShape(e, t) {
        for (var r = [], n = [], a = 0, o = 0; o < e.length; ++o) {
            if (null != t) {
                if (t[a] === o && e[o] > 1) throw new Error("Can't squeeze axis " + o + " since its dim '" + e[o] + "' is not 1");
                (null == t[a] || t[a] > o) && 1 === e[o] && (r.push(e[o]), n.push(o)), t[a] <= o && a++
            }
            e[o] > 1 && (r.push(e[o]), n.push(o))
        }
        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 isTensorInList(e, t) {
        for (var r = 0; r < t.length; r++)
            if (t[r].id === e.id) return !0;
        return !1
    }

    function checkForNaN(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 flattenNameArrayMap(e, t) {
        var r = [];
        if (e instanceof Tensor) r.push(e);
        else
            for (var n = e, a = 0; a < t.length; a++) r.push(n[t[a]]);
        return r
    }

    function unflattenToNameArrayMap(e, t) {
        if (e.length !== t.length) throw new Error("Cannot unflatten Tensor[], keys and arrays are not of same length.");
        for (var r = {}, n = 0; n < e.length; n++) r[e[n]] = t[n];
        return r
    }

    function hasEncodingLoss(e, t) {
        return "float32" !== t && (("int32" !== t || "float32" === e) && ("bool" !== t || "bool" !== e))
    }

    function copyTypedArray(e, t) {
        if (null == t || "float32" === t) return new Float32Array(e);
        if ("int32" === t) return new Int32Array(e);
        if ("bool" === t) {
            for (var r = new Uint8Array(e.length), n = 0; n < r.length; ++n) 0 !== Math.round(e[n]) && (r[n] = 1);
            return r
        }
        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 ("bool" === e) return 1;
        throw new Error("Unknown dtype " + e)
    }

    function isFunction(e) {
        return !!(e && e.constructor && e.call && e.apply)
    }

    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 a in n) {
                var o = n[a];
                r.has(o) || (r.add(o), walkTensorContainer(o, t, r))
            }
        }
    }

    function isIterable(e) {
        return Array.isArray(e) || "object" == typeof e
    }

    function tensorToString(e, t) {
        var r = e.dataSync(),
            n = computeMaxSizePerColumn(e),
            a = subTensorToString(r, e.shape, e.strides, n),
            o = ["Tensor"];
        return t && (o.push("  dtype: " + e.dtype), o.push("  rank: " + e.rank), o.push("  shape: [" + e.shape + "]"), o.push("  values:")), o.push(a.map(function(e) {
            return "    " + e
        }).join("\n")), o.join("\n")
    }

    function computeMaxSizePerColumn(e) {
        var t = e.dataSync(),
            r = e.size,
            n = e.strides[e.strides.length - 1],
            a = new Array(n).fill(0);
        if (e.rank > 1)
            for (var o = 0; o < r / n; o++)
                for (var i = o * n, s = 0; s < n; s++) a[s] = Math.max(a[s], valToString(t[i + s], 0).length);
        return a
    }

    function valToString(e, t) {
        return rightPad(parseFloat(e.toFixed(FORMAT_NUM_SIG_DIGITS)).toString(), t)
    }

    function subTensorToString(e, t, r, n, a) {
        void 0 === a && (a = !0);
        var o = t[0],
            i = t.length;
        if (0 === i) return [e[0].toString()];
        if (1 === i) {
            if (o > FORMAT_LIMIT_NUM_VALS) {
                var s = Array.from(e.subarray(0, FORMAT_NUM_FIRST_LAST_VALS)),
                    u = Array.from(e.subarray(o - FORMAT_NUM_FIRST_LAST_VALS, o));
                return ["[" + s.map(function(e, t) {
                    return valToString(e, n[t])
                }).join(", ") + ", ..., " + u.map(function(e, t) {
                    return valToString(e, n[o - FORMAT_NUM_FIRST_LAST_VALS + t])
                }).join(", ") + "]"]
            }
            return ["[" + Array.from(e).map(function(e, t) {
                return valToString(e, n[t])
            }).join(", ") + "]"]
        }
        var l = t.slice(1),
            c = r.slice(1),
            p = r[0],
            d = [];
        if (o > FORMAT_LIMIT_NUM_VALS) {
            for (y = 0; y < FORMAT_NUM_FIRST_LAST_VALS; y++) {
                f = (h = y * p) + p;
                d.push.apply(d, subTensorToString(e.subarray(h, f), l, c, n, !1))
            }
            d.push("...");
            for (y = o - FORMAT_NUM_FIRST_LAST_VALS; y < o; y++) {
                f = (h = y * p) + p;
                d.push.apply(d, subTensorToString(e.subarray(h, f), l, c, n, y === o - 1))
            }
        } else
            for (y = 0; y < o; y++) {
                var h = y * p,
                    f = h + p;
                d.push.apply(d, subTensorToString(e.subarray(h, f), l, c, n, y === o - 1))
            }
        var m = 2 === i ? "," : "";
        d[0] = "[" + d[0] + m;
        for (y = 1; y < d.length - 1; y++) d[y] = " " + d[y] + m;
        for (var g = ",\n", y = 2; y < i; y++) g += "\n";
        return d[d.length - 1] = " " + d[d.length - 1] + "]" + (a ? "" : g), d
    }

    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, a = [], o = 0, i = 0, s = 0; s < n; s++) - 1 === r.indexOf(s) ? a.push(e[o++]) : a.push(t[i++]);
        return a
    }

    function computeOutAndReduceShapes(e, t) {
        for (var r = [], n = e.length, a = 0; a < n; a++) - 1 === t.indexOf(a) && r.push(e[a]);
        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 e = null == e ? t.map(function(e, t) {
            return t
        }) : [].concat(e), assert(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 assertParams(e, t, r) {
        var n = e.length,
            a = t.length;
        assert(e.length === t.length, "Error in concat" + n + "D: rank of x1 (" + n + ") and x2 (" + a + ") must be the same."), assert(r >= 0 && r < n, "Error in concat" + n + "D: axis must be between 0 and " + (n - 1) + ".");
        for (var o = 0; o < n; o++) assert(o === r || e[o] === t[o], "Error in concat" + n + "D: Shape (" + e + ") does not match (" + t + ") along the non-concatenated axis " + o + ".")
    }

    function computeOutShape(e, t, r) {
        assert(e.length === t.length, "x1 and x2 should have the same rank.");
        var n = e.slice();
        return n[r] += t[r], n
    }

    function computeGradientSliceShapes(e, t) {
        return {
            aBegin: [0, 0],
            aSize: e,
            bBegin: [0, e[1]],
            bSize: t
        }
    }

    function operation(e, t, r) {
        var n = r.value;
        return r.value = function() {
            for (var e = [], r = 0; r < arguments.length; r++) e[r] = arguments[r];
            return tidy(t, function() {
                return n.apply(void 0, e)
            })
        }, r
    }

    function concat2Tensors(e, t, r) {
        assertParams(e.shape, t.shape, r);
        var n = computeOutShape(e.shape, t.shape, r),
            a = e.as2D(-1, sizeFromShape(e.shape.slice(r))),
            o = t.as2D(-1, sizeFromShape(t.shape.slice(r))),
            i = computeGradientSliceShapes(a.shape, o.shape),
            s = i.aBegin,
            u = i.aSize,
            l = i.bBegin,
            c = i.bSize;
        return ENV.engine.runKernel(function(e) {
            return e.concat(a, o)
        }, {
            a: a,
            b: o
        }, function(e) {
            return {
                a: function() {
                    return e.slice(s, u)
                },
                b: function() {
                    return e.slice(l, c)
                }
            }
        }).reshape(n)
    }

    function createCommonjsModule(e, t) {
        return t = {
            exports: {}
        }, e(t, t.exports), t.exports
    }

    function makeZerosTypedArray(e, t) {
        if (null == t || "float32" === 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 $ {dtype}")
    }

    function makeOnesTypedArray(e, t) {
        for (var r = makeZerosTypedArray(e, t), n = 0; n < r.length; n++) r[n] = 1;
        return r
    }

    function toTypedArray(e, t) {
        return noConversionNeeded(e, t) ? e : (Array.isArray(e) && (e = flatten(e)), copyTypedArray(e, t))
    }

    function noConversionNeeded(e, t) {
        return e instanceof Float32Array && "float32" === t || e instanceof Int32Array && "int32" === t || e instanceof Uint8Array && "bool" === t
    }

    function getBroadcastDims(e, t) {
        for (var r = e.length, n = [], a = 0; a < r; a++) {
            var o = r - 1 - a,
                i = e[o] || 1;
            (t[t.length - 1 - a] || 1) > 1 && 1 === i && n.unshift(o)
        }
        return n
    }

    function getReductionAxes(e, t) {
        for (var r = [], n = 0; n < t.length; n++) {
            var a = e[e.length - n - 1],
                o = t.length - n - 1,
                i = t[o];
            (null == a || 1 === a && i > 1) && r.unshift(o)
        }
        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 = "Operands could not be broadcast together with shapes " + e + " and " + t + ".", a = Math.max(e.length, t.length), o = 0; o < a; o++) {
            var i = e[e.length - o - 1] || 1,
                s = t[t.length - o - 1] || 1;
            if (i > 1 && s > 1 && i !== s) throw Error(n);
            r.unshift(Math.max(i, s))
        }
        return r
    }

    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
    }

    function upcastType(e, t) {
        return upcastTypeMap[e][t]
    }

    function sumOutType(e) {
        return upcastType(e, "int32")
    }

    function computePool2DInfo(e, t, r, n, a, o) {
        void 0 === o && (o = "channelsLast");
        var i, s = parseTupleParam(t),
            u = s[0],
            l = s[1];
        if ("channelsLast" === o) i = [u, l, e[3], e[3]];
        else {
            if ("channelsFirst" !== o) throw new Error("Unknown dataFormat " + o);
            i = [u, l, e[1], e[1]]
        }
        return computeConv2DInfo(e, i, r, 1, n, a, !1, o)
    }

    function computeConv2DInfo(e, t, r, n, a, o, 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],
            y = parseTupleParam(r),
            v = y[0],
            b = y[1],
            x = parseTupleParam(n),
            w = x[0],
            _ = x[1],
            S = getPadAndOutInfo(a, c, p, v, b, getEffectiveFilterSize(f, w), getEffectiveFilterSize(m, _), o),
            N = S.padInfo,
            A = S.outHeight,
            E = S.outWidth,
            T = i ? g * d : g;
        return "channelsFirst" === s ? h = [l, T, A, E] : "channelsLast" === s && (h = [l, A, E, T]), {
            batchSize: l,
            dataFormat: s,
            inHeight: c,
            inWidth: p,
            inChannels: d,
            outHeight: A,
            outWidth: E,
            outChannels: T,
            padInfo: N,
            strideHeight: v,
            strideWidth: b,
            filterHeight: f,
            filterWidth: m,
            dilationHeight: w,
            dilationWidth: _,
            inShape: e,
            outShape: h,
            filterShape: t
        }
    }

    function computeOutputShape3D(e, t, r, n, a, o) {
        null == a && (a = computeDefaultPad(e, t, n));
        var i = e[0],
            s = e[1],
            u = conditionalRound((i - t + 2 * a) / n + 1, o);
        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 * a) / n + 1, o);
        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 a = getEffectiveFilterSize(t, n);
        return Math.floor((e[0] * (r - 1) - r + a) / 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, a, o, 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], o, 1, n, e, s);
            l = p[0], c = p[1]
        } else if ("same" === e) {
            var d = ((l = Math.ceil(t / n)) - 1) * n + o - t,
                h = ((c = Math.ceil(r / a)) - 1) * a + 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 - o + 1) / n), c = Math.ceil((r - i + 1) / a)
        }
        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 parseTupleParam$1(e) {
        return "number" == typeof e ? [e, e] : e
    }

    function tupleValuesAreOne(e) {
        var t = parseTupleParam$1(e),
            r = t[0],
            n = t[1];
        return 1 === r && 1 === n
    }

    function eitherStridesOrDilationsAreOne(e, t) {
        return tupleValuesAreOne(e) || tupleValuesAreOne(t)
    }

    function depthwiseConv2dDerInput(e, t, r, n) {
        var a = t,
            o = !1;
        3 === t.rank && (o = !0, a = t.as4D(1, t.shape[0], t.shape[1], t.shape[2]));
        var i = ENV.engine.runKernel(function(e) {
            return e.depthwiseConv2DDerInput(a, r, n)
        }, {
            dy4D: a
        });
        return o ? i.as3D(i.shape[1], i.shape[2], i.shape[3]) : i
    }

    function depthwiseConv2dDerFilter(e, t, r, n) {
        var a = e;
        3 === e.rank && (a = e.as4D(1, e.shape[0], e.shape[1], e.shape[2]));
        var o = t;
        return 3 === o.rank && (o = t.as4D(1, t.shape[0], t.shape[1], t.shape[2])), ENV.engine.runKernel(function(e) {
            return e.depthwiseConv2DDerFilter(a, o, n)
        }, {
            x4D: a,
            dy4D: o
        })
    }

    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)
    }

    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, a, o) {
        void 0 === a && (a = 0), void 0 === o && (o = 0);
        for (var i = [], s = [], u = 0; u < e.length; u++) i[u] = startForAxis(a, t, n, e, u), s[u] = stopForAxis(o, r, n, e, u);
        var l = new Array(e.length).fill(0);
        return l = l.map(function(e, t) {
            for (var r = 0, a = i[t]; !(n[t] > 0 ? a >= s[t] : a <= s[t]); a += n[t]) r += 1;
            return r
        }), [i, l]
    }

    function startForAxis(e, t, r, n, a) {
        var o = t[a];
        e & 1 << a && (o = r[a] > 0 ? Number.MIN_SAFE_INTEGER : Number.MAX_SAFE_INTEGER);
        var i = n[a];
        return o < 0 && (o += i), o = clamp(0, o, i - 1)
    }

    function stopForAxis(e, t, r, n, a) {
        var o = t[a];
        e & 1 << a && (o = r[a] > 0 ? Number.MAX_SAFE_INTEGER : Number.MIN_SAFE_INTEGER);
        var i = n[a];
        return o < 0 && (o += i), o = r[a] > 0 ? clamp(0, o, i) : clamp(-1, o, i - 1)
    }

    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 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.")
    }

    function getFilteredNodesXToY(e, t, r) {
        for (var n = {}, a = {}, o = 0; o < t.length; o++) n[t[o].id] = !0;
        for (o = 0; o < e.length; o++) {
            d = (m = e[o]).inputs;
            for (var i in d) {
                for (var s = d[i], u = !1, l = 0; l < t.length; l++)
                    if (n[s.id]) {
                        n[m.output.id] = !0, u = !0, a[m.id] = !0;
                        break
                    }
                if (u) break
            }
        }
        var c = {};
        c[r.id] = !0;
        for (var p = {}, o = e.length - 1; o >= 0; o--) {
            var d = (m = e[o]).inputs,
                h = [];
            h.push(m.output);
            for (l = 0; l < h.length; l++)
                if (c[h[l].id]) {
                    for (var i in d) c[d[i].id] = !0, p[m.id] = !0;
                    break
                }
        }
        for (var f = [], o = 0; o < e.length; o++) {
            var m = e[o];
            if (a[m.id] && p[m.id]) {
                var g = {};
                for (var i in m.inputs) {
                    var y = m.inputs[i];
                    n[y.id] && (g[i] = y)
                }
                var v = Object.assign({}, m);
                v.inputs = g, v.output = m.output, f.push(v)
            }
        }
        return f
    }

    function backpropagateGradients(e, t) {
        for (var r = t.length - 1; r >= 0; r--) {
            var n = t[r],
                a = e[n.output.id];
            if (null == n.gradient) throw new Error("Cannot compute gradient: gradient function not found for " + n.name + ".");
            var o = n.gradient(a);
            for (var i in n.inputs) {
                if (!(i in o)) throw new Error("Cannot backprop through input " + i + ". Available gradients found: " + Object.keys(o) + ".");
                var s = o[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()
                }
            }
        }
    }

    function hasExtension(e, t) {
        return null != e.getExtension(t)
    }

    function getWebGLRenderingContext(e) {
        if (0 === e || !ENV.get("IS_BROWSER")) throw new Error("Cannot get WebGL rendering context, WebGL is disabled.");
        var t = document.createElement("canvas");
        return 1 === e ? t.getContext("webgl") || t.getContext("experimental-webgl") : t.getContext("webgl2")
    }

    function loseContext(e) {
        if (null != e) {
            var t = e.getExtension("WEBGL_lose_context");
            if (null == t) throw new Error("Extension WEBGL_lose_context not supported on this browser.");
            t.loseContext()
        }
    }

    function isWebGLVersionEnabled(e) {
        var t = getWebGLRenderingContext(e);
        return null != t && (loseContext(t), !0)
    }

    function getWebGLDisjointQueryTimerVersion(e) {
        if (0 === e) return 0;
        var t, r = getWebGLRenderingContext(e);
        return t = hasExtension(r, "EXT_disjoint_timer_query_webgl2") && 2 === e ? 2 : hasExtension(r, "EXT_disjoint_timer_query") ? 1 : 0, null != r && loseContext(r), t
    }

    function isFloatTextureReadPixelsEnabled(e) {
        if (0 === e) return !1;
        var t = getWebGLRenderingContext(e);
        if (1 === e) {
            if (!hasExtension(t, "OES_texture_float")) return !1
        } else if (!hasExtension(t, "EXT_color_buffer_float")) return !1;
        var r = t.createFramebuffer(),
            n = t.createTexture();
        t.bindTexture(t.TEXTURE_2D, n);
        var a = 2 === e ? t.RGBA32F : t.RGBA;
        t.texImage2D(t.TEXTURE_2D, 0, a, 1, 1, 0, t.RGBA, t.FLOAT, null), t.bindFramebuffer(t.FRAMEBUFFER, r), t.framebufferTexture2D(t.FRAMEBUFFER, t.COLOR_ATTACHMENT0, t.TEXTURE_2D, n, 0);
        var o = t.checkFramebufferStatus(t.FRAMEBUFFER) === t.FRAMEBUFFER_COMPLETE;
        t.readPixels(0, 0, 1, 1, t.RGBA, t.FLOAT, new Float32Array(4));
        var i = t.getError() === t.NO_ERROR;
        return loseContext(t), o && i
    }

    function isWebGLGetBufferSubDataAsyncExtensionEnabled(e) {
        if (e > 0) return !1;
        if (2 !== e) return !1;
        var t = getWebGLRenderingContext(e),
            r = hasExtension(t, "WEBGL_get_buffer_sub_data_async");
        return loseContext(t), r
    }

    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],
                    a = t[1];
                r[n] = a
            }), 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 getGlobalNamespace() {
        var e;
        if ("undefined" != typeof window) e = window;
        else {
            if ("undefined" == typeof global) throw new Error("Could not find a global object");
            e = global
        }
        return e
    }

    function getOrMakeEnvironment() {
        var e = getGlobalNamespace();
        return e.ENV = e.ENV || new Environment(getFeaturesFromURL()), e.ENV
    }

    function computeOptimalWindowSize(e) {
        return e <= PARALLELIZE_THRESHOLD ? e : nearestDivisor(e, Math.floor(Math.sqrt(e)))
    }

    function nearestDivisor(e, t) {
        for (var r = t; r < e; ++r)
            if (e % r == 0) return r;
        return e
    }

    function castTensor(e, t, r) {
        if (!hasEncodingLoss(e.dtype, t)) return Tensor.make(e.shape, {
            dataId: e.dataId
        }, t);
        if ("int32" === t) return r.int(e);
        if ("bool" === t) return r.notEqual(e, ArrayOps.scalar(0, e.dtype));
        throw new Error("Error in Cast: unknown dtype argument (" + t + ")")
    }

    function reshapeTensor(e, t) {
        return Tensor.make(t, {
            dataId: e.dataId
        }, e.dtype)
    }

    function getUnpackedMatrixTextureShapeWidthHeight(e, t) {
        return [t, e]
    }

    function getUnpackedArraySizeFromMatrixSize(e, t) {
        return e * t
    }

    function getColorMatrixTextureShapeWidthHeight(e, t) {
        return [4 * t, e]
    }

    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 a = 0, o = 0; o < e.length; ++o) t[a] = e[o], a += r
    }

    function encodeFloatArray(e) {
        for (var t = new Uint8Array(4 * e.length), r = 0; r < t.length; r += 4) ! function(r) {
            var n = e[r / 4];
            if (isNaN(n)) return t[r] = BYTE_NAN_VALUE, t[r + 1] = BYTE_NAN_VALUE, t[r + 2] = BYTE_NAN_VALUE, t[r + 3] = BYTE_NAN_VALUE, "continue";
            var a = (n - FLOAT_MIN) / FLOAT_RANGE,
                o = FLOAT_POWERS.map(function(e) {
                    return e * a
                }).map(function(e) {
                    return Math.floor(e % 1 * 255)
                });
            t[r] = Math.floor(a), t[r + 1] = o[0], t[r + 2] = o[1], t[r + 3] = o[2]
        }(r);
        return t
    }

    function decodeToFloatArray(e) {
        for (var t = new Float32Array(e.length / 4), r = 0; r < e.length; r += 4) ! function(r) {
            if (e[r] === BYTE_NAN_VALUE && e[r + 1] === BYTE_NAN_VALUE && e[r + 2] === BYTE_NAN_VALUE && e[r + 3] === BYTE_NAN_VALUE) return t[r / 4] = NaN, "continue";
            var n = 0;
            FLOAT_DELTAS.forEach(function(t, a) {
                n += t * e[r + a]
            });
            var a = n * FLOAT_RANGE + FLOAT_MIN;
            t[r / 4] = a
        }(r);
        return t
    }

    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 a = 0, o = 0; o < e.length; o += r) t[a++] = e[o]
    }

    function decodeMatrixFromUnpackedColorRGBAArray(e, t, r) {
        var n = e.length * r / 4;
        if (t.length < n) throw new Error("matrix length (" + t.length + ") must be >= " + n);
        for (var a = 0, o = 0; o < e.length; o += 4)
            for (var i = 0; i < r; i++) t[a++] = e[o + i]
    }

    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) {
        var a = getPackedRGBAArraySizeFromMatrixShape(t, r);
        if (n.length < a) throw new Error("packedRGBA length (" + n.length + ") must be >= " + a);
        for (var o = getPackedMatrixTextureShapeWidthHeight(t, r), i = o[0], s = o[1], u = r % 2 == 1, l = t % 2 == 1, c = Math.floor(r / 2), p = Math.floor(t / 2), d = u ? 4 : 0, h = r, f = 0, m = 0; m < p; ++m) {
            for (var g = 2 * m * r, y = 0; y < c; ++y) {
                v = g + 2 * y;
                n[f] = e[v], n[f + 1] = e[v + 1], n[f + 2] = e[v + h], n[f + 3] = e[v + h + 1], f += 4
            }
            f += d
        }
        if (u)
            for (var v = r - 1, f = 4 * (i - 1), b = 2 * r, d = 4 * i, m = 0; m < p; ++m) n[f] = e[v], n[f + 2] = e[v + r], v += b, f += d;
        if (l)
            for (var v = (t - 1) * r, f = (s - 1) * i * 4, y = 0; y < c; ++y) n[f++] = e[v++], n[f++] = e[v++], f += 2;
        return u && l && (n[n.length - 4] = e[e.length - 1]), n
    }

    function decodeMatrixFromPackedRGBA(e, t, r, n) {
        var a = t * r;
        if (a < n.length) throw new Error("matrix length (" + n.length + ") must be >= " + a);
        for (var o = r % 2 == 1, i = t % 2 == 1, s = Math.floor(r / 2), u = Math.floor(t / 2), l = getPackedMatrixTextureShapeWidthHeight(t, r), c = l[0], p = l[1], d = o ? 4 : 0, h = r + (o ? 1 : 0), f = 0, m = 0, g = r, y = 0; y < u; ++y) {
            for (b = 0; b < s; ++b) n[m++] = e[f++], n[m++] = e[f++], n[g++] = e[f++], n[g++] = e[f++];
            f += d, m += h, g += h
        }
        if (o)
            for (var f = 4 * (c - 1), v = r - 1, d = 4 * c, h = 2 * r, y = 0; y < u; ++y) n[v] = e[f], n[v + r] = e[f + 2], f += d, v += h;
        if (i)
            for (var f = (p - 1) * c * 4, v = (t - 1) * r, b = 0; b < s; ++b) n[v++] = e[f++], n[v++] = e[f++], f += 2;
        return o && i && (n[n.length - 1] = e[e.length - 4]), n
    }

    function makeShader(e, t, r, n) {
        var a = getSampleSnippet(),
            o = getSetOutputSnippet(),
            i = e.map(function(e) {
                return "uniform sampler2D " + e.name + ";"
            }).join("\n"),
            s = e.map(function(e) {
                return getInputSamplingSnippet(e, t, n)
            }).join("\n"),
            u = t.texShape,
            l = getOutputSamplingSnippet(t.logicalShape, u);
        return [SHADER_PREFIX, a, o, i, l, s, r].join("\n")
    }

    function getSampleSnippet() {
        return ENV.get("WEBGL_FLOAT_TEXTURE_ENABLED") ? FLOAT_TEXTURE_SAMPLE_SNIPPET : UNSIGNED_BYTE_TEXTURE_SAMPLE_SNIPPET
    }

    function getSetOutputSnippet() {
        return ENV.get("WEBGL_FLOAT_TEXTURE_ENABLED") ? FLOAT_TEXTURE_SETOUTPUT_SNIPPET : UNSIGNED_BYTE_TEXTURE_SETOUTPUT_SNIPPET
    }

    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);
            default:
                throw new Error(t.length + "-D input sampling is not yet supported")
        }
    }

    function getInputSamplingSnippet(e, t, r) {
        var n = getSamplerFlat(e);
        return n += getSamplerFromInInfo(e), (r || arraysEqual(e.shapeInfo.logicalShape, t.logicalShape)) && (n += getSamplerAtOutputCoords(e, t, r)), n
    }

    function getOutputSamplingSnippet(e, t) {
        switch (e.length) {
            case 0:
                return "\n    int getOutputCoords() {\n      return 0;\n    }\n  ";
            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);
            default:
                throw new Error(e.length + "-D output sampling is not yet supported")
        }
    }

    function getOutputScalarCoords() {
        return "\n    int getOutputCoords() {\n      return 0;\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 getOutput4DCoords(e, t) {
        var r = e[3],
            n = e[2] * r,
            a = 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 / " + a + ";\n      index -= r * " + a + ";\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,
            a = e[2] * n,
            o = e[1] * a;
        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 / " + o + ";\n      index -= r * " + o + ";\n\n      int c = index / " + a + ";\n      index -= c * " + a + ";\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 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;
        return "\n    float " + ("get" + t.charAt(0).toUpperCase() + t.slice(1)) + "() {\n      return sampleTexture(" + t + ", halfCR);\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 getSampler2D(e) {
        var t = e.shapeInfo.logicalShape,
            r = e.shapeInfo.texShape,
            n = e.name,
            a = "get" + n.charAt(0).toUpperCase() + n.slice(1),
            o = r[0],
            i = r[1];
        if (arraysEqual(t, r)) return "\n    float " + a + "(int row, int col) {\n      vec2 uv = (vec2(col, row) + halfCR) / vec2(" + i + ".0, " + o + ".0);\n      return sampleTexture(" + n + ", uv);\n    }\n  ";
        var s = squeezeShape(t),
            u = s.newShape,
            l = s.keptDims,
            c = u;
        if (c.length < t.length) {
            var p = ["row", "col"];
            return "\n      " + getSamplerFromInInfo(squeezeInputInfo(e, c)) + "\n      float " + a + "(int row, int col) {\n        return " + a + "(" + getSqueezedParams(p, l) + ");\n      }\n    "
        }
        return 1 === i ? "\n    float " + a + "(int row, int col) {\n      int index = row * " + t[1] + " + col;\n      vec2 uv = vec2(0.5, (float(index) + 0.5) / " + o + ".0);\n      return sampleTexture(" + n + ", uv);\n    }\n  " : 1 === o ? "\n    float " + a + "(int row, int col) {\n      int index = row * " + t[1] + " + col;\n      vec2 uv = vec2((float(index) + 0.5) / " + i + ".0, 0.5);\n      return sampleTexture(" + n + ", uv);\n    }\n  " : "\n  float " + a + "(int row, int col) {\n    vec2 uv = UVfrom2D(" + o + ", " + i + ", " + t[1] + ", row, col);\n    return sampleTexture(" + n + ", uv);\n  }\n"
    }

    function getSampler3D(e) {
        var t = e.shapeInfo.texShape,
            r = e.shapeInfo.logicalShape,
            n = e.name,
            a = "get" + n.charAt(0).toUpperCase() + n.slice(1),
            o = t[0],
            i = t[1],
            s = r[1] * r[2],
            u = r[2],
            l = squeezeShape(r),
            c = l.newShape,
            p = l.keptDims,
            d = c;
        if (d.length < r.length) {
            var h = ["row", "col", "depth"];
            return "\n        " + getSamplerFromInInfo(squeezeInputInfo(e, d)) + "\n        float " + a + "(int row, int col, int depth) {\n          return " + a + "(" + getSqueezedParams(h, p) + ");\n        }\n      "
        }
        return i === s ? "\n        float " + a + "(int row, int col, int depth) {\n          int texR = row;\n          int texC = col * " + u + " + depth;\n          vec2 uv = (vec2(texC, texR) + halfCR) /\n                     vec2(" + i + ".0, " + o + ".0);\n          return sampleTexture(" + n + ", uv);\n        }\n      " : i === u ? "\n    float " + a + "(int row, int col, int depth) {\n      int texR = row * " + r[1] + " + col;\n      int texC = depth;\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(" + i + ".0, " + o + ".0);\n      return sampleTexture(" + n + ", uv);\n    }\n  " : "\n      float " + a + "(int row, int col, int depth) {\n        vec2 uv = UVfrom3D(\n            " + o + ", " + i + ", " + s + ", " + u + ", row, col, depth);\n        return sampleTexture(" + n + ", uv);\n      }\n  "
    }

    function getSampler4D(e) {
        var t = e.shapeInfo.logicalShape,
            r = e.shapeInfo.texShape,
            n = e.name,
            a = "get" + n.charAt(0).toUpperCase() + n.slice(1),
            o = r[0],
            i = r[1],
            s = t[3],
            u = t[2] * s,
            l = t[1] * u,
            c = squeezeShape(t),
            p = c.newShape,
            d = c.keptDims;
        if (p.length < t.length) {
            var h = ["row", "col", "depth", "depth2"];
            return "\n      " + getSamplerFromInInfo(squeezeInputInfo(e, p)) + "\n      float " + a + "(int row, int col, int depth, int depth2) {\n        return " + a + "(" + getSqueezedParams(h, d) + ");\n      }\n    "
        }
        return i === l ? "\n      float " + a + "(int row, int col, int depth, int depth2) {\n        int texR = row;\n        int texC = col * " + u + " + depth * " + s + " + depth2;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(" + i + ".0, " + o + ".0);\n        return sampleTexture(" + n + ", uv);\n      }\n    " : i === s ? "\n      float " + a + "(int row, int col, int depth, int depth2) {\n        int texR = row * " + t[1] * t[2] + " + col * " + t[2] + " + depth;\n        int texC = depth2;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(" + i + ".0, " + o + ".0);\n        return sampleTexture(" + n + ", uv);\n      }\n    " : "\n    float " + a + "(int row, int col, int depth, int depth2) {\n      vec2 uv = UVfrom4D(" + o + ", " + i + ", " + l + ", " + u + ",\n          " + s + ", row, col, depth, depth2);\n      return sampleTexture(" + n + ", uv);\n    }\n  "
    }

    function getSampler5D(e) {
        var t = e.shapeInfo.logicalShape,
            r = e.shapeInfo.texShape,
            n = e.name,
            a = "get" + n.charAt(0).toUpperCase() + n.slice(1),
            o = r[0],
            i = r[1],
            s = t[4],
            u = t[3] * s,
            l = t[2] * u,
            c = t[1] * l,
            p = squeezeShape(t),
            d = p.newShape,
            h = p.keptDims;
        if (d.length < t.length) {
            var f = ["row", "col", "depth", "depth2", "depth3"];
            return "\n      " + getSamplerFromInInfo(squeezeInputInfo(e, d)) + "\n      float " + a + "(int row, int col, int depth, int depth2, int depth3) {\n        return " + a + "(" + getSqueezedParams(f, h) + ");\n      }\n    "
        }
        return i === c ? "\n      float " + a + "(int row, int col, int depth, int depth2, int depth3) {\n        int texR = row;\n        int texC = col * " + l + " + depth * " + u + " +\n                   depth2 * " + s + " + depth3;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(" + i + ".0, " + o + ".0);\n        return sampleTexture(" + n + ", uv);\n      }\n    " : i === s ? "\n      float " + a + "(int row, int col, int depth, int depth2, int depth3) {\n        int texR = row * " + t[1] * t[2] + " + col * " + t[2] + " +\n                   depth * " + t[3] + " + depth2;\n        int texC = depth3;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(" + i + ".0, " + o + ".0);\n        return sampleTexture(" + n + ", uv);\n      }\n    " : "\n    float " + a + "(int row, int col, int depth, int depth2, int depth3) {\n      vec2 uv = UVfrom5D(" + o + ", " + i + ", " + c + ", " + l + ",\n          " + u + ", " + s + ", row, col, depth, depth2, depth3);\n      return sampleTexture(" + n + ", uv);\n    }\n  "
    }

    function getSamplerFlat(e) {
        var t = e.name,
            r = e.shapeInfo.texShape,
            n = "get" + t.charAt(0).toUpperCase() + t.slice(1) + "Flat",
            a = r[0],
            o = r[1];
        return 1 === o && 1 === a ? "\n      float " + n + "(int index) {\n        return sampleTexture(" + t + ", halfCR);\n      }\n    " : 1 === o ? "\n      float " + n + "(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 " + n + "(int index) {\n        vec2 uv = vec2((float(index) + 0.5) / " + o + ".0, 0.5);\n        return sampleTexture(" + t + ", uv);\n      }\n    " : "\n    float " + n + "(int index) {\n      vec2 uv = UVfrom1D(" + a + ", " + o + ", index);\n      return sampleTexture(" + t + ", uv);\n    }\n  "
    }

    function getBroadcastOutputCoordsSampler(e, t, r, n) {
        var a = e.shapeInfo.logicalShape.length,
            o = t.logicalShape.length,
            i = "int";
        2 === o ? i = "ivec2" : 3 === o ? i = "ivec3" : 4 === o && (i = "ivec4");
        var s, u = getBroadcastDims(e.shapeInfo.logicalShape, t.logicalShape),
            l = o - a;
        s = 0 === a ? "" : o < 2 && u.length >= 1 ? "coords = 0;" : u.map(function(e) {
            return "coords[" + (e + l) + "] = 0;"
        }).join("\n");
        var c = "";
        return c = o < 2 && a > 0 ? "coords" : e.shapeInfo.logicalShape.map(function(e, t) {
            return "coords[" + (t + l) + "]"
        }).join(", "), "\n    float " + n + "() {\n      " + i + " coords = getOutputCoords();\n      " + s + "\n      return get" + r + "(" + c + ");\n    }\n  "
    }

    function getSamplerAtOutputCoords(e, t, r) {
        var n = e.shapeInfo.texShape,
            a = e.name,
            o = a.charAt(0).toUpperCase() + a.slice(1),
            i = "get" + o + "AtOutCoords",
            s = getBroadcastDims(e.shapeInfo.logicalShape, t.logicalShape),
            u = e.shapeInfo.logicalShape.length,
            l = t.logicalShape.length,
            c = r && (l > u || s.length > 0),
            p = broadcastDimsAreOuter(s);
        if (c && !p) return getBroadcastOutputCoordsSampler(e, t, o, i);
        var d = t.texShape;
        if (arraysEqual(n, d)) return "\n      float " + i + "() {\n        return sampleTexture(" + a + ", resultUV);\n      }\n    ";
        var h = sizeFromShape(n),
            f = "";
        return c && p && (f = "\n        int mainPart = index / " + h + ";\n        index -= mainPart * " + h + ";\n      "), "\n    float " + i + "() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(" + d[0] + ", " + d[1] + "));\n      int index = resTexRC.x * " + d[1] + " + resTexRC.y;\n      " + f + "\n      int texR = index / " + n[1] + ";\n      int texC = index - texR * " + n[1] + ";\n      vec2 uv = (vec2(texC, texR) + halfCR) /\n                 vec2(" + n[1] + ".0, " + n[0] + ".0);\n\n      return sampleTexture(" + a + ", 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";
        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(", ")
    }

    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")
    }

    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"], a = [], o = 0; o < e.length; o++) o === t ? a.push("int(getIndices(" + n[o] + "))") : a.push("" + n[o]);
        return a.join()
    }

    function createWebGLRenderingContext(e) {
        var t = document.createElement("canvas");
        return t.width = 1, t.height = 1, createWebGLRenderingContextFromCanvas(t, e)
    }

    function createWebGLRenderingContextFromCanvas(e, t) {
        var r, n = ENV.get("WEBGL_VERSION");
        if (2 === n ? r = e.getContext("webgl2", t) : 1 === n && (r = e.getContext("webgl", t) || e.getContext("experimental-webgl", t)), 0 === n || null == r) throw new Error("This browser does not support WebGL.");
        return r
    }

    function callAndCheck(e, t) {
        var r = t();
        return checkWebGLError(e), r
    }

    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
    }

    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], a = e.split("\n"), o = a.length.toString().length + 2, i = a.map(function(e, t) {
                return rightPad((t + 1).toString(), o) + 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 queryMaxTextureSize(e) {
        return null != MAX_TEXTURE_SIZE ? MAX_TEXTURE_SIZE : MAX_TEXTURE_SIZE = callAndCheck(e, function() {
            return e.getParameter(e.MAX_TEXTURE_SIZE)
        })
    }

    function getChannelsPerTexture() {
        return ENV.get("WEBGL_FLOAT_TEXTURE_ENABLED") && 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, r) {
        var n = queryMaxTextureSize(e);
        if (t <= 0 || r <= 0) {
            a = "[" + t + "x" + r + "]";
            throw new Error("Requested texture size " + a + " is invalid.")
        }
        if (t > n || r > n) {
            var a = "[" + t + "x" + r + "]",
                o = "[" + n + "x" + n + "]";
            throw new Error("Requested texture size " + a + " greater than WebGL maximum on this browser / GPU " + o + ".")
        }
    }

    function createFramebuffer(e) {
        return throwIfNull(e, function() {
            return e.createFramebuffer()
        }, "Unable to create WebGLFramebuffer.")
    }

    function bindVertexBufferToProgramAttribute(e, t, r, n, a, o, 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, a, e.FLOAT, !1, o, 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, a) {
        callAndCheck(e, function() {
            return bindTextureUnit(e, r, a)
        }), callAndCheck(e, function() {
            return e.uniform1i(n, a)
        })
    }

    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) {
            var a = "[gl.TEXTURE0, gl.TEXTURE" + r + "]";
            throw new Error("textureUnit must be in " + a + ".")
        }
    }

    function getTextureShapeFromLogicalShape(e, t) {
        2 !== t.length && (t = squeezeShape(t).newShape);
        var r = queryMaxTextureSize(e),
            n = sizeFromShape(t);
        return t.length <= 1 && n <= r ? [n, 1] : 2 === t.length && t[0] <= r && t[1] <= r ? t : 3 === t.length && t[0] <= r && t[1] * t[2] <= r ? [t[0], t[1] * t[2]] : 4 === t.length && t[0] <= r && t[1] * t[2] * t[3] <= r ? [t[0], t[1] * t[2] * t[3]] : sizeToSquarishShape(n)
    }

    function getWebGLContextAttributes() {
        return {
            alpha: !1,
            antialias: !1,
            premultipliedAlpha: !1,
            preserveDrawingBuffer: !1,
            depth: !1,
            stencil: !1,
            failIfMajorPerformanceCaveat: !0
        }
    }

    function createWebGLContext(e) {
        var t, r = getWebGLContextAttributes();
        return t = null != e ? createWebGLRenderingContextFromCanvas(e, r) : createWebGLRenderingContext(r), callAndCheck(t, function() {
            return t.disable(t.DEPTH_TEST)
        }), callAndCheck(t, function() {
            return t.disable(t.STENCIL_TEST)
        }), callAndCheck(t, function() {
            return t.disable(t.BLEND)
        }), callAndCheck(t, function() {
            return t.disable(t.DITHER)
        }), callAndCheck(t, function() {
            return t.disable(t.POLYGON_OFFSET_FILL)
        }), callAndCheck(t, function() {
            return t.disable(t.SAMPLE_COVERAGE)
        }), callAndCheck(t, function() {
            return t.enable(t.SCISSOR_TEST)
        }), callAndCheck(t, function() {
            return t.enable(t.CULL_FACE)
        }), callAndCheck(t, function() {
            return t.cullFace(t.BACK)
        }), t
    }

    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 getTextureInternalFormat(e, t) {
        return ENV.get("WEBGL_FLOAT_TEXTURE_ENABLED") && 2 === ENV.get("WEBGL_VERSION") ? 4 === t ? e.RGBA32F : e.R32F : e.RGBA
    }

    function getTextureFormat(e, t) {
        return ENV.get("WEBGL_FLOAT_TEXTURE_ENABLED") && 2 === ENV.get("WEBGL_VERSION") ? 4 === t ? e.RGBA : e.RED : e.RGBA
    }

    function getTextureType(e) {
        return ENV.get("WEBGL_FLOAT_TEXTURE_ENABLED") ? e.FLOAT : e.UNSIGNED_BYTE
    }

    function createAndConfigureTexture(e, t, r, n) {
        validateTextureSize(e, t, r);
        var a = createTexture(e),
            o = e.TEXTURE_2D,
            i = getTextureInternalFormat(e, n),
            s = getTextureFormat(e, n);
        return callAndCheck(e, function() {
            return e.bindTexture(o, a)
        }), callAndCheck(e, function() {
            return e.texParameteri(o, e.TEXTURE_WRAP_S, e.CLAMP_TO_EDGE)
        }), callAndCheck(e, function() {
            return e.texParameteri(o, e.TEXTURE_WRAP_T, e.CLAMP_TO_EDGE)
        }), callAndCheck(e, function() {
            return e.texParameteri(o, e.TEXTURE_MIN_FILTER, e.NEAREST)
        }), callAndCheck(e, function() {
            return e.texParameteri(o, e.TEXTURE_MAG_FILTER, e.NEAREST)
        }), callAndCheck(e, function() {
            return e.texImage2D(o, 0, i, t, r, 0, s, getTextureType(e), null)
        }), callAndCheck(e, function() {
            return e.bindTexture(e.TEXTURE_2D, null)
        }), a
    }

    function createMatrixTexture(e, t, r) {
        var n = getUnpackedMatrixTextureShapeWidthHeight(t, r);
        return createAndConfigureTexture(e, n[0], n[1], 1)
    }

    function createColorMatrixTexture(e, t, r) {
        var n = getColorMatrixTextureShapeWidthHeight(t, r);
        return createAndConfigureTexture(e, n[0], n[1], 4)
    }

    function createPackedMatrixTexture(e, t, r) {
        var n = getPackedMatrixTextureShapeWidthHeight(t, r);
        return createAndConfigureTexture(e, n[0], n[1], 4)
    }

    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, a, o) {
        var i = getTextureFormat(e, o);
        validateTextureSize(e, 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, i, getTextureType(e), a)
        }), callAndCheck(e, function() {
            return e.bindTexture(e.TEXTURE_2D, null)
        })
    }

    function uploadMatrixToTexture(e, t, r, n, a, o) {
        var i, s = getUnpackedMatrixTextureShapeWidthHeight(r, n),
            u = s[0],
            l = s[1];
        if (ENV.get("WEBGL_FLOAT_TEXTURE_ENABLED")) {
            var c = 1 === o ? getChannelsPerTexture() : o;
            1 === c ? i = a : encodeMatrixToUnpackedArray(a, i = new Float32Array(getUnpackedArraySizeFromMatrixSize(a.length, c)), c)
        } else i = encodeFloatArray(a);
        uploadDataToTexture(e, t, u, l, i, o)
    }

    function uploadMatrixToPackedTexture(e, t, r, n, a) {
        var o = getPackedMatrixTextureShapeWidthHeight(r, n),
            i = o[0],
            s = o[1],
            u = new Float32Array(getPackedRGBAArraySizeFromMatrixShape(r, n));
        encodeMatrixToPackedRGBA(a, r, n, u);
        uploadDataToTexture(e, t, i, s, u, 4)
    }

    function getDownloadTargetArrayBuffer(e, t, r) {
        var n = ENV.get("WEBGL_FLOAT_TEXTURE_ENABLED"),
            a = e * t * r;
        return n ? (null == floatDownloadBuffer || floatDownloadBuffer.length < a) && (floatDownloadBuffer = new Float32Array(a)) : (null == byteDownloadBuffer || byteDownloadBuffer.length < a) && (byteDownloadBuffer = new Uint8Array(a)), (n ? floatDownloadBuffer : byteDownloadBuffer).subarray(0, a)
    }

    function decodeDownloadTargetArrayBuffer(e, t, r, n) {
        if (ENV.get("WEBGL_FLOAT_TEXTURE_ENABLED")) {
            var a = new Float32Array(t * r);
            return decodeMatrixFromUnpackedArray(e, a, n), a
        }
        return decodeToFloatArray(e)
    }

    function downloadMatrixFromOutputTextureAsync(e, t, r, n) {
        return __awaiter$3(this, void 0, void 0, function() {
            var a, o, i, s, u;
            return __generator$3(this, function(l) {
                switch (l.label) {
                    case 0:
                        return a = e, o = 4, i = getDownloadTargetArrayBuffer(r, n, o), s = i instanceof Float32Array ? 4 * i.length : i, u = e.createBuffer(), callAndCheck(e, function() {
                            return e.bindBuffer(a.PIXEL_PACK_BUFFER, u)
                        }), callAndCheck(e, function() {
                            return e.bufferData(a.PIXEL_PACK_BUFFER, s, e.STATIC_DRAW)
                        }), callAndCheck(e, function() {
                            return a.readPixels(0, 0, n, r, e.RGBA, getTextureType(e), 0)
                        }), [4, t.getBufferSubDataAsync(a.PIXEL_PACK_BUFFER, 0, i)];
                    case 1:
                        return l.sent(), [2, decodeDownloadTargetArrayBuffer(i, r, n, o)]
                }
            })
        })
    }

    function downloadMatrixFromOutputTexture(e, t, r) {
        var n = getUnpackedMatrixTextureShapeWidthHeight(t, r),
            a = n[0],
            o = n[1],
            i = getDownloadTargetArrayBuffer(t, r, 4);
        return callAndCheck(e, function() {
            return e.readPixels(0, 0, a, o, e.RGBA, getTextureType(e), i)
        }), decodeDownloadTargetArrayBuffer(i, t, r, 4)
    }

    function downloadMatrixFromRGBAColorTexture(e, t, r, n) {
        var a = t * r * 4,
            o = new Uint8Array(a);
        callAndCheck(e, function() {
            return e.readPixels(0, 0, r, t, e.RGBA, e.UNSIGNED_BYTE, o)
        });
        for (var i = new Float32Array(a), s = 0; s < o.length; s++) i[s] = o[s];
        var u = new Float32Array(t * r * n);
        return decodeMatrixFromUnpackedColorRGBAArray(i, u, n), u
    }

    function downloadMatrixFromPackedOutputTexture(e, t, r) {
        var n = getPackedMatrixTextureShapeWidthHeight(t, r),
            a = n[0],
            o = n[1],
            i = new Float32Array(getPackedRGBAArraySizeFromMatrixShape(t, r));
        callAndCheck(e, function() {
            return e.readPixels(0, 0, a, o, e.RGBA, getTextureType(e), i)
        });
        var s = new Float32Array(t * r);
        return decodeMatrixFromPackedRGBA(i, t, r, s)
    }

    function binSearchLastTrue(e) {
        for (var t = 0, r = e.length - 1, n = -1; t <= r;) {
            var a = t + r >> 1;
            e[a]() ? (n = a, t = a + 1) : r = a - 1
        }
        return n
    }

    function shouldUploadNaNUniform() {
        return !ENV.get("WEBGL_FLOAT_TEXTURE_ENABLED")
    }

    function compileProgram(e, t, r, n) {
        for (var a = t.userCode, o = r.map(function(e, r) {
                var n = {
                    logicalShape: e.tensor.shape,
                    texShape: e.texData.texShape
                };
                return {
                    name: t.variableNames[r],
                    shapeInfo: n
                }
            }), i = o.map(function(e) {
                return e.shapeInfo
            }), s = {
                logicalShape: n.tensor.shape,
                texShape: n.texData.texShape
            }, u = makeShader(o, s, a, !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)
        }
        if (shouldUploadNaNUniform()) {
            c[NAN_UNIFORM_NAME] = e.getUniformLocation(l, NAN_UNIFORM_NAME, !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,
                a = e.texShape,
                o = t[r].tensor.shape,
                i = t[r].texData.texShape;
            if (!arraysEqual(n, o)) throw Error("Binary was compiled with different shapes than the current args. Shapes " + n + " and " + o + " must match");
            if (!arraysEqual(a, i)) throw Error("Binary was compiled with different texture shapes than the current args. Shape " + a + " and " + i + " must match")
        })
    }

    function runProgram(e, t, r, n) {
        validateBinaryAndProgram(e.inShapeInfos, t), validateBinaryAndProgram([e.outShapeInfo], [r]);
        var a = r.texData.texture,
            o = r.texData.texShape,
            i = e.gpgpu;
        i.setOutputMatrixTexture(a, o[0], o[1]), i.setProgram(e.webGLProgram), t.forEach(function(t, r) {
            var n = t.texData.texture,
                a = e.program.variableNames[r],
                o = e.uniformLocations[a];
            i.setInputMatrixTexture(n, o, r)
        }), shouldUploadNaNUniform() && i.gl.uniform1f(e.uniformLocations[NAN_UNIFORM_NAME], NaN), null != n && n(i, e.webGLProgram), i.executeProgram()
    }

    function makeShaderKey(e, t, r) {
        var n = "";
        t.concat(r).forEach(function(e) {
            n += e.tensor.shape + "_" + e.texData.texShape
        });
        var a = e.userCode,
            o = (!0 === e.supportsBroadcasting).toString(),
            i = e.constructor.name;
        return i += "_" + o + "_" + n + "_" + a
    }

    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")
    }

    function getKeyFromTextureShape(e, t) {
        return e[0] + "_" + e[1] + "_" + t
    }

    function getSourceCoords$1(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 = [], a = 0; a < e.length; a++) n.push("imod(" + r[a] + ", " + e[a] + ")");
        return n.join()
    }

    function getSwitchedCoords(e) {
        var t = e.length;
        if (t > 5) throw Error("Transpose for rank " + t + " is not yet supported");
        for (var r = ["resRC.x", "resRC.y", "resRC.z", "resRC.w", "resRC.u"], n = new Array(t), a = 0; a < e.length; a++) n[e[a]] = r[a];
        return n.join()
    }

    function STEP(e) {
        return void 0 === e && (e = 0), CHECK_NAN_SNIPPET$1 + "\n    return x > 0.0 ? 1.0 : float(" + e + ");\n  "
    }

    function float32ToTypedArray(e, t) {
        if ("float32" === 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 encodeWeights(e) {
        return __awaiter$7(this, void 0, void 0, function() {
            var t, r, n, a, o;
            return __generator$7(this, function(i) {
                switch (i.label) {
                    case 0:
                        t = [], r = [];
                        for (n in e) {
                            if ("float32" !== (a = e[n]).dtype && "int32" !== a.dtype && "bool" !== a.dtype) throw new Error("Unsupported dtype in weight '" + n + "': " + a.dtype);
                            t.push({
                                name: n,
                                shape: a.shape,
                                dtype: a.dtype
                            }), r.push(a.data())
                        }
                        return [4, Promise.all(r)];
                    case 1:
                        return o = i.sent(), [2, {
                            data: concatenateTypedArrays(o),
                            specs: t
                        }]
                }
            })
        })
    }

    function decodeWeights(e, t) {
        for (var r = {}, n = 0, a = 0, o = t; a < o.length; a++) {
            var i = o[a],
                s = i.name,
                u = i.dtype,
                l = i.shape;
            if (null != i.quantization) throw new Error("decodeWeights does not support quantization yet, but encountered weight '" + s + " with quantization.'");
            var c = sizeFromShape(l),
                p = void 0;
            if ("float32" === u) p = ArrayOps.tensor(new Float32Array(e, n, c), l, "float32");
            else if ("int32" === u) p = ArrayOps.tensor(new Int32Array(e, n, c), l, "int32");
            else {
                if ("bool" !== u) throw new Error("Unsupported dtype in weight '" + s + "': " + u);
                p = ArrayOps.tensor(new Uint8Array(e, n, c), l, "bool")
            }
            r[s] = p, n += c * DTYPE_VALUE_SIZE_MAP[u]
        }
        return r
    }

    function concatenateTypedArrays(e) {
        if (null === e) throw new Error("Invalid input value: " + JSON.stringify(e));
        var t = 0;
        e.forEach(function(e) {
            if (e instanceof Float32Array || e instanceof Int32Array) t += 4 * e.length;
            else {
                if (!(e instanceof Uint8Array)) throw new Error("Unsupported TypedArray subtype: " + e.constructor.name);
                t += e.length
            }
        });
        var r = new Uint8Array(t),
            n = 0;
        return e.forEach(function(e) {
            r.set(new Uint8Array(e.buffer), n), e instanceof Float32Array || e instanceof Int32Array ? n += 4 * e.length : n += e.length
        }), r.buffer
    }

    function stringByteLength(e) {
        return new Blob([e]).size
    }

    function arrayBufferToBase64String(e) {
        return btoa(String.fromCharCode.apply(null, new Uint8Array(e)))
    }

    function base64StringToArrayBuffer(e) {
        for (var t = atob(e), r = new Uint8Array(t.length), n = 0; n < t.length; ++n) r.set([t.charCodeAt(n)], n);
        return r.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
        }
    }

    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$8(this, void 0, void 0, function() {
            var n, a, o, i, s, u, l, c, p;
            return __generator$8(this, function(d) {
                switch (d.label) {
                    case 0:
                        return assert(e !== t, "Old path and new path are the same: '" + e + "'"), n = IORouterRegistry.getLoadHandlers(e), assert(n.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 + "."), a = n[0], o = IORouterRegistry.getSaveHandlers(t), assert(o.length > 0, "Copying failed because no save handler is found for destination URL " + t + "."), assert(o.length < 2, "Copying failed because more than one (" + n.length + ") save handlers for destination URL " + t + "."), i = o[0], s = parseURL(e).scheme, u = parseURL(e).path, l = s === parseURL(e).scheme, [4, a.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 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"
        })
    }

    function browserIndexedDB(e) {
        return new BrowserIndexedDB(e)
    }

    function maybeStripScheme(e) {
        return e.startsWith(BrowserIndexedDB.URL_SCHEME) ? e.slice(BrowserIndexedDB.URL_SCHEME.length) : e
    }

    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
    }

    function browserLocalStorage(e) {
        return new BrowserLocalStorage(e)
    }

    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$12(this, void 0, void 0, function() {
            var r, n, a;
            return __generator$12(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 a = o.sent(), [2, a]
                }
            })
        })
    }

    function loadWeights(e, t, r, n) {
        return void 0 === t && (t = ""), __awaiter$12(this, void 0, void 0, function() {
            var a, o, i, s, u, l, c, p, d, h;
            return __generator$12(this, function(f) {
                switch (f.label) {
                    case 0:
                        if (a = e.map(function() {
                                return !1
                            }), o = {}, 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() {
                                            a[t] = !0, null == o[t] && (o[t] = []), o[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 = a.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, a = 0; a < r; a++) n += p[h + a].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
                            }
                            o[t].forEach(function(e) {
                                var t, r = i.slice(e.groupOffset, e.groupOffset + e.sizeBytes),
                                    n = e.manifestEntry.dtype;
                                if ("quantization" in e.manifestEntry) {
                                    var a = e.manifestEntry.quantization;
                                    if ("uint8" !== a.dtype && "uint16" !== a.dtype) throw new Error("Weight " + e.manifestEntry.name + " has unknown quantization dtype " + a.dtype + ".");
                                    var o = "uint8" === a.dtype ? new Uint8Array(r) : new Uint16Array(r);
                                    if ("float32" === n) t = Float32Array.from(o, function(e) {
                                        return e * a.scale + a.min
                                    });
                                    else {
                                        if ("int32" !== n) throw new Error("Weight " + e.manifestEntry.name + " has a dtype not supported by quantization: " + n);
                                        t = Int32Array.from(o, function(e) {
                                            return Math.round(e * a.scale + a.min)
                                        })
                                    }
                                } else if ("float32" === n) t = new Float32Array(r);
                                else {
                                    if ("int32" !== n) throw new Error("Weight " + e.manifestEntry.name + " has unknown dtype " + n + ".");
                                    t = new Int32Array(r)
                                }
                                var s = e.manifestEntry.name;
                                if (null != d[s]) throw new Error("Duplicate weight with name " + s + ". Please make sure weights names are unique in the manifest JSON.");
                                d[s] = tensor(t, e.manifestEntry.shape, e.manifestEntry.dtype)
                            }), h += r
                        }), [2, d]
                }
            })
        })
    }

    function browserHTTPRequest(e, t) {
        return new BrowserHTTPRequest(e, t)
    }

    function expectArraysClose(e, t, r) {
        if (void 0 === r && (r = 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,
                a = t.constructor.name;
            if (n !== a) throw new Error("Arrays are of different type actual: " + n + " vs expected: " + a)
        }
        var o, i;
        if (o = e instanceof Tensor ? e.dataSync() : e, i = t instanceof Tensor ? t.dataSync() : t, o.length !== i.length) throw new Error("Arrays have different lengths actual: " + o.length + " vs expected: " + i.length + ".\nActual:   " + o + ".\nExpected: " + i + ".");
        for (var s = 0; s < i.length; ++s) {
            var u = o[s],
                l = i[s];
            if (!areClose(u, Number(l), r)) throw new Error("Arrays differ: actual[" + s + "] = " + u + ", expected[" + s + "] = " + l + ".\nActual:   " + o + ".\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 (void 0 === r && (r = 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 a = 0; a < n.length; a++)
            if (n[a] < t || n[a] > r) throw new Error("Value out of range:" + n[a] + " low: " + t + ", high: " + r)
    }

    function pyListRepeat(e, t) {
        if (Array.isArray(e)) {
            for (var r = [], n = 0; n < t; n++) r = r.concat(e);
            return r
        }
        return (r = new Array(t)).fill(e), r
    }

    function assert$1(e, t) {
        if (!e) throw new AssertionError(t)
    }

    function count(e, t) {
        for (var r = 0, n = 0, a = e; n < a.length; n++) a[n] === t && r++;
        return r
    }

    function singletonOrArray(e) {
        return 1 === e.length ? e[0] : e
    }

    function toList(e) {
        return Array.isArray(e) ? e : [e]
    }

    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 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()
        })
    }

    function serializeKerasObject(e) {
        return null === e || void 0 === e ? null : {
            className: e.getClassName(),
            config: e.getConfig()
        }
    }

    function deserializeKerasObject(e, t, r, n) {
        if (void 0 === t && (t = {}), void 0 === r && (r = {}), void 0 === n && (n = "object"), "string" == typeof e) {
            var a = e,
                o = void 0;
            if (a in r) o = r[a];
            else if (a in _GLOBAL_CUSTOM_OBJECTS) o = _GLOBAL_CUSTOM_OBJECTS[a];
            else if (null == (o = t[a])) throw new ValueError("Unknown " + n + ": " + e);
            return o
        }
        var i = e;
        if (null == i.className || null == i.config) throw new ValueError(n + ": Improper config format: " + JSON.stringify(i) + ".\n'className' and 'config' must set.");
        var s = i.className,
            u = void 0,
            l = void 0;
        if (s in r ? (u = (_ = r.get(s))[0], l = _[1]) : s in _GLOBAL_CUSTOM_OBJECTS ? (u = (S = _GLOBAL_CUSTOM_OBJECTS.className)[0], l = S[1]) : s in t && (u = (N = t[s])[0], l = N[1]), null == u) throw new ValueError("Unknown " + n + ": " + s);
        if (null != l) {
            for (var c = {}, p = 0, d = Object.keys(_GLOBAL_CUSTOM_OBJECTS); p < d.length; p++) c[x = d[p]] = _GLOBAL_CUSTOM_OBJECTS[x];
            for (var h = 0, f = Object.keys(r); h < f.length; h++) c[x = f[h]] = r[x];
            i.config.customObjects = c;
            for (var m = __assign({}, _GLOBAL_CUSTOM_OBJECTS), g = 0, y = Object.keys(r); g < y.length; g++) {
                x = y[g];
                _GLOBAL_CUSTOM_OBJECTS[x] = r[x]
            }
            w = l(u, i.config);
            return _GLOBAL_CUSTOM_OBJECTS = __assign({}, m), w
        }
        for (var m = __assign({}, _GLOBAL_CUSTOM_OBJECTS), v = 0, b = Object.keys(r); v < b.length; v++) {
            var x = b[v];
            _GLOBAL_CUSTOM_OBJECTS[x] = r[x]
        }
        var w = new u(i.config);
        return _GLOBAL_CUSTOM_OBJECTS = __assign({}, m), w;
        var _, S, N
    }

    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 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 = [], r = 0, n = e; r < n.length; r++) {
            var a = n[r]; - 1 === t.indexOf(a) && t.push(a)
        }
        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, r) {
        if (null != r && e.indexOf(r) < 0) throw new ValueError(r + " is not a valid " + t + ".  Valid values are " + e + " or null/undefined.")
    }

    function checkDataFormat(e) {
        checkStringTypeUnionValue(VALID_DATA_FORMAT_VALUES, "DataFormat", e)
    }

    function checkPaddingMode(e) {
        checkStringTypeUnionValue(VALID_PADDING_MODE_VALUES, "PaddingMode", e)
    }

    function checkPoolMode(e) {
        checkStringTypeUnionValue(VALID_POOL_MODE_VALUES, "PoolMode", e)
    }

    function nameScope(e, t) {
        _nameScopeStack.push(e);
        try {
            var r = t();
            return _nameScopeStack.pop(), r
        } 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 r = e + "_" + t;
            return nameMap.set(r, 1), r
        }
        return e
    }

    function isValidTensorName(e) {
        return !!e.match(tensorNameRegex)
    }

    function isInteger(e) {
        return e === parseInt(e.toString(), 10)
    }

    function arrayProd(e, t, r) {
        null == t && (t = 0), null == r && (r = e.length);
        for (var n = 1, a = t; a < r; ++a) n *= e[a];
        return n
    }

    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 r = [], n = e; n < t; ++n) r.push(n);
        return r
    }

    function getNextUniqueTensorId() {
        return _nextUniqueTensorId++
    }

    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])
        })
    }

    function epsilon() {
        return _epsilon
    }

    function imageDataFormat() {
        return "channelsLast"
    }

    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]
    }

    function shape(e) {
        return e.shape
    }

    function intShape(e) {
        return e.shape
    }

    function dtype(e) {
        return e instanceof Tensor ? DEFAULT_DTYPE : e.dtype
    }

    function cast$1(e, t) {
        return e.asType(t)
    }

    function expandDims$1(e, t) {
        void 0 === t && (t = -1);
        var r = shape(e).slice();
        return t < 0 && (t = r.length + t + 1), r.splice(t, 0, 1), e.reshape(r)
    }

    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, r) {
        return tidy(function() {
            switch (e.rank) {
                case 1:
                    return slice1d(e, t, r);
                case 2:
                    return slice2d(e, [t, 0], [r, e.shape[1]]);
                case 3:
                    return slice3d(e, [t, 0, 0], [r, e.shape[1], e.shape[2]]);
                case 4:
                    return slice4d(e, [t, 0, 0, 0], [r, 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, r) {
        return tidy(function() {
            switch (e.rank) {
                case 1:
                    return slice1d(e, t, r);
                case 2:
                    return slice2d(e, [0, t], [e.shape[0], r]);
                case 3:
                    return slice3d(e, [0, 0, t], [e.shape[0], e.shape[1], r]);
                case 4:
                    return slice4d(e, [0, 0, 0, t], [e.shape[0], e.shape[1], e.shape[2], r]);
                default:
                    throw new ValueError("sliceAlongLastAxis() received an unsupported tensor rank: " + e.rank)
            }
        })
    }

    function sliceAlongAxis(e, t, r, n) {
        return tidy(function() {
            switch (e.rank) {
                case 1:
                    return slice1d(e, t, r);
                case 2:
                    switch (n) {
                        case 1:
                            return sliceAlongFirstAxis(e, t, r);
                        case 2:
                            return sliceAlongLastAxis(e, t, r);
                        default:
                            throw new ValueError("The axis is not within the rank of the tensor " + n)
                    }
                case 3:
                    switch (n) {
                        case 1:
                            return sliceAlongFirstAxis(e, t, r);
                        case 2:
                            return slice3d(e, [0, t, 0], [e.shape[0], r, e.shape[2]]);
                        case 3:
                            return sliceAlongLastAxis(e, t, r);
                        default:
                            throw new ValueError("The axis is not within the rank of the tensor " + n)
                    }
                case 4:
                    switch (n) {
                        case 1:
                            return sliceAlongFirstAxis(e, t, r);
                        case 2:
                            return slice4d(e, [0, t, 0, 0], [e.shape[0], r, e.shape[2], e.shape[3]]);
                        case 3:
                            return slice4d(e, [0, 0, t, 0], [e.shape[0], e.shape[1], r, e.shape[3]]);
                        case 4:
                            return sliceAlongLastAxis(e, t, r);
                        default:
                            throw new ValueError("The axis is not within the rank of the tensor " + n)
                    }
                default:
                    throw new ValueError("sliceAlongLastAxis() received an unsupported tensor rank: " + e.rank)
            }
        })
    }

    function concatenate(e, t) {
        void 0 === t && (t = -1);
        var r;
        return t < 0 && (t = 0 !== (r = e[0].rank) ? r : 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 identity(e) {
        return e.clone()
    }

    function scalarTimesArray(e, t) {
        return mul(e, t)
    }

    function scalarPlusArray(e, t) {
        return add(e, t)
    }

    function randomNormal$1(e, t, r, n, a) {
        return void 0 === t && (t = 0), void 0 === r && (r = 1), randomNormal(e, t, r, n, a)
    }

    function dot$1(e, t) {
        if (2 !== t.rank) throw new NotImplementedError("dot support for y other than rank 2 is not yet implemented: y shape = " + shape);
        if (2 === e.rank) return matMul(e, t);
        if (3 === e.rank) {
            var r = e.shape[0],
                n = e.shape[1],
                a = e.shape[2];
            return e = e.reshape([r * n, a]), matMul(e, t).reshape([r, n, t.shape[1]])
        }
        throw new NotImplementedError("dot support for x of rank " + e.rank + " is not yet implemented: x shape = " + shape)
    }

    function gather$1(e, t, r) {
        return tidy(function() {
            return t = Array.isArray(t) ? tensor1d(t, "int32") : t.toInt(), gather(e, t, r)
        })
    }

    function square$1(e) {
        return mulStrict(e, e)
    }

    function biasAdd(e, t, r) {
        return tidy(function() {
            if (null == r && (r = imageDataFormat()), checkDataFormat(r), 1 !== t.rank && t.rank !== e.rank) throw new ValueError("Unexpected bias dimensions: " + t.rank + "; expected it to be 1 or " + e.rank);
            var n, a = t.shape;
            if (5 === e.rank) "channelsFirst" === r ? n = 1 === a.length ? e.add(t.reshape([1, a[0], 1, 1, 1])) : e.add(t.reshape([1, a[3], a[0], a[1], a[2]])) : "channelsLast" === r && (n = 1 === a.length ? e.add(t.reshape([1, 1, 1, 1, a[0]])) : e.add(t.reshape([1].concat(a))));
            else if (4 === e.rank) "channelsFirst" === r ? n = 1 === a.length ? e.add(t.reshape([1, a[0], 1, 1])) : e.add(t.reshape([1, a[2], a[0], a[1]])) : "channelsLast" === r && (n = 1 === a.length ? e.add(t.reshape([1, 1, 1, a[0]])) : e.add(t.reshape([1].concat(a))));
            else if (3 === e.rank) "channelsFirst" === r ? n = 1 === a.length ? e.add(t.reshape([1, a[0], 1])) : e.add(t.reshape([1, a[1], a[0]])) : "channelsLast" === r && (n = 1 === a.length ? e.add(t.reshape([1, 1, a[0]])) : e.add(t.reshape([1].concat(a))));
            else {
                if (!(e.rank < 3)) throw new ValueError("Unsupported input rank by biasAdd: " + e.rank);
                n = e.add(t)
            }
            return n
        })
    }

    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, r, n) {
        return tidy(function() {
            if (null != r && !arraysEqual(e.shape, r)) throw new NotImplementedError("Non-default noise shape is not implemented yet: " + JSON.stringify(r));
            if (null != n) throw new NotImplementedError("seed is not implemented for dropout yet.");
            var a = step(add(neg(t), randomUniform(e.shape, 0, 1, "float32")));
            return a = mul(div(getScalar(1), sub(getScalar(1), t)), a), mul(e, a)
        })
    }

    function nameScope$1(e, t) {
        return nameScope(e, t)
    }

    function floatx() {
        return "float32"
    }

    function getUid(e) {
        return void 0 === e && (e = ""), e in _uidPrefixes || (_uidPrefixes[e] = 0), _uidPrefixes[e] += 1, e + _uidPrefixes[e].toString()
    }

    function hardSigmoid(e) {
        return tidy(function() {
            var t = scalarPlusArray(getScalar(.5), scalarTimesArray(getScalar(.2), e));
            return clipByValue(t, 0, 1)
        })
    }

    function inTrainPhase(e, t, r) {
        return void 0 === r && (r = !1), r ? e() : t()
    }

    function calcL2Norms(e, t) {
        return tidy(function() {
            return sqrt(sum(square$1(e), t, !0))
        })
    }

    function serializeConstraint(e) {
        return serializeKerasObject(e)
    }

    function deserializeConstraint(e, t) {
        return void 0 === t && (t = {}), deserializeKerasObject(e, 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 deserialize(e, t) {
        return void 0 === t && (t = {}), deserializeKerasObject(e, SerializationMap.getMap().classNameMap, t, "layer")
    }

    function isArrayItemInputOrOutputName(e, t, r) {
        return ("inboundNodes" === e || "outputLayers" === e || "inputLayers" === e) && 0 === t && "string" == typeof r
    }

    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 r = [], n = e.length, a = 0; a < n; ++a) {
                var o = e[a];
                isArrayItemInputOrOutputName(t, a, o) ? r.push(o) : r.push(convertPythonicToTs(o, t))
            }
            return r
        }
        for (var i = {}, s = 0, u = Object.keys(e); s < u.length; s++) {
            var l = u[s],
                c = e[l];
            if ("name" === l && "string" == typeof c) i[l] = c;
            else {
                var p = toCamelCase(l);
                i[p] = convertPythonicToTs(c, p)
            }
        }
        return i
    }

    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 r = [], n = e.length, a = 0; a < n; ++a) {
                var o = e[a];
                isArrayItemInputOrOutputName(t, a, o) ? r.push(o) : r.push(convertTsToPythonic(o, t))
            }
            return r
        }
        for (var i = {}, s = 0, u = Object.keys(e); s < u.length; s++) {
            var l = u[s],
                c = e[l],
                p = toSnakeCase(l);
            i[p] = "name" !== l && "className" !== l || "string" != typeof c ? convertTsToPythonic(c, l) : c
        }
        return i
    }

    function collectInputShape(e) {
        for (var t = [], r = 0, n = e = toList(e); r < n.length; r++) {
            var a = n[r];
            t.push(intShape(a))
        }
        return singletonOrArray(t)
    }

    function guessOutputDType(e) {
        return "float32"
    }

    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 r = e.dtype;
        return null == r && (r = floatx()), new InputLayer({
            batchInputShape: t,
            name: e.name,
            dtype: r,
            sparse: e.sparse
        }).inboundNodes[0].outputTensors[0]
    }

    function getSourceInputs(e, t, r) {
        if ((null == t || null != r && r > 0) && (t = e.sourceLayer, r = e.nodeIndex), 0 === t.inboundNodes.length) return [e];
        var n = t.inboundNodes[r];
        if (0 === n.inboundLayers.length) return n.inputTensors;
        for (var a = [], o = 0; o < n.inboundLayers.length; o++)
            for (var i = 0, s = getSourceInputs(n.inputTensors[o], n.inboundLayers[o], n.nodeIndices[o]); i < s.length; i++) {
                var u = s[i]; - 1 === a.indexOf(u) && a.push(u)
            }
        return a
    }

    function loadTensor(e, t, r) {
        var n = stringToDType(e);
        return Tensor.make(t, {
            values: 0 === t.length ? r : flatten(r)
        }, n)
    }

    function preprocessWeightsForLoading(e, t, r, n) {
        if (!r.startsWith("2.")) throw new ValueError("Unsupported Keras version in weights being loaded: " + r);
        return t
    }

    function loadWeightsFromNamedTensorMap(e, t) {
        for (var r = {}, n = 0, a = 0, o = t; a < o.length; a++)
            for (var i = 0, s = o[a].weights; i < s.length; i++) {
                var u = s[i];
                if (null != r[u.originalName]) throw new ValueError("Duplicate weight name: " + u.originalName);
                r[u.originalName] = u, n++
            }
        var l = [];
        for (var c in e) l.push([r[c], e[c]]), delete r[c];
        var p = [];
        for (var d in r) p.push(d);
        if (p.length > 0) throw new ValueError(p.length + " of " + n + " weights are not set: " + p);
        batchSetValue(l)
    }

    function loadWeightsFromJson(e, t, r) {
        void 0 === r && (r = !1);
        for (var n = e.keras_version, a = e.backend, o = t.map(function(e) {
                return e.name
            }), i = {}, s = 0, u = t; s < u.length; s++) null != (b = u[s]).name && (null == i[b.name] && (i[b.name] = []), i[b.name].push(b));
        for (var l = e.weights, c = [], p = 0; p < o.length; ++p) {
            var d = o[p],
                h = l[d];
            null == h && (h = []);
            for (var f = [], m = 0; m < h.length; ++m) {
                var g = h[m];
                f.push(new LayerVariable(loadTensor(g.dtype, g.shape, g.value)))
            }
            for (var y = 0, v = i[d]; y < v.length; y++) {
                var b = v[y],
                    x = b.weights;
                if ((f = preprocessWeightsForLoading(b, f, n, a)).length !== x.length) {
                    if (!r) throw new ValueError("Layer #" + p + ' (named "' + b.name + '") expects ' + x.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 " + x.length + ").")
                }
                for (var w = 0; w < f.length; ++w) !r || arraysEqual(x[w].shape, f[w].shape) ? c.push([x[w], f[w].read()]) : console.warn("Skipping loading of weights for layer " + b.name + " due to mismatch in shape (" + x[w].shape + " vs " + f[w].shape + ")")
            }
        }
        batchSetValue(c)
    }

    function resolveScalarsInLogs(e) {
        return __awaiter$14(this, void 0, void 0, function() {
            var t, r, n, a, o, i, s;
            return __generator$14(this, function(u) {
                switch (u.label) {
                    case 0:
                        if (null == e) return [2];
                        t = [], r = [];
                        for (n in e) "number" != typeof(a = e[n]) && (o = a, t.push(o.data()), r.push(n));
                        return [4, Promise.all(t)];
                    case 1:
                        for (i = u.sent(), s = 0; s < i.length; ++s) e[r[s]] = i[s][0];
                        return [2]
                }
            })
        })
    }

    function disposeTensorsInLogs(e) {
        if (null != e)
            for (var t in e) {
                var r = e[t];
                "number" != typeof r && r.dispose()
            }
    }

    function standardizeCallbacks(e) {
        return null == e ? null : e instanceof Callback ? [e] : Array.isArray(e) && e[0] instanceof Callback ? e : toList(e).map(function(e) {
            return new CustomCallback(e)
        })
    }

    function l2Normalize(e, t) {
        return tidy(function() {
            var r = sum(square$1(e), t, !0),
                n = scalarTimesArray(scalar(epsilon$1()), onesLike(e)),
                a = sqrt(maximum(r, n));
            return div(e, a)
        })
    }

    function meanSquaredError(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 r = sub(e, t),
                n = clipByValue(abs(e), epsilon$1(), Number.MAX_VALUE),
                a = abs(div(r, n));
            return scalarTimesArray(getScalar(100), mean(a, -1))
        })
    }

    function meanSquaredLogarithmicError(e, t) {
        return tidy(function() {
            var r = getScalar(1),
                n = clipByValue(t, epsilon$1(), Number.MAX_VALUE),
                a = log(scalarPlusArray(r, n)),
                o = clipByValue(e, epsilon$1(), Number.MAX_VALUE),
                i = log(scalarPlusArray(r, o));
            return mean(square$1(sub(a, i)), -1)
        })
    }

    function squaredHinge(e, t) {
        return tidy(function() {
            var r = getScalar(0),
                n = getScalar(1),
                a = maximum(r, sub(n, mul(e, t)));
            return mean(square$1(a), -1)
        })
    }

    function hinge(e, t) {
        return tidy(function() {
            var r = getScalar(0),
                n = getScalar(1),
                a = maximum(r, sub(n, mul(e, t)));
            return mean(a, -1)
        })
    }

    function categoricalHinge(e, t) {
        return tidy(function() {
            var r = getScalar(0),
                n = getScalar(1),
                a = sum(mul(e, t), -1),
                o = max(mul(sub(n, e), t), -1);
            return maximum(r, scalarPlusArray(n, sub(o, a)))
        })
    }

    function logcosh(e, t) {
        return tidy(function() {
            var r = getScalar(Math.log(2)),
                n = sub(t, e),
                a = sub(add(n, softplus(scalarTimesArray(getScalar(-2), n))), r);
            return mean(a, -1)
        })
    }

    function categoricalCrossentropy(e, t, r) {
        return void 0 === r && (r = !1), tidy(function() {
            if (r) t = softmax(t);
            else {
                var n = sum(t, shape(t).length - 1, !0);
                t = div(t, n)
            }
            return t = clipByValue(t, epsilon$1(), 1 - epsilon$1()), neg(sum(mul(e.toFloat(), log(t)), shape(t).length - 1))
        })
    }

    function sparseCategoricalCrossentropy(e, t, r) {
        return void 0 === r && (r = !1), tidy(function() {
            var n = floor(flatten$1(e)).toInt(),
                a = shape(t);
            return categoricalCrossentropy(oneHot(n, a[a.length - 1]).reshape(a), t, r)
        })
    }

    function sigmoidCrossEntropyWithLogits(e, t) {
        return tidy(function() {
            var r = maximum(t, zerosLike(t)),
                n = mul(t, e),
                a = log(add(getScalar(1), exp(neg(abs(t)))));
            return add(sub(r, n), a)
        })
    }

    function binaryCrossentropy(e, t) {
        return tidy(function() {
            var r;
            return r = clipByValue(t, epsilon$1(), 1 - epsilon$1()), r = log(div(r, sub(onesLike(r), r))), mean(sigmoidCrossEntropyWithLogits(e, r), -1)
        })
    }

    function kullbackLeiblerDivergence(e, t) {
        return tidy(function() {
            var r = clipByValue(e, epsilon$1(), 1),
                n = clipByValue(t, epsilon$1(), 1);
            return sum(mul(e, log(div(r, n))), -1)
        })
    }

    function poisson(e, t) {
        return tidy(function() {
            var r = log(scalarPlusArray(getScalar(epsilon$1()), t));
            return mean(sub(t, mul(e, r)), -1)
        })
    }

    function cosineProximity(e, t) {
        return tidy(function() {
            var r = l2Normalize(e, -1),
                n = l2Normalize(t, -1),
                a = mul(r, n);
            return neg(sum(a, -1))
        })
    }

    function get(e) {
        var t = {
            meanSquaredError: meanSquaredError,
            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];
            throw new ValueError("Unknown loss " + e)
        }
        return e
    }

    function binaryAccuracy(e, t) {
        return tidy(function() {
            var r = scalarTimesArray(getScalar(.5), onesLike(t)),
                n = cast$1(greater(t, r), e.dtype);
            return mean(equal(e, n), -1)
        })
    }

    function categoricalAccuracy(e, t) {
        return tidy(function() {
            return cast$1(equal(argMax(e, -1), argMax(t, -1)), "float32")
        })
    }

    function binaryCrossentropy$1(e, t) {
        return binaryCrossentropy(e, t)
    }

    function sparseCategoricalAccuracy(e, t) {
        throw new NotImplementedError
    }

    function get$1(e) {
        var t = {
            binaryAccuracy: binaryAccuracy,
            categoricalAccuracy: categoricalAccuracy,
            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)
            },
            Adam: function() {
                return train.adam(.001, .9, .999, epsilon$1())
            },
            RMSProp: function() {
                return train.rmsprop(.001, .9, null, epsilon$1())
            },
            SGD: function() {
                return train.sgd(.01)
            }
        };
        if (t.adagrad = t.Adagrad, t.adam = t.Adam, t.rmsprop = t.RMSProp, t.sgd = t.SGD, e in t) return t[e]();
        throw new ValueError("Unknown Optimizer " + e)
    }

    function assertFeedCompatibility(e, t) {
        if (null != e.dtype && e.dtype !== t.dtype) throw new ValueError("The dtype of the feed (" + t.dtype + ") is incompatible with that of the key '" + e.name + "' (" + e.dtype + ").");
        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 r = 0; r < e.shape.length; ++r)
                if (null != e.shape[r] && e.shape[r] !== t.shape[r]) throw new ValueError("The " + r + "-th dimension of the feed (" + t.shape[r] + ") is incompatible with that of the key (" + e.shape[r] + ").")
        }
    }

    function execute(e, t, r) {
        for (var n = Array.isArray(e), a = n ? e : [e], o = [], i = new FeedDict(t), s = 0, u = a; s < u.length; s++) {
            var l = u[s];
            o.push(executeInternal(l, i, r))
        }
        return n ? o : o[0]
    }

    function executeInternal(e, t, r) {
        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 n = [], a = 0, o = e.inputs; a < o.length; a++) {
            var i = executeInternal(o[a], t, r);
            n.push(i)
        }
        var s = e.sourceLayer.apply(n, r);
        Array.isArray(s) || (s = [s]);
        for (var u = getNodeOutputs(e), l = Array.isArray(u) ? u : [u], c = 0; c < l.length; ++c) t.add(l[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 r = null, n = 0; n < e.sourceLayer.inboundNodes.length; ++n)
                for (var a = 0, o = e.sourceLayer.inboundNodes[n].outputTensors; a < o.length; a++)
                    if (o[a].id === e.id) {
                        r = n;
                        break
                    }
            t = e.sourceLayer.getOutputAt(r)
        }
        return t
    }

    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, r, n, a) {
        if (void 0 === n && (n = !0), void 0 === a && (a = ""), null == t || 0 === t.length) {
            if (null != e) {
                var o = !1;
                if (isDataArray(e) && e.length > 0) o = !0;
                else if (isDataDict(e)) {
                    for (var i in e)
                        if (e.hasOwnProperty(i)) {
                            o = !0;
                            break
                        }
                } else o = !0;
                if (o) throw new ValueError("Error when checking model " + a + " 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 u = 0, l = t; u < l.length; u++) {
                var c = l[u];
                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 " + a + ": 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 " + a + " expects " + t.length + " Tensor(s), but only received one Tensor. Found: Tensor with shape " + e.shape);
            s = [e]
        }
        for (p = 0; p < t.length; ++p) 1 === (d = s[p]).shape.length && (s[p] = expandDims$1(d, 1));
        if (null != r)
            for (var p = 0; p < t.length; ++p)
                if (null != r[p]) {
                    var d = s[p];
                    if (d.shape.length !== r[p].length) throw new ValueError("Error when checking " + a + ": expected " + t[p] + " to have " + r[p].length + " dimension(s). but got array with shape " + d.shape);
                    for (var h = 0; h < r[p].length; ++h)
                        if (0 !== h || n) {
                            var f = d.shape[h],
                                m = r[p][h];
                            if (null != m && m >= 0 && f !== m) throw new ValueError("Error when checking " + a + ": expected " + t[p] + " to have shape [" + r[p] + "], but got array with shape [" + d.shape + "].")
                        }
                }
        return s
    }

    function checkArrayLengths(e, t, r) {
        var n = unique(e.map(function(e) {
            return e.shape[0]
        }));
        n.sort();
        var a = unique(t.map(function(e) {
            return e.shape[0]
        }));
        if (a.sort(), n.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 (a.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 (n.length > 0 && a.length > 0 && !arraysEqual(n, a)) throw new ValueError("Input Tensors should have the same number of samples as target Tensors. Found " + n[0] + " input sample(s) and " + a[0] + " target sample(s).")
    }

    function checkLossAndTargetCompatibility(e, t, r) {
        for (var n = [meanSquaredError, binaryCrossentropy, categoricalCrossentropy], a = 0; a < e.length; ++a) {
            var o = e[a],
                i = t[a],
                s = r[a];
            if (null != i) {
                if (i === categoricalCrossentropy && 1 === o.shape[o.shape.length - 1]) throw new ValueError("You are passing a target array of shape " + o.shape + " while using a loss 'categorical_crossentropy'. 'categorical_crossentropy'expects targets to be binary matrices (1s and 0s) of shape [samples, classes].");
                if (-1 !== n.indexOf(i))
                    for (var u = o.shape.slice(1), l = s.slice(1), c = 0; c < u.length; ++c) {
                        var p = u[c],
                            d = l[c];
                        if (null != d && p !== d) throw new ValueError("A target Tensor with shape " + o.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 makeBatches(e, t) {
        for (var r = [], n = 0, a = null; n < e;)(a = n + t) >= e && (a = e), r.push([n, a]), n = a;
        return r
    }

    function sliceArrays(e, t, r) {
        return null == e ? [null] : Array.isArray(e) ? e.map(function(e) {
            return sliceAlongFirstAxis(e, t, r - t)
        }) : sliceAlongFirstAxis(e, t, r - 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 checkInputData(e, t, r, n, a) {
        void 0 === n && (n = !0), void 0 === a && (a = "");
        var o;
        if (Array.isArray(e)) {
            if (e.length !== t.length) throw new ValueError("Error when checking model " + a + ": 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).");
            o = e
        } else {
            if (t.length > 1) throw new ValueError("The model expects " + t.length + " " + a + " Tensors, but only received one Tensor. Found: array with shape " + JSON.stringify(e.shape) + ".");
            o = [e]
        }
        if (null != r)
            for (var i = 0; i < t.length; ++i)
                if (null != r[i]) {
                    var s = o[i];
                    if (s.shape.length !== r[i].length) throw new ValueError("Error when checking " + a + ": expected " + t[i] + " to have " + r[i].length + " dimension(s), but got array with shape " + JSON.stringify(s.shape));
                    for (var u = 0; u < r[i].length; ++u)
                        if (0 !== u || n) {
                            var l = s.shape[u],
                                c = r[i][u];
                            if (null != c && c !== l) throw new ValueError("Error when checking " + a + ": expected " + t[i] + " to have shape " + JSON.stringify(r[i]) + " 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 r = [], n = 0, a = t; n < a.length; n++) {
                var o = a[n],
                    i = e.hasOwnProperty(o) ? e[o] : [];
                Array.isArray(i) || (i = [i]), r.push(i)
            }
            return r
        }
        throw new TypeError("Type of metrics argument not understood. Expected an Array or Object, found: " + e)
    }

    function checkFanMode(e) {
        checkStringTypeUnionValue(VALID_FAN_MODE_VALUES, "FanMode", e)
    }

    function checkDistribution(e) {
        checkStringTypeUnionValue(VALID_DISTRIBUTION_VALUES, "Distribution", e)
    }

    function computeFans(e, t) {
        void 0 === t && (t = "channelsLast");
        var r, n;
        if (checkDataFormat(t), 2 === e.length) r = e[0], n = e[1];
        else if (-1 !== [3, 4, 5].indexOf(e.length)) {
            if ("channelsFirst" === t) {
                a = arrayProd(e, 2);
                r = e[1] * a, n = e[0] * a
            } else if ("channelsLast" === t) {
                var a = arrayProd(e, 0, e.length - 2);
                r = e[e.length - 2] * a, n = e[e.length - 1] * a
            }
        } else {
            var o = arrayProd(e);
            r = Math.sqrt(o), n = Math.sqrt(o)
        }
        return [r, n]
    }

    function deserializeInitializer(e, t) {
        return void 0 === t && (t = {}), deserializeKerasObject(e, 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 serializeActivation(e) {
        return e.getClassName()
    }

    function deserializeActivation(e, t) {
        return void 0 === t && (t = {}), deserializeKerasObject(e, SerializationMap.getMap().classNameMap, t, "activation")
    }

    function getActivation(e) {
        if (null == e) return deserializeActivation(t = {
            className: "linear",
            config: {}
        });
        if ("string" == typeof e) {
            var t = {
                className: e,
                config: {}
            };
            return deserializeActivation(t)
        }
        return e instanceof Activation ? e : deserializeActivation(e)
    }

    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
        })
    }

    function serializeRegularizer(e) {
        return serializeKerasObject(e)
    }

    function deserializeRegularizer(e, t) {
        return void 0 === t && (t = {}), deserializeKerasObject(e, 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, r) {
        if ("number" == typeof e) return pyListRepeat(e, t);
        if (e.length !== t) throw new ValueError("The " + r + " argument must be a tuple of " + t + " integers. Received: " + e.length + " elements.");
        for (var n = 0; n < t; ++n) {
            var a = e[n];
            if (!isInteger(a)) throw new ValueError("The " + r + " argument must be a tuple of " + t + " integers. Received: " + JSON.stringify(e) + " including a non-integer number " + a)
        }
        return e
    }

    function convOutputLength(e, t, r, n, a) {
        if (void 0 === a && (a = 1), null == e) return e;
        var o, i = t + (t - 1) * (a - 1);
        return o = "same" === r ? e : e - i + 1, Math.floor((o + n - 1) / n)
    }

    function deconvLength(e, t, r, n) {
        if (null == e) return null;
        if ("valid" === n) e = e * t + max$1([r - t, 0]);
        else {
            if ("same" !== n) throw new ValueError("Unsupport padding mode: " + n + ".");
            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, r, n, a, o, i) {
        return void 0 === n && (n = 1), void 0 === a && (a = "valid"), void 0 === i && (i = 1), tidy(function() {
            if (null == o && (o = imageDataFormat()), checkDataFormat(o), 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 != r && 1 !== r.shape.length) throw new ValueError("The bias for a conv1dWithBias operation should be 1, but is " + t.shape.length + " instead");
            if ("channelsFirst" === o && (e = transpose(e, [0, 2, 1])), "causal" === a) throw new NotImplementedError("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");
            var s = conv1d(e, t, n, "same" === a ? "same" : "valid", "NWC", i);
            return null != r && (s = biasAdd(s, r)), s
        })
    }

    function conv2dWithBias(e, t, r, n, a, o, i) {
        return void 0 === n && (n = [1, 1]), void 0 === a && (a = "valid"), tidy(function() {
            if (null == o && (o = imageDataFormat()), checkDataFormat(o), 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, o);
            if ("causal" === a) throw new NotImplementedError("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");
            return s = conv2d(s, t, n, "same" === a ? "same" : "valid", "NHWC", i), null != r && (s = biasAdd(s, r)), "channelsFirst" === o && (s = transpose(s, [0, 3, 1, 2])), s
        })
    }

    function depthwiseConv2d$1(e, t, r, n, a, o) {
        return void 0 === r && (r = [1, 1]), void 0 === n && (n = "valid"), tidy(function() {
            null == a && (a = imageDataFormat()), checkDataFormat(a);
            var i = preprocessConv2DInput(e, a);
            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 i = depthwiseConv2d(i, t, r, "same" === n ? "same" : "valid", "NHWC", o), "channelsFirst" === a && (i = transpose(i, [0, 3, 1, 2])), i
        })
    }

    function batchNormalization$1(e, t, r, n, a, o) {
        void 0 === o && (o = .001);
        var i;
        if (2 === e.rank) i = batchNormalization2d(e, t, r, o, a, n);
        else if (3 === e.rank) i = batchNormalization3d(e, t, r, o, a, n);
        else {
            if (4 !== e.rank) throw new NotImplementedError("batchNormalization is not implememnted for array of rank " + e.rank + " yet");
            i = batchNormalization4d(e, t, r, o, a, n)
        }
        return i
    }

    function regularNormalizeBatchInTraining(e, t, r, n, a) {
        return void 0 === a && (a = .001), tidy(function() {
            var o = moments(e, n),
                i = o.mean,
                s = o.variance;
            return [batchNormalization$1(e, i, s, r, t, a), i, s]
        })
    }

    function broadcastNormalizeBatchInTraining(e, t, r, n, a) {
        return void 0 === a && (a = .001), tidy(function() {
            for (var o = moments(e, n), i = o.mean, s = o.variance, u = [], l = 0, c = range$1(0, e.rank); l < c.length; l++) {
                var p = c[l]; - 1 !== n.indexOf(p) ? u.push(1) : u.push(e.shape[p])
            }
            var d = i.reshape(u),
                h = s.reshape(u),
                f = null == t ? null : t.reshape(u),
                m = null == r ? null : r.reshape(u);
            return [batchNormalization$1(e, d, h, m, f, a), i, s]
        })
    }

    function normalizeBatchInTraining(e, t, r, n, a) {
        return void 0 === a && (a = .001), arraysEqual(n.slice().sort(), range$1(0, e.rank - 1)) ? regularNormalizeBatchInTraining(e, t, r, n, a) : broadcastNormalizeBatchInTraining(e, t, r, n, a)
    }

    function spatial2dPadding(e, t, r) {
        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 == r && (r = imageDataFormat()), "channelsLast" !== r && "channelsFirst" !== r) throw new ValueError("Unknown data format: " + r + ". Supported data formats are 'channelsLast' and 'channelsFirst.");
            var n;
            return n = "channelsFirst" === r ? [
                [0, 0],
                [0, 0], t[0], t[1]
            ] : [
                [0, 0], t[0], t[1],
                [0, 0]
            ], pad(e, n)
        })
    }

    function pool2d(e, t, r, n, a, o) {
        return tidy(function() {
            checkDataFormat(a), checkPoolMode(o), checkPaddingMode(n), null == r && (r = [1, 1]), null == n && (n = "valid"), null == a && (a = imageDataFormat()), null == o && (o = "max"), e = preprocessConv2DInput(e, a);
            var i, s = "same" === n ? "same" : "valid";
            return i = "max" === o ? maxPool(e, t, r, s) : avgPool(e, t, r, s), "channelsFirst" === a && (i = transpose(i, [0, 3, 1, 2])), i
        })
    }

    function rnn(e, t, r, n, a, o, i, s) {
        void 0 === n && (n = !1), void 0 === i && (i = !1);
        var u = t.shape.length;
        if (u < 3) throw new ValueError("Input should be at least 3D, but is " + u + "D.");
        var l = [1, 0].concat(range$1(2, u));
        if (t = transpose(t, l), null != a) throw new NotImplementedError("The rnn() function of the deeplearn.js backend does not support masking yet.");
        if (null != o) throw new NotImplementedError("The rnn() functoin of the deeplearn.js backend does not support constants yet.");
        i && console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."), n && (t = reverse(t, 0));
        for (var c, p, d = r, h = t.shape[0], f = 0; f < h; ++f) {
            var m = sliceAlongFirstAxis(t, f, 1),
                g = e(m = m.reshape(m.shape.slice(1)), d);
            p = g[0], c = 0 === f ? p.reshape([1].concat(p.shape)) : concatAlongFirstAxis(c, p.reshape([1].concat(p.shape))), d = g[1]
        }
        return [p, transpose(c, [1, 0].concat(range$1(2, c.shape.length))), d]
    }

    function checkBidirectionalMergeMode(e) {
        checkStringTypeUnionValue(VALID_BIDIRECTIONAL_MERGE_MODES, "BidirectionalMergeMode", e)
    }

    function loadModelInternal(e) {
        return __awaiter$16(this, void 0, void 0, function() {
            var t;
            return __generator$16(this, function(r) {
                if ("string" == typeof e) {
                    if (0 === (t = getLoadHandlers(e)).length) t.push(browserHTTPRequest(e));
                    else if (t.length > 1) throw new ValueError("Found more than one (" + t.length + ") load handlers for URL '" + e + "'");
                    e = t[0]
                }
                return [2, loadModelFromIOHandler(e)]
            })
        })
    }

    function loadModelFromIOHandler(e, t) {
        return __awaiter$16(this, void 0, void 0, function() {
            var r, n, a, o, i;
            return __generator$16(this, function(s) {
                switch (s.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 = s.sent(), null != (n = r.modelTopology).model_config && (n = n.model_config), a = deserialize(convertPythonicToTs(n), 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, i = !0, a.loadWeights(decodeWeights(r.weightData, r.weightSpecs), o, i)
                        }
                        return [2, a]
                }
            })
        })
    }

    function hasOwnProperty(e, t) {
        return Object.prototype.hasOwnProperty.call(e, t)
    }

    function Url() {
        this.protocol = null, this.slashes = null, this.auth = null, this.host = null, this.port = null, this.hostname = null, this.hash = null, this.search = null, this.query = null, this.pathname = null, this.path = null, this.href = null
    }

    function urlParse(e, t, r) {
        if (e && util$1.isObject(e) && e instanceof Url) return e;
        var n = new Url;
        return n.parse(e, t, r), n
    }

    function urlFormat(e) {
        return util$1.isString(e) && (e = urlParse(e)), e instanceof Url ? e.format() : Url.prototype.format.call(e)
    }

    function asPromise(e, t) {
        for (var r = new Array(arguments.length - 1), n = 0, a = 2, o = !0; a < arguments.length;) r[n++] = arguments[a++];
        return new Promise(function(a, i) {
            r[n] = function(e) {
                if (o)
                    if (o = !1, e) i(e);
                    else {
                        for (var t = new Array(arguments.length - 1), r = 0; r < t.length;) t[r++] = arguments[r];
                        a.apply(null, t)
                    }
            };
            try {
                e.apply(t || null, r)
            } catch (e) {
                o && (o = !1, i(e))
            }
        })
    }

    function EventEmitter() {
        this._listeners = {}
    }

    function factory(e) {
        return "undefined" != typeof Float32Array ? function() {
            function t(e, t, r) {
                o[0] = e, t[r] = i[0], t[r + 1] = i[1], t[r + 2] = i[2], t[r + 3] = i[3]
            }

            function r(e, t, r) {
                o[0] = e, t[r] = i[3], t[r + 1] = i[2], t[r + 2] = i[1], t[r + 3] = i[0]
            }

            function n(e, t) {
                return i[0] = e[t], i[1] = e[t + 1], i[2] = e[t + 2], i[3] = e[t + 3], o[0]
            }

            function a(e, t) {
                return i[3] = e[t], i[2] = e[t + 1], i[1] = e[t + 2], i[0] = e[t + 3], o[0]
            }
            var o = new Float32Array([-0]),
                i = new Uint8Array(o.buffer),
                s = 128 === i[3];
            e.writeFloatLE = s ? t : r, e.writeFloatBE = s ? r : t, e.readFloatLE = s ? n : a, e.readFloatBE = s ? a : n
        }() : function() {
            function t(e, t, r, n) {
                var a = t < 0 ? 1 : 0;
                if (a && (t = -t), 0 === t) e(1 / t > 0 ? 0 : 2147483648, r, n);
                else if (isNaN(t)) e(2143289344, r, n);
                else if (t > 3.4028234663852886e38) e((a << 31 | 2139095040) >>> 0, r, n);
                else if (t < 1.1754943508222875e-38) e((a << 31 | Math.round(t / 1.401298464324817e-45)) >>> 0, r, n);
                else {
                    var o = Math.floor(Math.log(t) / Math.LN2);
                    e((a << 31 | o + 127 << 23 | 8388607 & Math.round(t * Math.pow(2, -o) * 8388608)) >>> 0, r, n)
                }
            }

            function r(e, t, r) {
                var n = e(t, r),
                    a = 2 * (n >> 31) + 1,
                    o = n >>> 23 & 255,
                    i = 8388607 & n;
                return 255 === o ? i ? NaN : a * (1 / 0) : 0 === o ? 1.401298464324817e-45 * a * i : a * Math.pow(2, o - 150) * (i + 8388608)
            }
            e.writeFloatLE = t.bind(null, writeUintLE), e.writeFloatBE = t.bind(null, writeUintBE), e.readFloatLE = r.bind(null, readUintLE), e.readFloatBE = r.bind(null, readUintBE)
        }(), "undefined" != typeof Float64Array ? function() {
            function t(e, t, r) {
                o[0] = e, t[r] = i[0], t[r + 1] = i[1], t[r + 2] = i[2], t[r + 3] = i[3], t[r + 4] = i[4], t[r + 5] = i[5], t[r + 6] = i[6], t[r + 7] = i[7]
            }

            function r(e, t, r) {
                o[0] = e, t[r] = i[7], t[r + 1] = i[6], t[r + 2] = i[5], t[r + 3] = i[4], t[r + 4] = i[3], t[r + 5] = i[2], t[r + 6] = i[1], t[r + 7] = i[0]
            }

            function n(e, t) {
                return i[0] = e[t], i[1] = e[t + 1], i[2] = e[t + 2], i[3] = e[t + 3], i[4] = e[t + 4], i[5] = e[t + 5], i[6] = e[t + 6], i[7] = e[t + 7], o[0]
            }

            function a(e, t) {
                return i[7] = e[t], i[6] = e[t + 1], i[5] = e[t + 2], i[4] = e[t + 3], i[3] = e[t + 4], i[2] = e[t + 5], i[1] = e[t + 6], i[0] = e[t + 7], o[0]
            }
            var o = new Float64Array([-0]),
                i = new Uint8Array(o.buffer),
                s = 128 === i[7];
            e.writeDoubleLE = s ? t : r, e.writeDoubleBE = s ? r : t, e.readDoubleLE = s ? n : a, e.readDoubleBE = s ? a : n
        }() : function() {
            function t(e, t, r, n, a, o) {
                var i = n < 0 ? 1 : 0;
                if (i && (n = -n), 0 === n) e(0, a, o + t), e(1 / n > 0 ? 0 : 2147483648, a, o + r);
                else if (isNaN(n)) e(0, a, o + t), e(2146959360, a, o + r);
                else if (n > 1.7976931348623157e308) e(0, a, o + t), e((i << 31 | 2146435072) >>> 0, a, o + r);
                else {
                    var s;
                    if (n < 2.2250738585072014e-308) e((s = n / 5e-324) >>> 0, a, o + t), e((i << 31 | s / 4294967296) >>> 0, a, o + r);
                    else {
                        var u = Math.floor(Math.log(n) / Math.LN2);
                        1024 === u && (u = 1023), e(4503599627370496 * (s = n * Math.pow(2, -u)) >>> 0, a, o + t), e((i << 31 | u + 1023 << 20 | 1048576 * s & 1048575) >>> 0, a, o + r)
                    }
                }
            }

            function r(e, t, r, n, a) {
                var o = e(n, a + t),
                    i = e(n, a + r),
                    s = 2 * (i >> 31) + 1,
                    u = i >>> 20 & 2047,
                    l = 4294967296 * (1048575 & i) + o;
                return 2047 === u ? l ? NaN : s * (1 / 0) : 0 === u ? 5e-324 * s * l : s * Math.pow(2, u - 1075) * (l + 4503599627370496)
            }
            e.writeDoubleLE = t.bind(null, writeUintLE, 0, 4), e.writeDoubleBE = t.bind(null, writeUintBE, 4, 0), e.readDoubleLE = r.bind(null, readUintLE, 0, 4), e.readDoubleBE = r.bind(null, readUintBE, 4, 0)
        }(), e
    }

    function writeUintLE(e, t, r) {
        t[r] = 255 & e, t[r + 1] = e >>> 8 & 255, t[r + 2] = e >>> 16 & 255, t[r + 3] = e >>> 24
    }

    function writeUintBE(e, t, r) {
        t[r] = e >>> 24, t[r + 1] = e >>> 16 & 255, t[r + 2] = e >>> 8 & 255, t[r + 3] = 255 & e
    }

    function readUintLE(e, t) {
        return (e[t] | e[t + 1] << 8 | e[t + 2] << 16 | e[t + 3] << 24) >>> 0
    }

    function readUintBE(e, t) {
        return (e[t] << 24 | e[t + 1] << 16 | e[t + 2] << 8 | e[t + 3]) >>> 0
    }

    function inquire(moduleName) {
        try {
            var mod = eval("quire".replace(/^/, "re"))(moduleName);
            if (mod && (mod.length || Object.keys(mod).length)) return mod
        } catch (e) {}
        return null
    }

    function pool(e, t, r) {
        var n = r || 8192,
            a = n >>> 1,
            o = null,
            i = n;
        return function(r) {
            if (r < 1 || r > a) return e(r);
            i + r > n && (o = e(n), i = 0);
            var s = t.call(o, i, i += r);
            return 7 & i && (i = 1 + (7 | i)), s
        }
    }

    function LongBits(e, t) {
        this.lo = e >>> 0, this.hi = t >>> 0
    }

    function Op(e, t, r) {
        this.fn = e, this.len = t, this.next = void 0, this.val = r
    }

    function noop() {}

    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, 0, 0), this.tail = this.head, this.states = null
    }

    function writeByte(e, t, r) {
        t[r] = 255 & e
    }

    function writeVarint32(e, t, r) {
        for (; e > 127;) t[r++] = 127 & e | 128, e >>>= 7;
        t[r] = e
    }

    function VarintOp(e, t) {
        this.len = e, this.next = void 0, this.val = t
    }

    function writeVarint64(e, t, r) {
        for (; e.hi;) t[r++] = 127 & e.lo | 128, e.lo = (e.lo >>> 7 | e.hi << 25) >>> 0, e.hi >>>= 7;
        for (; e.lo > 127;) t[r++] = 127 & e.lo | 128, e.lo = e.lo >>> 7;
        t[r++] = e.lo
    }

    function writeFixed32(e, t, r) {
        t[r] = 255 & e, t[r + 1] = e >>> 8 & 255, t[r + 2] = e >>> 16 & 255, t[r + 3] = e >>> 24
    }

    function BufferWriter$1() {
        writer.call(this)
    }

    function writeStringBuffer(e, t, r) {
        e.length < 40 ? minimal.utf8.write(e, t, r) : t.utf8Write(e, r)
    }

    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
    }

    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))
    }

    function BufferReader$1(e) {
        reader.call(this, e)
    }

    function Service(e, t, r) {
        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(r)
    }

    function getParamValue(e, t, r, n) {
        var a = t.params[e];
        if (a && void 0 !== a.inputIndex) {
            if ("tensor" === a.type) return getTensor(t.inputNames[a.inputIndex], r, n);
            if ("tensors" === a.type) return (0 === a.inputIndex ? 0 === a.inputParamLength ? t.inputNames : t.inputNames.slice(a.inputIndex, -a.inputParamLength) : t.inputNames.splice(a.inputIndex)).map(function(e) {
                return getTensor(e, r, n)
            });
            var o = Array.prototype.slice.call(getTensor(t.inputNames.slice(a.inputIndex)[0], r, n).dataSync());
            return "number" === a.type ? o[0] : o
        }
        return a && a.value
    }

    function getTensor(e, t, r) {
        var n = parseNodeName(e),
            a = n[0],
            o = n[1],
            i = r.currentContextIds.find(function(e) {
                return !!t[getNodeNameWithContextId(a, e)]
            });
        return void 0 !== i ? t[getNodeNameWithContextId(a, i)][o] : void 0
    }

    function getNodeNameAndIndex(e, t) {
        var r = parseNodeName(e),
            n = r[0],
            a = r[1];
        return [getNodeNameWithContextId(n, t && t.currentContextId), a]
    }

    function getNodeNameWithContextId(e, t) {
        return t ? e + "-" + t : e
    }

    function parseNodeName(e) {
        var t = e.lastIndexOf(":");
        return -1 === t ? [e, 0] : [e.substring(0, t), Number(e.substring(t + 1))]
    }

    function split$1(e, t) {
        for (var r = [], n = 0; n < e.length; n += t) r.push(e.slice(n, n + t));
        return r
    }

    function executeOp$2(e, t, r) {
        return __awaiter$17(this, void 0, void 0, function() {
            var n, a, o, i, s, u, l, c;
            return __generator$17(this, function(p) {
                switch (p.label) {
                    case 0:
                        switch (n = 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]
                        }
                        return [3, 8];
                    case 1:
                        return [2, [getParamValue("pred", e, t, r)]];
                    case 2:
                        return a = getParamValue("pred", e, t, r), o = getParamValue("data", e, t, r), [4, a.data()];
                    case 3:
                        return [2, p.sent()[0] ? [void 0, o] : [o, void 0]];
                    case 4:
                        return i = e.inputNames.find(function(e) {
                            return void 0 !== getTensor(e, t, r)
                        }), [2, i ? [getTensor(i, t, r)] : void 0];
                    case 5:
                        return s = getParamValue("frameName", e, t, r), u = getParamValue("tensor", e, t, r), r.enterFrame(s), [2, [u]];
                    case 6:
                        return l = getParamValue("tensor", e, t, r), r.exitFrame(), [2, [l]];
                    case 7:
                        return c = getParamValue("tensor", e, t, r), r.nextIteration(), [2, [c]];
                    case 8:
                        throw TypeError("Node type " + e.op + " is not implemented")
                }
            })
        })
    }

    function executeOp$13(e, t, r) {
        switch (e.category) {
            case "arithmetic":
                return executeOp(e, t, r);
            case "basic_math":
                return executeOp$1(e, t, r);
            case "control":
                return executeOp$2(e, t, r);
            case "convolution":
                return executeOp$3(e, t, r);
            case "creation":
                return executeOp$4(e, t, r);
            case "image":
                return executeOp$6(e, t, r);
            case "graph":
                return executeOp$5(e, t, r);
            case "logical":
                return executeOp$7(e, t, r);
            case "matrices":
                return executeOp$8(e, t, r);
            case "normalization":
                return executeOp$9(e, t, r);
            case "reduction":
                return executeOp$10(e, t, r);
            case "slice_join":
                return executeOp$11(e, t, r);
            case "transformation":
                return executeOp$12(e, t, r);
            default:
                throw TypeError("Node type " + e.op + " is not implemented")
        }
    }

    function loadFrozenModel(e, t, r) {
        return __awaiter$19(this, void 0, void 0, function() {
            var n;
            return __generator$19(this, function(a) {
                switch (a.label) {
                    case 0:
                        return n = new FrozenModel(e, t, r), [4, n.load()];
                    case 1:
                        return a.sent(), [2, n]
                }
            })
        })
    }
    var util = Object.freeze({
            assertArgumentsAreTensors: assertArgumentsAreTensors,
            shuffle: shuffle,
            clamp: clamp,
            randUniform: randUniform,
            distSquared: distSquared,
            assert: assert,
            assertShapesMatch: assertShapesMatch,
            assertTypesMatch: assertTypesMatch,
            flatten: flatten,
            inferShape: inferShape,
            sizeFromShape: sizeFromShape,
            isScalarShape: isScalarShape,
            arraysEqual: arraysEqual,
            isInt: isInt,
            tanh: tanh,
            sizeToSquarishShape: sizeToSquarishShape,
            createShuffledIndices: createShuffledIndices,
            rightPad: rightPad,
            repeatedTry: repeatedTry,
            getQueryParams: getQueryParams,
            inferFromImplicitShape: inferFromImplicitShape,
            squeezeShape: squeezeShape,
            getTypedArrayFromDType: getTypedArrayFromDType,
            isTensorInList: isTensorInList,
            checkForNaN: checkForNaN,
            flattenNameArrayMap: flattenNameArrayMap,
            unflattenToNameArrayMap: unflattenToNameArrayMap,
            hasEncodingLoss: hasEncodingLoss,
            copyTypedArray: copyTypedArray,
            isTypedArray: isTypedArray,
            bytesPerElement: bytesPerElement,
            isFunction: isFunction,
            getTensorsInContainer: getTensorsInContainer
        }),
        FORMAT_LIMIT_NUM_VALS = 20,
        FORMAT_NUM_FIRST_LAST_VALS = 3,
        FORMAT_NUM_SIG_DIGITS = 7,
        __decorate = function(e, t, r, n) {
            var a, o = arguments.length,
                i = o < 3 ? t : null === n ? n = Object.getOwnPropertyDescriptor(t, r) : n;
            if ("object" == typeof Reflect && "function" == typeof Reflect.decorate) i = Reflect.decorate(e, t, r, n);
            else
                for (var s = e.length - 1; s >= 0; s--)(a = e[s]) && (i = (o < 3 ? a(i) : o > 3 ? a(t, r, i) : a(t, r)) || i);
            return o > 3 && i && Object.defineProperty(t, r, i), i
        },
        ConcatOps = function() {
            function e() {}
            return e.concat1d = function(t) {
                return e.concat(t, 0)
            }, e.concat2d = function(t, r) {
                return e.concat(t, r)
            }, e.concat3d = function(t, r) {
                return e.concat(t, r)
            }, e.concat4d = function(t, r) {
                return e.concat(t, r)
            }, e.concat = function(e, t) {
                void 0 === t && (t = 0), assert(e.length >= 1, "Pass at least one tensor to concat"), assertArgumentsAreTensors({
                    tensors: e
                }, "concat");
                var r = e[0];
                if (1 === e.length) return r;
                for (var n = parseAxisParam(t, r.shape), a = 1; a < e.length; ++a) r = concat2Tensors(r, e[a], n[0]);
                return r
            }, __decorate([doc(), operation], e, "concat", null), e
        }(),
        commonjsGlobal = "undefined" != typeof window ? window : "undefined" != typeof global ? global : "undefined" != typeof self ? self : {},
        alea = createCommonjsModule(function(e) {
            ! function(e, t, r) {
                function n(e) {
                    var t = this,
                        r = i();
                    t.next = function() {
                        var e = 2091639 * t.s0 + 2.3283064365386963e-10 * t.c;
                        return t.s0 = t.s1, t.s1 = t.s2, t.s2 = e - (t.c = 0 | e)
                    }, t.c = 1, t.s0 = r(" "), t.s1 = r(" "), t.s2 = r(" "), t.s0 -= r(e), t.s0 < 0 && (t.s0 += 1), t.s1 -= r(e), t.s1 < 0 && (t.s1 += 1), t.s2 -= r(e), t.s2 < 0 && (t.s2 += 1), r = null
                }

                function a(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 n(e),
                        o = t && t.state,
                        i = r.next;
                    return i.int32 = function() {
                        return 4294967296 * r.next() | 0
                    }, i.double = function() {
                        return i() + 1.1102230246251565e-16 * (2097152 * i() | 0)
                    }, i.quick = i, o && ("object" == typeof o && a(o, r), i.state = function() {
                        return a(r, {})
                    }), i
                }

                function i() {
                    var e = 4022871197;
                    return function(t) {
                        t = t.toString();
                        for (var r = 0; r < t.length; r++) {
                            var n = .02519603282416938 * (e += t.charCodeAt(r));
                            n -= e = n >>> 0, e = (n *= e) >>> 0, e += 4294967296 * (n -= e)
                        }
                        return 2.3283064365386963e-10 * (e >>> 0)
                    }
                }
                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) {
                    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()
                }

                function a(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 n(e),
                        o = t && t.state,
                        i = function() {
                            return (r.next() >>> 0) / 4294967296
                        };
                    return i.double = function() {
                        do {
                            var e = ((r.next() >>> 11) + (r.next() >>> 0) / 4294967296) / (1 << 21)
                        } while (0 === e);
                        return e
                    }, i.int32 = r.next, i.quick = i, o && ("object" == typeof o && a(o, r), i.state = function() {
                        return a(r, {})
                    }), i
                }
                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) {
                    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 n = 0; n < r.length + 64; n++) t.x ^= 0 | r.charCodeAt(n), n == r.length && (t.d = t.x << 10 ^ t.x >>> 4), t.next()
                }

                function a(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 n(e),
                        o = t && t.state,
                        i = function() {
                            return (r.next() >>> 0) / 4294967296
                        };
                    return i.double = function() {
                        do {
                            var e = ((r.next() >>> 11) + (r.next() >>> 0) / 4294967296) / (1 << 21)
                        } while (0 === e);
                        return e
                    }, i.int32 = r.next, i.quick = i, o && ("object" == typeof o && a(o, r), i.state = function() {
                        return a(r, {})
                    }), i
                }
                t && t.exports ? t.exports = o : r && r.amd ? r(function() {
                    return o
                }) : this.xorwow = o
            }(0, e, !1)
        }),
        xorshift7 = createCommonjsModule(function(e) {
            ! function(e, t, r) {
                function n(e) {
                    var t = this;
                    t.next = function() {
                            var e, r, n = t.x,
                                a = t.i;
                            return e = n[a], e ^= e >>> 7, r = e ^ e << 24, e = n[a + 1 & 7], r ^= e ^ e >>> 10, e = n[a + 3 & 7], r ^= e ^ e >>> 3, e = n[a + 4 & 7], r ^= e ^ e << 7, e = n[a + 7 & 7], e ^= e << 13, r ^= e ^ e << 9, n[a] = r, t.i = a + 1 & 7, r
                        },
                        function(e, t) {
                            var r, n = [];
                            if (t === (0 | t)) n[0] = t;
                            else
                                for (t = "" + t, r = 0; r < t.length; ++r) n[7 & r] = n[7 & r] << 15 ^ t.charCodeAt(r) + n[r + 1 & 7] << 13;
                            for (; n.length < 8;) n.push(0);
                            for (r = 0; r < 8 && 0 === n[r]; ++r);
                            for (8 == r ? n[7] = -1 : n[r], e.x = n, e.i = 0, r = 256; r > 0; --r) e.next()
                        }(t, e)
                }

                function a(e, t) {
                    return t.x = e.x.slice(), t.i = e.i, t
                }

                function o(e, t) {
                    null == e && (e = +new Date);
                    var r = new n(e),
                        o = t && t.state,
                        i = function() {
                            return (r.next() >>> 0) / 4294967296
                        };
                    return i.double = function() {
                        do {
                            var e = ((r.next() >>> 11) + (r.next() >>> 0) / 4294967296) / (1 << 21)
                        } while (0 === e);
                        return e
                    }, i.int32 = r.next, i.quick = i, o && (o.x && a(o, r), i.state = function() {
                        return a(r, {})
                    }), i
                }
                t && t.exports ? t.exports = o : r && r.amd ? r(function() {
                    return o
                }) : this.xorshift7 = o
            }(0, e, !1)
        }),
        xor4096 = createCommonjsModule(function(e) {
            ! function(e, t, r) {
                function n(e) {
                    var t = this;
                    t.next = function() {
                            var e, r, n = t.w,
                                a = t.X,
                                o = t.i;
                            return t.w = n = n + 1640531527 | 0, r = a[o + 34 & 127], e = a[o = o + 1 & 127], r ^= r << 13, e ^= e << 17, r ^= r >>> 15, e ^= e >>> 12, r = a[o] = r ^ e, t.i = o, r + (n ^ n >>> 16) | 0
                        },
                        function(e, t) {
                            var r, n, a, o, i, s = [],
                                u = 128;
                            for (t === (0 | t) ? (n = t, t = null) : (t += "\0", n = 0, u = Math.max(u, t.length)), a = 0, o = -32; o < u; ++o) t && (n ^= t.charCodeAt((o + 32) % t.length)), 0 === o && (i = n), n ^= n << 10, n ^= n >>> 15, n ^= n << 4, n ^= n >>> 13, o >= 0 && (i = i + 1640531527 | 0, a = 0 == (r = s[127 & o] ^= n + i) ? a + 1 : 0);
                            for (a >= 128 && (s[127 & (t && t.length || 0)] = -1), a = 127, o = 512; o > 0; --o) n = s[a + 34 & 127], r = s[a = a + 1 & 127], n ^= n << 13, r ^= r << 17, n ^= n >>> 15, r ^= r >>> 12, s[a] = n ^ r;
                            e.w = i, e.X = s, e.i = a
                        }(t, e)
                }

                function a(e, t) {
                    return t.i = e.i, t.w = e.w, t.X = e.X.slice(), t
                }

                function o(e, t) {
                    null == e && (e = +new Date);
                    var r = new n(e),
                        o = t && t.state,
                        i = function() {
                            return (r.next() >>> 0) / 4294967296
                        };
                    return i.double = function() {
                        do {
                            var e = ((r.next() >>> 11) + (r.next() >>> 0) / 4294967296) / (1 << 21)
                        } while (0 === e);
                        return e
                    }, i.int32 = r.next, i.quick = i, o && (o.X && a(o, r), i.state = function() {
                        return a(r, {})
                    }), i
                }
                t && t.exports ? t.exports = o : r && r.amd ? r(function() {
                    return o
                }) : this.xor4096 = o
            }(0, e, !1)
        }),
        tychei = createCommonjsModule(function(e) {
            ! function(e, t, r) {
                function n(e) {
                    var t = this,
                        r = "";
                    t.next = function() {
                        var e = t.b,
                            r = t.c,
                            n = t.d,
                            a = t.a;
                        return e = e << 25 ^ e >>> 7 ^ r, r = r - n | 0, n = n << 24 ^ n >>> 8 ^ a, a = a - e | 0, t.b = e = e << 20 ^ e >>> 12 ^ r, t.c = r = r - n | 0, t.d = n << 16 ^ r >>> 16 ^ a, t.a = a - e | 0
                    }, t.a = 0, t.b = 0, t.c = -1640531527, t.d = 1367130551, e === Math.floor(e) ? (t.a = e / 4294967296 | 0, t.b = 0 | e) : r += e;
                    for (var n = 0; n < r.length + 20; n++) t.b ^= 0 | r.charCodeAt(n), t.next()
                }

                function a(e, t) {
                    return t.a = e.a, t.b = e.b, t.c = e.c, t.d = e.d, t
                }

                function o(e, t) {
                    var r = new n(e),
                        o = t && t.state,
                        i = function() {
                            return (r.next() >>> 0) / 4294967296
                        };
                    return i.double = function() {
                        do {
                            var e = ((r.next() >>> 11) + (r.next() >>> 0) / 4294967296) / (1 << 21)
                        } while (0 === e);
                        return e
                    }, i.int32 = r.next, i.quick = i, o && ("object" == typeof o && a(o, r), i.state = function() {
                        return a(r, {})
                    }), i
                }
                t && t.exports ? t.exports = o : r && r.amd ? r(function() {
                    return o
                }) : this.tychei = o
            }(0, e, !1)
        }),
        seedrandom = createCommonjsModule(function(e) {
            ! function(t, r) {
                function n(e, n, c) {
                    var p = [],
                        v = s(i((n = 1 == n ? {
                            entropy: !0
                        } : n || {}).entropy ? [e, l(t)] : null == e ? u() : e, 3), p),
                        b = new a(p),
                        x = function() {
                            for (var e = b.g(h), t = m, r = 0; e < g;) e = (e + r) * d, t *= d, r = b.g(1);
                            for (; e >= y;) e /= 2, t /= 2, r >>>= 1;
                            return (e + r) / t
                        };
                    return x.int32 = function() {
                        return 0 | b.g(4)
                    }, x.quick = function() {
                        return b.g(4) / 4294967296
                    }, x.double = x, s(l(b.S), t), (n.pass || c || function(e, t, n, a) {
                        return a && (a.S && o(a, b), e.state = function() {
                            return o(b, {})
                        }), n ? (r[f] = e, t) : e
                    })(x, v, "global" in n ? n.global : this == r, n.state)
                }

                function a(e) {
                    var t, r = e.length,
                        n = this,
                        a = 0,
                        o = n.i = n.j = 0,
                        i = n.S = [];
                    for (r || (e = [r++]); a < d;) i[a] = a++;
                    for (a = 0; a < d; a++) i[a] = i[o = v & o + e[a % r] + (t = i[a])], i[o] = t;
                    (n.g = function(e) {
                        for (var t, r = 0, a = n.i, o = n.j, i = n.S; e--;) t = i[a = v & a + 1], r = r * d + i[v & (i[a] = i[o = v & o + t]) + (i[o] = t)];
                        return n.i = a, n.j = o, r
                    })(d)
                }

                function o(e, t) {
                    return t.i = e.i, t.j = e.j, t.S = e.S.slice(), t
                }

                function i(e, t) {
                    var r, n = [],
                        a = typeof e;
                    if (t && "object" == a)
                        for (r in e) try {
                            n.push(i(e[r], t - 1))
                        } catch (e) {}
                    return n.length ? n : "string" == a ? e : e + "\0"
                }

                function s(e, t) {
                    for (var r, n = e + "", a = 0; a < n.length;) t[v & a] = v & (r ^= 19 * t[v & a]) + n.charCodeAt(a++);
                    return l(t)
                }

                function u() {
                    try {
                        var e;
                        return c && (e = c.randomBytes) ? e = e(d) : (e = new Uint8Array(d), (p.crypto || p.msCrypto).getRandomValues(e)), l(e)
                    } catch (e) {
                        var r = p.navigator,
                            n = r && r.plugins;
                        return [+new Date, p, n, p.screen, l(t)]
                    }
                }

                function l(e) {
                    return String.fromCharCode.apply(0, e)
                }
                var c, p = this,
                    d = 256,
                    h = 6,
                    f = "random",
                    m = r.pow(d, h),
                    g = r.pow(2, 52),
                    y = 2 * g,
                    v = d - 1;
                if (r["seed" + f] = n, s(r.random(), t), e.exports) {
                    e.exports = n;
                    try {
                        c = require("crypto")
                    } catch (e) {}
                }
            }([], Math)
        });
    seedrandom.alea = alea, seedrandom.xor128 = xor128, seedrandom.xorwow = xorwow, seedrandom.xorshift7 = xorshift7, seedrandom.xor4096 = xor4096, seedrandom.tychei = tychei;
    var seedrandom$1 = seedrandom,
        seedrandom_1 = seedrandom$1.alea,
        MPRandGauss = function() {
            function e(e, t, r, n, a) {
                this.mean = e, this.stdDev = t, this.dtype = r, this.nextVal = NaN, this.truncated = n, this.truncated && (this.upper = this.mean + 2 * this.stdDev, this.lower = this.mean - 2 * this.stdDev);
                var o = a || Math.random();
                this.random = seedrandom_1(o.toString())
            }
            return e.prototype.nextValue = function() {
                if (!isNaN(this.nextVal)) {
                    var e = this.nextVal;
                    return this.nextVal = NaN, e
                }
                for (var t, r, n = !1; !n;) {
                    var a = void 0,
                        o = void 0,
                        i = void 0;
                    do {
                        i = (a = 2 * this.random() - 1) * a + (o = 2 * this.random() - 1) * o
                    } while (i >= 1 || 0 === i);
                    var s = Math.sqrt(-2 * Math.log(i) / i);
                    t = this.mean + this.stdDev * a * s, r = this.mean + this.stdDev * o * s, this.truncated && !this.isValidTruncated(t) || (n = !0)
                }
                return this.truncated && !this.isValidTruncated(r) || (this.nextVal = this.convertValue(r)), this.convertValue(t)
            }, e.prototype.convertValue = function(e) {
                return null == this.dtype || "float32" === this.dtype ? e : Math.round(e)
            }, e.prototype.isValidTruncated = function(e) {
                return e <= this.upper && e >= this.lower
            }, e
        }(),
        __decorate$1 = function(e, t, r, n) {
            var a, o = arguments.length,
                i = o < 3 ? t : null === n ? n = Object.getOwnPropertyDescriptor(t, r) : n;
            if ("object" == typeof Reflect && "function" == typeof Reflect.decorate) i = Reflect.decorate(e, t, r, n);
            else
                for (var s = e.length - 1; s >= 0; s--)(a = e[s]) && (i = (o < 3 ? a(i) : o > 3 ? a(t, r, i) : a(t, r)) || i);
            return o > 3 && i && Object.defineProperty(t, r, i), i
        },
        ReductionOps = function() {
            function e() {}
            return e.logSumExp = function(e, t, r) {
                void 0 === t && (t = null), void 0 === r && (r = !1), assertArgumentsAreTensors({
                    x: e
                }, "logSumExp");
                var n = parseAxisParam(t, e.shape),
                    a = e.max(n, !0),
                    o = e.sub(a).exp().sum(n).log(),
                    i = a.reshape(o.shape).add(o);
                if (r) {
                    var s = expandShapeToKeepDim(i.shape, n);
                    return i.reshape(s)
                }
                return i
            }, e.sum = function(e, t, r) {
                void 0 === t && (t = null), void 0 === r && (r = !1), assertArgumentsAreTensors({
                    x: e
                }, "sum"), "bool" === e.dtype && (e = e.toInt());
                var n = parseAxisParam(t, e.shape);
                return customGrad(function(e) {
                    var t = getAxesPermutation(n, e.rank),
                        a = n,
                        o = e;
                    null != t && (o = e.transpose(t), a = getInnerMostAxes(a.length, e.rank));
                    var i = ENV.engine.runKernel(function(e) {
                        return e.sum(o, a)
                    }, {
                        permutedX: o
                    });
                    if (r) {
                        var s = expandShapeToKeepDim(i.shape, n);
                        i = i.reshape(s)
                    }
                    return {
                        value: i,
                        gradFunc: function(t) {
                            var r = e.shape.slice();
                            return n.forEach(function(e) {
                                r[e] = 1
                            }), t.reshape(r).mul(ones(e.shape, "float32"))
                        }
                    }
                })(e)
            }, e.mean = function(e, t, r) {
                void 0 === t && (t = null), void 0 === r && (r = !1), assertArgumentsAreTensors({
                    x: e
                }, "mean");
                var n = parseAxisParam(t, e.shape),
                    a = sizeFromShape(computeOutAndReduceShapes(e.shape, n)[1]);
                return customGrad(function(e) {
                    var o = scalar(a);
                    return {
                        value: (o.dtype === e.dtype ? e : e.cast(o.dtype)).div(o).sum(t, r),
                        gradFunc: function(t) {
                            var r = e.shape.slice();
                            return n.forEach(function(e) {
                                r[e] = 1
                            }), t.reshape(r).mul(ones(e.shape, "float32")).div(o)
                        }
                    }
                })(e)
            }, e.min = function(e, t, r) {
                void 0 === t && (t = null), void 0 === r && (r = !1), assertArgumentsAreTensors({
                    x: e
                }, "min");
                var n = parseAxisParam(t, e.shape),
                    a = n,
                    o = getAxesPermutation(a, e.rank);
                null != o && (e = e.transpose(o), a = getInnerMostAxes(a.length, e.rank));
                var i = ENV.engine.runKernel(function(t) {
                    return t.min(e, a)
                }, {
                    x: e
                });
                if (r) {
                    var s = expandShapeToKeepDim(i.shape, n);
                    return i.reshape(s)
                }
                return i
            }, e.max = function(e, t, r) {
                void 0 === t && (t = null), void 0 === r && (r = !1), assertArgumentsAreTensors({
                    x: e
                }, "max");
                var n = parseAxisParam(t, e.shape),
                    a = n,
                    o = getAxesPermutation(a, e.rank);
                null != o && (e = e.transpose(o), a = getInnerMostAxes(a.length, e.rank));
                var i = ENV.engine.runKernel(function(t) {
                    return t.max(e, a)
                }, {
                    x: e
                });
                if (r) {
                    var s = expandShapeToKeepDim(i.shape, n);
                    return i.reshape(s)
                }
                return i
            }, e.argMin = function(e, t) {
                void 0 === t && (t = 0), assertArgumentsAreTensors({
                    x: e
                }, "argMin"), null == t && (t = 0);
                var r = parseAxisParam(t, e.shape),
                    n = getAxesPermutation(r, e.rank);
                return null != n && (e = e.transpose(n), r = getInnerMostAxes(r.length, e.rank)), ENV.engine.runKernel(function(t) {
                    return t.argMin(e, r[0])
                }, {
                    x: e
                })
            }, e.argMax = function(e, t) {
                void 0 === t && (t = 0), assertArgumentsAreTensors({
                    x: e
                }, "argMax"), null == t && (t = 0);
                var r = parseAxisParam(t, e.shape),
                    n = getAxesPermutation(r, e.rank);
                return null != n && (e = e.transpose(n), r = getInnerMostAxes(r.length, e.rank)), ENV.engine.runKernel(function(t) {
                    return t.argMax(e, r[0])
                }, {
                    x: e
                })
            }, e.moments = function(e, t, r) {
                void 0 === t && (t = null), void 0 === r && (r = !1), assertArgumentsAreTensors({
                    x: e
                }, "moments");
                var n = parseAxisParam(t, e.shape),
                    a = e.mean(n, r),
                    o = a.shape;
                return r || (o = expandShapeToKeepDim(a.shape, n)), {
                    mean: a,
                    variance: e.toFloat().sub(a.reshape(o)).square().mean(n, r)
                }
            }, e.unsortedSegmentSum = function(e, t, r, n) {
                void 0 === n && (n = 0), assertArgumentsAreTensors({
                    x: e,
                    segmentIds: t
                }, "unsortedSegmentSum"), assert("int32" === t.dtype, "Segment Ids must be of dtype `int32`"), n = parseAxisParam(n, e.shape)[0];
                for (var a = [], o = t.shape[0], i = [], s = 0; s < e.shape.length; s++) s === n ? i.push(o) : i.push(1);
                for (var u = reshape(t, i), s = 0; s < r; s++) {
                    var l = scalar(s, "int32"),
                        c = equal(l, u).asType("float32").mul(e).sum(n);
                    a.push(c)
                }
                return stack(a, n)
            }, __decorate$1([doc(), operation], e, "logSumExp", null), __decorate$1([doc(), operation], e, "sum", null), __decorate$1([doc(), operation], e, "mean", null), __decorate$1([doc(), operation], e, "min", null), __decorate$1([doc(), operation], e, "max", null), __decorate$1([doc(), operation], e, "argMin", null), __decorate$1([doc(), operation], e, "argMax", null), __decorate$1([doc(), operation], e, "moments", null), __decorate$1([doc(), operation], e, "unsortedSegmentSum", null), e
        }(),
        __decorate$2 = function(e, t, r, n) {
            var a, o = arguments.length,
                i = o < 3 ? t : null === n ? n = Object.getOwnPropertyDescriptor(t, r) : n;
            if ("object" == typeof Reflect && "function" == typeof Reflect.decorate) i = Reflect.decorate(e, t, r, n);
            else
                for (var s = e.length - 1; s >= 0; s--)(a = e[s]) && (i = (o < 3 ? a(i) : o > 3 ? a(t, r, i) : a(t, r)) || i);
            return o > 3 && i && Object.defineProperty(t, r, i), i
        },
        __awaiter = function(e, t, r, n) {
            return new(r || (r = Promise))(function(a, o) {
                function i(e) {
                    try {
                        u(n.next(e))
                    } catch (e) {
                        o(e)
                    }
                }

                function s(e) {
                    try {
                        u(n.throw(e))
                    } catch (e) {
                        o(e)
                    }
                }

                function u(e) {
                    e.done ? a(e.value) : new r(function(t) {
                        t(e.value)
                    }).then(i, s)
                }
                u((n = n.apply(e, t || [])).next())
            })
        },
        __generator = function(e, t) {
            function r(e) {
                return function(t) {
                    return n([e, t])
                }
            }

            function n(r) {
                if (a) throw new TypeError("Generator is already executing.");
                for (; u;) try {
                    if (a = 1, o && (i = o[2 & r[0] ? "return" : r[0] ? "throw" : "next"]) && !(i = i.call(o, r[1])).done) return i;
                    switch (o = 0, i && (r = [0, i.value]), r[0]) {
                        case 0:
                        case 1:
                            i = r;
                            break;
                        case 4:
                            return u.label++, {
                                value: r[1],
                                done: !1
                            };
                        case 5:
                            u.label++, o = r[1], r = [0];
                            continue;
                        case 7:
                            r = u.ops.pop(), u.trys.pop();
                            continue;
                        default:
                            if (i = u.trys, !(i = i.length > 0 && i[i.length - 1]) && (6 === r[0] || 2 === r[0])) {
                                u = 0;
                                continue
                            }
                            if (3 === r[0] && (!i || r[1] > i[0] && r[1] < i[3])) {
                                u.label = r[1];
                                break
                            }
                            if (6 === r[0] && u.label < i[1]) {
                                u.label = i[1], i = r;
                                break
                            }
                            if (i && u.label < i[2]) {
                                u.label = i[2], u.ops.push(r);
                                break
                            }
                            i[2] && u.ops.pop(), u.trys.pop();
                            continue
                    }
                    r = t.call(e, u)
                } catch (e) {
                    r = [6, e], o = 0
                } finally {
                    a = i = 0
                }
                if (5 & r[0]) throw r[1];
                return {
                    value: r[0] ? r[1] : void 0,
                    done: !0
                }
            }
            var a, o, i, s, u = {
                label: 0,
                sent: function() {
                    if (1 & i[0]) throw i[1];
                    return i[1]
                },
                trys: [],
                ops: []
            };
            return s = {
                next: r(0),
                throw: r(1),
                return: r(2)
            }, "function" == typeof Symbol && (s[Symbol.iterator] = function() {
                return this
            }), s
        },
        ArrayOps = function() {
            function e() {}
            return e.tensor = function(e, t, r) {
                void 0 === r && (r = "float32");
                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)
                }, r)
            }, e.scalar = function(t, r) {
                if (void 0 === r && (r = "float32"), isTypedArray(t) || Array.isArray(t)) throw new Error("Error creating a new Scalar: value must be a primitive (number|boolean)");
                return e.tensor(t, [], r)
            }, e.tensor1d = function(t, r) {
                void 0 === r && (r = "float32");
                var n = inferShape(t);
                if (1 !== n.length) throw new Error("tensor1d() requires values to be a flat/TypedArray");
                return e.tensor(t, n, r)
            }, e.tensor2d = function(t, r, n) {
                if (void 0 === n && (n = "float32"), null != r && 2 !== r.length) throw new Error("tensor2d() requires shape to have two numbers");
                var a = inferShape(t);
                if (2 !== a.length && 1 !== a.length) throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray");
                if (1 === a.length && null == r) throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray");
                return r = r || a, e.tensor(t, r, n)
            }, e.tensor3d = function(t, r, n) {
                if (void 0 === n && (n = "float32"), null != r && 3 !== r.length) throw new Error("tensor3d() requires shape to have three numbers");
                var a = inferShape(t);
                if (3 !== a.length && 1 !== a.length) throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray");
                if (1 === a.length && null == r) throw new Error("tensor3d() requires shape to be provided when `values` are a flat array");
                return r = r || a, e.tensor(t, r, n)
            }, e.tensor4d = function(t, r, n) {
                if (void 0 === n && (n = "float32"), null != r && 4 !== r.length) throw new Error("tensor4d() requires shape to have four numbers");
                var a = inferShape(t);
                if (4 !== a.length && 1 !== a.length) throw new Error("tensor4d() requires values to be number[][][][] or flat/TypedArray");
                if (1 === a.length && null == r) throw new Error("tensor4d() requires shape to be provided when `values` are a flat array");
                return r = r || a, e.tensor(t, r, n)
            }, e.tensor5d = function(t, r, n) {
                if (void 0 === n && (n = "float32"), null != r && 5 !== r.length) throw new Error("tensor5d() requires shape to have five numbers");
                var a = inferShape(t);
                if (5 !== a.length && 1 !== a.length) throw new Error("tensor5d() requires values to be            number[][][][][] or flat/TypedArray");
                if (1 === a.length && null == r) throw new Error("tensor5d() requires shape to be provided when `values` are a flat array");
                return r = r || a, e.tensor(t, r, n)
            }, e.ones = function(e, t) {
                void 0 === t && (t = "float32");
                var r = makeOnesTypedArray(sizeFromShape(e), t);
                return Tensor.make(e, {
                    values: r
                }, t)
            }, e.zeros = function(e, t) {
                void 0 === t && (t = "float32");
                var r = makeZerosTypedArray(sizeFromShape(e), t);
                return Tensor.make(e, {
                    values: r
                }, t)
            }, e.fill = function(e, t, r) {
                void 0 === r && (r = "float32");
                var n = getTypedArrayFromDType(r, sizeFromShape(e));
                return n.fill(t), Tensor.make(e, {
                    values: n
                }, r)
            }, e.onesLike = function(t) {
                return assertArgumentsAreTensors({
                    x: t
                }, "onesLike"), e.ones(t.shape, t.dtype)
            }, e.zerosLike = function(t) {
                return assertArgumentsAreTensors({
                    x: t
                }, "zerosLike"), e.zeros(t.shape, t.dtype)
            }, e.clone = function(e) {
                assertArgumentsAreTensors({
                    x: e
                }, "clone");
                return ENV.engine.runKernel(function(t) {
                    return Tensor.make(e.shape, {
                        dataId: e.dataId
                    }, e.dtype)
                }, {
                    x: e
                }, function(e) {
                    return {
                        x: function() {
                            return e.toFloat()
                        }
                    }
                })
            }, e.eye = function(t, r, n, a) {
                void 0 === a && (a = "float32"), null == r && (r = t);
                for (var o = e.buffer([t, r], a), i = t <= r ? t : r, s = 0; s < i; ++s) o.set(1, s, s);
                var u = o.toTensor().as2D(t, r);
                if (null == n) return u;
                if (1 === n.length) return e.tile(e.expandDims(u, 0), [n[0], 1, 1]);
                if (2 === n.length) return e.tile(e.expandDims(e.expandDims(u, 0), 0), [n[0], n[1], 1, 1]);
                throw new Error("eye() currently supports only 1D and 2D batchShapes, but received " + n.length + "D.")
            }, e.randomNormal = function(t, r, n, a, o) {
                if (void 0 === r && (r = 0), void 0 === n && (n = 1), null != a && "bool" === a) throw new Error("Unsupported data type " + a);
                for (var i = new MPRandGauss(r, n, a, !1, o), s = e.buffer(t, a), u = 0; u < s.values.length; u++) s.values[u] = i.nextValue();
                return s.toTensor()
            }, e.truncatedNormal = function(t, r, n, a, o) {
                if (void 0 === r && (r = 0), void 0 === n && (n = 1), null != a && "bool" === a) throw new Error("Unsupported data type " + a);
                for (var i = new MPRandGauss(r, n, a, !0, o), s = e.buffer(t, a), u = 0; u < s.values.length; u++) s.values[u] = i.nextValue();
                return s.toTensor()
            }, e.randomUniform = function(t, r, n, a) {
                void 0 === r && (r = 0), void 0 === n && (n = 1), void 0 === a && (a = "float32");
                for (var o = e.buffer(t, a), i = 0; i < o.values.length; i++) o.values[i] = randUniform(r, n);
                return o.toTensor()
            }, e.rand = function(e, t, r) {
                var n = sizeFromShape(e),
                    a = null;
                if (null == r || "float32" === r) a = new Float32Array(n);
                else if ("int32" === r) a = new Int32Array(n);
                else {
                    if ("bool" !== r) throw new Error("Unknown data type " + r);
                    a = new Uint8Array(n)
                }
                for (var o = 0; o < n; o++) a[o] = t();
                return Tensor.make(e, {
                    values: a
                }, r)
            }, e.multinomial = function(e, t, r, n) {
                void 0 === n && (n = !1), assertArgumentsAreTensors({
                    logits: e
                }, "multinomial");
                var a = e.size,
                    o = e.rank;
                if (a < 2) throw new Error("Error in multinomial: you need at least 2 outcomes, but got " + a + ".");
                if (o > 2) throw new Error("Rank of probabilities must be 1 or 2, but is " + o);
                r = r || Math.random();
                var i = 1 === o ? e.as2D(1, -1) : e,
                    s = ENV.engine.runKernel(function(e) {
                        return e.multinomial(i, n, t, r)
                    }, {
                        logits2D: i
                    });
                return 1 === o ? s.as1D() : s
            }, e.oneHot = function(e, t, r, n) {
                if (void 0 === r && (r = 1), void 0 === n && (n = 0), assert("int32" === e.dtype, "Indices must be of dtype `int32`"), t < 2) throw new Error("Error in oneHot: depth must be >=2, but it is " + t);
                return ENV.engine.runKernel(function(a) {
                    return a.oneHot(e, t, r, n)
                }, {
                    indices: e
                })
            }, e.fromPixels = function(e, t) {
                if (void 0 === t && (t = 3), t > 4) throw new Error("Cannot construct Tensor with more than 4 channels from pixels.");
                return ENV.engine.fromPixels(e, t)
            }, e.toPixels = function(e, t) {
                return __awaiter(this, void 0, void 0, function() {
                    var r, n, a, o, i, s, u, l, c, p, d, h, f, m, g, y, v, b, x;
                    return __generator(this, function(w) {
                        switch (w.label) {
                            case 0:
                                if (assertArgumentsAreTensors({
                                        img: e
                                    }, "toPixels"), 2 !== e.rank && 3 !== e.rank) throw new Error("toPixels only supports rank 2 or 3 tensors, got rank " + e.rank + ".");
                                if (r = e.shape.slice(0, 2), n = r[0], a = r[1], (o = 2 === e.rank ? 1 : e.shape[2]) > 4 || 2 === o) throw new Error("toPixels only supports depth of size 1, 3 or 4 but got " + o);
                                return i = e.min(), s = e.max(), [4, i.data()];
                            case 1:
                                return u = w.sent()[0], [4, s.data()];
                            case 2:
                                if (l = w.sent()[0], i.dispose(), s.dispose(), "float32" === e.dtype) {
                                    if (u < 0 || l > 1) throw new Error("Tensor values for a float32 Tensor must be in the range [0 - 1] but got range [" + u + " - " + l + "].")
                                } else {
                                    if ("int32" !== e.dtype) throw new Error("Unsupported type for toPixels: " + e.dtype + ". Please use float32 or int32 tensors.");
                                    if (u < 0 || l > 255) throw new Error("Tensor values for a int32 Tensor must be in the range [0 - 255] but got range [" + u + " - " + l + "].")
                                }
                                return [4, e.data()];
                            case 3:
                                for (c = w.sent(), p = "float32" === e.dtype ? 255 : 1, d = new Uint8ClampedArray(a * n * 4), h = 0; h < n * a; ++h) f = void 0, m = void 0, g = void 0, y = void 0, 1 === o ? (f = c[h] * p, m = c[h] * p, g = c[h] * p, y = 255) : 3 === o ? (f = c[3 * h] * p, m = c[3 * h + 1] * p, g = c[3 * h + 2] * p, y = 255) : 4 === o && (f = c[4 * h] * p, m = c[4 * h + 1] * p, g = c[4 * h + 2] * p, y = c[4 * h + 3] * p), d[(v = 4 * h) + 0] = Math.round(f), d[v + 1] = Math.round(m), d[v + 2] = Math.round(g), d[v + 3] = Math.round(y);
                                return null != t && (t.width = a, t.height = n, b = t.getContext("2d"), x = new ImageData(d, a, n), b.putImageData(x, 0, 0)), [2, d]
                        }
                    })
                })
            }, e.reshape = function(e, t) {
                assertArgumentsAreTensors({
                    x: e
                }, "reshape"), t = inferFromImplicitShape(t, e.size), assert(e.size === sizeFromShape(t), "new shape and old shape must have the same number of elements.");
                return ENV.engine.runKernel(function(r) {
                    return r.reshape(e, t)
                }, {
                    x: e
                }, function(t) {
                    return {
                        x: function() {
                            return t.reshape(e.shape)
                        }
                    }
                })
            }, e.squeeze = function(t, r) {
                return assertArgumentsAreTensors({
                    x: t
                }, "squeeze"), e.reshape(t, squeezeShape(t.shape, r).newShape)
            }, e.cast = function(e, t) {
                assertArgumentsAreTensors({
                    x: e
                }, "cast");
                return ENV.engine.runKernel(function(r) {
                    return r.cast(e, t)
                }, {
                    x: e
                }, function(e) {
                    return {
                        x: function() {
                            return e.clone()
                        }
                    }
                })
            }, e.tile = function(t, r) {
                assertArgumentsAreTensors({
                    x: t
                }, "tile"), assert(t.rank === r.length, "Error in transpose: rank of input " + t.rank + " must match length of reps " + r + ".");
                return ENV.engine.runKernel(function(e) {
                    return e.tile(t, r)
                }, {
                    x: t
                }, function(n) {
                    return {
                        x: function() {
                            var a = e.zerosLike(t);
                            if (1 === t.rank)
                                for (o = 0; o < r[0]; ++o) a = a.add(n.slice([o * t.shape[0]], [t.shape[0]]));
                            else if (2 === t.rank)
                                for (o = 0; o < r[0]; ++o)
                                    for (i = 0; i < r[1]; ++i) a = a.add(n.slice([o * t.shape[0], i * t.shape[1]], [t.shape[0], t.shape[1]]));
                            else if (3 === t.rank)
                                for (o = 0; o < r[0]; ++o)
                                    for (i = 0; i < r[1]; ++i)
                                        for (s = 0; s < r[2]; ++s) a = a.add(n.slice([o * t.shape[0], i * t.shape[1], s * t.shape[2]], [t.shape[0], t.shape[1], t.shape[2]]));
                            else {
                                if (4 !== t.rank) throw new Error("Gradient for tile operation is not implemented for rank-" + t.rank + " tensors yet.");
                                for (var o = 0; o < r[0]; ++o)
                                    for (var i = 0; i < r[1]; ++i)
                                        for (var s = 0; s < r[2]; ++s)
                                            for (var u = 0; u < r[3]; ++u) a = a.add(n.slice([o * t.shape[0], i * t.shape[1], s * t.shape[2], u * t.shape[3]], [t.shape[0], t.shape[1], t.shape[2], t.shape[3]]))
                            }
                            return a
                        }
                    }
                })
            }, e.gather = function(e, t, r) {
                void 0 === r && (r = 0), assertArgumentsAreTensors({
                    x: e,
                    indices: t
                }, "gather"), assert("int32" === t.dtype, "Indices must be of dtype `int32`"), r = parseAxisParam(r, e.shape)[0];
                return ENV.engine.runKernel(function(n) {
                    return n.gather(e, t, r)
                }, {
                    x: e
                }, function(n) {
                    return {
                        x: function() {
                            return ReductionOps.unsortedSegmentSum(n, t, e.shape[r], r)
                        }
                    }
                })
            }, e.pad1d = function(t, r, n) {
                return void 0 === n && (n = 0), assert(2 === r.length, "Invalid number of paddings. Must be length of 2."), e.pad(t, [r], n)
            }, e.pad2d = function(t, r, n) {
                return void 0 === n && (n = 0), assert(2 === r.length && 2 === r[0].length && 2 === r[1].length, "Invalid number of paddings. Must be length of 2 each."), e.pad(t, r, n)
            }, e.pad3d = function(t, r, n) {
                return void 0 === n && (n = 0), assert(3 === r.length && 2 === r[0].length && 2 === r[1].length && 2 === r[2].length, "Invalid number of paddings. Must be length of 2 each."), e.pad(t, r, n)
            }, e.pad4d = function(t, r, n) {
                return void 0 === n && (n = 0), assert(4 === r.length && 2 === r[0].length && 2 === r[1].length && 2 === r[2].length && 2 === r[3].length, "Invalid number of paddings. Must be length of 2 each."), e.pad(t, r, n)
            }, e.pad = function(e, t, r) {
                if (void 0 === r && (r = 0), assertArgumentsAreTensors({
                        x: e
                    }, "pad"), 0 === e.rank) throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");
                var n = t.map(function(e) {
                    return e[0]
                });
                return ENV.engine.runKernel(function(n) {
                    return n.pad(e, t, r)
                }, {
                    x: e
                }, function(t) {
                    return {
                        x: function() {
                            return t.slice(n, e.shape)
                        }
                    }
                })
            }, e.stack = function(e, t) {
                if (void 0 === t && (t = 0), assertArgumentsAreTensors({
                        tensors: e
                    }, "stack"), assert(e.length >= 1, "Pass at least one tensor to tf.stack"), 1 === e.length) return e[0].expandDims(t);
                var r = e[0].rank,
                    n = e[0].shape,
                    a = e[0].dtype;
                assert(t <= r, "Axis must be <= rank of the tensor"), e.forEach(function(e) {
                    assertShapesMatch(n, e.shape, "All tensors passed to stack must have matching shapes")
                }), e.forEach(function(e) {
                    assert(a === e.dtype, "All tensors passed to stack must have matching dtypes")
                });
                var o = e.map(function(e) {
                    return e.expandDims(t)
                });
                return ConcatOps.concat(o, t)
            }, e.unstack = function(e, t) {
                void 0 === t && (t = 0);
                for (var r = e.shape[t], n = Array(e.rank - 1).fill(0), a = 0, o = 0; o < e.rank; o++) o !== t && (n[a] = e.shape[o], a++);
                var i;
                i = Array(r).fill(1);
                var s = Array(e.rank).fill(0),
                    u = e.shape.slice();
                return i.map(function(r) {
                    u[t] = r;
                    var a = e.slice(s, u);
                    return s[t] += r, a.reshape(n)
                })
            }, e.split = function(e, t, r) {
                void 0 === r && (r = 0), assertArgumentsAreTensors({
                    x: e
                }, "split"), r = parseAxisParam(r, e.shape)[0];
                var n;
                "number" == typeof t ? (assert(e.shape[r] % t == 0, "Number of splits must evenly divide the axis."), n = Array(t).fill(e.shape[r] / t)) : (assert(e.shape[r] === t.reduce(function(e, t) {
                    return e + t
                }), "The sum of sizes must match the size of the axis dimension."), n = t);
                var a = Array(e.rank).fill(0),
                    o = e.shape.slice();
                return n.map(function(t) {
                    o[r] = t;
                    var n = e.slice(a, o);
                    return a[r] += t, n
                })
            }, e.cumsum = function(e, t, r, n) {
                void 0 === t && (t = 0), void 0 === r && (r = !1), void 0 === n && (n = !1), assertArgumentsAreTensors({
                    x: e
                }, "cumsum");
                var a = getAxesPermutation([t |= 0], e.rank),
                    o = e;
                null != a && (o = e.transpose(a));
                var i = getInnerMostAxes(1, e.rank)[0],
                    s = ENV.engine.runKernel(function(e) {
                        return e.cumsum(o, i, r, n)
                    }, {
                        permutedX: o
                    }, function(e) {
                        return {
                            permutedX: function() {
                                return e.cumsum(t, r, !n)
                            }
                        }
                    });
                return null != a && (s = s.transpose(a)), s
            }, e.expandDims = function(t, r) {
                void 0 === r && (r = 0), assertArgumentsAreTensors({
                    x: t
                }, "expandDims"), assert(r <= t.rank, "Axis must be <= rank of the tensor");
                var n = t.shape.slice();
                return n.splice(r, 0, 1), e.reshape(t, n)
            }, e.linspace = function(t, r, n) {
                if (0 === n) throw new Error("Cannot request zero samples");
                var a = (r - t) / (n - 1),
                    o = makeZerosTypedArray(n, "float32");
                o[0] = t;
                for (var i = 1; i < o.length; i++) o[i] = o[i - 1] + a;
                return e.tensor1d(o, "float32")
            }, e.range = function(t, r, n, a) {
                if (void 0 === n && (n = 1), void 0 === a && (a = "float32"), 0 === n) throw new Error("Cannot have a step of zero");
                var o = t === r,
                    i = t < r && n < 0,
                    s = r < t && n > 1;
                if (o || i || s) return e.zeros([0], a);
                var u = makeZerosTypedArray(Math.abs(Math.ceil((r - t) / n)), a);
                r < t && 1 === n && (n = -1), u[0] = t;
                for (var l = 1; l < u.length; l++) u[l] = u[l - 1] + n;
                return e.tensor1d(u, a)
            }, e.buffer = function(e, t, r) {
                return void 0 === t && (t = "float32"), new TensorBuffer(e, t, r)
            }, e.print = function(e, t) {
                void 0 === t && (t = !1), console.log(tensorToString(e, t))
            }, __decorate$2([doc()], e, "tensor", null), __decorate$2([doc()], e, "scalar", null), __decorate$2([doc()], e, "tensor1d", null), __decorate$2([doc()], e, "tensor2d", null), __decorate$2([doc()], e, "tensor3d", null), __decorate$2([doc()], e, "tensor4d", null), __decorate$2([doc()], e, "tensor5d", null), __decorate$2([doc(), operation], e, "ones", null), __decorate$2([doc(), operation], e, "zeros", null), __decorate$2([doc(), operation], e, "fill", null), __decorate$2([doc(), operation], e, "onesLike", null), __decorate$2([doc(), operation], e, "zerosLike", null), __decorate$2([doc(), operation], e, "clone", null), __decorate$2([doc(), operation], e, "eye", null), __decorate$2([doc(), operation], e, "randomNormal", null), __decorate$2([doc(), operation], e, "truncatedNormal", null), __decorate$2([doc(), operation], e, "randomUniform", null), __decorate$2([operation], e, "rand", null), __decorate$2([operation], e, "multinomial", null), __decorate$2([doc(), operation], e, "oneHot", null), __decorate$2([doc(), operation], e, "fromPixels", null), __decorate$2([doc()], e, "toPixels", null), __decorate$2([doc(), operation], e, "reshape", null), __decorate$2([doc()], e, "squeeze", null), __decorate$2([doc(), operation], e, "cast", null), __decorate$2([doc(), operation], e, "tile", null), __decorate$2([doc(), operation], e, "gather", null), __decorate$2([doc(), operation], e, "pad", null), __decorate$2([doc(), operation], e, "stack", null), __decorate$2([doc(), operation], e, "unstack", null), __decorate$2([doc(), operation], e, "split", null), __decorate$2([doc()], e, "cumsum", null), __decorate$2([doc(), operation], e, "expandDims", null), __decorate$2([operation, doc()], e, "linspace", null), __decorate$2([operation, doc()], e, "range", null), __decorate$2([doc()], e, "buffer", null), __decorate$2([doc()], e, "print", null), e
        }(),
        __decorate$3 = function(e, t, r, n) {
            var a, o = arguments.length,
                i = o < 3 ? t : null === n ? n = Object.getOwnPropertyDescriptor(t, r) : n;
            if ("object" == typeof Reflect && "function" == typeof Reflect.decorate) i = Reflect.decorate(e, t, r, n);
            else
                for (var s = e.length - 1; s >= 0; s--)(a = e[s]) && (i = (o < 3 ? a(i) : o > 3 ? a(t, r, i) : a(t, r)) || i);
            return o > 3 && i && Object.defineProperty(t, r, i), i
        },
        BatchNormOps = function() {
            function e() {}
            return e.batchNormalization2d = function(t, r, n, a, o, i) {
                return void 0 === a && (a = .001), assert(2 === t.rank, "Error in batchNormalization3D: x must be rank 3 but got rank " + t.rank + "."), assert(2 === r.rank || 1 === r.rank, "Error in batchNormalization2D: mean must be rank 2 or rank 1 but got rank " + r.rank + "."), assert(2 === n.rank || 1 === n.rank, "Error in batchNormalization2D: variance must be rank 2 or rank 1 but got rank " + n.rank + "."), null != o && assert(2 === o.rank || 1 === o.rank, "Error in batchNormalization2D: scale must be rank 2 or rank 1 but got rank " + o.rank + "."), null != i && assert(2 === i.rank || 1 === i.rank, "Error in batchNormalization2D: offset must be rank 2 or rank 1 but got rank " + i.rank + "."), e.batchNormalization(t, r, n, a, o, i)
            }, e.batchNormalization3d = function(t, r, n, a, o, i) {
                return void 0 === a && (a = .001), assert(3 === t.rank, "Error in batchNormalization3D: x must be rank 3 but got rank " + t.rank + "."), assert(3 === r.rank || 1 === r.rank, "Error in batchNormalization3D: mean must be rank 3 or rank 1 but got rank " + r.rank + "."), assert(3 === n.rank || 1 === n.rank, "Error in batchNormalization3D: variance must be rank 3 or rank 1 but got rank " + n.rank + "."), null != o && assert(3 === o.rank || 1 === o.rank, "Error in batchNormalization3D: scale must be rank 3 or rank 1 but got rank " + o.rank + "."), null != i && assert(3 === i.rank || 1 === i.rank, "Error in batchNormalization3D: offset must be rank 3 or rank 1 but got rank " + i.rank + "."), e.batchNormalization(t, r, n, a, o, i)
            }, e.batchNormalization4d = function(t, r, n, a, o, i) {
                return void 0 === a && (a = .001), assert(4 === t.rank, "Error in batchNormalization4D: x must be rank 4 but got rank " + t.rank + "."), assert(4 === r.rank || 1 === r.rank, "Error in batchNormalization4D: mean must be rank 4 or rank 1 but got rank " + r.rank + "."), assert(4 === n.rank || 1 === n.rank, "Error in batchNormalization4D: variance must be rank 4 or rank 1 but got rank " + n.rank + "."), null != o && assert(4 === o.rank || 1 === o.rank, "Error in batchNormalization4D: scale must be rank 4 or rank 1 but got rank " + o.rank + "."), null != i && assert(4 === i.rank || 1 === i.rank, "Error in batchNormalization4D: offset must be rank 4 or rank 1 but got rank " + i.rank + "."), e.batchNormalization(t, r, n, a, o, i)
            }, e.batchNormalization = function(e, t, r, n, a, o) {
                void 0 === n && (n = .001), assertArgumentsAreTensors({
                    x: e,
                    mean: t,
                    variance: r
                }, "batchNormalization"), null != a && assertArgumentsAreTensors({
                    scale: a
                }, "batchNormalization"), null != o && assertArgumentsAreTensors({
                    offset: o
                }, "batchNormalization"), assert(t.rank === r.rank, "Batch normalization gradient requires mean and variance to have equal ranks."), assert(null == o || t.rank === o.rank, "Batch normalization gradient requires mean and offset to have equal ranks."), assert(null == a || t.rank === a.rank, "Batch normalization gradient requires mean and scale to have equal ranks.");
                var i;
                i = 0 === e.rank || 1 === e.rank ? e.as4D(1, 1, 1, e.size) : 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;
                return ENV.engine.runKernel(function(e) {
                    return e.batchNormalization(i, batchnormReshape4D(t), batchnormReshape4D(r), n, batchnormReshape4D(a), batchnormReshape4D(o))
                }, {
                    x: e,
                    mean: t,
                    variance: r,
                    scale: a,
                    offset: o
                }, function(o) {
                    var s = null == a ? ArrayOps.scalar(1) : a,
                        u = getReductionAxes(t.shape, i.shape),
                        l = [];
                    if (1 === t.rank) {
                        for (var c = 0; c < i.shape.length - 1; ++c) l.push(i.shape[c]);
                        l.push(1)
                    }
                    var p = e.sub(t),
                        d = o.mul(s),
                        h = rsqrt(r.add(ArrayOps.scalar(n))),
                        f = h.mul(h).mul(h).mul(ArrayOps.scalar(-.5));
                    return {
                        x: function() {
                            return 1 === t.rank ? o.mul(ArrayOps.tile(h.as4D(1, 1, 1, t.shape[0]), l)).mul(s).reshape(e.shape) : o.mul(h).mul(s).reshape(e.shape)
                        },
                        mean: function() {
                            var e = h.mul(ArrayOps.scalar(-1)).mul(d);
                            return 1 === t.rank && (e = e.sum(u)), e.reshape(t.shape)
                        },
                        variance: function() {
                            var e = f.mul(p).mul(d);
                            return 1 === t.rank && (e = e.sum(u)), e.reshape(t.shape)
                        },
                        scale: function() {
                            var e = p.mul(h),
                                r = o.mul(e);
                            return 1 === t.rank && (r = r.sum(u)), r.reshape(t.shape)
                        },
                        offset: function() {
                            var e = o;
                            return 1 === t.rank && (e = e.sum(u)), e.reshape(t.shape)
                        }
                    }
                }).reshape(e.shape)
            }, __decorate$3([operation], e, "batchNormalization2d", null), __decorate$3([operation], e, "batchNormalization3d", null), __decorate$3([operation], e, "batchNormalization4d", null), __decorate$3([doc()], e, "batchNormalization", null), e
        }(),
        DType;
    ! 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"
    }(exports.Rank || (exports.Rank = {}));
    var UpcastInt32AndMap;
    ! function(e) {
        e.float32 = "float32", e.int32 = "int32", e.bool = "int32"
    }(UpcastInt32AndMap || (UpcastInt32AndMap = {}));
    var UpcastBoolAndMap;
    ! function(e) {
        e.float32 = "float32", e.int32 = "int32", e.bool = "bool"
    }(UpcastBoolAndMap || (UpcastBoolAndMap = {}));
    var UpcastFloat32AndMap;
    ! function(e) {
        e.float32 = "float32", e.int32 = "float32", e.bool = "float32"
    }(UpcastFloat32AndMap || (UpcastFloat32AndMap = {}));
    var upcastTypeMap = {
            float32: UpcastFloat32AndMap,
            int32: UpcastInt32AndMap,
            bool: UpcastBoolAndMap
        },
        __decorate$4 = function(e, t, r, n) {
            var a, o = arguments.length,
                i = o < 3 ? t : null === n ? n = Object.getOwnPropertyDescriptor(t, r) : n;
            if ("object" == typeof Reflect && "function" == typeof Reflect.decorate) i = Reflect.decorate(e, t, r, n);
            else
                for (var s = e.length - 1; s >= 0; s--)(a = e[s]) && (i = (o < 3 ? a(i) : o > 3 ? a(t, r, i) : a(t, r)) || i);
            return o > 3 && i && Object.defineProperty(t, r, i), i
        },
        BinaryOps = function() {
            function e() {}
            return e.add = function(e, t) {
                assertArgumentsAreTensors({
                    a: e,
                    b: t
                }, "add"), assertTypesMatch(e, t);
                var r = assertAndGetBroadcastShape(e.shape, t.shape);
                return ENV.engine.runKernel(function(r) {
                    return r.add(e, t)
                }, {
                    a: e,
                    b: t
                }, function(n) {
                    return {
                        a: function() {
                            var t = n,
                                a = getReductionAxes(e.shape, r);
                            return a.length > 0 && (t = t.sum(a)), t.reshape(e.shape)
                        },
                        b: function() {
                            var e = n,
                                a = getReductionAxes(t.shape, r);
                            return a.length > 0 && (e = e.sum(a)), e.reshape(t.shape)
                        }
                    }
                })
            }, e.addStrict = function(e, t) {
                return assertShapesMatch(e.shape, t.shape, "Error in addStrict: "), e.add(t)
            }, e.sub = function(e, t) {
                assertArgumentsAreTensors({
                    a: e,
                    b: t
                }, "sub"), assertTypesMatch(e, t);
                var r = assertAndGetBroadcastShape(e.shape, t.shape);
                return ENV.engine.runKernel(function(r) {
                    return r.subtract(e, t)
                }, {
                    a: e,
                    b: t
                }, function(n) {
                    return {
                        a: function() {
                            var t = n,
                                a = getReductionAxes(e.shape, r);
                            return a.length > 0 && (t = t.sum(a)), t.reshape(e.shape)
                        },
                        b: function() {
                            var e = n,
                                a = getReductionAxes(t.shape, r);
                            return a.length > 0 && (e = e.sum(a)), e.neg().reshape(t.shape)
                        }
                    }
                })
            }, e.subStrict = function(e, t) {
                return assertShapesMatch(e.shape, t.shape, "Error in subStrict: "), e.sub(t)
            }, e.pow = function(e, t) {
                assertArgumentsAreTensors({
                    base: e,
                    exp: t
                }, "pow");
                var r = assertAndGetBroadcastShape(e.shape, t.shape);
                e = e.cast(upcastType(e.dtype, t.dtype)), t = t.cast(upcastType(e.dtype, t.dtype));
                return ENV.engine.runKernel(function(r, n) {
                    return n(r.pow(e, t))
                }, {
                    base: e,
                    exp: t
                }, function(n, a) {
                    var o = a[0];
                    return {
                        base: function() {
                            var a = n.mul(t.toFloat().mul(o.div(e))),
                                i = getReductionAxes(e.shape, r);
                            return i.length > 0 && (a = a.sum(i)), a.reshape(e.shape)
                        },
                        exp: function() {
                            var a = n.mul(o.mul(e.log()).toFloat()),
                                i = getReductionAxes(t.shape, r);
                            return i.length > 0 && (a = a.sum(i)), a.reshape(t.shape)
                        }
                    }
                })
            }, e.powStrict = function(e, t) {
                return assertShapesMatch(e.shape, t.shape, "Error in powStrict: "), e.pow(t)
            }, e.mul = function(e, t) {
                assertArgumentsAreTensors({
                    a: e,
                    b: t
                }, "mul"), assertTypesMatch(e, t);
                var r = assertAndGetBroadcastShape(e.shape, t.shape);
                return ENV.engine.runKernel(function(r) {
                    return r.multiply(e, t)
                }, {
                    a: e,
                    b: t
                }, function(n) {
                    return {
                        a: function() {
                            var a = n.mul(t.toFloat()),
                                o = getReductionAxes(e.shape, r);
                            return o.length > 0 ? a.sum(o).reshape(e.shape) : a
                        },
                        b: function() {
                            var a = n.mul(e.toFloat()),
                                o = getReductionAxes(t.shape, r);
                            return o.length > 0 ? a.sum(o).reshape(t.shape) : a
                        }
                    }
                })
            }, e.mulStrict = function(e, t) {
                return assertShapesMatch(e.shape, t.shape, "Error in multiplyStrict: "), e.mul(t)
            }, e.div = function(t, r) {
                assertArgumentsAreTensors({
                    a: t,
                    b: r
                }, "div"), assertTypesMatch(t, r);
                var n;
                if ("int32" === t.dtype && "int32" === r.dtype) return e.floorDiv(t, r);
                n = function(e) {
                    return e.realDivide(t, r)
                };
                var a = assertAndGetBroadcastShape(t.shape, r.shape);
                return ENV.engine.runKernel(n, {
                    a: t,
                    b: r
                }, function(e) {
                    return {
                        a: function() {
                            var n = e.div(r.toFloat()),
                                o = getReductionAxes(t.shape, a);
                            return o.length > 0 ? n.sum(o).reshape(t.shape) : n
                        },
                        b: function() {
                            var n = e.mul(t.toFloat()),
                                o = getReductionAxes(r.shape, a);
                            o.length > 0 && (n = n.sum(o).reshape(r.shape));
                            var i = r.square();
                            return n.div(i.toFloat()).neg()
                        }
                    }
                })
            }, e.floorDiv = function(e, t) {
                assertArgumentsAreTensors({
                    a: e,
                    b: t
                }, "floorDiv"), assertTypesMatch(e, t);
                var r = assertAndGetBroadcastShape(e.shape, t.shape);
                return ENV.engine.runKernel(function(r) {
                    return r.floorDiv(e, t)
                }, {
                    a: e,
                    b: t
                }, function(n) {
                    return {
                        a: function() {
                            var a = n.div(t.toFloat()),
                                o = getReductionAxes(e.shape, r);
                            return o.length > 0 ? a.sum(o).reshape(e.shape) : a
                        },
                        b: function() {
                            var a = n.mul(e.toFloat()),
                                o = getReductionAxes(t.shape, r);
                            o.length > 0 && (a = a.sum(o).reshape(t.shape));
                            var i = t.square();
                            return a.div(i.toFloat()).neg()
                        }
                    }
                })
            }, e.divStrict = function(e, t) {
                return assertShapesMatch(e.shape, t.shape, "Error in divideStrict: "), e.div(t)
            }, e.mod = function(e, t) {
                assertArgumentsAreTensors({
                    a: e,
                    b: t
                }, "mod"), assertTypesMatch(e, t);
                var r = assertAndGetBroadcastShape(e.shape, t.shape);
                return ENV.engine.runKernel(function(r) {
                    return r.mod(e, t)
                }, {
                    a: e,
                    b: t
                }, function(n) {
                    return {
                        a: function() {
                            var t = getReductionAxes(e.shape, r);
                            return t.length > 0 ? n.sum(t).reshape(e.shape) : n
                        },
                        b: function() {
                            var a = n.mul(e.div(t).floor().neg()),
                                o = getReductionAxes(t.shape, r);
                            return o.length > 0 ? a.sum(o).reshape(t.shape) : a
                        }
                    }
                })
            }, e.modStrict = function(e, t) {
                return assertShapesMatch(e.shape, t.shape, "Error in modStrict: "), e.mod(t)
            }, e.minimum = function(e, t) {
                assertArgumentsAreTensors({
                    a: e,
                    b: t
                }, "minimum"), assertTypesMatch(e, t), "bool" === e.dtype && (e = e.toInt()), "bool" === t.dtype && (t = t.toInt()), assertAndGetBroadcastShape(e.shape, t.shape);
                return ENV.engine.runKernel(function(r) {
                    return r.minimum(e, t)
                }, {
                    a: e,
                    b: t
                }, function(r) {
                    return {
                        a: function() {
                            return r.mul(e.lessEqual(t).toFloat())
                        },
                        b: function() {
                            return r.mul(e.greater(t).toFloat())
                        }
                    }
                })
            }, e.minimumStrict = function(e, t) {
                return assertShapesMatch(e.shape, t.shape, "Error in minimumStrict: "), e.minimum(t)
            }, e.maximum = function(e, t) {
                assertArgumentsAreTensors({
                    a: e,
                    b: t
                }, "maximum"), assertTypesMatch(e, t), "bool" === e.dtype && (e = e.toInt()), "bool" === t.dtype && (t = t.toInt()), assertAndGetBroadcastShape(e.shape, t.shape);
                return ENV.engine.runKernel(function(r) {
                    return r.maximum(e, t)
                }, {
                    a: e,
                    b: t
                }, function(r) {
                    return {
                        a: function() {
                            return r.mul(e.greaterEqual(t).toFloat())
                        },
                        b: function() {
                            return r.mul(e.less(t).toFloat())
                        }
                    }
                })
            }, e.maximumStrict = function(e, t) {
                return assertShapesMatch(e.shape, t.shape, "Error in minimumStrict: "), e.maximum(t)
            }, e.squaredDifference = function(e, t) {
                assertArgumentsAreTensors({
                    a: e,
                    b: t
                }, "squaredDifference"), assertTypesMatch(e, t), assertAndGetBroadcastShape(e.shape, t.shape);
                return ENV.engine.runKernel(function(r) {
                    return r.squaredDifference(e, t)
                }, {
                    a: e,
                    b: t
                }, function(r) {
                    var n = scalar(2);
                    return {
                        a: function() {
                            return r.mul(e.sub(t).mul(n))
                        },
                        b: function() {
                            return r.mul(t.sub(e).mul(n))
                        }
                    }
                })
            }, e.squaredDifferenceStrict = function(e, t) {
                return assertShapesMatch(e.shape, t.shape, "Error in squaredDifferenceStrict: "), e.squaredDifference(t)
            }, e.atan2 = function(t, r) {
                assertArgumentsAreTensors({
                    a: t,
                    b: r
                }, "atan2"), assertTypesMatch(t, r);
                var n = assertAndGetBroadcastShape(t.shape, r.shape);
                return ENV.engine.runKernel(function(e) {
                    return e.atan2(t, r)
                }, {
                    a: t,
                    b: r
                }, function(a) {
                    return {
                        a: function() {
                            var o = e.add(square(t), square(r)),
                                i = a.mul(r.div(o)),
                                s = getReductionAxes(t.shape, n);
                            return s.length > 0 && (i = i.sum(s)), i.reshape(t.shape)
                        },
                        b: function() {
                            var o = e.add(square(t), square(r)),
                                i = neg(a.mul(t.div(o))),
                                s = getReductionAxes(r.shape, n);
                            return s.length > 0 && (i = i.sum(s)), i.reshape(r.shape)
                        }
                    }
                })
            }, __decorate$4([doc(), operation], e, "add", null), __decorate$4([operation], e, "addStrict", null), __decorate$4([doc(), operation], e, "sub", null), __decorate$4([operation], e, "subStrict", null), __decorate$4([doc(), operation], e, "pow", null), __decorate$4([operation], e, "powStrict", null), __decorate$4([doc(), operation], e, "mul", null), __decorate$4([operation], e, "mulStrict", null), __decorate$4([doc(), operation], e, "div", null), __decorate$4([doc(), operation], e, "floorDiv", null), __decorate$4([operation], e, "divStrict", null), __decorate$4([doc(), operation], e, "mod", null), __decorate$4([operation], e, "modStrict", null), __decorate$4([doc(), operation], e, "minimum", null), __decorate$4([operation], e, "minimumStrict", null), __decorate$4([doc(), operation], e, "maximum", null), __decorate$4([operation], e, "maximumStrict", null), __decorate$4([doc(), operation], e, "squaredDifference", null), __decorate$4([operation], e, "squaredDifferenceStrict", null), __decorate$4([operation], e, "atan2", null), e
        }(),
        __decorate$5 = function(e, t, r, n) {
            var a, o = arguments.length,
                i = o < 3 ? t : null === n ? n = Object.getOwnPropertyDescriptor(t, r) : n;
            if ("object" == typeof Reflect && "function" == typeof Reflect.decorate) i = Reflect.decorate(e, t, r, n);
            else
                for (var s = e.length - 1; s >= 0; s--)(a = e[s]) && (i = (o < 3 ? a(i) : o > 3 ? a(t, r, i) : a(t, r)) || i);
            return o > 3 && i && Object.defineProperty(t, r, i), i
        },
        CompareOps = function() {
            function e() {}
            return e.notEqual = function(e, t) {
                return assertArgumentsAreTensors({
                    a: e,
                    b: t
                }, "notEqual"), assertTypesMatch(e, t), assertAndGetBroadcastShape(e.shape, t.shape), ENV.engine.runKernel(function(r) {
                    return r.notEqual(e, t)
                }, {
                    a: e,
                    b: t
                })
            }, e.notEqualStrict = function(e, t) {
                return assertShapesMatch(e.shape, t.shape, "Error in notEqualStrict: "), e.notEqual(t)
            }, e.less = function(e, t) {
                return assertArgumentsAreTensors({
                    a: e,
                    b: t
                }, "less"), assertTypesMatch(e, t), assertAndGetBroadcastShape(e.shape, t.shape), ENV.engine.runKernel(function(r) {
                    return r.less(e, t)
                }, {
                    a: e,
                    b: t
                })
            }, e.lessStrict = function(e, t) {
                return assertShapesMatch(e.shape, t.shape, "Error in lessStrict: "), e.less(t)
            }, e.equal = function(e, t) {
                return assertArgumentsAreTensors({
                    a: e,
                    b: t
                }, "equal"), assertTypesMatch(e, t), assertAndGetBroadcastShape(e.shape, t.shape), ENV.engine.runKernel(function(r) {
                    return r.equal(e, t)
                }, {
                    a: e,
                    b: t
                })
            }, e.equalStrict = function(e, t) {
                return assertShapesMatch(e.shape, t.shape, "Error in equalStrict: "), e.equal(t)
            }, e.lessEqual = function(e, t) {
                return assertArgumentsAreTensors({
                    a: e,
                    b: t
                }, "lessEqual"), assertTypesMatch(e, t), assertAndGetBroadcastShape(e.shape, t.shape), ENV.engine.runKernel(function(r) {
                    return r.lessEqual(e, t)
                }, {
                    a: e,
                    b: t
                })
            }, e.lessEqualStrict = function(e, t) {
                return assertShapesMatch(e.shape, t.shape, "Error in lessEqualStrict: "), e.lessEqual(t)
            }, e.greater = function(e, t) {
                return assertArgumentsAreTensors({
                    a: e,
                    b: t
                }, "greater"), assertTypesMatch(e, t), assertAndGetBroadcastShape(e.shape, t.shape), ENV.engine.runKernel(function(r) {
                    return r.greater(e, t)
                }, {
                    a: e,
                    b: t
                })
            }, e.greaterStrict = function(e, t) {
                return assertShapesMatch(e.shape, t.shape, "Error in greaterStrict: "), e.greater(t)
            }, e.greaterEqual = function(e, t) {
                return assertArgumentsAreTensors({
                    a: e,
                    b: t
                }, "greaterEqual"), assertTypesMatch(e, t), assertAndGetBroadcastShape(e.shape, t.shape), ENV.engine.runKernel(function(r) {
                    return r.greaterEqual(e, t)
                }, {
                    a: e,
                    b: t
                })
            }, e.greaterEqualStrict = function(e, t) {
                return assertShapesMatch(e.shape, t.shape, "Error in greaterEqualStrict: "), e.greaterEqual(t)
            }, __decorate$5([doc(), operation], e, "notEqual", null), __decorate$5([operation], e, "notEqualStrict", null), __decorate$5([doc(), operation], e, "less", null), __decorate$5([operation], e, "lessStrict", null), __decorate$5([doc(), operation], e, "equal", null), __decorate$5([operation], e, "equalStrict", null), __decorate$5([doc(), operation], e, "lessEqual", null), __decorate$5([operation], e, "lessEqualStrict", null), __decorate$5([doc(), operation], e, "greater", null), __decorate$5([operation], e, "greaterStrict", null), __decorate$5([doc(), operation], e, "greaterEqual", null), __decorate$5([operation], e, "greaterEqualStrict", null), e
        }(),
        __decorate$6 = function(e, t, r, n) {
            var a, o = arguments.length,
                i = o < 3 ? t : null === n ? n = Object.getOwnPropertyDescriptor(t, r) : n;
            if ("object" == typeof Reflect && "function" == typeof Reflect.decorate) i = Reflect.decorate(e, t, r, n);
            else
                for (var s = e.length - 1; s >= 0; s--)(a = e[s]) && (i = (o < 3 ? a(i) : o > 3 ? a(t, r, i) : a(t, r)) || i);
            return o > 3 && i && Object.defineProperty(t, r, i), i
        },
        ConvOps = function() {
            function e() {}
            return e.conv1d = function(t, r, n, a, o, i, s) {
                void 0 === o && (o = "NWC"), void 0 === i && (i = 1), assertArgumentsAreTensors({
                    x: t,
                    filter: r
                }, "conv1d");
                var u = t,
                    l = !1;
                2 === t.rank && (l = !0, u = t.as3D(1, t.shape[0], t.shape[1])), assert(3 === u.rank, "Error in conv1d: input must be rank 3, but got rank " + u.rank + "."), assert(3 === r.rank, "Error in conv1d: filter must be rank 3, but got rank " + r.rank + "."), null != s && assert(isInt(a), "Error in conv1d: pad must be an integer when using, dimRoundingMode " + s + " but got pad " + a + "."), assert(u.shape[2] === r.shape[1], "Error in conv1d: depth of input (" + u.shape[2] + ") must match input depth for filter " + r.shape[1] + "."), assert(eitherStridesOrDilationsAreOne(n, i), "Error in conv1D: Either stride or dilation must be 1. Got stride " + n + " and dilation '" + i + "'"), assert("NWC" === o, "Error in conv1d: got dataFormat of " + o + " but only NWC is currently supported.");
                var c = r.as4D(1, r.shape[0], r.shape[1], r.shape[2]),
                    p = u.as4D(u.shape[0], 1, u.shape[1], u.shape[2]),
                    d = [1, n],
                    h = [1, i],
                    f = e.conv2d(p, c, d, a, "NHWC", h, s);
                return l ? f.as2D(f.shape[2], f.shape[3]) : f.as3D(f.shape[0], f.shape[2], f.shape[3])
            }, e.conv2d = function(t, r, n, a, o, i, s) {
                void 0 === o && (o = "NHWC"), void 0 === i && (i = [1, 1]), assertArgumentsAreTensors({
                    x: t,
                    filter: r
                }, "conv2d");
                var u = t,
                    l = !1;
                3 === t.rank && (l = !0, u = t.as4D(1, t.shape[0], t.shape[1], t.shape[2])), assert(4 === u.rank, "Error in conv2d: input must be rank 4, but got rank " + u.rank + "."), assert(4 === r.rank, "Error in conv2d: filter must be rank 4, but got rank " + r.rank + "."), null != s && assert(isInt(a), "Error in conv2d: pad must be an integer when using, dimRoundingMode " + s + " but got pad " + a + "."), assert(u.shape[3] === r.shape[2], "Error in conv2d: depth of input (" + u.shape[3] + ") must match input depth for filter " + r.shape[2] + "."), assert(eitherStridesOrDilationsAreOne(n, i), "Error in conv2D: Either strides or dilations must be 1. Got strides " + n + " and dilations '" + i + "'"), assert("NHWC" === o, "Error in conv2d: got dataFormat of " + o + " but only NHWC is currently supported.");
                var c = computeConv2DInfo(u.shape, r.shape, n, i, a, s),
                    p = ENV.engine.runKernel(function(e) {
                        return e.conv2d(u, r, c)
                    }, {
                        x: u,
                        filter: r
                    }, function(t) {
                        return assert(tupleValuesAreOne(i), "Error in gradient of conv2D: dilation rates greater than 1 are notyet supported in gradients. Got dilations '" + i + "'"), {
                            x: function() {
                                return e.conv2dDerInput(u.shape, t, r, n, a)
                            },
                            filter: function() {
                                return e.conv2dDerFilter(u, t, r.shape, n, a)
                            }
                        }
                    });
                return l ? p.as3D(p.shape[1], p.shape[2], p.shape[3]) : p
            }, e.conv2dDerInput = function(e, t, r, n, a, o) {
                assertArgumentsAreTensors({
                    dy: t,
                    filter: r
                }, "conv2dDerInput"), 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 != o && assert(isInt(a), "Error in conv2dDerInput: pad must be an integer when using, dimRoundingMode " + o + " but got pad " + a + ".");
                var p = computeConv2DInfo(i, r.shape, n, 1, a, o),
                    d = ENV.engine.runKernel(function(e) {
                        return e.conv2dDerInput(s, r, p)
                    }, {
                        dy4D: s
                    });
                return u ? d.as3D(d.shape[1], d.shape[2], d.shape[3]) : d
            }, e.conv2dDerFilter = function(e, t, r, n, a, o) {
                assertArgumentsAreTensors({
                    x: e,
                    dy: t
                }, "conv2dDerFilter");
                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 != o && assert(isInt(a), "Error in conv2dDerFilter: pad must be an integer when using, dimRoundingMode " + o + " but got pad " + a + ".");
                var u = computeConv2DInfo(i.shape, r, n, 1, a, o);
                return ENV.engine.runKernel(function(e) {
                    return e.conv2dDerFilter(i, s, u)
                }, {
                    x4D: i,
                    dy4D: s
                })
            }, e.conv2dTranspose = function(t, r, n, a, o, i) {
                return assertArgumentsAreTensors({
                    x: t,
                    filter: r
                }, "conv2dTranspose"), e.conv2dDerInput(n, t, r, a, o, i)
            }, e.depthwiseConv2d = function(e, t, r, n, a, o, i) {
                void 0 === a && (a = "NHWC"), void 0 === o && (o = [1, 1]), assertArgumentsAreTensors({
                    x: e,
                    filter: t
                }, "depthwiseConv2d");
                var s = e,
                    u = !1;
                3 === e.rank && (u = !0, s = e.as4D(1, e.shape[0], e.shape[1], e.shape[2])), assert(4 === s.rank, "Error in depthwiseConv2d: input must be rank 4, but got rank " + s.rank + "."), assert(4 === t.rank, "Error in depthwiseConv2d: filter must be rank 4, but got rank " + t.rank + "."), assert(s.shape[3] === t.shape[2], "Error in depthwiseConv2d: number of input channels (" + s.shape[3] + ") must match the inChannels dimension in filter " + t.shape[2] + "."), null == o && (o = [1, 1]), assert(eitherStridesOrDilationsAreOne(r, o), "Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides " + r + " and dilations '" + o + "'"), null != i && assert(isInt(n), "Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode " + i + " but got pad " + n + ".");
                var l = computeConv2DInfo(s.shape, t.shape, r, o, n, i, !0),
                    c = ENV.engine.runKernel(function(e) {
                        return e.depthwiseConv2D(s, t, l)
                    }, {
                        x: s,
                        filter: t
                    }, function(e) {
                        return assert(tupleValuesAreOne(o), "Error in gradient of depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '" + o + "'"), {
                            x: function() {
                                return depthwiseConv2dDerInput(s.shape, e, t, l)
                            },
                            filter: function() {
                                return depthwiseConv2dDerFilter(s, e, t.shape, l)
                            }
                        }
                    });
                return u ? c.as3D(c.shape[1], c.shape[2], c.shape[3]) : c
            }, e.separableConv2d = function(t, r, n, a, o, i, s) {
                void 0 === i && (i = [1, 1]), void 0 === s && (s = "NHWC"), assertArgumentsAreTensors({
                    x: t,
                    depthwiseFilter: r,
                    pointwiseFilter: n
                }, "separableConv2d");
                var u = t,
                    l = !1;
                if (3 === t.rank && (l = !0, u = t.as4D(1, t.shape[0], t.shape[1], t.shape[2])), "NCHW" === s) throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported");
                assert(4 === u.rank, "Error in separableConv2d: input must be rank 4, but got rank " + u.rank + "."), assert(4 === r.rank, "Error in separableConv2d: depthwise filter must be rank 4, but got rank " + r.rank + "."), assert(4 === n.rank, "Error in separableConv2d: pointwise filter must be rank 4, but got rank " + r.rank + "."), assert(1 === n.shape[0], "Error in separableConv2d: the first dimension of pointwise filter  must be 1, but got " + n.shape[0] + "."), assert(1 === n.shape[1], "Error in separableConv2d: the second dimension of pointwise filter  must be 1, but got " + n.shape[1] + ".");
                var c = r.shape[2],
                    p = r.shape[3];
                assert(n.shape[2] === c * p, "Error in separableConv2d: the third dimension of pointwise filter must be " + c * p + ", but got " + n.shape[2] + ".");
                var d = e.depthwiseConv2d(u, r, a, o, s, i),
                    h = e.conv2d(d, n, 1, "valid", s);
                return l ? h.as3D(h.shape[1], h.shape[2], h.shape[3]) : h
            }, __decorate$6([doc(), operation], e, "conv1d", null), __decorate$6([doc(), operation], e, "conv2d", null), __decorate$6([operation], e, "conv2dDerInput", null), __decorate$6([operation], e, "conv2dDerFilter", null), __decorate$6([doc(), operation], e, "conv2dTranspose", null), __decorate$6([doc(), operation], e, "depthwiseConv2d", null), __decorate$6([doc(), operation], e, "separableConv2d", null), e
        }(),
        __decorate$7 = function(e, t, r, n) {
            var a, o = arguments.length,
                i = o < 3 ? t : null === n ? n = Object.getOwnPropertyDescriptor(t, r) : n;
            if ("object" == typeof Reflect && "function" == typeof Reflect.decorate) i = Reflect.decorate(e, t, r, n);
            else
                for (var s = e.length - 1; s >= 0; s--)(a = e[s]) && (i = (o < 3 ? a(i) : o > 3 ? a(t, r, i) : a(t, r)) || i);
            return o > 3 && i && Object.defineProperty(t, r, i), i
        },
        ImageOps = function() {
            function e() {}
            return e.resizeBilinear = function(e, t, r) {
                void 0 === r && (r = !1), assertArgumentsAreTensors({
                    images: e
                }, "resizeBilinear"), assert(3 === e.rank || 4 === e.rank, "Error in resizeBilinear: x must be rank 3 or 4, but got rank " + e.rank + "."), assert(2 === t.length, "Error in resizeBilinear: new shape must 2D, but got shape " + t + ".");
                var n = e,
                    a = !1;
                3 === e.rank && (a = !0, n = e.as4D(1, e.shape[0], e.shape[1], e.shape[2]));
                var o = t[0],
                    i = t[1],
                    s = ENV.engine.runKernel(function(e, t) {
                        return e.resizeBilinear(n, o, i, r)
                    }, {
                        batchImages: n
                    }, function(e, t) {
                        return {
                            batchImages: function() {
                                return ENV.engine.runKernel(function(t) {
                                    return t.resizeBilinearBackprop(e, n, r)
                                }, {})
                            }
                        }
                    });
                return a ? s.as3D(s.shape[1], s.shape[2], s.shape[3]) : s
            }, e.resizeNearestNeighbor = function(e, t, r) {
                void 0 === r && (r = !1), assertArgumentsAreTensors({
                    images: e
                }, "resizeNearestNeighbor"), assert(3 === e.rank || 4 === e.rank, "Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank " + e.rank + "."), assert(2 === t.length, "Error in resizeNearestNeighbor: new shape must 2D, but got shape " + t + "."), assert("float32" === e.dtype || "int32" === e.dtype, "`images` must have `int32` or `float32` as dtype");
                var n = e,
                    a = !1;
                3 === e.rank && (a = !0, n = e.as4D(1, e.shape[0], e.shape[1], e.shape[2]));
                var o = t[0],
                    i = t[1],
                    s = ENV.engine.runKernel(function(e) {
                        return e.resizeNearestNeighbor(n, o, i, r)
                    }, {
                        batchImages: n
                    });
                return a ? s.as3D(s.shape[1], s.shape[2], s.shape[3]) : s
            }, __decorate$7([doc(), operation], e, "resizeBilinear", null), __decorate$7([doc(), operation], e, "resizeNearestNeighbor", null), e
        }(),
        __decorate$8 = function(e, t, r, n) {
            var a, o = arguments.length,
                i = o < 3 ? t : null === n ? n = Object.getOwnPropertyDescriptor(t, r) : n;
            if ("object" == typeof Reflect && "function" == typeof Reflect.decorate) i = Reflect.decorate(e, t, r, n);
            else
                for (var s = e.length - 1; s >= 0; s--)(a = e[s]) && (i = (o < 3 ? a(i) : o > 3 ? a(t, r, i) : a(t, r)) || i);
            return o > 3 && i && Object.defineProperty(t, r, i), i
        },
        Tracking = function() {
            function e() {}
            return e.tidy = function(e, t, r) {
                void 0 === r && (r = !1);
                var n = 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");
                    n = e
                }
                ENV.engine.startScope(n, r);
                var a = t();
                return a instanceof Promise && console.error("Cannot return a Promise inside of tidy."), ENV.engine.endScope(a, r), a
            }, 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)
            }, __decorate$8([doc()], e, "tidy", null), __decorate$8([doc()], e, "dispose", null), __decorate$8([doc()], e, "keep", null), __decorate$8([doc()], e, "time", null), e
        }(),
        __decorate$9 = function(e, t, r, n) {
            var a, o = arguments.length,
                i = o < 3 ? t : null === n ? n = Object.getOwnPropertyDescriptor(t, r) : n;
            if ("object" == typeof Reflect && "function" == typeof Reflect.decorate) i = Reflect.decorate(e, t, r, n);
            else
                for (var s = e.length - 1; s >= 0; s--)(a = e[s]) && (i = (o < 3 ? a(i) : o > 3 ? a(t, r, i) : a(t, r)) || i);
            return o > 3 && i && Object.defineProperty(t, r, i), i
        },
        LinalgOps = function() {
            function e() {}
            return e.gramSchmidt = function(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(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] + ").");
                for (var a = [], o = e, n = 0; n < e.length; ++n) ! function(e) {
                    a.push(Tracking.tidy(function() {
                        var t = o[e];
                        if (e > 0)
                            for (var r = 0; r < e; ++r) {
                                var n = sum(a[r].mulStrict(t)).mul(a[r]);
                                t = t.sub(n)
                            }
                        return t.div(norm(t, "euclidean"))
                    }))
                }(n);
                return t ? stack(a, 0) : a
            }, __decorate$9([doc(), operation], e, "gramSchmidt", null), e
        }(),
        __decorate$10 = function(e, t, r, n) {
            var a, o = arguments.length,
                i = o < 3 ? t : null === n ? n = Object.getOwnPropertyDescriptor(t, r) : n;
            if ("object" == typeof Reflect && "function" == typeof Reflect.decorate) i = Reflect.decorate(e, t, r, n);
            else
                for (var s = e.length - 1; s >= 0; s--)(a = e[s]) && (i = (o < 3 ? a(i) : o > 3 ? a(t, r, i) : a(t, r)) || i);
            return o > 3 && i && Object.defineProperty(t, r, i), i
        },
        LogicalOps = function() {
            function e() {}
            return e.logicalNot = function(e) {
                return assertArgumentsAreTensors({
                    x: e
                }, "logicalNot"), assert("bool" === e.dtype, "Error Array must be of type bool."), ENV.engine.runKernel(function(t) {
                    return t.logicalNot(e)
                }, {
                    x: e
                })
            }, e.logicalAnd = function(e, t) {
                return assertArgumentsAreTensors({
                    a: e,
                    b: t
                }, "logicalAnd"), assert("bool" === e.dtype && "bool" === t.dtype, "Error Array must be of type bool."), assertAndGetBroadcastShape(e.shape, t.shape), ENV.engine.runKernel(function(r) {
                    return r.logicalAnd(e, t)
                }, {
                    a: e,
                    b: t
                })
            }, e.logicalOr = function(e, t) {
                return assertArgumentsAreTensors({
                    a: e,
                    b: t
                }, "logicalOr"), assert("bool" === e.dtype && "bool" === t.dtype, "Error Array must be of type bool."), assertAndGetBroadcastShape(e.shape, t.shape), ENV.engine.runKernel(function(r) {
                    return r.logicalOr(e, t)
                }, {
                    a: e,
                    b: t
                })
            }, e.logicalXor = function(t, r) {
                return assertArgumentsAreTensors({
                    a: t,
                    b: r
                }, "logicalXor"), assert("bool" === t.dtype && "bool" === r.dtype, "Error Array must be of type bool."), assertAndGetBroadcastShape(t.shape, r.shape), e.logicalOr(t, r).logicalAnd(e.logicalAnd(t, r).logicalNot())
            }, e.where = function(e, t, r) {
                assertArgumentsAreTensors({
                    condition: e,
                    a: t,
                    b: r
                }, "where"), assert("bool" === e.dtype, "Error Condition must be of type bool."), assertShapesMatch(t.shape, r.shape, "Error in where: "), 1 === e.rank ? assert(e.shape[0] === t.shape[0], "The first dimension of `a` must match the size of `condition`.") : assertShapesMatch(e.shape, r.shape, "Error in where: ");
                var n = upcastType(t.dtype, r.dtype);
                return ENV.engine.runKernel(function(a) {
                    return a.where(e, t, r, n)
                }, {
                    condition: e,
                    a: t,
                    b: r
                })
            }, __decorate$10([doc(), operation], e, "logicalNot", null), __decorate$10([doc(), operation], e, "logicalAnd", null), __decorate$10([doc(), operation], e, "logicalOr", null), __decorate$10([doc(), operation], e, "logicalXor", null), __decorate$10([doc(), operation], e, "where", null), e
        }(),
        __decorate$11 = function(e, t, r, n) {
            var a, o = arguments.length,
                i = o < 3 ? t : null === n ? n = Object.getOwnPropertyDescriptor(t, r) : n;
            if ("object" == typeof Reflect && "function" == typeof Reflect.decorate) i = Reflect.decorate(e, t, r, n);
            else
                for (var s = e.length - 1; s >= 0; s--)(a = e[s]) && (i = (o < 3 ? a(i) : o > 3 ? a(t, r, i) : a(t, r)) || i);
            return o > 3 && i && Object.defineProperty(t, r, i), i
        };
    ! 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"
    }(exports.Reduction || (exports.Reduction = {}));
    var LossOps = function() {
            function e() {}
            return e.computeWeightedLoss = function(e, t, r) {
                void 0 === r && (r = exports.Reduction.SUM_BY_NONZERO_WEIGHTS), assertArgumentsAreTensors({
                    losses: e
                }, "computeWeightedLoss"), null != t && assertArgumentsAreTensors({
                    weights: t
                }, "computeWeightedLoss");
                var n = null == t ? e : e.mul(t);
                if (r === exports.Reduction.NONE) return n;
                if (r === exports.Reduction.SUM) return n.sum();
                if (r === exports.Reduction.MEAN) return null == t ? n.mean() : n.sum().div(t.sum());
                if (r === exports.Reduction.SUM_BY_NONZERO_WEIGHTS) {
                    if (null == t) return n.sum().div(scalar(e.size));
                    var a = t.notEqual(scalar(0)).sum().toFloat();
                    return n.sum().div(a)
                }
                throw Error("Unknown reduction: " + r)
            }, e.absoluteDifference = function(t, r, n, a) {
                void 0 === a && (a = exports.Reduction.SUM_BY_NONZERO_WEIGHTS), assertArgumentsAreTensors({
                    labels: t,
                    predictions: r
                }, "absoluteDifference"), null != n && assertArgumentsAreTensors({
                    weights: n
                }, "absoluteDifference"), assertShapesMatch(t.shape, r.shape, "Error in absoluteDifference: ");
                var o = t.sub(r).abs();
                return e.computeWeightedLoss(o, n, a)
            }, e.meanSquaredError = function(t, r, n, a) {
                void 0 === a && (a = exports.Reduction.SUM_BY_NONZERO_WEIGHTS), assertArgumentsAreTensors({
                    labels: t,
                    predictions: r
                }, "meanSquaredError"), null != n && assertArgumentsAreTensors({
                    weights: n
                }, "meanSquaredError"), assertShapesMatch(t.shape, r.shape, "Error in meanSquaredError: ");
                var o = t.squaredDifference(r);
                return e.computeWeightedLoss(o, n, a)
            }, e.cosineDistance = function(t, r, n, a, o) {
                void 0 === o && (o = exports.Reduction.SUM_BY_NONZERO_WEIGHTS), assertArgumentsAreTensors({
                    labels: t,
                    predictions: r
                }, "cosineDistance"), null != a && assertArgumentsAreTensors({
                    weights: a
                }, "cosineDistance"), assertShapesMatch(t.shape, r.shape, "Error in cosineDistance: ");
                var i = scalar(1).sub(t.mul(r).sum(n, !0));
                return e.computeWeightedLoss(i, a, o)
            }, e.hingeLoss = function(t, r, n, a) {
                void 0 === a && (a = exports.Reduction.SUM_BY_NONZERO_WEIGHTS), assertArgumentsAreTensors({
                    labels: t,
                    predictions: r
                }, "hingeLoss"), null != n && assertArgumentsAreTensors({
                    weights: n
                }, "hingeLoss"), assertShapesMatch(t.shape, r.shape, "Error in hingeLoss: ");
                var o = scalar(1);
                t = scalar(2).mul(t).sub(o);
                var i = o.sub(t.mul(r)).relu();
                return e.computeWeightedLoss(i, n, a)
            }, e.logLoss = function(t, r, n, a, o) {
                void 0 === a && (a = 1e-7), void 0 === o && (o = exports.Reduction.SUM_BY_NONZERO_WEIGHTS), assertArgumentsAreTensors({
                    labels: t,
                    predictions: r
                }, "logLoss"), null != n && assertArgumentsAreTensors({
                    weights: n
                }, "logLoss"), assertShapesMatch(t.shape, r.shape, "Error in logLoss: ");
                var i = scalar(1),
                    s = scalar(a),
                    u = t.mul(r.add(s).log()).neg().sub(i.sub(t).mul(i.sub(r).add(s).log()));
                return e.computeWeightedLoss(u, n, o)
            }, e.huberLoss = function(t, r, n, a, o) {
                void 0 === a && (a = 1), void 0 === o && (o = exports.Reduction.SUM_BY_NONZERO_WEIGHTS), assertArgumentsAreTensors({
                    labels: t,
                    predictions: r
                }, "huberLoss"), null != n && assertArgumentsAreTensors({
                    weights: n
                }, "huberLoss"), assertShapesMatch(t.shape, r.shape, "Error in huberLoss: ");
                var i = scalar(a),
                    s = r.sub(t).abs(),
                    u = minimum(s, i),
                    l = s.sub(u),
                    c = scalar(.5).mul(u.square()).add(i.mul(l));
                return e.computeWeightedLoss(c, n, o)
            }, __decorate$11([doc(), operation], e, "computeWeightedLoss", null), __decorate$11([doc(), operation], e, "absoluteDifference", null), __decorate$11([doc(), operation], e, "meanSquaredError", null), __decorate$11([doc(), operation], e, "cosineDistance", null), __decorate$11([doc(), operation], e, "hingeLoss", null), __decorate$11([doc(), operation], e, "logLoss", null), __decorate$11([doc(), operation], e, "huberLoss", null), e
        }(),
        __decorate$12 = function(e, t, r, n) {
            var a, o = arguments.length,
                i = o < 3 ? t : null === n ? n = Object.getOwnPropertyDescriptor(t, r) : n;
            if ("object" == typeof Reflect && "function" == typeof Reflect.decorate) i = Reflect.decorate(e, t, r, n);
            else
                for (var s = e.length - 1; s >= 0; s--)(a = e[s]) && (i = (o < 3 ? a(i) : o > 3 ? a(t, r, i) : a(t, r)) || i);
            return o > 3 && i && Object.defineProperty(t, r, i), i
        },
        LRNOps = function() {
            function e() {}
            return e.localResponseNormalization = function(e, t, r, n, a) {
                void 0 === t && (t = 5), void 0 === r && (r = 1), void 0 === n && (n = 1), void 0 === a && (a = .5), assertArgumentsAreTensors({
                    x: e
                }, "localResponseNormalization"), assert(4 === e.rank || 3 === e.rank, "Error in localResponseNormalization: x must be rank 3 or 4 but got\n               rank " + e.rank + "."), assert(isInt(t), "Error in localResponseNormalization: depthRadius must be an integer\n                     but got depthRadius " + t + ".");
                var o = e,
                    i = !1;
                3 === e.rank && (i = !0, o = e.as4D(1, e.shape[0], e.shape[1], e.shape[2]));
                var s = ENV.engine.runKernel(function(e) {
                    return e.localResponseNormalization4D(o, t, r, n, a)
                }, {
                    x4D: o
                });
                return i ? s.as3D(s.shape[1], s.shape[2], s.shape[3]) : s
            }, __decorate$12([doc(), operation], e, "localResponseNormalization", null), e
        }(),
        __decorate$13 = function(e, t, r, n) {
            var a, o = arguments.length,
                i = o < 3 ? t : null === n ? n = Object.getOwnPropertyDescriptor(t, r) : n;
            if ("object" == typeof Reflect && "function" == typeof Reflect.decorate) i = Reflect.decorate(e, t, r, n);
            else
                for (var s = e.length - 1; s >= 0; s--)(a = e[s]) && (i = (o < 3 ? a(i) : o > 3 ? a(t, r, i) : a(t, r)) || i);
            return o > 3 && i && Object.defineProperty(t, r, i), i
        },
        LSTMOps = function() {
            function e() {}
            return e.multiRNNCell = function(e, t, r, n) {
                assertArgumentsAreTensors({
                    data: t,
                    c: r,
                    h: n
                }, "multiRNNCell");
                for (var a = t, o = [], i = 0; i < e.length; i++) {
                    var s = e[i](a, r[i], n[i]);
                    o.push(s[0]), o.push(s[1]), a = s[1]
                }
                for (var u = [], l = [], i = 0; i < o.length; i += 2) u.push(o[i]), l.push(o[i + 1]);
                return [u, l]
            }, e.basicLSTMCell = function(e, t, r, n, a, o) {
                assertArgumentsAreTensors({
                    forgetBias: e,
                    lstmKernel: t,
                    lstmBias: r,
                    data: n,
                    c: a,
                    h: o
                }, "basicLSTMCell");
                var i = n.concat(o, 1).matMul(t).add(r),
                    s = i.shape[0],
                    u = i.shape[1] / 4,
                    l = [s, u],
                    c = i.slice([0, 0], l),
                    p = i.slice([0, u], l),
                    d = i.slice([0, 2 * u], l),
                    h = i.slice([0, 3 * u], l),
                    f = c.sigmoid().mulStrict(p.tanh()).addStrict(a.mulStrict(e.add(d).sigmoid()));
                return [f, f.tanh().mulStrict(h.sigmoid())]
            }, __decorate$13([doc(), operation], e, "multiRNNCell", null), __decorate$13([doc(), operation], e, "basicLSTMCell", null), e
        }(),
        __decorate$14 = function(e, t, r, n) {
            var a, o = arguments.length,
                i = o < 3 ? t : null === n ? n = Object.getOwnPropertyDescriptor(t, r) : n;
            if ("object" == typeof Reflect && "function" == typeof Reflect.decorate) i = Reflect.decorate(e, t, r, n);
            else
                for (var s = e.length - 1; s >= 0; s--)(a = e[s]) && (i = (o < 3 ? a(i) : o > 3 ? a(t, r, i) : a(t, r)) || i);
            return o > 3 && i && Object.defineProperty(t, r, i), i
        },
        MatmulOps = function() {
            function e() {}
            return e.matMul = function(e, t, r, n) {
                void 0 === r && (r = !1), void 0 === n && (n = !1), assertArgumentsAreTensors({
                    a: e,
                    b: t
                }, "matMul");
                var a = r ? e.shape[0] : e.shape[1],
                    o = n ? t.shape[1] : t.shape[0];
                assert(2 === e.rank && 2 === t.rank, "Error in matMul: inputs must be rank 2, got ranks " + e.rank + " and " + t.rank + "."), assert(a === o, "Error in matMul: inner shapes (" + a + ") and (" + o + ") of Tensors with shapes " + e.shape + " and " + t.shape + " and transposeA=" + r + " and transposeB=" + n + " must match.");
                return ENV.engine.runKernel(function(a) {
                    return a.matMul(e, t, r, n)
                }, {
                    a: e,
                    b: t
                }, function(a) {
                    return r || n ? !r && n ? {
                        a: function() {
                            return a.matMul(t.toFloat(), !1, !1)
                        },
                        b: function() {
                            return a.matMul(e.toFloat(), !0, !1)
                        }
                    } : r && !n ? {
                        a: function() {
                            return t.toFloat().matMul(a, !1, !0)
                        },
                        b: function() {
                            return e.toFloat().matMul(a, !1, !1)
                        }
                    } : {
                        a: function() {
                            return t.toFloat().matMul(a, !0, !0)
                        },
                        b: function() {
                            return a.matMul(e.toFloat(), !0, !0)
                        }
                    } : {
                        a: function() {
                            return a.matMul(t.toFloat(), !1, !0)
                        },
                        b: function() {
                            return e.toFloat().matMul(a, !0, !1)
                        }
                    }
                })
            }, e.vectorTimesMatrix = function(e, t) {
                return assert(1 === e.rank, "Error in vectorTimesMatrix: first input must be rank 1, but got rank " + e.rank + "."), assert(2 === t.rank, "Error in vectorTimesMatrix: second input must be rank 2, but got rank " + t.rank + "."), assert(e.size === t.shape[0], "Error in vectorTimesMatrix: size of vector (" + e.size + ") must match first dimension of matrix (" + t.shape[0] + ")"), e.as2D(1, -1).matMul(t).as1D()
            }, e.matrixTimesVector = function(e, t) {
                return assert(1 === t.rank, "Error in matrixTimesVector: second input must rank 1, but got rank " + t.rank + "."), assert(2 === e.rank, "Error in matrixTimesVector: first input must be a rank 2, but got rank " + e.rank + "."), assert(t.size === e.shape[1], "Error in matrixTimesVector: size of first rank 1 input " + t.size + " must match inner dimension of second rank 2 input, but got shape " + e.shape + "."), e.matMul(t.as2D(-1, 1)).as1D()
            }, e.dotProduct = function(e, t) {
                return assert(1 === e.rank && 1 === t.rank, "Error in dotProduct: inputs must be rank 1, but got ranks " + e.rank + " and " + t.rank + "."), assert(e.size === t.size, "Error in dotProduct: size of inputs (" + e.size + ") and (" + t.size + ") must match."), e.as2D(1, -1).matMul(t.as2D(-1, 1)).asScalar()
            }, e.outerProduct = function(e, t) {
                return assert(1 === e.rank && 1 === t.rank, "Error in outerProduct: inputs must be rank 1, but got ranks " + e.rank + " and " + t.rank + "."), e.as2D(-1, 1).matMul(t.as2D(1, -1))
            }, e.dot = function(e, t) {
                assert(!(1 !== e.rank && 2 !== e.rank || 1 !== t.rank && 2 !== t.rank), "Error in dot: inputs must all be rank 1 or 2, but got ranks " + e.rank + " and " + t.rank + ".");
                var r = 1 === e.rank ? e.size : e.shape[1],
                    n = 1 === t.rank ? t.size : t.shape[0];
                return assert(r === n, "Error in dot: inner dimensions of inputs must match, but got " + r + " and " + n + "."), 1 === e.rank && 1 === t.rank ? e.as2D(1, -1).matMul(t.as2D(-1, 1)).asScalar() : 1 === e.rank && 2 === t.rank ? e.as2D(1, -1).matMul(t.as2D(t.shape[0], t.shape[1])).as1D() : 2 === e.rank && 1 === t.rank ? e.matMul(t.as2D(-1, 1)).as1D() : e.matMul(t.as2D(t.shape[0], t.shape[1]))
            }, __decorate$14([doc(), operation], e, "matMul", null), __decorate$14([operation], e, "vectorTimesMatrix", null), __decorate$14([operation], e, "matrixTimesVector", null), __decorate$14([operation], e, "dotProduct", null), __decorate$14([doc(), operation], e, "outerProduct", null), __decorate$14([doc(), operation], e, "dot", null), e
        }(),
        __decorate$15 = function(e, t, r, n) {
            var a, o = arguments.length,
                i = o < 3 ? t : null === n ? n = Object.getOwnPropertyDescriptor(t, r) : n;
            if ("object" == typeof Reflect && "function" == typeof Reflect.decorate) i = Reflect.decorate(e, t, r, n);
            else
                for (var s = e.length - 1; s >= 0; s--)(a = e[s]) && (i = (o < 3 ? a(i) : o > 3 ? a(t, r, i) : a(t, r)) || i);
            return o > 3 && i && Object.defineProperty(t, r, i), i
        },
        MovingAverageOps = function() {
            function e() {}
            return e.movingAverage = function(e, t, r, n, a) {
                void 0 === a && (a = !0), assertArgumentsAreTensors({
                    v: e,
                    x: t
                }, "movingAverage"), assertTypesMatch(e, t), assert(arraysEqual(e.shape, t.shape), "Shape mismatch in v and x");
                var o = ArrayOps.scalar(1);
                r = "number" == typeof r ? ArrayOps.scalar(r) : r;
                var i = o.sub(r),
                    s = t.sub(e).mul(i);
                return a && (assert(null != n, "When using zeroDebias: true, step is required."), n = "number" == typeof n ? ArrayOps.scalar(n) : n, s = s.div(o.sub(BinaryOps.pow(r, n)))), e.add(s)
            }, __decorate$15([doc(), operation], e, "movingAverage", null), e
        }(),
        __decorate$16 = function(e, t, r, n) {
            var a, o = arguments.length,
                i = o < 3 ? t : null === n ? n = Object.getOwnPropertyDescriptor(t, r) : n;
            if ("object" == typeof Reflect && "function" == typeof Reflect.decorate) i = Reflect.decorate(e, t, r, n);
            else
                for (var s = e.length - 1; s >= 0; s--)(a = e[s]) && (i = (o < 3 ? a(i) : o > 3 ? a(t, r, i) : a(t, r)) || i);
            return o > 3 && i && Object.defineProperty(t, r, i), i
        },
        NormOps = function() {
            function e() {}
            return e.norm = function(e, t, r, n) {
                void 0 === t && (t = "euclidean"), void 0 === r && (r = null), void 0 === n && (n = !1), assertArgumentsAreTensors({
                    x: e
                }, "norm");
                var a = normImpl(e, t, r),
                    o = a.shape;
                if (n) {
                    var i = parseAxisParam(r, e.shape);
                    o = expandShapeToKeepDim(a.shape, i)
                }
                return a.reshape(o)
            }, __decorate$16([doc(), operation], e, "norm", null), e
        }(),
        __decorate$17 = function(e, t, r, n) {
            var a, o = arguments.length,
                i = o < 3 ? t : null === n ? n = Object.getOwnPropertyDescriptor(t, r) : n;
            if ("object" == typeof Reflect && "function" == typeof Reflect.decorate) i = Reflect.decorate(e, t, r, n);
            else
                for (var s = e.length - 1; s >= 0; s--)(a = e[s]) && (i = (o < 3 ? a(i) : o > 3 ? a(t, r, i) : a(t, r)) || i);
            return o > 3 && i && Object.defineProperty(t, r, i), i
        },
        PoolOps = function() {
            function e() {}
            return e.maxPool = function(t, r, n, a, o) {
                assertArgumentsAreTensors({
                    x: t
                }, "maxPool");
                var i = t,
                    s = !1;
                3 === t.rank && (s = !0, i = t.as4D(1, t.shape[0], t.shape[1], t.shape[2])), assert(4 === i.rank, "Error in maxPool: input must be rank 4 but got rank " + i.rank + "."), null != o && assert(isInt(a), "Error in maxPool: pad must be an integer when using, dimRoundingMode " + o + " but got pad " + a + ".");
                var u = computePool2DInfo(i.shape, r, n, a, o),
                    l = ENV.engine.runKernel(function(e, t) {
                        return t(e.maxPool(i, u))
                    }, {
                        x: i
                    }, function(t, o) {
                        var s = o[0];
                        return {
                            x: function() {
                                return e.maxPoolBackprop(t, i, s, r, n, a)
                            }
                        }
                    });
                return s ? l.as3D(l.shape[1], l.shape[2], l.shape[3]) : l
            }, e.maxPoolBackprop = function(e, t, r, n, a, o, i) {
                assertArgumentsAreTensors({
                    dy: e,
                    input: t,
                    output: r
                }, "maxPoolBackprop"), assert(t.rank === e.rank, "Rank of input (" + t.rank + ") does not match rank of dy (" + e.rank + ")"), assert(4 === e.rank, "Error in maxPoolBackprop: dy must be rank 4 but got rank " + e.rank + "."), assert(4 === t.rank, "Error in maxPoolBackprop: input must be rank 4 but got rank " + t.rank + "."), null != i && assert(isInt(o), "Error in maxPoolBackprop: pad must be an integer when using, dimRoundingMode " + i + " but got pad " + o + ".");
                var s = computePool2DInfo(t.shape, n, a, o, i);
                return ENV.engine.runKernel(function(n) {
                    return n.maxPoolBackprop(e, t, r, s)
                }, {
                    dy: e,
                    input: t
                })
            }, e.avgPool = function(t, r, n, a, o) {
                assertArgumentsAreTensors({
                    x: t
                }, "avgPool"), assert("float32" === t.dtype, "The input dtype to avgPool must be float32");
                var i = t,
                    s = !1;
                3 === t.rank && (s = !0, i = t.as4D(1, t.shape[0], t.shape[1], t.shape[2])), assert(4 === i.rank, "Error in avgPool: x must be rank 4 but got rank " + i.rank + "."), null != o && assert(isInt(a), "Error in avgPool: pad must be an integer when using, dimRoundingMode " + o + " but got pad " + a + ".");
                var u = computePool2DInfo(i.shape, r, n, a),
                    l = ENV.engine.runKernel(function(e) {
                        return e.avgPool(i, u)
                    }, {
                        x: i
                    }, function(t) {
                        return {
                            x: function() {
                                return e.avgPoolBackprop(t, i, r, n, a)
                            }
                        }
                    });
                return l = l.cast(t.dtype), s ? l.as3D(l.shape[1], l.shape[2], l.shape[3]) : l
            }, e.avgPoolBackprop = function(e, t, r, n, a) {
                assertArgumentsAreTensors({
                    dy: e,
                    input: t
                }, "avgPoolBackprop"), assert(t.rank === e.rank, "Rank of input (" + t.rank + ") does not match rank of dy (" + e.rank + ")");
                var o = t,
                    i = e,
                    s = !1;
                3 === t.rank && (s = !0, o = t.as4D(1, t.shape[0], t.shape[1], t.shape[2]), i = e.as4D(1, e.shape[0], e.shape[1], e.shape[2])), assert(4 === i.rank, "Error in avgPoolBackprop: dy must be rank 4 but got rank " + i.rank + "."), assert(4 === o.rank, "Error in avgPoolBackprop: input must be rank 4 but got rank " + o.rank + ".");
                var u = computePool2DInfo(o.shape, r, n, a),
                    l = ENV.engine.runKernel(function(e) {
                        return e.avgPoolBackprop(i, o, u)
                    }, {
                        dy4D: i,
                        input4D: o
                    });
                return s ? l.as3D(l.shape[1], l.shape[2], l.shape[3]) : l
            }, __decorate$17([doc(), operation], e, "maxPool", null), __decorate$17([operation], e, "maxPoolBackprop", null), __decorate$17([doc(), operation], e, "avgPool", null), __decorate$17([operation], e, "avgPoolBackprop", null), e
        }(),
        __decorate$18 = function(e, t, r, n) {
            var a, o = arguments.length,
                i = o < 3 ? t : null === n ? n = Object.getOwnPropertyDescriptor(t, r) : n;
            if ("object" == typeof Reflect && "function" == typeof Reflect.decorate) i = Reflect.decorate(e, t, r, n);
            else
                for (var s = e.length - 1; s >= 0; s--)(a = e[s]) && (i = (o < 3 ? a(i) : o > 3 ? a(t, r, i) : a(t, r)) || i);
            return o > 3 && i && Object.defineProperty(t, r, i), i
        },
        ReverseOps = function() {
            function e() {}
            return e.reverse1d = function(t) {
                return assert(1 === t.rank, "Error in reverse1D: x must be rank 1 but got\n             rank " + t.rank + "."), e.reverse(t, 0)
            }, e.reverse2d = function(t, r) {
                return assert(2 === t.rank, "Error in reverse2D: x must be rank 2 but got\n             rank " + t.rank + "."), e.reverse(t, r)
            }, e.reverse3d = function(t, r) {
                return assert(3 === t.rank, "Error in reverse3D: x must be rank 3 but got\n             rank " + t.rank + "."), e.reverse(t, r)
            }, e.reverse4d = function(t, r) {
                return assert(4 === t.rank, "Error in reverse4D: x must be rank 4 but got\n             rank " + t.rank + "."), e.reverse(t, r)
            }, e.reverse = function(e, t) {
                if (assertArgumentsAreTensors({
                        x: e
                    }, "reverse"), 0 === e.rank) return e.clone();
                var r = parseAxisParam(t, e.shape);
                return ENV.engine.runKernel(function(t) {
                    return t.reverse(e, r)
                }, {
                    x: e
                }, function(e) {
                    return {
                        x: function() {
                            return e.reverse(r)
                        }
                    }
                }).reshapeAs(e)
            }, __decorate$18([doc(), operation], e, "reverse", null), e
        }(),
        __decorate$19 = function(e, t, r, n) {
            var a, o = arguments.length,
                i = o < 3 ? t : null === n ? n = Object.getOwnPropertyDescriptor(t, r) : n;
            if ("object" == typeof Reflect && "function" == typeof Reflect.decorate) i = Reflect.decorate(e, t, r, n);
            else
                for (var s = e.length - 1; s >= 0; s--)(a = e[s]) && (i = (o < 3 ? a(i) : o > 3 ? a(t, r, i) : a(t, r)) || i);
            return o > 3 && i && Object.defineProperty(t, r, i), i
        },
        SliceOps = function() {
            function e() {}
            return e.slice1d = function(t, r, n) {
                return assert(1 === t.rank, "slice1d expects a rank-1 tensor, but got a rank-" + t.rank + " tensor"), e.slice(t, [r], [n])
            }, e.slice2d = function(t, r, n) {
                return assert(2 === t.rank, "slice1d expects a rank-2 tensor, but got a rank-" + t.rank + " tensor"), e.slice(t, r, n)
            }, e.slice3d = function(t, r, n) {
                return assert(3 === t.rank, "slice1d expects a rank-3 tensor, but got a rank-" + t.rank + " tensor"), e.slice(t, r, n)
            }, e.slice4d = function(t, r, n) {
                return assert(4 === t.rank, "slice1d expects a rank-4 tensor, but got a rank-" + t.rank + " tensor"), e.slice(t, r, n)
            }, e.slice = function(e, t, r) {
                if (assertArgumentsAreTensors({
                        x: e
                    }, "slice"), 0 === e.rank) throw new Error("Slicing scalar is not possible");
                var n;
                n = "number" == typeof t ? [t].concat(new Array(e.rank - 1).fill(0)) : t.length < e.rank ? t.concat(new Array(e.rank - t.length).fill(0)) : t;
                var a;
                a = null == r ? new Array(e.rank).fill(-1) : "number" == typeof r ? [r].concat(new Array(e.rank - 1).fill(-1)) : r.length < e.rank ? r.concat(new Array(e.rank - r.length).fill(-1)) : r, a = a.map(function(t, r) {
                    return t >= 0 ? t : (assert(-1 === t, "Bad value in size"), e.shape[r] - n[r])
                }), assertParamsValid(e, n, a);
                var o = e.shape;
                return ENV.engine.runKernel(function(t) {
                    return t.slice(e, n, a)
                }, {
                    x: e
                }, function(e) {
                    for (var t = [], r = 0; r < e.rank; r++) t.push([n[r], o[r] - n[r] - a[r]]);
                    return {
                        x: function() {
                            return e.pad(t)
                        }
                    }
                })
            }, __decorate$19([doc(), operation], e, "slice", null), e
        }(),
        __decorate$20 = function(e, t, r, n) {
            var a, o = arguments.length,
                i = o < 3 ? t : null === n ? n = Object.getOwnPropertyDescriptor(t, r) : n;
            if ("object" == typeof Reflect && "function" == typeof Reflect.decorate) i = Reflect.decorate(e, t, r, n);
            else
                for (var s = e.length - 1; s >= 0; s--)(a = e[s]) && (i = (o < 3 ? a(i) : o > 3 ? a(t, r, i) : a(t, r)) || i);
            return o > 3 && i && Object.defineProperty(t, r, i), i
        },
        SoftmaxOps = function() {
            function e() {}
            return e.softmax = function(e, t) {
                if (void 0 === t && (t = -1), assertArgumentsAreTensors({
                        logits: e
                    }, "softmax"), -1 === t && (t = e.rank - 1), t !== e.rank - 1) throw Error("Softmax along a non-last dimension is not yet supported. Logits was rank " + e.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))
                        }
                    }
                })(e)
            }, e.softmaxCrossEntropy = function(e, t, r) {
                if (void 0 === r && (r = -1), assertArgumentsAreTensors({
                        labels: e,
                        logits: t
                    }, "softmaxCrossEntropy"), assertShapesMatch(e.shape, t.shape, "Error in softmaxCrossEntropy: "), -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.softmax(r);
                    return {
                        value: scalar(1e-5).add(n).log().mul(e).neg().sum([r]),
                        gradFunc: function(t) {
                            var a = expandShapeToKeepDim(t.shape, [r]);
                            return [t.reshape(a).mul(e.toFloat().sub(n)), t.reshape(a).mul(n.sub(e.toFloat()))]
                        }
                    }
                })(e, t)
            }, __decorate$20([doc(), operation], e, "softmax", null), __decorate$20([doc(), operation], e, "softmaxCrossEntropy", null), e
        }(),
        __decorate$21 = function(e, t, r, n) {
            var a, o = arguments.length,
                i = o < 3 ? t : null === n ? n = Object.getOwnPropertyDescriptor(t, r) : n;
            if ("object" == typeof Reflect && "function" == typeof Reflect.decorate) i = Reflect.decorate(e, t, r, n);
            else
                for (var s = e.length - 1; s >= 0; s--)(a = e[s]) && (i = (o < 3 ? a(i) : o > 3 ? a(t, r, i) : a(t, r)) || i);
            return o > 3 && i && Object.defineProperty(t, r, i), i
        },
        StridedSliceOps = function() {
            function e() {}
            return e.stridedSlice = function(e, t, r, n, a, o) {
                return void 0 === a && (a = 0), void 0 === o && (o = 0), assertArgumentsAreTensors({
                    x: e
                }, "stridedSlice"), ENV.engine.runKernel(function(i) {
                    return i.stridedSlice(e, t, r, n, a, o)
                }, {
                    x: e
                })
            }, __decorate$21([doc(), operation], e, "stridedSlice", null), e
        }(),
        __decorate$22 = function(e, t, r, n) {
            var a, o = arguments.length,
                i = o < 3 ? t : null === n ? n = Object.getOwnPropertyDescriptor(t, r) : n;
            if ("object" == typeof Reflect && "function" == typeof Reflect.decorate) i = Reflect.decorate(e, t, r, n);
            else
                for (var s = e.length - 1; s >= 0; s--)(a = e[s]) && (i = (o < 3 ? a(i) : o > 3 ? a(t, r, i) : a(t, r)) || i);
            return o > 3 && i && Object.defineProperty(t, r, i), i
        },
        TransposeOps = function() {
            function e() {}
            return e.transpose = function(e, t) {
                if (assertArgumentsAreTensors({
                        x: e
                    }, "transpose"), null == t && (t = e.shape.map(function(e, t) {
                        return t
                    }).reverse()), assert(e.rank === t.length, "Error in transpose: rank of input " + e.rank + " must match length of perm " + t + "."), t.forEach(function(r) {
                        assert(r >= 0 && r < e.rank, "All entries in 'perm' must be between 0 and " + (e.rank - 1) + " but got " + t)
                    }), e.rank <= 1) return e.clone();
                return ENV.engine.runKernel(function(r) {
                    return r.transpose(e, t)
                }, {
                    x: e
                }, function(e) {
                    var r = getUndoAxesPermutation(t);
                    return {
                        x: function() {
                            return e.transpose(r)
                        }
                    }
                })
            }, __decorate$22([doc(), operation], e, "transpose", null), e
        }(),
        SELU_SCALEALPHA = 1.7580993408473768,
        SELU_SCALE = 1.0507009873554805,
        __decorate$23 = function(e, t, r, n) {
            var a, o = arguments.length,
                i = o < 3 ? t : null === n ? n = Object.getOwnPropertyDescriptor(t, r) : n;
            if ("object" == typeof Reflect && "function" == typeof Reflect.decorate) i = Reflect.decorate(e, t, r, n);
            else
                for (var s = e.length - 1; s >= 0; s--)(a = e[s]) && (i = (o < 3 ? a(i) : o > 3 ? a(t, r, i) : a(t, r)) || i);
            return o > 3 && i && Object.defineProperty(t, r, i), i
        },
        UnaryOps = function() {
            function e() {}
            return e.neg = function(e) {
                assertArgumentsAreTensors({
                    x: e
                }, "neg");
                return ENV.engine.runKernel(function(t) {
                    return t.neg(e)
                }, {
                    x: e
                }, function(e) {
                    return {
                        x: function() {
                            return e.neg()
                        }
                    }
                })
            }, e.ceil = function(e) {
                assertArgumentsAreTensors({
                    x: e
                }, "ceil");
                return ENV.engine.runKernel(function(t) {
                    return t.ceil(e)
                }, {
                    x: e
                }, function(e) {
                    return {
                        x: function() {
                            return zerosLike(e)
                        }
                    }
                })
            }, e.floor = function(e) {
                assertArgumentsAreTensors({
                    x: e
                }, "floor");
                return ENV.engine.runKernel(function(t) {
                    return t.floor(e)
                }, {
                    x: e
                }, function(e) {
                    return {
                        x: function() {
                            return zerosLike(e)
                        }
                    }
                })
            }, e.sign = function(e) {
                assertArgumentsAreTensors({
                    x: e
                }, "sign");
                return ENV.engine.runKernel(function(t) {
                    return t.sign(e)
                }, {
                    x: e
                }, function(e) {
                    return {
                        x: function() {
                            return zerosLike(e)
                        }
                    }
                })
            }, e.round = function(e) {
                assertArgumentsAreTensors({
                    x: e
                }, "round");
                return ENV.engine.runKernel(function(t) {
                    return t.round(e)
                }, {
                    x: e
                }, function(e) {
                    return {
                        x: function() {
                            return zerosLike(e)
                        }
                    }
                })
            }, e.exp = function(e) {
                assertArgumentsAreTensors({
                    x: e
                }, "exp");
                return ENV.engine.runKernel(function(t, r) {
                    return r(t.exp(e))
                }, {
                    x: e
                }, function(e, t) {
                    var r = t[0];
                    return {
                        x: function() {
                            return e.mulStrict(r)
                        }
                    }
                })
            }, e.expm1 = function(e) {
                assertArgumentsAreTensors({
                    x: e
                }, "expm1");
                return ENV.engine.runKernel(function(t) {
                    return t.expm1(e)
                }, {
                    x: e
                }, function(t) {
                    return {
                        x: function() {
                            return t.mulStrict(e.exp())
                        }
                    }
                })
            }, e.log = function(e) {
                assertArgumentsAreTensors({
                    x: e
                }, "log");
                return ENV.engine.runKernel(function(t) {
                    return t.log(e)
                }, {
                    x: e
                }, function(t) {
                    return {
                        x: function() {
                            return t.divStrict(e.toFloat())
                        }
                    }
                })
            }, e.log1p = function(e) {
                assertArgumentsAreTensors({
                    x: e
                }, "log1p");
                return ENV.engine.runKernel(function(t) {
                    return t.log1p(e)
                }, {
                    x: e
                }, function(t) {
                    return {
                        x: function() {
                            return t.divStrict(e.add(scalar(1)))
                        }
                    }
                })
            }, e.sqrt = function(e) {
                assertArgumentsAreTensors({
                    x: e
                }, "sqrt");
                return ENV.engine.runKernel(function(t) {
                    return t.sqrt(e)
                }, {
                    x: e
                }, function(t) {
                    return {
                        x: function() {
                            return t.divStrict(e.toFloat().sqrt().mul(scalar(2)))
                        }
                    }
                })
            }, e.rsqrt = function(e) {
                assertArgumentsAreTensors({
                    x: e
                }, "rsqrt");
                return ENV.engine.runKernel(function(t) {
                    return t.rsqrt(e)
                }, {
                    x: e
                }, function(t) {
                    return {
                        x: function() {
                            return t.divStrict(e.pow(scalar(1.5)).mul(scalar(2))).neg()
                        }
                    }
                })
            }, e.square = function(e) {
                assertArgumentsAreTensors({
                    x: e
                }, "square");
                return ENV.engine.runKernel(function(t) {
                    return t.square(e)
                }, {
                    x: e
                }, function(t) {
                    return {
                        x: function() {
                            return t.mulStrict(e.toFloat().mul(scalar(2)))
                        }
                    }
                })
            }, e.reciprocal = function(e) {
                assertArgumentsAreTensors({
                    x: e
                }, "reciprocal");
                return ENV.engine.runKernel(function(t) {
                    return t.reciprocal(e)
                }, {
                    x: e
                }, function(t) {
                    return {
                        x: function() {
                            return t.divStrict(e.square().neg())
                        }
                    }
                })
            }, e.abs = function(e) {
                assertArgumentsAreTensors({
                    x: e
                }, "abs");
                return ENV.engine.runKernel(function(t) {
                    return t.abs(e)
                }, {
                    x: e
                }, function(t) {
                    return {
                        x: function() {
                            return t.mulStrict(e.toFloat().step(-1))
                        }
                    }
                })
            }, e.clipByValue = function(e, t, r) {
                assertArgumentsAreTensors({
                    x: e
                }, "clipByValue"), assert(t <= r, "Error in clip: min (" + t + ") must be less than or equal to max (" + r + ").");
                return ENV.engine.runKernel(function(n) {
                    return n.clip(e, t, r)
                }, {
                    x: e
                }, function(n) {
                    return {
                        x: function() {
                            return n.where(e.greaterEqual(scalar(t)).logicalAnd(e.lessEqual(scalar(r))), zerosLike(n))
                        }
                    }
                })
            }, e.relu = function(e) {
                if (assertArgumentsAreTensors({
                        x: e
                    }, "relu"), "bool" === e.dtype) return e.toInt();
                return ENV.engine.runKernel(function(t) {
                    return t.relu(e)
                }, {
                    x: e
                }, function(t) {
                    var r = e.step();
                    return {
                        x: function() {
                            return t.mulStrict(r.toFloat())
                        }
                    }
                })
            }, e.elu = function(e) {
                assertArgumentsAreTensors({
                    x: e
                }, "elu");
                return ENV.engine.runKernel(function(t, r) {
                    return r(t.elu(e))
                }, {
                    x: e
                }, 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
                            })
                        }
                    }
                })
            }, e.selu = function(e) {
                assertArgumentsAreTensors({
                    x: e
                }, "selu");
                return ENV.engine.runKernel(function(t) {
                    return t.selu(e)
                }, {
                    x: e
                }, function(t) {
                    return {
                        x: function() {
                            var r = e.greater(scalar(0)),
                                n = scalar(SELU_SCALEALPHA),
                                a = scalar(SELU_SCALE),
                                o = t.mul(a),
                                i = t.mul(n).mul(e.toFloat().exp());
                            return where(r, o, i)
                        }
                    }
                })
            }, e.leakyRelu = function(e, t) {
                return void 0 === t && (t = .2), assertArgumentsAreTensors({
                    x: e
                }, "leakyRelu"), maximum(scalar(t).mul(e), e)
            }, e.prelu = function(e, t) {
                assertArgumentsAreTensors({
                    x: e,
                    alpha: t
                }, "prelu");
                var r = scalar(0);
                return maximum(r, e).add(t.mul(minimum(r, e)))
            }, e.sigmoid = function(e) {
                assertArgumentsAreTensors({
                    x: e
                }, "sigmoid");
                return ENV.engine.runKernel(function(t, r) {
                    return r(t.sigmoid(e))
                }, {
                    x: e
                }, function(e, t) {
                    var r = t[0];
                    return {
                        x: function() {
                            return e.mulStrict(r.mul(scalar(1).sub(r)))
                        }
                    }
                })
            }, e.logSigmoid = function(e) {
                assertArgumentsAreTensors({
                    x: e
                }, "logSigmoid");
                return ENV.engine.runKernel(function(t) {
                    return t.softplus(e.neg()).neg()
                }, {
                    x: e
                }, function(t) {
                    return {
                        x: function() {
                            return t.mulStrict(e.neg().sigmoid())
                        }
                    }
                })
            }, e.softplus = function(e) {
                assertArgumentsAreTensors({
                    x: e
                }, "softplus");
                return ENV.engine.runKernel(function(t) {
                    return t.softplus(e)
                }, {
                    x: e
                }, function(t) {
                    return {
                        x: function() {
                            return t.mulStrict(e.sigmoid())
                        }
                    }
                })
            }, e.sin = function(e) {
                assertArgumentsAreTensors({
                    x: e
                }, "sin");
                return ENV.engine.runKernel(function(t) {
                    return t.sin(e)
                }, {
                    x: e
                }, function(t) {
                    return {
                        x: function() {
                            return e.toFloat().cos().mulStrict(t)
                        }
                    }
                })
            }, e.cos = function(e) {
                assertArgumentsAreTensors({
                    x: e
                }, "cos");
                return ENV.engine.runKernel(function(t) {
                    return t.cos(e)
                }, {
                    x: e
                }, function(t) {
                    return {
                        x: function() {
                            return e.toFloat().sin().neg().mulStrict(t)
                        }
                    }
                })
            }, e.tan = function(e) {
                assertArgumentsAreTensors({
                    x: e
                }, "tan");
                return ENV.engine.runKernel(function(t) {
                    return t.tan(e)
                }, {
                    x: e
                }, function(t) {
                    return {
                        x: function() {
                            return t.divStrict(e.cos().square())
                        }
                    }
                })
            }, e.asin = function(t) {
                assertArgumentsAreTensors({
                    x: t
                }, "asin");
                return ENV.engine.runKernel(function(e) {
                    return e.asin(t)
                }, {
                    x: t
                }, function(r) {
                    return {
                        x: function() {
                            return r.divStrict(e.sqrt(scalar(1).sub(t.toFloat().square())))
                        }
                    }
                })
            }, e.acos = function(t) {
                assertArgumentsAreTensors({
                    x: t
                }, "acos");
                return ENV.engine.runKernel(function(e) {
                    return e.acos(t)
                }, {
                    x: t
                }, function(r) {
                    return {
                        x: function() {
                            return r.divStrict(e.sqrt(scalar(1).sub(t.toFloat().square()))).neg()
                        }
                    }
                })
            }, e.atan = function(e) {
                assertArgumentsAreTensors({
                    x: e
                }, "atan");
                return ENV.engine.runKernel(function(t) {
                    return t.atan(e)
                }, {
                    x: e
                }, function(t) {
                    return {
                        x: function() {
                            return t.divStrict(scalar(1).add(e.toFloat().square()))
                        }
                    }
                })
            }, e.sinh = function(e) {
                assertArgumentsAreTensors({
                    x: e
                }, "sinh");
                return ENV.engine.runKernel(function(t) {
                    return t.sinh(e)
                }, {
                    x: e
                }, function(t) {
                    return {
                        x: function() {
                            return e.toFloat().cosh().mulStrict(t)
                        }
                    }
                })
            }, e.cosh = function(e) {
                assertArgumentsAreTensors({
                    x: e
                }, "cosh");
                return ENV.engine.runKernel(function(t) {
                    return t.cosh(e)
                }, {
                    x: e
                }, function(t) {
                    return {
                        x: function() {
                            return e.toFloat().sinh().mulStrict(t)
                        }
                    }
                })
            }, e.tanh = function(e) {
                assertArgumentsAreTensors({
                    x: e
                }, "tanh");
                return ENV.engine.runKernel(function(t, r) {
                    return r(t.tanh(e))
                }, {
                    x: e
                }, function(e, t) {
                    var r = t[0];
                    return {
                        x: function() {
                            return scalar(1).sub(r.square()).mulStrict(e)
                        }
                    }
                })
            }, e.asinh = function(t) {
                assertArgumentsAreTensors({
                    x: t
                }, "asinh");
                return ENV.engine.runKernel(function(e) {
                    return e.asinh(t)
                }, {
                    x: t
                }, function(r) {
                    return {
                        x: function() {
                            return r.divStrict(e.sqrt(scalar(1).add(t.toFloat().square())))
                        }
                    }
                })
            }, e.acosh = function(t) {
                assertArgumentsAreTensors({
                    x: t
                }, "acosh");
                return ENV.engine.runKernel(function(e) {
                    return e.acosh(t)
                }, {
                    x: t
                }, function(r) {
                    return {
                        x: function() {
                            return r.divStrict(e.sqrt(t.toFloat().square().sub(scalar(1))))
                        }
                    }
                })
            }, e.atanh = function(e) {
                assertArgumentsAreTensors({
                    x: e
                }, "atanh");
                return ENV.engine.runKernel(function(t) {
                    return t.atanh(e)
                }, {
                    x: e
                }, function(t) {
                    return {
                        x: function() {
                            return t.divStrict(scalar(1).sub(e.toFloat().square()))
                        }
                    }
                })
            }, e.erf = function(e) {
                assert("int32" === e.dtype || "float32" === e.dtype, "Input dtype must be `int32` or `float32`."), "int32" === e.dtype && (e = e.toFloat());
                return ENV.engine.runKernel(function(t) {
                    return t.erf(e)
                }, {
                    x: e
                }, function(t) {
                    return {
                        x: function() {
                            return t.mulStrict(scalar(2 / Math.sqrt(Math.PI)).mul(e.square().neg().exp()))
                        }
                    }
                })
            }, e.step = function(e, t) {
                void 0 === t && (t = 0), assertArgumentsAreTensors({
                    x: e
                }, "step");
                return ENV.engine.runKernel(function(r) {
                    return r.step(e, t)
                }, {
                    x: e
                }, function(e) {
                    return {
                        x: function() {
                            return zerosLike(e)
                        }
                    }
                })
            }, __decorate$23([doc(), operation], e, "neg", null), __decorate$23([doc(), operation], e, "ceil", null), __decorate$23([doc(), operation], e, "floor", null), __decorate$23([doc(), operation], e, "sign", null), __decorate$23([doc(), operation], e, "round", null), __decorate$23([doc(), operation], e, "exp", null), __decorate$23([doc(), operation], e, "expm1", null), __decorate$23([doc(), operation], e, "log", null), __decorate$23([doc(), operation], e, "log1p", null), __decorate$23([doc(), operation], e, "sqrt", null), __decorate$23([doc(), operation], e, "rsqrt", null), __decorate$23([doc(), operation], e, "square", null), __decorate$23([doc(), operation], e, "reciprocal", null), __decorate$23([doc(), operation], e, "abs", null), __decorate$23([doc(), operation], e, "clipByValue", null), __decorate$23([doc(), operation], e, "relu", null), __decorate$23([doc(), operation], e, "elu", null), __decorate$23([doc(), operation], e, "selu", null), __decorate$23([doc(), operation], e, "leakyRelu", null), __decorate$23([doc(), operation], e, "prelu", null), __decorate$23([doc(), operation], e, "sigmoid", null), __decorate$23([doc(), operation], e, "logSigmoid", null), __decorate$23([doc(), operation], e, "softplus", null), __decorate$23([doc(), operation], e, "sin", null), __decorate$23([doc(), operation], e, "cos", null), __decorate$23([doc(), operation], e, "tan", null), __decorate$23([doc(), operation], e, "asin", null), __decorate$23([doc(), operation], e, "acos", null), __decorate$23([doc(), operation], e, "atan", null), __decorate$23([doc(), operation], e, "sinh", null), __decorate$23([doc(), operation], e, "cosh", null), __decorate$23([doc(), operation], e, "tanh", null), __decorate$23([doc(), operation], e, "asinh", null), __decorate$23([doc(), operation], e, "acosh", null), __decorate$23([doc(), operation], e, "atanh", null), __decorate$23([doc(), operation], e, "erf", null), __decorate$23([doc(), operation], e, "step", null), e
        }(),
        batchNormalization = BatchNormOps.batchNormalization,
        batchNormalization2d = BatchNormOps.batchNormalization2d,
        batchNormalization3d = BatchNormOps.batchNormalization3d,
        batchNormalization4d = BatchNormOps.batchNormalization4d,
        concat = ConcatOps.concat,
        concat1d = ConcatOps.concat1d,
        concat2d = ConcatOps.concat2d,
        concat3d = ConcatOps.concat3d,
        concat4d = ConcatOps.concat4d,
        conv1d = ConvOps.conv1d,
        conv2d = ConvOps.conv2d,
        conv2dTranspose = ConvOps.conv2dTranspose,
        depthwiseConv2d = ConvOps.depthwiseConv2d,
        separableConv2d = ConvOps.separableConv2d,
        matMul = MatmulOps.matMul,
        matrixTimesVector = MatmulOps.matrixTimesVector,
        outerProduct = MatmulOps.outerProduct,
        vectorTimesMatrix = MatmulOps.vectorTimesMatrix,
        dot = MatmulOps.dot,
        avgPool = PoolOps.avgPool,
        maxPool = PoolOps.maxPool,
        transpose = TransposeOps.transpose,
        reverse = ReverseOps.reverse,
        reverse1d = ReverseOps.reverse1d,
        reverse2d = ReverseOps.reverse2d,
        reverse3d = ReverseOps.reverse3d,
        reverse4d = ReverseOps.reverse4d,
        slice = SliceOps.slice,
        slice1d = SliceOps.slice1d,
        slice2d = SliceOps.slice2d,
        slice3d = SliceOps.slice3d,
        slice4d = SliceOps.slice4d,
        stridedSlice = StridedSliceOps.stridedSlice,
        argMax = ReductionOps.argMax,
        argMin = ReductionOps.argMin,
        logSumExp = ReductionOps.logSumExp,
        max = ReductionOps.max,
        mean = ReductionOps.mean,
        min = ReductionOps.min,
        moments = ReductionOps.moments,
        sum = ReductionOps.sum,
        unsortedSegmentSum = ReductionOps.unsortedSegmentSum,
        equal = CompareOps.equal,
        equalStrict = CompareOps.equalStrict,
        greater = CompareOps.greater,
        greaterStrict = CompareOps.greaterStrict,
        greaterEqual = CompareOps.greaterEqual,
        greaterEqualStrict = CompareOps.greaterEqualStrict,
        less = CompareOps.less,
        lessStrict = CompareOps.lessStrict,
        lessEqual = CompareOps.lessEqual,
        lessEqualStrict = CompareOps.lessEqualStrict,
        notEqual = CompareOps.notEqual,
        notEqualStrict = CompareOps.notEqualStrict,
        logicalNot = LogicalOps.logicalNot,
        logicalAnd = LogicalOps.logicalAnd,
        logicalOr = LogicalOps.logicalOr,
        logicalXor = LogicalOps.logicalXor,
        where = LogicalOps.where,
        abs = UnaryOps.abs,
        acos = UnaryOps.acos,
        acosh = UnaryOps.acosh,
        asin = UnaryOps.asin,
        asinh = UnaryOps.asinh,
        atan = UnaryOps.atan,
        atanh = UnaryOps.atanh,
        ceil = UnaryOps.ceil,
        clipByValue = UnaryOps.clipByValue,
        cos = UnaryOps.cos,
        cosh = UnaryOps.cosh,
        elu = UnaryOps.elu,
        exp = UnaryOps.exp,
        expm1 = UnaryOps.expm1,
        floor = UnaryOps.floor,
        sign = UnaryOps.sign,
        leakyRelu = UnaryOps.leakyRelu,
        log = UnaryOps.log,
        log1p = UnaryOps.log1p,
        logSigmoid = UnaryOps.logSigmoid,
        neg = UnaryOps.neg,
        prelu = UnaryOps.prelu,
        relu = UnaryOps.relu,
        reciprocal = UnaryOps.reciprocal,
        round = UnaryOps.round,
        selu = UnaryOps.selu,
        sigmoid = UnaryOps.sigmoid,
        sin = UnaryOps.sin,
        sinh = UnaryOps.sinh,
        softplus = UnaryOps.softplus,
        sqrt = UnaryOps.sqrt,
        rsqrt = UnaryOps.rsqrt,
        square = UnaryOps.square,
        step = UnaryOps.step,
        tan = UnaryOps.tan,
        tanh$1 = UnaryOps.tanh,
        erf = UnaryOps.erf,
        add = BinaryOps.add,
        addStrict = BinaryOps.addStrict,
        atan2 = BinaryOps.atan2,
        div = BinaryOps.div,
        floorDiv = BinaryOps.floorDiv,
        divStrict = BinaryOps.divStrict,
        maximum = BinaryOps.maximum,
        maximumStrict = BinaryOps.maximumStrict,
        minimum = BinaryOps.minimum,
        minimumStrict = BinaryOps.minimumStrict,
        mod = BinaryOps.mod,
        modStrict = BinaryOps.modStrict,
        mul = BinaryOps.mul,
        mulStrict = BinaryOps.mulStrict,
        pow = BinaryOps.pow,
        powStrict = BinaryOps.powStrict,
        sub = BinaryOps.sub,
        subStrict = BinaryOps.subStrict,
        squaredDifference = BinaryOps.squaredDifference,
        squaredDifferenceStrict = BinaryOps.squaredDifferenceStrict,
        norm = NormOps.norm,
        cast = ArrayOps.cast,
        clone = ArrayOps.clone,
        fromPixels = ArrayOps.fromPixels,
        toPixels = ArrayOps.toPixels,
        ones = ArrayOps.ones,
        onesLike = ArrayOps.onesLike,
        zeros = ArrayOps.zeros,
        zerosLike = ArrayOps.zerosLike,
        eye = ArrayOps.eye,
        rand = ArrayOps.rand,
        randomNormal = ArrayOps.randomNormal,
        truncatedNormal = ArrayOps.truncatedNormal,
        randomUniform = ArrayOps.randomUniform,
        multinomial = ArrayOps.multinomial,
        reshape = ArrayOps.reshape,
        squeeze = ArrayOps.squeeze,
        tile = ArrayOps.tile,
        gather = ArrayOps.gather,
        oneHot = ArrayOps.oneHot,
        linspace = ArrayOps.linspace,
        range = ArrayOps.range,
        buffer = ArrayOps.buffer,
        fill = ArrayOps.fill,
        tensor = ArrayOps.tensor,
        scalar = ArrayOps.scalar,
        tensor1d = ArrayOps.tensor1d,
        tensor2d = ArrayOps.tensor2d,
        tensor3d = ArrayOps.tensor3d,
        tensor4d = ArrayOps.tensor4d,
        tensor5d = ArrayOps.tensor5d,
        print = ArrayOps.print,
        expandDims = ArrayOps.expandDims,
        stack = ArrayOps.stack,
        unstack = ArrayOps.unstack,
        split = ArrayOps.split,
        cumsum = ArrayOps.cumsum,
        pad = ArrayOps.pad,
        pad1d = ArrayOps.pad1d,
        pad2d = ArrayOps.pad2d,
        pad3d = ArrayOps.pad3d,
        pad4d = ArrayOps.pad4d,
        movingAverage = MovingAverageOps.movingAverage,
        basicLSTMCell = LSTMOps.basicLSTMCell,
        multiRNNCell = LSTMOps.multiRNNCell,
        softmax = SoftmaxOps.softmax,
        localResponseNormalization = LRNOps.localResponseNormalization,
        linalg = LinalgOps,
        losses = {
            absoluteDifference: LossOps.absoluteDifference,
            computeWeightedLoss: LossOps.computeWeightedLoss,
            cosineDistance: LossOps.cosineDistance,
            hingeLoss: LossOps.hingeLoss,
            huberLoss: LossOps.huberLoss,
            logLoss: LossOps.logLoss,
            meanSquaredError: LossOps.meanSquaredError,
            softmaxCrossEntropy: SoftmaxOps.softmaxCrossEntropy
        },
        image = {
            resizeBilinear: ImageOps.resizeBilinear,
            resizeNearestNeighbor: ImageOps.resizeNearestNeighbor
        },
        __extends = function() {
            var e = 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])
            };
            return function(t, r) {
                function n() {
                    this.constructor = t
                }
                e(t, r), t.prototype = null === r ? Object.create(r) : (n.prototype = r.prototype, new n)
            }
        }(),
        __decorate$24 = function(e, t, r, n) {
            var a, o = arguments.length,
                i = o < 3 ? t : null === n ? n = Object.getOwnPropertyDescriptor(t, r) : n;
            if ("object" == typeof Reflect && "function" == typeof Reflect.decorate) i = Reflect.decorate(e, t, r, n);
            else
                for (var s = e.length - 1; s >= 0; s--)(a = e[s]) && (i = (o < 3 ? a(i) : o > 3 ? a(t, r, i) : a(t, r)) || i);
            return o > 3 && i && Object.defineProperty(t, r, i), i
        },
        __awaiter$1 = function(e, t, r, n) {
            return new(r || (r = Promise))(function(a, o) {
                function i(e) {
                    try {
                        u(n.next(e))
                    } catch (e) {
                        o(e)
                    }
                }

                function s(e) {
                    try {
                        u(n.throw(e))
                    } catch (e) {
                        o(e)
                    }
                }

                function u(e) {
                    e.done ? a(e.value) : new r(function(t) {
                        t(e.value)
                    }).then(i, s)
                }
                u((n = n.apply(e, t || [])).next())
            })
        },
        __generator$1 = function(e, t) {
            function r(e) {
                return function(t) {
                    return n([e, t])
                }
            }

            function n(r) {
                if (a) throw new TypeError("Generator is already executing.");
                for (; u;) try {
                    if (a = 1, o && (i = o[2 & r[0] ? "return" : r[0] ? "throw" : "next"]) && !(i = i.call(o, r[1])).done) return i;
                    switch (o = 0, i && (r = [0, i.value]), r[0]) {
                        case 0:
                        case 1:
                            i = r;
                            break;
                        case 4:
                            return u.label++, {
                                value: r[1],
                                done: !1
                            };
                        case 5:
                            u.label++, o = r[1], r = [0];
                            continue;
                        case 7:
                            r = u.ops.pop(), u.trys.pop();
                            continue;
                        default:
                            if (i = u.trys, !(i = i.length > 0 && i[i.length - 1]) && (6 === r[0] || 2 === r[0])) {
                                u = 0;
                                continue
                            }
                            if (3 === r[0] && (!i || r[1] > i[0] && r[1] < i[3])) {
                                u.label = r[1];
                                break
                            }
                            if (6 === r[0] && u.label < i[1]) {
                                u.label = i[1], i = r;
                                break
                            }
                            if (i && u.label < i[2]) {
                                u.label = i[2], u.ops.push(r);
                                break
                            }
                            i[2] && u.ops.pop(), u.trys.pop();
                            continue
                    }
                    r = t.call(e, u)
                } catch (e) {
                    r = [6, e], o = 0
                } finally {
                    a = i = 0
                }
                if (5 & r[0]) throw r[1];
                return {
                    value: r[0] ? r[1] : void 0,
                    done: !0
                }
            }
            var a, o, i, s, u = {
                label: 0,
                sent: function() {
                    if (1 & i[0]) throw i[1];
                    return i[1]
                },
                trys: [],
                ops: []
            };
            return s = {
                next: r(0),
                throw: r(1),
                return: r(2)
            }, "function" == typeof Symbol && (s[Symbol.iterator] = function() {
                return this
            }), s
        },
        TensorBuffer = function() {
            function e(e, t, r) {
                if (this.dtype = t, null != r) {
                    var n = r.length,
                        a = sizeFromShape(e);
                    assert(n === a, "Length of values '" + n + "' does not match the size inferred by the shape '" + a + "'")
                }
                this.shape = e.slice(), this.values = r || getTypedArrayFromDType(t, sizeFromShape(e)), this.strides = computeStrides(e), this.size = sizeFromShape(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)
            }, __decorate$24([doc()], e.prototype, "set", null), __decorate$24([doc()], e.prototype, "get", null), __decorate$24([doc()], e.prototype, "toTensor", null), e = __decorate$24([doc()], e)
        }(),
        Tensor = function() {
            function e(e, r, n, a) {
                this.isDisposedInternal = !1, this.size = sizeFromShape(e), null != n && assert(this.size === n.length, "Constructing tensor of shape (" + this.size + ") should match the length of values (" + n.length + ")"), this.shape = e.slice(), this.dtype = r || "float32", this.strides = computeStrides(e), this.dataId = null != a ? a : {}, this.id = t.nextId++, this.rankType = this.rank < 5 ? this.rank.toString() : "higher", ENV.engine.registerTensor(this), null != n && ENV.engine.write(this.dataId, n)
            }
            return t = e, e.make = function(e, r, n) {
                return new t(e, 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(), 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"), 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 buffer(this.shape, this.dtype, this.dataSync())
            }, e.prototype.data = function() {
                return __awaiter$1(this, void 0, void 0, function() {
                    return __generator$1(this, function(e) {
                        return this.throwIfDisposed(), [2, ENV.engine.read(this.dataId)]
                    })
                })
            }, e.prototype.dataSync = function() {
                return this.throwIfDisposed(), ENV.engine.readSync(this.dataId)
            }, e.prototype.dispose = function() {
                this.isDisposed || (ENV.engine.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), print(this, e)
            }, e.prototype.reshape = function(e) {
                return this.throwIfDisposed(), 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), 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), cumsum(this, e, t, r)
            }, e.prototype.squeeze = function(e) {
                return this.throwIfDisposed(), squeeze(this, e)
            }, e.prototype.clone = function() {
                return this.throwIfDisposed(), clone(this)
            }, e.prototype.toString = function(e) {
                return void 0 === e && (e = !1), tensorToString(this, e)
            }, e.prototype.tile = function(e) {
                return this.throwIfDisposed(), tile(this, e)
            }, e.prototype.gather = function(e, t) {
                return void 0 === t && (t = 0), this.throwIfDisposed(), gather(this, e, t)
            }, e.prototype.matMul = function(e, t, r) {
                return void 0 === t && (t = !1), void 0 === r && (r = !1), this.throwIfDisposed(), matMul(this, e, t, r)
            }, e.prototype.dot = function(e) {
                return this.throwIfDisposed(), 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(), norm(this, e, t, r)
            }, e.prototype.slice = function(e, t) {
                return this.throwIfDisposed(), slice(this, e, t)
            }, e.prototype.reverse = function(e) {
                return this.throwIfDisposed(), reverse(this, e)
            }, e.prototype.concat = function(e, t) {
                return void 0 === t && (t = 0), this.throwIfDisposed(), concat([this, e], t)
            }, e.prototype.stack = function(e, t) {
                return void 0 === t && (t = 0), stack([this, e], t)
            }, e.prototype.unstack = function(e, t) {
                return void 0 === t && (t = 0), unstack(this, t)
            }, e.prototype.pad = function(e, t) {
                return void 0 === t && (t = 0), pad(this, e, t)
            }, e.prototype.batchNormalization = function(e, t, r, n, a) {
                return void 0 === r && (r = .001), this.throwIfDisposed(), batchNormalization(this, e, t, r, n, a)
            }, e.prototype.logSumExp = function(e, t) {
                return void 0 === e && (e = null), void 0 === t && (t = !1), this.throwIfDisposed(), logSumExp(this, e, t)
            }, e.prototype.sum = function(e, t) {
                return void 0 === e && (e = null), void 0 === t && (t = !1), this.throwIfDisposed(), sum(this, e, t)
            }, e.prototype.mean = function(e, t) {
                return void 0 === e && (e = null), void 0 === t && (t = !1), this.throwIfDisposed(), mean(this, e, t)
            }, e.prototype.min = function(e, t) {
                return void 0 === e && (e = null), void 0 === t && (t = !1), this.throwIfDisposed(), min(this, e, t)
            }, e.prototype.max = function(e, t) {
                return void 0 === e && (e = null), void 0 === t && (t = !1), this.throwIfDisposed(), max(this, e, t)
            }, e.prototype.argMin = function(e) {
                return void 0 === e && (e = null), this.throwIfDisposed(), argMin(this, e)
            }, e.prototype.argMax = function(e) {
                return void 0 === e && (e = null), this.throwIfDisposed(), argMax(this, e)
            }, e.prototype.cast = function(e) {
                return this.throwIfDisposed(), cast(this, e)
            }, e.prototype.add = function(e) {
                return this.throwIfDisposed(), add(this, e)
            }, e.prototype.addStrict = function(e) {
                return this.throwIfDisposed(), addStrict(this, e)
            }, e.prototype.sub = function(e) {
                return this.throwIfDisposed(), sub(this, e)
            }, e.prototype.subStrict = function(e) {
                return this.throwIfDisposed(), subStrict(this, e)
            }, e.prototype.pow = function(e) {
                return this.throwIfDisposed(), pow(this, e)
            }, e.prototype.powStrict = function(e) {
                return this.throwIfDisposed(), powStrict(this, e)
            }, e.prototype.mul = function(e) {
                return this.throwIfDisposed(), mul(this, e)
            }, e.prototype.mulStrict = function(e) {
                return this.throwIfDisposed(), mulStrict(this, e)
            }, e.prototype.div = function(e) {
                return this.throwIfDisposed(), div(this, e)
            }, e.prototype.floorDiv = function(e) {
                return this.throwIfDisposed(), floorDiv(this, e)
            }, e.prototype.divStrict = function(e) {
                return this.throwIfDisposed(), divStrict(this, e)
            }, e.prototype.minimum = function(e) {
                return this.throwIfDisposed(), minimum(this, e)
            }, e.prototype.minimumStrict = function(e) {
                return this.throwIfDisposed(), minimumStrict(this, e)
            }, e.prototype.maximum = function(e) {
                return this.throwIfDisposed(), maximum(this, e)
            }, e.prototype.maximumStrict = function(e) {
                return this.throwIfDisposed(), maximumStrict(this, e)
            }, e.prototype.mod = function(e) {
                return this.throwIfDisposed(), mod(this, e)
            }, e.prototype.modStrict = function(e) {
                return this.throwIfDisposed(), modStrict(this, e)
            }, e.prototype.squaredDifference = function(e) {
                return this.throwIfDisposed(), squaredDifference(this, e)
            }, e.prototype.squaredDifferenceStrict = function(e) {
                return this.throwIfDisposed(), squaredDifferenceStrict(this, e)
            }, e.prototype.transpose = function(e) {
                return this.throwIfDisposed(), transpose(this, e)
            }, e.prototype.notEqual = function(e) {
                return this.throwIfDisposed(), notEqual(this, e)
            }, e.prototype.notEqualStrict = function(e) {
                return this.throwIfDisposed(), notEqualStrict(this, e)
            }, e.prototype.less = function(e) {
                return this.throwIfDisposed(), less(this, e)
            }, e.prototype.lessStrict = function(e) {
                return this.throwIfDisposed(), lessStrict(this, e)
            }, e.prototype.equal = function(e) {
                return this.throwIfDisposed(), equal(this, e)
            }, e.prototype.equalStrict = function(e) {
                return this.throwIfDisposed(), equalStrict(this, e)
            }, e.prototype.lessEqual = function(e) {
                return this.throwIfDisposed(), lessEqual(this, e)
            }, e.prototype.lessEqualStrict = function(e) {
                return this.throwIfDisposed(), lessEqualStrict(this, e)
            }, e.prototype.greater = function(e) {
                return this.throwIfDisposed(), greater(this, e)
            }, e.prototype.greaterStrict = function(e) {
                return this.throwIfDisposed(), greaterStrict(this, e)
            }, e.prototype.greaterEqual = function(e) {
                return this.throwIfDisposed(), greaterEqual(this, e)
            }, e.prototype.greaterEqualStrict = function(e) {
                return this.throwIfDisposed(), greaterEqualStrict(this, e)
            }, e.prototype.logicalAnd = function(e) {
                return this.throwIfDisposed(), logicalAnd(this, e)
            }, e.prototype.logicalOr = function(e) {
                return this.throwIfDisposed(), logicalOr(this, e)
            }, e.prototype.logicalNot = function() {
                return this.throwIfDisposed(), logicalNot(this)
            }, e.prototype.logicalXor = function(e) {
                return this.throwIfDisposed(), logicalXor(this, e)
            }, e.prototype.where = function(e, t) {
                return this.throwIfDisposed(), where(e, this, t)
            }, e.prototype.neg = function() {
                return this.throwIfDisposed(), neg(this)
            }, e.prototype.ceil = function() {
                return this.throwIfDisposed(), ceil(this)
            }, e.prototype.floor = function() {
                return this.throwIfDisposed(), floor(this)
            }, e.prototype.sign = function() {
                return this.throwIfDisposed(), sign(this)
            }, e.prototype.exp = function() {
                return this.throwIfDisposed(), exp(this)
            }, e.prototype.expm1 = function() {
                return this.throwIfDisposed(), expm1(this)
            }, e.prototype.log = function() {
                return this.throwIfDisposed(), log(this)
            }, e.prototype.log1p = function() {
                return this.throwIfDisposed(), log1p(this)
            }, e.prototype.sqrt = function() {
                return this.throwIfDisposed(), sqrt(this)
            }, e.prototype.rsqrt = function() {
                return this.throwIfDisposed(), rsqrt(this)
            }, e.prototype.square = function() {
                return this.throwIfDisposed(), square(this)
            }, e.prototype.reciprocal = function() {
                return this.throwIfDisposed(), reciprocal(this)
            }, e.prototype.abs = function() {
                return this.throwIfDisposed(), abs(this)
            }, e.prototype.clipByValue = function(e, t) {
                return this.throwIfDisposed(), clipByValue(this, e, t)
            }, e.prototype.relu = function() {
                return this.throwIfDisposed(), relu(this)
            }, e.prototype.elu = function() {
                return this.throwIfDisposed(), elu(this)
            }, e.prototype.selu = function() {
                return this.throwIfDisposed(), selu(this)
            }, e.prototype.leakyRelu = function(e) {
                return void 0 === e && (e = .2), this.throwIfDisposed(), leakyRelu(this, e)
            }, e.prototype.prelu = function(e) {
                return this.throwIfDisposed(), prelu(this, e)
            }, e.prototype.sigmoid = function() {
                return this.throwIfDisposed(), sigmoid(this)
            }, e.prototype.logSigmoid = function() {
                return this.throwIfDisposed(), logSigmoid(this)
            }, e.prototype.softplus = function() {
                return this.throwIfDisposed(), softplus(this)
            }, e.prototype.sin = function() {
                return this.throwIfDisposed(), sin(this)
            }, e.prototype.cos = function() {
                return this.throwIfDisposed(), cos(this)
            }, e.prototype.tan = function() {
                return this.throwIfDisposed(), tan(this)
            }, e.prototype.asin = function() {
                return this.throwIfDisposed(), asin(this)
            }, e.prototype.acos = function() {
                return this.throwIfDisposed(), acos(this)
            }, e.prototype.atan = function() {
                return this.throwIfDisposed(), atan(this)
            }, e.prototype.sinh = function() {
                return this.throwIfDisposed(), sinh(this)
            }, e.prototype.cosh = function() {
                return this.throwIfDisposed(), cosh(this)
            }, e.prototype.tanh = function() {
                return this.throwIfDisposed(), tanh$1(this)
            }, e.prototype.asinh = function() {
                return this.throwIfDisposed(), asinh(this)
            }, e.prototype.acosh = function() {
                return this.throwIfDisposed(), acosh(this)
            }, e.prototype.atanh = function() {
                return this.throwIfDisposed(), atanh(this)
            }, e.prototype.erf = function() {
                return this.throwIfDisposed(), erf(this)
            }, e.prototype.round = function() {
                return this.throwIfDisposed(), round(this)
            }, e.prototype.step = function(e) {
                return void 0 === e && (e = 0), this.throwIfDisposed(), step(this, e)
            }, e.prototype.softmax = function(e) {
                return void 0 === e && (e = -1), this.throwIfDisposed(), softmax(this, e)
            }, e.prototype.resizeBilinear = function(e, t) {
                return void 0 === t && (t = !1), this.throwIfDisposed(), image.resizeBilinear(this, e, t)
            }, e.prototype.resizeNearestNeighbor = function(e, t) {
                return void 0 === t && (t = !1), this.throwIfDisposed(), image.resizeNearestNeighbor(this, e, t)
            }, e.prototype.conv1d = function(e, t, r, n, a, o) {
                return void 0 === n && (n = "NWC"), void 0 === a && (a = 1), this.throwIfDisposed(), conv1d(this, e, t, r, n, a, o)
            }, e.prototype.conv2d = function(e, t, r, n, a, o) {
                return void 0 === n && (n = "NHWC"), void 0 === a && (a = [1, 1]), this.throwIfDisposed(), conv2d(this, e, t, r, n, a, o)
            }, e.prototype.conv2dTranspose = function(e, t, r, n, a) {
                return this.throwIfDisposed(), conv2dTranspose(this, e, t, r, n, a)
            }, e.prototype.depthwiseConv2D = function(e, t, r, n, a, o) {
                return void 0 === n && (n = "NHWC"), void 0 === a && (a = [1, 1]), this.throwIfDisposed(), depthwiseConv2d(this, e, t, r, n, a, o)
            }, e.prototype.avgPool = function(e, t, r, n) {
                return this.throwIfDisposed(), avgPool(this, e, t, r, n)
            }, e.prototype.maxPool = function(e, t, r, n) {
                return this.throwIfDisposed(), 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), localResponseNormalization(this, e, t, r, n)
            }, 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, r) {
                return void 0 === r && (r = 0), this.throwIfDisposed(), unsortedSegmentSum(this, e, t, r)
            }, e.nextId = 0, __decorate$24([doc()], e.prototype, "flatten", null), __decorate$24([doc()], e.prototype, "asScalar", null), __decorate$24([doc()], e.prototype, "as1D", null), __decorate$24([doc()], e.prototype, "as2D", null), __decorate$24([doc()], e.prototype, "as3D", null), __decorate$24([doc()], e.prototype, "as4D", null), __decorate$24([doc()], e.prototype, "asType", null), __decorate$24([doc()], e.prototype, "buffer", null), __decorate$24([doc()], e.prototype, "data", null), __decorate$24([doc()], e.prototype, "dataSync", null), __decorate$24([doc()], e.prototype, "dispose", null), __decorate$24([doc()], e.prototype, "toFloat", null), __decorate$24([doc()], e.prototype, "toInt", null), __decorate$24([doc()], e.prototype, "toBool", null), __decorate$24([doc()], e.prototype, "print", null), __decorate$24([doc()], e.prototype, "reshape", null), __decorate$24([doc()], e.prototype, "reshapeAs", null), __decorate$24([doc()], e.prototype, "expandDims", null), __decorate$24([doc()], e.prototype, "cumsum", null), __decorate$24([doc()], e.prototype, "squeeze", null), __decorate$24([doc()], e.prototype, "clone", null), __decorate$24([doc()], e.prototype, "toString", null), e = t = __decorate$24([doc()], e);
            var t
        }(),
        Variable = function(e) {
            function t(t, n, a) {
                void 0 === n && (n = !0);
                var o = e.call(this, t.shape, t.dtype, null, t.dataId) || this;
                return o.trainable = n, o.name = a, null == o.name && (o.name = r.nextVarId.toString(), r.nextVarId++), ENV.engine.registerVariable(o), o
            }
            return __extends(t, e), r = t, t.variable = function(e, t, n, a) {
                return void 0 === t && (t = !0), null != a && a !== e.dtype && (e = e.asType(a)), new r(e, t, 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");
                ENV.engine.disposeTensor(this), this.dataId = e.dataId, ENV.engine.registerTensor(this)
            }, t.nextVarId = 0, __decorate$24([doc()], t.prototype, "assign", null), __decorate$24([doc()], t, "variable", null), t = r = __decorate$24([doc()], t);
            var r
        }(Tensor),
        variable = Variable.variable,
        __decorate$25 = function(e, t, r, n) {
            var a, o = arguments.length,
                i = o < 3 ? t : null === n ? n = Object.getOwnPropertyDescriptor(t, r) : n;
            if ("object" == typeof Reflect && "function" == typeof Reflect.decorate) i = Reflect.decorate(e, t, r, n);
            else
                for (var s = e.length - 1; s >= 0; s--)(a = e[s]) && (i = (o < 3 ? a(i) : o > 3 ? a(t, r, i) : a(t, r)) || i);
            return o > 3 && i && Object.defineProperty(t, r, i), i
        },
        Gradients = function() {
            function e() {}
            return e.gradScope = function(e, t) {
                return tidy(e, t, !0)
            }, e.grad = function(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"), tidy(function() {
                            var n = ENV.engine.gradients(function() {
                                    return e(t)
                                }, [t], r),
                                a = n.value,
                                o = n.grads;
                            return null != r && assertShapesMatch(a.shape, r.shape, "The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"), checkGrads(o), o[0]
                        })
                    }
            }, e.grads = function(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"), tidy(function() {
                            var n = ENV.engine.gradients(function() {
                                    return e.apply(void 0, t)
                                }, t, r),
                                a = n.value,
                                o = n.grads;
                            return null != r && assertShapesMatch(a.shape, r.shape, "The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"), checkGrads(o), o
                        })
                    }
            }, e.valueAndGrad = function(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),
                            a = n.grads,
                            o = n.value;
                        return checkGrads(a), {
                            grad: a[0],
                            value: o
                        }
                    }
            }, e.valueAndGrads = function(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
                    }
            }, e.variableGrads = function(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) {
                    t = [];
                    for (var r in 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 a = ENV.engine.gradients(e, t, null, !0),
                    o = a.value,
                    i = a.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 === o.rank, "The f passed in variableGrads(f) must return a scalar, but it returned a rank-" + o.rank + " tensor");
                var s = {};
                return t.forEach(function(e, t) {
                    null != i[t] && (s[e.name] = i[t])
                }), {
                    value: o,
                    grads: s
                }
            }, e.customGrad = function(e) {
                return ENV.engine.customGrad(e)
            }, __decorate$25([doc()], e, "grad", null), __decorate$25([doc()], e, "grads", null), __decorate$25([doc()], e, "valueAndGrad", null), __decorate$25([doc()], e, "valueAndGrads", null), __decorate$25([doc()], e, "variableGrads", null), __decorate$25([doc()], e, "customGrad", null), e
        }(),
        tidy = Tracking.tidy,
        keep = Tracking.keep,
        dispose = Tracking.dispose,
        time = Tracking.time,
        grad = Gradients.grad,
        valueAndGrad = Gradients.valueAndGrad,
        grads = Gradients.grads,
        valueAndGrads = Gradients.valueAndGrads,
        variableGrads = Gradients.variableGrads,
        customGrad = Gradients.customGrad,
        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,
                    a = this.backendTimer.time(function() {
                        r = t()
                    }),
                    o = r.dataSync();
                return checkForNaN(o, r.dtype, e), a.then(function(t) {
                    n.logger.logKernelProfile(e, r, o, t.kernelMs)
                }), r
            }, e
        }(),
        Logger = function() {
            function e() {}
            return e.prototype.logKernelProfile = function(e, t, r, n) {
                var a = rightPad(n + "ms", 9),
                    o = rightPad(e, 25),
                    i = t.rank,
                    s = t.size,
                    u = rightPad(t.shape.toString(), 14);
                console.log("%c" + o + "\t%c" + a + "\t%c" + i + "D " + u + "\t%c" + s, "font-weight:bold", "color:red", "color:blue", "color: orange")
            }, e
        }(),
        __awaiter$2 = function(e, t, r, n) {
            return new(r || (r = Promise))(function(a, o) {
                function i(e) {
                    try {
                        u(n.next(e))
                    } catch (e) {
                        o(e)
                    }
                }

                function s(e) {
                    try {
                        u(n.throw(e))
                    } catch (e) {
                        o(e)
                    }
                }

                function u(e) {
                    e.done ? a(e.value) : new r(function(t) {
                        t(e.value)
                    }).then(i, s)
                }
                u((n = n.apply(e, t || [])).next())
            })
        },
        __generator$2 = function(e, t) {
            function r(e) {
                return function(t) {
                    return n([e, t])
                }
            }

            function n(r) {
                if (a) throw new TypeError("Generator is already executing.");
                for (; u;) try {
                    if (a = 1, o && (i = o[2 & r[0] ? "return" : r[0] ? "throw" : "next"]) && !(i = i.call(o, r[1])).done) return i;
                    switch (o = 0, i && (r = [0, i.value]), r[0]) {
                        case 0:
                        case 1:
                            i = r;
                            break;
                        case 4:
                            return u.label++, {
                                value: r[1],
                                done: !1
                            };
                        case 5:
                            u.label++, o = r[1], r = [0];
                            continue;
                        case 7:
                            r = u.ops.pop(), u.trys.pop();
                            continue;
                        default:
                            if (i = u.trys, !(i = i.length > 0 && i[i.length - 1]) && (6 === r[0] || 2 === r[0])) {
                                u = 0;
                                continue
                            }
                            if (3 === r[0] && (!i || r[1] > i[0] && r[1] < i[3])) {
                                u.label = r[1];
                                break
                            }
                            if (6 === r[0] && u.label < i[1]) {
                                u.label = i[1], i = r;
                                break
                            }
                            if (i && u.label < i[2]) {
                                u.label = i[2], u.ops.push(r);
                                break
                            }
                            i[2] && u.ops.pop(), u.trys.pop();
                            continue
                    }
                    r = t.call(e, u)
                } catch (e) {
                    r = [6, e], o = 0
                } finally {
                    a = i = 0
                }
                if (5 & r[0]) throw r[1];
                return {
                    value: r[0] ? r[1] : void 0,
                    done: !0
                }
            }
            var a, o, i, s, u = {
                label: 0,
                sent: function() {
                    if (1 & i[0]) throw i[1];
                    return i[1]
                },
                trys: [],
                ops: []
            };
            return s = {
                next: r(0),
                throw: r(1),
                return: r(2)
            }, "function" == typeof Symbol && (s[Symbol.iterator] = function() {
                return this
            }), s
        },
        Engine = function() {
            function e(e, t) {
                this.backend = e, this.safeMode = t, this.registeredVariables = {}, this.refCounter = new WeakMap, this.nextTapeNodeId = 0, this.numBytes = 0, this.numTensors = 0, this.numDataBuffers = 0, this.gradientScopeCount = 0, this.customGradientDepth = 0, this.keepTensors = new Set, this.activeScope = {
                    track: []
                }, this.scopeStack = [this.activeScope], this.profiler = new Profiler(e)
            }
            return e.prototype.runKernel = function(e, t, r) {
                var n, a = this,
                    o = [],
                    i = function(e) {
                        return o.push(e), e
                    },
                    s = this.activeScope.name;
                if (this.customGradientDepth++, n = ENV.get("DEBUG") ? this.profiler.profileKernel(s, function() {
                        return e(a.backend, i)
                    }) : e(this.backend, i), this.customGradientDepth--, this.shouldRecord()) {
                    var u = {
                        id: this.nextTapeNodeId++,
                        name: s,
                        inputs: t,
                        output: n
                    };
                    null != r && (u.gradient = function(e) {
                        return r(e, o)
                    }), this.activeTape.push(u)
                }
                return n
            }, e.prototype.registerTensor = function(e) {
                var t = this.refCounter.has(e.dataId) ? this.refCounter.get(e.dataId) : 0;
                this.numTensors++, 0 === t && (this.numDataBuffers++, this.numBytes += sizeFromShape(e.shape) * bytesPerElement(e.dtype), this.backend.register(e.dataId, e.shape, e.dtype)), this.refCounter.set(e.dataId, t + 1), 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) {
                if (this.refCounter.has(e.dataId)) {
                    this.numTensors--;
                    var t = this.refCounter.get(e.dataId);
                    t <= 1 ? (this.refCounter.delete(e.dataId), this.backend.disposeData(e.dataId), this.numDataBuffers--, this.numBytes -= sizeFromShape(e.shape) * bytesPerElement(e.dtype)) : this.refCounter.set(e.dataId, t - 1)
                }
            }, 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.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 a = {
                    id: this.nextTapeNodeId++,
                    name: this.activeScope.name,
                    inputs: n,
                    output: t,
                    gradient: function(e) {
                        var t = {};
                        return r(e).forEach(function(e, r) {
                            t[r] = function() {
                                return e
                            }
                        }), t
                    }
                };
                this.activeTape.push(a)
            }, e.prototype.keep = function(e) {
                if (1 === this.scopeStack.length && ENV.engine.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: []
                };
                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 && 0 === --this.gradientScopeCount && (this.activeTape = null);
                var n = new Set(this.keepTensors),
                    a = getTensorsInContainer(e);
                a.forEach(function(e) {
                    return n.add(e.id)
                });
                for (var o = 0; o < this.activeScope.track.length; o++) {
                    var i = this.activeScope.track[o];
                    n.has(i.id) || (null != this.activeTape ? a.push(i) : i.dispose())
                }
                var s = this.scopeStack.pop();
                this.activeScope = 0 === this.scopeStack.length ? {
                    track: []
                } : this.scopeStack[this.scopeStack.length - 1], a.forEach(function(e) {
                    !r.keepTensors.has(e.id) && isTensorInList(e, s.track) && r.track(e)
                })
            }, e.prototype.gradients = function(e, t, r, n) {
                var a = this;
                return void 0 === n && (n = !1), assert(t.length > 0, "gradients() received an empty list of xs."), tidy("gradients", function() {
                    var o = e();
                    assert(o instanceof Tensor, "The result y returned by f() must be a tensor.");
                    var i = getFilteredNodesXToY(a.activeTape, t, o);
                    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[o.id] = null == r ? ones(o.shape) : r, backpropagateGradients(s, i), {
                        value: o,
                        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 = 0; n < arguments.length; n++) r[n] = arguments[n];
                        assert(r.every(function(e) {
                            return e instanceof Tensor
                        }), "The args passed in customGrad(f)(x1, x2,...) must all be tensors"), t.customGradientDepth++;
                        var a, o = tidy(e.name, function() {
                            var t = e.apply(void 0, r),
                                n = t.value,
                                o = 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(o), "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."), a = o, n
                        }, !0);
                        if (t.customGradientDepth--, t.shouldRecord()) {
                            t.addTapeNode(r, o, function(e) {
                                var t = a(e),
                                    n = Array.isArray(t) ? t : [t];
                                return assert(n.length === r.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 o
                    }
            }, e.prototype.write = function(e, t) {
                this.backend.write(e, t)
            }, e.prototype.readSync = function(e) {
                return this.backend.readSync(e)
            }, e.prototype.read = function(e) {
                return this.backend.read(e)
            }, e.prototype.fromPixels = function(e, t) {
                return this.backend.fromPixels(e, t)
            }, e.prototype.time = function(e) {
                return __awaiter$2(this, void 0, void 0, function() {
                    var t, r;
                    return __generator$2(this, function(n) {
                        switch (n.label) {
                            case 0:
                                return t = performance.now(), [4, this.backend.time(e)];
                            case 1:
                                return r = n.sent(), r.wallMs = performance.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
        }(),
        __decorate$26 = function(e, t, r, n) {
            var a, o = arguments.length,
                i = o < 3 ? t : null === n ? n = Object.getOwnPropertyDescriptor(t, r) : n;
            if ("object" == typeof Reflect && "function" == typeof Reflect.decorate) i = Reflect.decorate(e, t, r, n);
            else
                for (var s = e.length - 1; s >= 0; s--)(a = e[s]) && (i = (o < 3 ? a(i) : o > 3 ? a(t, r, i) : a(t, r)) || i);
            return o > 3 && i && Object.defineProperty(t, r, i), i
        },
        Type;
    ! function(e) {
        e[e.NUMBER = 0] = "NUMBER", e[e.BOOLEAN = 1] = "BOOLEAN", e[e.STRING = 2] = "STRING"
    }(Type || (Type = {}));
    var URL_PROPERTIES = [{
            name: "DEBUG",
            type: Type.BOOLEAN
        }, {
            name: "IS_BROWSER",
            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_FLOAT_TEXTURE_ENABLED",
            type: Type.BOOLEAN
        }, {
            name: "WEBGL_GET_BUFFER_SUB_DATA_ASYNC_EXTENSION_ENABLED",
            type: Type.BOOLEAN
        }, {
            name: "BACKEND",
            type: Type.STRING
        }],
        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 type '" + e + "' not found in registry");
                ENV.initBackend(e, t)
            }, e.getBackend = function() {
                return ENV.initDefaultBackend(), ENV.currentBackend
            }, e.disposeVariables = function() {
                ENV.engine.disposeVariables()
            }, e.memory = function() {
                return ENV.engine.memory()
            }, e.prototype.get = function(e) {
                return e in this.features ? this.features[e] : (this.features[e] = this.evaluateFeature(e), this.features[e])
            }, e.prototype.set = function(e, t) {
                this.features[e] = t
            }, e.prototype.getBestBackendType = 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 ("BACKEND" === e) return this.getBestBackendType();
                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 ("WEBGL_VERSION" === e) return isWebGLVersionEnabled(2) ? 2 : isWebGLVersionEnabled(1) ? 1 : 0;
                if ("WEBGL_FLOAT_TEXTURE_ENABLED" === e) return isFloatTextureReadPixelsEnabled(this.get("WEBGL_VERSION"));
                if ("WEBGL_GET_BUFFER_SUB_DATA_ASYNC_EXTENSION_ENABLED" === e) return isWebGLGetBufferSubDataAsyncExtensionEnabled(this.get("WEBGL_VERSION"));
                throw new Error("Unknown feature " + e + ".")
            }, e.prototype.setFeatures = function(e) {
                this.features = e
            }, e.prototype.reset = function() {
                this.features = getFeaturesFromURL(), null != this.globalEngine && (this.globalEngine = null)
            }, e.prototype.initBackend = function(e, t) {
                void 0 === t && (t = !1), this.currentBackend = e;
                var r = ENV.findBackend(e);
                this.globalEngine = new Engine(r, t)
            }, e.prototype.findBackend = function(e) {
                return e in this.registry ? this.registry[e].backend : null
            }, e.prototype.registerBackend = function(e, t, r) {
                void 0 === r && (r = 1), e in this.registry && console.warn(e + " backend was already registered");
                try {
                    var n = t();
                    return this.registry[e] = {
                        backend: n,
                        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.initDefaultBackend(), this.globalEngine
                },
                enumerable: !0,
                configurable: !0
            }), e.prototype.initDefaultBackend = function() {
                null == this.globalEngine && this.initBackend(ENV.get("BACKEND"), !1)
            }, __decorate$26([doc()], e, "setBackend", null), __decorate$26([doc()], e, "getBackend", null), __decorate$26([doc()], e, "disposeVariables", null), __decorate$26([doc()], e, "memory", null), e
        }(),
        TENSORFLOWJS_FLAGS_PREFIX = "tfjsflags",
        ENV = getOrMakeEnvironment(),
        environment = Object.freeze({get Type() {
                return Type
            },
            URL_PROPERTIES: URL_PROPERTIES,
            Environment: Environment,
            ENV: ENV
        }),
        PARALLELIZE_THRESHOLD = 30,
        ArgMinMaxProgram = function() {
            return function(e, t, r) {
                this.variableNames = ["A"];
                var n = e.windowSize,
                    a = e.batchSize,
                    o = e.inSize,
                    i = Math.ceil(o / n);
                r || this.variableNames.push("bestIndicesA"), this.outputShape = [a, 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 = 0;\n        float bestValue = getA(batch, inOffset);\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,
                    a = e.strideWidth,
                    o = t - 1 - e.padInfo.top,
                    i = r - 1 - e.padInfo.left,
                    s = 1 / (t * r);
                this.userCode = "\n      const ivec2 pads = ivec2(" + o + ", " + i + ");\n      const float avgMultiplier = float(" + 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 < " + 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          for (int wC = 0; wC < " + r + "; wC++) {\n            float dyC = float(dyCCorner + wC) / " + a + ".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    "
            }
        }(),
        BatchNormProgram = function() {
            return function(e, t, r, n, a, o) {
                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 != a && (assertAndGetBroadcastShape(e, a), 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(" + o + "));\n        setOutput((x - mean) * inv + offset);\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 = "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 = "\n  return (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 = "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() {
            return function(e, t, r) {
                this.variableNames = ["A", "B"], this.supportsBroadcasting = !0, this.outputShape = assertAndGetBroadcastShape(t, r), this.userCode = "\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    "
            }
        }(),
        ClipProgram = function() {
            return function(e, t, r) {
                this.variableNames = ["A"], this.outputShape = e;
                var n = t.toFixed(20),
                    a = r.toFixed(20);
                this.userCode = "\n      void main() {\n        float value = getAAtOutCoords();\n        if (isNaN(value)) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, " + n + ", " + a + "));\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,
                    a = 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 + " - " + a + ";\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,
                    a = e.strideWidth,
                    o = t - 1 - e.padInfo.top,
                    i = r - 1 - e.padInfo.left;
                this.userCode = "\n      const ivec2 pads = ivec2(" + o + ", " + 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) / " + a + ".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,
                    a = e.padInfo.left,
                    o = 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 * " + o + " + 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 + " - " + a + ";\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,
                    a = e.strideWidth,
                    o = t - 1 - e.padInfo.top,
                    i = r - 1 - e.padInfo.left,
                    s = e.outChannels / e.inChannels;
                this.userCode = "\n      const ivec2 pads = ivec2(" + o + ", " + 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) / " + a + ".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,
                    a = e.strideWidth,
                    o = 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 + ", " + a + ");\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 * " + o + ";\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,
                    a = e.padInfo.left,
                    o = 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(" + o + ", " + i + ");\n      const ivec2 pads = ivec2(" + n + ", " + a + ");\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    "
            }
        }(),
        TextureType;
    ! function(e) {
        e[e.FLOAT = 0] = "FLOAT", e[e.UNSIGNED_BYTE = 1] = "UNSIGNED_BYTE"
    }(TextureType || (TextureType = {}));
    var FLOAT_MAX = 2e4,
        FLOAT_MIN = -FLOAT_MAX,
        FLOAT_RANGE = (FLOAT_MAX - FLOAT_MIN) / 255,
        FLOAT_DELTAS = [1, 1 / 255, 1 / 65025, 1 / 16581375],
        FLOAT_POWERS = [1, 255, 65025],
        BYTE_NAN_VALUE = 0,
        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}\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}\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}\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",
        UNSIGNED_BYTE_TEXTURE_SAMPLE_SNIPPET = "\n  uniform float NaN;\n\n  const vec4 floatDeltas = vec4(\n      1.0,\n      1.0 / 255.0,\n      1.0 / (255.0 * 255.0),\n      1.0 / (255.0 * 255.0 * 255.0)\n  );\n  const float minValue = " + FLOAT_MIN + ".0;\n  const float maxValue = " + FLOAT_MAX + ".0;\n  const float range = (maxValue - minValue) / 255.0;\n  const vec2 dotRange = vec2(1.0, range);\n\n  float sampleTexture(sampler2D textureSampler, vec2 uv) {\n    vec4 sampleValue = texture2D(textureSampler, uv);\n    if (all(equal(sampleValue, vec4(" + BYTE_NAN_VALUE + ")))) {\n      return NaN;\n    }\n\n    vec4 encValue = floor(sampleValue * 255.0 + 0.5);\n    float decodedValue = dot(encValue, floatDeltas);\n    return dot(vec2(minValue, decodedValue), dotRange);\n  }\n",
        UNSIGNED_BYTE_TEXTURE_SETOUTPUT_SNIPPET = "\n  const vec4 floatPowers = vec4(\n    1.0,\n    255.0,\n    255.0 * 255.0,\n    255.0 * 255.0 * 255.0\n  );\n  const vec2 recipRange = vec2(1.0/range);\n  const vec2 recipRange255 = vec2(1.0/(maxValue - minValue));\n\n  void setOutput(float decodedValue) {\n    if (isNaN(decodedValue)) {\n      gl_FragColor = vec4(" + BYTE_NAN_VALUE + ");\n      return;\n    }\n\n    float a = dot(vec2(decodedValue, -minValue), recipRange);\n    float b = fract(a) * 255.0;\n    float c = fract(b) * 255.0;\n    float d = fract(c) * 255.0;\n    gl_FragColor = floor(vec4(a, b, c, d)) / 255.0;\n\n    // TODO(dsmilkov): Version above gets better accuracy but probably slower\n    // than the version below. Benchmark to determine if the accuracy is worth\n    // the cost.\n\n    // float normValue = dot(vec2(decodedValue, -minValue), recipRange255);\n    // vec4 f = normValue * floatPowers;\n    // gl_FragColor = floor(fract(f) * 255.0) / 255.0;\n  }\n",
        FLOAT_TEXTURE_SAMPLE_SNIPPET = "\n  float sampleTexture(sampler2D textureSampler, vec2 uv) {\n    return texture2D(textureSampler, uv).r;\n  }\n",
        FLOAT_TEXTURE_SETOUTPUT_SNIPPET = "\n  void setOutput(float val) {\n    gl_FragColor = vec4(val, 0, 0, 0);\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  bool isNaN(float val) {\n    float v1 = val * val;\n    float v2 = val * val;\n    return v1 == v2 ? 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",
        CumSumProgram = function() {
            return function(e, t, r) {
                this.variableNames = ["x"], this.outputShape = e;
                var n = e.length,
                    a = e[e.length - 1],
                    o = r ? "<" : ">";
                this.userCode = "\n      int getIndex(int i) {\n        " + (r ? "return " + a + " -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 = " + a + " - 1; i >= 0; i -= 1) {\n          int idx = getIndex(i);\n          if (idx " + o + " 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    "
            }
        }(),
        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 a = getCoordsDataType(this.rank),
                    o = getSourceCoords(e, r);
                this.userCode = "\n      void main() {\n        " + a + " resRC = getOutputCoords();\n        setOutput(getA(" + o + "));\n      }\n    "
            }
        }(),
        MAX_TEXTURE_SIZE = null,
        webGLDebugErrorCheckingEnabled = !1,
        lineNumberRegex = /ERROR: [0-9]+:([0-9]+):/g,
        webgl_util = Object.freeze({
            createWebGLRenderingContext: createWebGLRenderingContext,
            createWebGLRenderingContextFromCanvas: createWebGLRenderingContextFromCanvas,
            callAndCheck: callAndCheck,
            enableDebugWebGLErrorChecking: enableDebugWebGLErrorChecking,
            checkWebGLError: checkWebGLError,
            getWebGLErrorMessage: getWebGLErrorMessage,
            getExtensionOrThrow: getExtensionOrThrow,
            createVertexShader: createVertexShader,
            createFragmentShader: createFragmentShader,
            createProgram: createProgram,
            linkProgram: linkProgram,
            validateProgram: validateProgram,
            createStaticVertexBuffer: createStaticVertexBuffer,
            createStaticIndexBuffer: createStaticIndexBuffer,
            queryMaxTextureSize: queryMaxTextureSize,
            getChannelsPerTexture: getChannelsPerTexture,
            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
        }),
        __awaiter$3 = function(e, t, r, n) {
            return new(r || (r = Promise))(function(a, o) {
                function i(e) {
                    try {
                        u(n.next(e))
                    } catch (e) {
                        o(e)
                    }
                }

                function s(e) {
                    try {
                        u(n.throw(e))
                    } catch (e) {
                        o(e)
                    }
                }

                function u(e) {
                    e.done ? a(e.value) : new r(function(t) {
                        t(e.value)
                    }).then(i, s)
                }
                u((n = n.apply(e, t || [])).next())
            })
        },
        __generator$3 = function(e, t) {
            function r(e) {
                return function(t) {
                    return n([e, t])
                }
            }

            function n(r) {
                if (a) throw new TypeError("Generator is already executing.");
                for (; u;) try {
                    if (a = 1, o && (i = o[2 & r[0] ? "return" : r[0] ? "throw" : "next"]) && !(i = i.call(o, r[1])).done) return i;
                    switch (o = 0, i && (r = [0, i.value]), r[0]) {
                        case 0:
                        case 1:
                            i = r;
                            break;
                        case 4:
                            return u.label++, {
                                value: r[1],
                                done: !1
                            };
                        case 5:
                            u.label++, o = r[1], r = [0];
                            continue;
                        case 7:
                            r = u.ops.pop(), u.trys.pop();
                            continue;
                        default:
                            if (i = u.trys, !(i = i.length > 0 && i[i.length - 1]) && (6 === r[0] || 2 === r[0])) {
                                u = 0;
                                continue
                            }
                            if (3 === r[0] && (!i || r[1] > i[0] && r[1] < i[3])) {
                                u.label = r[1];
                                break
                            }
                            if (6 === r[0] && u.label < i[1]) {
                                u.label = i[1], i = r;
                                break
                            }
                            if (i && u.label < i[2]) {
                                u.label = i[2], u.ops.push(r);
                                break
                            }
                            i[2] && u.ops.pop(), u.trys.pop();
                            continue
                    }
                    r = t.call(e, u)
                } catch (e) {
                    r = [6, e], o = 0
                } finally {
                    a = i = 0
                }
                if (5 & r[0]) throw r[1];
                return {
                    value: r[0] ? r[1] : void 0,
                    done: !0
                }
            }
            var a, o, i, s, u = {
                label: 0,
                sent: function() {
                    if (1 & i[0]) throw i[1];
                    return i[1]
                },
                trys: [],
                ops: []
            };
            return s = {
                next: r(0),
                throw: r(1),
                return: r(2)
            }, "function" == typeof Symbol && (s[Symbol.iterator] = function() {
                return this
            }), s
        },
        floatDownloadBuffer = null,
        byteDownloadBuffer = null,
        gpgpu_util = Object.freeze({
            getWebGLContextAttributes: getWebGLContextAttributes,
            createWebGLContext: createWebGLContext,
            createVertexShader: createVertexShader$1,
            createVertexBuffer: createVertexBuffer,
            createIndexBuffer: createIndexBuffer,
            createMatrixTexture: createMatrixTexture,
            createColorMatrixTexture: createColorMatrixTexture,
            createPackedMatrixTexture: createPackedMatrixTexture,
            bindVertexProgramAttributeStreams: bindVertexProgramAttributeStreams,
            uploadPixelDataToTexture: uploadPixelDataToTexture,
            uploadMatrixToTexture: uploadMatrixToTexture,
            uploadMatrixToPackedTexture: uploadMatrixToPackedTexture,
            downloadMatrixFromOutputTextureAsync: downloadMatrixFromOutputTextureAsync,
            downloadMatrixFromOutputTexture: downloadMatrixFromOutputTexture,
            downloadMatrixFromRGBAColorTexture: downloadMatrixFromRGBAColorTexture,
            downloadMatrixFromPackedOutputTexture: downloadMatrixFromPackedOutputTexture
        }),
        __awaiter$4 = function(e, t, r, n) {
            return new(r || (r = Promise))(function(a, o) {
                function i(e) {
                    try {
                        u(n.next(e))
                    } catch (e) {
                        o(e)
                    }
                }

                function s(e) {
                    try {
                        u(n.throw(e))
                    } catch (e) {
                        o(e)
                    }
                }

                function u(e) {
                    e.done ? a(e.value) : new r(function(t) {
                        t(e.value)
                    }).then(i, s)
                }
                u((n = n.apply(e, t || [])).next())
            })
        },
        __generator$4 = function(e, t) {
            function r(e) {
                return function(t) {
                    return n([e, t])
                }
            }

            function n(r) {
                if (a) throw new TypeError("Generator is already executing.");
                for (; u;) try {
                    if (a = 1, o && (i = o[2 & r[0] ? "return" : r[0] ? "throw" : "next"]) && !(i = i.call(o, r[1])).done) return i;
                    switch (o = 0, i && (r = [0, i.value]), r[0]) {
                        case 0:
                        case 1:
                            i = r;
                            break;
                        case 4:
                            return u.label++, {
                                value: r[1],
                                done: !1
                            };
                        case 5:
                            u.label++, o = r[1], r = [0];
                            continue;
                        case 7:
                            r = u.ops.pop(), u.trys.pop();
                            continue;
                        default:
                            if (i = u.trys, !(i = i.length > 0 && i[i.length - 1]) && (6 === r[0] || 2 === r[0])) {
                                u = 0;
                                continue
                            }
                            if (3 === r[0] && (!i || r[1] > i[0] && r[1] < i[3])) {
                                u.label = r[1];
                                break
                            }
                            if (6 === r[0] && u.label < i[1]) {
                                u.label = i[1], i = r;
                                break
                            }
                            if (i && u.label < i[2]) {
                                u.label = i[2], u.ops.push(r);
                                break
                            }
                            i[2] && u.ops.pop(), u.trys.pop();
                            continue
                    }
                    r = t.call(e, u)
                } catch (e) {
                    r = [6, e], o = 0
                } finally {
                    a = i = 0
                }
                if (5 & r[0]) throw r[1];
                return {
                    value: r[0] ? r[1] : void 0,
                    done: !0
                }
            }
            var a, o, i, s, u = {
                label: 0,
                sent: function() {
                    if (1 & i[0]) throw i[1];
                    return i[1]
                },
                trys: [],
                ops: []
            };
            return s = {
                next: r(0),
                throw: r(1),
                return: r(2)
            }, "function" == typeof Symbol && (s[Symbol.iterator] = function() {
                return this
            }), s
        },
        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 : createWebGLContext(), 1 === ENV.get("WEBGL_VERSION") ? (this.textureFloatExtension = getExtensionOrThrow(this.gl, "OES_texture_float"), this.colorBufferFloatExtension = this.gl.getExtension("WEBGL_color_buffer_float")) : this.colorBufferFloatExtension = getExtensionOrThrow(this.gl, "EXT_color_buffer_float"), this.loseContextExtension = getExtensionOrThrow(this.gl, "WEBGL_lose_context"), ENV.get("WEBGL_GET_BUFFER_SUB_DATA_ASYNC_EXTENSION_ENABLED") && (this.getBufferSubDataAsyncExtension = this.gl.getExtension("WEBGL_get_buffer_sub_data_async")), this.vertexBuffer = createVertexBuffer(this.gl), this.indexBuffer = createIndexBuffer(this.gl), this.framebuffer = createFramebuffer(this.gl)
            }
            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.deleteBuffer(e.vertexBuffer)
                    }), callAndCheck(t, function() {
                        return t.bindBuffer(t.ELEMENT_ARRAY_BUFFER, null)
                    }), callAndCheck(t, function() {
                        return t.deleteBuffer(e.indexBuffer)
                    }), this.loseContextExtension.loseContext(), this.disposed = !0
                }
            }, e.prototype.enableAutomaticDebugValidation = function(e) {
                this.autoDebugValidate = e, enableDebugWebGLErrorChecking(e)
            }, e.prototype.createMatrixTexture = function(e, t) {
                return this.throwIfDisposed(), createMatrixTexture(this.gl, e, t)
            }, e.prototype.uploadPixelDataToTexture = function(e, t) {
                this.throwIfDisposed(), uploadPixelDataToTexture(this.gl, e, t)
            }, e.prototype.createPackedMatrixTexture = function(e, t) {
                return this.throwIfDisposed(), createPackedMatrixTexture(this.gl, e, t)
            }, 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();
                return uploadMatrixToTexture(this.gl, e, t, r, n, 1)
            }, e.prototype.uploadMatrixToPackedTexture = function(e, t, r, n) {
                return this.throwIfDisposed(), uploadMatrixToPackedTexture(this.gl, e, t, r, n)
            }, e.prototype.downloadMatrixFromTexture = function(e, t, r) {
                var n = this;
                return this.downloadMatrixDriver(e, function() {
                    return downloadMatrixFromOutputTexture(n.gl, t, r)
                })
            }, e.prototype.downloadMatrixFromTextureAsync = function(e, t, r) {
                return __awaiter$4(this, void 0, void 0, function() {
                    var n = this;
                    return __generator$4(this, function(a) {
                        if (null == this.getBufferSubDataAsyncExtension) throw new Error("Cannot download matrix from output texture asynchronously, WEBGL_get_buffer_sub_data_async is not enabled.");
                        return [2, this.downloadMatrixDriverAsync(e, function() {
                            return downloadMatrixFromOutputTextureAsync(n.gl, n.getBufferSubDataAsyncExtension, t, r)
                        })]
                    })
                })
            }, e.prototype.downloadMatrixFromRGBAColorTexture = function(e, t, r, n) {
                var a = this;
                return this.downloadMatrixDriver(e, function() {
                    return downloadMatrixFromRGBAColorTexture(a.gl, t, r, n)
                })
            }, e.prototype.downloadMatrixFromPackedTexture = function(e, t, r) {
                var n = this;
                return this.downloadMatrixDriver(e, function() {
                    return downloadMatrixFromPackedOutputTexture(n.gl, t, r)
                })
            }, e.prototype.createProgram = function(e) {
                this.throwIfDisposed();
                var t = this.gl,
                    r = createFragmentShader(t, e),
                    n = createVertexShader$1(t),
                    a = createProgram(t);
                return callAndCheck(t, function() {
                    return t.attachShader(a, n)
                }), callAndCheck(t, function() {
                    return t.attachShader(a, r)
                }), linkProgram(t, a), this.autoDebugValidate && validateProgram(t, a), this.vertexAttrsAreBound || (this.setProgram(a), this.vertexAttrsAreBound = bindVertexProgramAttributeStreams(t, this.program, this.vertexBuffer)), a
            }, 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),
                    a = n[0],
                    o = n[1];
                this.setOutputMatrixTextureDriver(e, a, o)
            }, 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.runQuery = function(e) {
                var t = this.beginQuery();
                return e(), this.endQuery(), this.pollQueryTime(t)
            }, 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(),
                    a = n.createQueryEXT();
                return n.beginQueryEXT(n.TIME_ELAPSED_EXT, a), a
            }, 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.isQueryAvailable = function(e, t) {
                if (0 === t) return !0;
                if (2 === t) {
                    var r = this.gl,
                        n = this.getQueryTimerExtensionWebGL2(),
                        a = r.getQueryParameter(e, r.QUERY_RESULT_AVAILABLE);
                    return null == this.disjoint && (this.disjoint = this.gl.getParameter(n.GPU_DISJOINT_EXT)), a && !this.disjoint
                }
                a = (n = this.getQueryTimerExtensionWebGL1()).getQueryObjectEXT(e, n.QUERY_RESULT_AVAILABLE_EXT);
                return null == this.disjoint && (this.disjoint = this.gl.getParameter(n.GPU_DISJOINT_EXT)), a && !this.disjoint
            }, e.prototype.pollQueryTime = function(e) {
                var t = this;
                return new Promise(function(r) {
                    var n = ENV.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION");
                    t.addItemToPoll(function() {
                        return t.isQueryAvailable(e, n)
                    }, function() {
                        return r(t.getQueryTime(e, n))
                    })
                })
            }, 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.getQueryTime = function(e, t) {
                if (0 === t) return null;
                if (2 === t) {
                    var r = this.gl;
                    return (a = r.getQueryParameter(e, r.QUERY_RESULT)) / 1e6
                }
                var n = this.getQueryTimerExtensionWebGL1(),
                    a = n.getQueryObjectEXT(e, n.QUERY_RESULT_EXT);
                return a / 1e6
            }, e.prototype.downloadMatrixDriverSetup = function(e) {
                this.throwIfDisposed(), bindColorTextureToFramebuffer(this.gl, e, this.framebuffer), this.autoDebugValidate && validateFramebuffer(this.gl)
            }, e.prototype.downloadMatrixDriverTeardown = 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.downloadMatrixDriverSetup(e);
                var r = t();
                return this.downloadMatrixDriverTeardown(), r
            }, e.prototype.downloadMatrixDriverAsync = function(e, t) {
                return __awaiter$4(this, void 0, void 0, function() {
                    var r;
                    return __generator$4(this, function(n) {
                        switch (n.label) {
                            case 0:
                                return this.downloadMatrixDriverSetup(e), [4, t()];
                            case 1:
                                return r = n.sent(), this.downloadMatrixDriverTeardown(), [2, 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 a = this;
                this.throwIfDisposed(), callAndCheck(this.gl, function() {
                    return a.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
        }(),
        NAN_UNIFORM_NAME = "NaN",
        WhereProgram = function() {
            return function(e, t, r) {
                this.variableNames = ["c", "a", "b"], this.outputShape = t;
                var n, a;
                if (r > 4) throw Error("Where for rank " + r + " is not yet supported");
                if (1 === r) a = "resRC", n = "resRC";
                else {
                    for (var o = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"], i = [], s = [], u = 0; u < t.length; u++) s.push("" + o[u]), u < e && i.push("" + o[u]);
                    n = i.join(), a = 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(" + a + "));\n        } else {\n          setOutput(getB(" + a + "));\n        }\n      }\n    "
            }
        }(),
        LRNProgram = function() {
            return function(e, t, r, n, a) {
                this.variableNames = ["x"], this.outputShape = [];
                var o = t,
                    i = e[3] - 1;
                this.outputShape = e;
                var s, u = "float(" + r + ") + float(" + n + ") * sum";
                s = .5 === a ? "inversesqrt(" + u + ")" : 1 === a ? "1.0/(" + u + ")" : "exp(log(" + u + ") * float(-" + a + "));", 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 = -" + o + "; j <= " + o + "; j++) {\n          int idx = d + j;\n          if (idx >= 0 && idx <=  " + i + ") {\n            float z = getX(b, r, c, idx);\n            sum += z * z;\n          }\n        }\n        float val = x * " + s + ";\n        setOutput(val);\n      }\n    "
            }
        }(),
        MaxPool2DBackpropProgram = function() {
            return function(e) {
                this.variableNames = ["dy", "maxPos"], this.outputShape = e.inShape;
                var t = e.filterHeight,
                    r = e.filterWidth,
                    n = e.strideHeight,
                    a = e.strideWidth,
                    o = t - 1 - e.padInfo.top,
                    i = r - 1 - e.padInfo.left,
                    s = t * r - 1;
                this.userCode = "\n      const ivec2 pads = ivec2(" + o + ", " + i + ");\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 < " + 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          for (int wC = 0; wC < " + r + "; wC++) {\n            float dyC = float(dyCCorner + wC) / " + a + ".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 = " + s + " - 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 * " + r + " + 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 a = r ? e[1] : e[0],
                    o = n ? t[0] : t[1],
                    i = r ? e[0] : e[1];
                this.outputShape = [a, o];
                var s = function(e, t) {
                        return r ? t + " + " + e + ", aRow" : "aRow, " + t + " + " + e
                    },
                    u = function(e, t) {
                        return n ? "bCol, " + t + " + " + e : t + " + " + e + ", bCol"
                    },
                    l = 4 * Math.floor(i / 4),
                    c = i % 4;
                this.userCode = " float dotARowBCol(int aRow, int bCol) {\n      float result = 0.0;\n      for (int i = 0; i < " + l + "; i += 4) {\n        vec4 a = vec4(\n          getMatrixA(" + s(0, "i") + "),\n          getMatrixA(" + s(1, "i") + "),\n          getMatrixA(" + s(2, "i") + "),\n          getMatrixA(" + s(3, "i") + ")\n        );\n        vec4 b = vec4(\n          getMatrixB(" + u(0, "i") + "),\n          getMatrixB(" + u(1, "i") + "),\n          getMatrixB(" + u(2, "i") + "),\n          getMatrixB(" + u(3, "i") + ")\n        );\n\n        result += dot(a, b);\n      }\n\n      if (" + (1 === c) + ") {\n        result += getMatrixA(" + s(0, l) + ") *\n          getMatrixB(" + u(0, l) + ");\n      } else if (" + (2 === c) + ") {\n        vec2 a = vec2(\n          getMatrixA(" + s(0, l) + "),\n          getMatrixA(" + s(1, l) + ")\n        );\n        vec2 b = vec2(\n          getMatrixB(" + u(0, l) + "),\n          getMatrixB(" + u(1, l) + ")\n        );\n        result += dot(a, b);\n      } else if (" + (3 === c) + ") {\n        vec3 a = vec3(\n          getMatrixA(" + s(0, l) + "),\n          getMatrixA(" + s(1, l) + "),\n          getMatrixA(" + s(2, l) + ")\n        );\n        vec3 b = vec3(\n          getMatrixB(" + u(0, l) + "),\n          getMatrixB(" + u(1, l) + "),\n          getMatrixB(" + u(2, l) + ")\n        );\n        result += dot(a, b);\n      }\n\n      return result;\n    }\n\n    void main() {\n      ivec2 resRC = getOutputCoords();\n      setOutput(dotARowBCol(resRC.x, resRC.y));\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    "
            }
        }(),
        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,
                    a = getCoordsDataType(n),
                    o = 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      " + a + " start = " + a + "(" + o + ");\n      " + a + " end = " + a + "(" + i + ");\n\n      void main() {\n        " + a + " outC = getOutputCoords();\n        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {\n          setOutput(float(" + r + "));\n        } else {\n          " + a + " coords = outC - start;\n          setOutput(getX(" + s + "));\n        }\n      }\n    " : "\n        int start = " + o + ";\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.filterHeight,
                    a = e.filterWidth,
                    o = e.strideHeight,
                    i = e.strideWidth,
                    s = e.padInfo.top,
                    u = e.padInfo.left;
                this.outputShape = e.outShape;
                var l = "avg" === t,
                    c = "0.0";
                if (l || (c = "-1.0 / 0.0"), r) this.userCode = "\n        const ivec2 strides = ivec2(" + o + ", " + i + ");\n        const ivec2 pads = ivec2(" + s + ", " + u + ");\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 < " + n + "; wR++) {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= " + e.inHeight + ") {\n              continue;\n            }\n\n            for (int wC = 0; wC < " + a + "; wC++) {\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 * " + a + " + wC;\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      ";
                else {
                    var p = t + "(" + t + "(" + t + "(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])";
                    "avg" === t && (p = "avgValue / count");
                    var d = 4 * Math.floor(a / 4),
                        h = a % 4,
                        f = "\n      if (" + l + ") {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = max(values, minMaxValue);\n      }\n    ";
                    this.userCode = "\n      const ivec2 strides = ivec2(" + o + ", " + i + ");\n      const ivec2 pads = ivec2(" + s + ", " + u + ");\n      const float initializationValue = " + c + ";\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(" + c + ");\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wR = 0; wR < " + n + "; wR++) {\n          int xR = xRCorner + wR;\n\n          if (xR < 0 || xR >= " + e.inHeight + ") {\n            continue;\n          }\n\n          for (int wC = 0; wC < " + d + "; wC += 4) {\n            int xC = xCCorner + wC;\n\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + 1, d),\n              getValue(batch, xR, xC + 2, d),\n              getValue(batch, xR, xC + 3, d)\n            );\n\n            " + f + "\n          }\n\n          int xC = xCCorner + " + d + ";\n          if (" + (1 === h) + ") {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              initializationValue,\n              initializationValue,\n              initializationValue\n            );\n\n            " + f + "\n          } else if (" + (2 === h) + ") {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + 1, d),\n              initializationValue,\n              initializationValue\n            );\n\n            " + f + "\n          } else if (" + (3 === h) + ") {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + 1, d),\n              getValue(batch, xR, xC + 2, d),\n              initializationValue\n            );\n\n            " + f + "\n          }\n        }\n        setOutput(" + p + ");\n      }\n    "
                }
            }
        }(),
        ReduceProgram = function() {
            return function(e, t) {
                this.variableNames = ["x"];
                var r = e.windowSize,
                    n = e.batchSize,
                    a = e.inSize,
                    o = Math.ceil(a / r);
                this.outputShape = [n, o];
                var i = "sum" === t,
                    s = "0.0";
                i || (s = "min" === t ? "1.0 / 0.0" : "-1.0 / 0.0");
                var u = "min" === t ? "min" : "max",
                    l = t + "(" + t + "(" + t + "(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])";
                "sum" === t && (l = "sumValue");
                var c = 4 * Math.floor(r / 4),
                    p = r % 4,
                    d = "\n      if (" + i + ") {\n        sumValue += dot(values, ones);\n      } else {\n        minMaxValue = " + u + "(values, minMaxValue);\n      }\n    ",
                    h = "";
                a % r > 0 && (h = "\n        if (inIdx < 0 || inIdx >= " + a + ") {\n          return initializationValue;\n        }\n      "), this.userCode = "\n      const float initializationValue = " + s + ";\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(" + s + ");\n        float sumValue = 0.0;\n\n        for (int i = 0; i < " + c + "; 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          " + d + "\n        }\n\n        int inIdx = inOffset + " + c + ";\n        if (" + (1 === p) + ") {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n          " + d + "\n        } else if (" + (2 === p) + ") {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n          " + d + "\n        } else if (" + (3 === p) + ") {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n          " + d + "\n        }\n        setOutput(" + l + ");\n      }\n    "
            }
        }(),
        ResizeBilinearBackpropProgram = function() {
            return function(e, t, r) {
                this.variableNames = ["dy"], this.outputShape = [], this.outputShape = t.shape;
                var n = t.shape,
                    a = n[1],
                    o = n[2],
                    i = e.shape,
                    s = i[1],
                    u = i[2],
                    l = [r && s > 1 ? a - 1 : a, r && u > 1 ? o - 1 : o],
                    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), " + (a - 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), " + (o - 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 a = e[0],
                    o = e[1],
                    i = e[2],
                    s = e[3];
                this.outputShape = [a, t, r, s];
                var u = [n && t > 1 ? o - 1 : o, 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(" + o + ".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    "
            }
        }(),
        ResizeNearestNeighborProgram = function() {
            return function(e, t, r, n) {
                this.variableNames = ["A"], this.outputShape = [];
                var a = e[0],
                    o = e[1],
                    i = e[2],
                    s = e[3];
                this.outputShape = [a, t, r, s];
                var u = n ? [o - 1, i - 1] : [o, i],
                    l = n ? [t - 1, r - 1] : [t, 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(" + o + ".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 = function(r) {
                            return -1 !== t.indexOf(r) && 1 !== e[r] ? e[r] + " - coords[" + r + "] - 1" : "coords[" + r + "]"
                        },
                        a = e.map(function(e, t) {
                            return n(t)
                        }).join(","),
                        o = getCoordsDataType(r);
                    this.userCode = "\n      void main() {\n        " + o + " coords = getOutputCoords();\n        setOutput(getX(" + a + "));\n      }\n    "
                } else this.userCode = "\n        void main() {\n          int coord = getOutputCoords();\n          setOutput(getX(" + e[0] + " - coord - 1));\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
        }(),
        StridedSliceProgram = function() {
            return function(e, t, r) {
                this.variableNames = ["x"], this.outputShape = r, this.rank = r.length;
                var n = getCoordsDataType(this.rank),
                    a = "";
                a = 1 === this.rank ? "coords * strides + begin" : r.map(function(e, t) {
                    return "coords[" + t + "] * strides[" + t + "] + begin[" + t + "]"
                }).join(","), this.userCode = "\n      " + n + " begin = " + n + "(" + e + ");\n      " + n + " strides = " + n + "(" + t + ");\n\n      void main() {\n        " + n + " coords = getOutputCoords();\n        setOutput(getX(" + a + "));\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) {
                void 0 === t && (t = TextureType.FLOAT);
                var r = getKeyFromTextureShape(e, t);
                if (r in this.freeTextures || (this.freeTextures[r] = []), r in this.usedTextures || (this.usedTextures[r] = []), this.freeTextures[r].length > 0) {
                    this.numFreeTextures--, this.numUsedTextures++, this.log();
                    var n = this.freeTextures[r].shift();
                    return this.usedTextures[r].push(n), n
                }
                this.numUsedTextures++, this.log();
                var a = this.gpgpu.createMatrixTexture(e[0], e[1]);
                return this.usedTextures[r].push(a), a
            }, e.prototype.releaseTexture = function(e, t, r) {
                void 0 === r && (r = TextureType.FLOAT);
                var n = getKeyFromTextureShape(t, r);
                n in this.freeTextures || (this.freeTextures[n] = []), this.freeTextures[n].push(e), this.numFreeTextures++, this.numUsedTextures--;
                var a = this.usedTextures[n],
                    o = a.indexOf(e);
                if (o < 0) throw new Error("Cannot release a texture that was never provided by this texture manager");
                a.splice(o, 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
        }(),
        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 a = getCoordsDataType(this.rank),
                    o = getSourceCoords$1(e);
                this.userCode = "\n      void main() {\n        " + a + " resRC = getOutputCoords();\n        setOutput(getA(" + o + "));\n      }\n    "
            }
        }(),
        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 a = getCoordsDataType(this.rank),
                    o = getSwitchedCoords(t);
                this.userCode = "\n    void main() {\n      " + a + " resRC = getOutputCoords();\n      setOutput(getA(" + o + "));\n    }\n    "
            }
        }(),
        ERF_P = .3275911,
        ERF_A1 = .254829592,
        ERF_A2 = -.284496736,
        ERF_A3 = 1.421413741,
        ERF_A4 = -1.453152027,
        ERF_A5 = 1.061405429,
        UnaryOpProgram = function() {
            return function(e, t) {
                this.variableNames = ["A"], this.outputShape = e, this.userCode = "\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    "
            }
        }(),
        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",
        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 = "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 = "return sin(x);",
        COS = "return cos(x);",
        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 = "return log(x + sqrt(x * x - 1.0));",
        ATANH = "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));",
        __awaiter$5 = function(e, t, r, n) {
            return new(r || (r = Promise))(function(a, o) {
                function i(e) {
                    try {
                        u(n.next(e))
                    } catch (e) {
                        o(e)
                    }
                }

                function s(e) {
                    try {
                        u(n.throw(e))
                    } catch (e) {
                        o(e)
                    }
                }

                function u(e) {
                    e.done ? a(e.value) : new r(function(t) {
                        t(e.value)
                    }).then(i, s)
                }
                u((n = n.apply(e, t || [])).next())
            })
        },
        __generator$5 = function(e, t) {
            function r(e) {
                return function(t) {
                    return n([e, t])
                }
            }

            function n(r) {
                if (a) throw new TypeError("Generator is already executing.");
                for (; u;) try {
                    if (a = 1, o && (i = o[2 & r[0] ? "return" : r[0] ? "throw" : "next"]) && !(i = i.call(o, r[1])).done) return i;
                    switch (o = 0, i && (r = [0, i.value]), r[0]) {
                        case 0:
                        case 1:
                            i = r;
                            break;
                        case 4:
                            return u.label++, {
                                value: r[1],
                                done: !1
                            };
                        case 5:
                            u.label++, o = r[1], r = [0];
                            continue;
                        case 7:
                            r = u.ops.pop(), u.trys.pop();
                            continue;
                        default:
                            if (i = u.trys, !(i = i.length > 0 && i[i.length - 1]) && (6 === r[0] || 2 === r[0])) {
                                u = 0;
                                continue
                            }
                            if (3 === r[0] && (!i || r[1] > i[0] && r[1] < i[3])) {
                                u.label = r[1];
                                break
                            }
                            if (6 === r[0] && u.label < i[1]) {
                                u.label = i[1], i = r;
                                break
                            }
                            if (i && u.label < i[2]) {
                                u.label = i[2], u.ops.push(r);
                                break
                            }
                            i[2] && u.ops.pop(), u.trys.pop();
                            continue
                    }
                    r = t.call(e, u)
                } catch (e) {
                    r = [6, e], o = 0
                } finally {
                    a = i = 0
                }
                if (5 & r[0]) throw r[1];
                return {
                    value: r[0] ? r[1] : void 0,
                    done: !0
                }
            }
            var a, o, i, s, u = {
                label: 0,
                sent: function() {
                    if (1 & i[0]) throw i[1];
                    return i[1]
                },
                trys: [],
                ops: []
            };
            return s = {
                next: r(0),
                throw: r(1),
                return: r(2)
            }, "function" == typeof Symbol && (s[Symbol.iterator] = function() {
                return this
            }), s
        },
        BEFORE_PAGING_CONSTANT = 300,
        MathBackendWebGL = function() {
            function e(e, t) {
                if (void 0 === t && (t = !0), this.gpgpu = e, this.delayedStorage = t, this.texData = new WeakMap, 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");
                ENV.get("IS_BROWSER") && (this.canvas = document.createElement("canvas")), null == e ? (this.gpgpu = new GPGPUContext(createWebGLContext(this.canvas)), this.gpgpuCreatedLocally = !0) : this.gpgpuCreatedLocally = !1, 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,
                    texShape: null,
                    texType: TextureType.FLOAT
                })
            }, e.prototype.fromPixels = function(e, t) {
                if (null == e) throw new Error("MathBackendWebGL.writePixels(): pixels can not be null");
                var r = [e.height, e.width],
                    n = [e.height, e.width, t];
                if (e instanceof HTMLVideoElement) {
                    if (null == this.fromPixelsCanvas) {
                        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.fromPixelsCanvas = document.createElement("canvas")
                    }
                    this.fromPixelsCanvas.width = e.width, this.fromPixelsCanvas.height = e.height, this.fromPixelsCanvas.getContext("2d").drawImage(e, 0, 0, e.width, e.height), e = this.fromPixelsCanvas
                }
                var a = Tensor.make(r, {}, "int32");
                this.texData.get(a.dataId).texType = TextureType.UNSIGNED_BYTE, this.gpgpu.uploadPixelDataToTexture(this.getTexture(a.dataId), e);
                var o = new FromPixelsProgram(n),
                    i = this.compileAndRun(o, [a]);
                return a.dispose(), i
            }, e.prototype.write = function(e, t) {
                if (null == t) throw new Error("MathBackendWebGL.write(): values can not be null");
                this.throwIfNoData(e);
                var r = this.texData.get(e),
                    n = r.texture,
                    a = r.texShape,
                    o = r.texType;
                null != n && (this.releaseTexture(e, n, a, o), r.texture = null, r.texShape = null), r.values = t, this.delayedStorage || this.uploadToGPU(e)
            }, e.prototype.readSync = function(e) {
                this.throwIfNoData(e);
                var t = this.texData.get(e),
                    r = t.texture,
                    n = t.values,
                    a = t.texShape;
                if (null != n) return this.cacheOnCPU(e), n;
                var o, i = null != this.activeTimers;
                i && (o = performance.now());
                var s = this.gpgpu.downloadMatrixFromTexture(r, a[0], a[1]);
                return i && (this.downloadWaitMs += performance.now() - o), this.cacheOnCPU(e, s), t.values
            }, e.prototype.read = function(e) {
                return __awaiter$5(this, void 0, void 0, function() {
                    var t, r, n, a, o, i, s, u;
                    return __generator$5(this, function(l) {
                        switch (l.label) {
                            case 0:
                                return this.pendingRead.has(e) ? (t = this.pendingRead.get(e), [2, new Promise(function(e) {
                                    return t.push(e)
                                })]) : (this.throwIfNoData(e), r = this.texData.get(e), n = r.texture, a = r.values, o = r.texShape, null != a ? (this.cacheOnCPU(e), [2, a]) : ENV.get("WEBGL_GET_BUFFER_SUB_DATA_ASYNC_EXTENSION_ENABLED") ? [4, this.gpgpu.downloadMatrixFromTextureAsync(n, o[0], o[1])] : [3, 2]);
                            case 1:
                                return i = l.sent(), this.cacheOnCPU(e, i), [2, r.values];
                            case 2:
                                return 0 === ENV.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") ? [2, this.readSync(e)] : (this.pendingRead.set(e, []), [4, this.gpgpu.runQuery(function() {})]);
                            case 3:
                                return l.sent(), s = this.pendingRead.get(e), this.pendingRead.delete(e), u = this.readSync(e), s.forEach(function(e) {
                                    return e(u)
                                }), this.pendingDisposal.has(e) && (this.pendingDisposal.delete(e), this.disposeData(e)), [2, u]
                        }
                    })
                })
            }, e.prototype.time = function(e) {
                return __awaiter$5(this, void 0, void 0, function() {
                    var t, r, n, a, o, i;
                    return __generator$5(this, function(s) {
                        switch (s.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(), a = flatten(this.activeTimers), this.activeTimers = t, n && (this.programTimersStack = null), [4, Promise.all(a).then(function(e) {
                                    var t = 0;
                                    return e.forEach(function(e) {
                                        return t += e
                                    }), t
                                })];
                            case 1:
                                return o = s.sent(), i = {
                                    uploadWaitMs: this.uploadWaitMs,
                                    downloadWaitMs: this.downloadWaitMs,
                                    kernelMs: o,
                                    wallMs: null
                                }, this.uploadWaitMs = 0, this.downloadWaitMs = 0, [2, i]
                        }
                    })
                })
            }, 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$5(this, void 0, void 0, function() {
                    var t;
                    return __generator$5(this, function(r) {
                        return ENV.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") > 0 ? [2, this.gpgpu.pollQueryTime(e)] : (t = e, [2, t.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,
                        a = t.texType;
                    null != r && this.releaseTexture(e, r, n, a), this.texData.delete(e)
                }
            }, e.prototype.getTexture = function(e) {
                return this.uploadToGPU(e), this.texData.get(e).texture
            }, e.prototype.getGPGPUContext = function() {
                return this.gpgpu
            }, e.prototype.getCanvas = function() {
                return this.canvas
            }, e.prototype.slice = function(e, t, r) {
                var n = new SliceProgram(r),
                    a = n.getCustomSetupFunc(t);
                return this.compileAndRun(n, [e], null, a)
            }, e.prototype.stridedSlice = function(e, t, r, n, a, o) {
                var i = getStridedSlicedInfo(e.shape, t, r, n, a, o),
                    s = i[0],
                    u = i[1];
                if (u.some(function(e) {
                        return 0 === e
                    })) return tensor([], u);
                var l = new StridedSliceProgram(s, n, u);
                return this.compileAndRun(l, [e])
            }, e.prototype.reverse = function(e, t) {
                var r = new ReverseProgram(e.shape, t);
                return this.compileAndRun(r, [e])
            }, e.prototype.concat = function(e, t) {
                var r = new ConcatProgram(e.shape, t.shape);
                return this.compileAndRun(r, [e, t])
            }, e.prototype.neg = function(e) {
                var t = new UnaryOpProgram(e.shape, NEG);
                return this.compileAndRun(t, [e])
            }, e.prototype.matMul = function(e, t, r, n) {
                var a = new MatMulProgram(e.shape, t.shape, r, n);
                return this.compileAndRun(a, [e, t])
            }, e.prototype.multiply = function(e, t) {
                var r = new BinaryOpProgram(MUL, e.shape, t.shape),
                    n = this.makeOutputArray(r.outputShape, upcastType(e.dtype, t.dtype));
                return this.compileAndRun(r, [e, t], n)
            }, e.prototype.batchNormalization = function(e, t, r, n, a, o) {
                var i = [e, t, r],
                    s = null;
                null != o && (s = o.shape, i.push(o));
                var u = null;
                null != a && (u = a.shape, i.push(a));
                var l = new BatchNormProgram(e.shape, t.shape, r.shape, s, u, n);
                return this.compileAndRun(l, i)
            }, e.prototype.localResponseNormalization4D = function(e, t, r, n, a) {
                var o = new LRNProgram(e.shape, t, r, n, a);
                return this.compileAndRun(o, [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.reduce = function(e, t, r) {
                var n = e.shape[0],
                    a = e.shape[1],
                    o = computeOptimalWindowSize(a),
                    i = new ReduceProgram({
                        windowSize: o,
                        inSize: a,
                        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],
                    a = e.shape[1];
                null != r && (n = r.shape[0], a = r.shape[1]);
                var o = computeOptimalWindowSize(a),
                    i = new ArgMinMaxProgram({
                        windowSize: o,
                        inSize: a,
                        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],
                    a = sizeFromShape(r[1]),
                    o = e.as2D(-1, a),
                    i = sumOutType(e.dtype);
                return this.reduce(o, "sum", i).reshape(n)
            }, e.prototype.argMin = function(e, t) {
                var r = [t];
                assertAxesAreInnerMostDims("argMin", r, e.rank);
                var n = computeOutAndReduceShapes(e.shape, r),
                    a = n[0],
                    o = sizeFromShape(n[1]),
                    i = e.as2D(-1, o);
                return this.argReduce(i, "min").reshape(a)
            }, e.prototype.argMax = function(e, t) {
                var r = [t];
                assertAxesAreInnerMostDims("argMax", r, e.rank);
                var n = computeOutAndReduceShapes(e.shape, r),
                    a = n[0],
                    o = sizeFromShape(n[1]),
                    i = e.as2D(-1, o);
                return this.argReduce(i, "max").reshape(a)
            }, 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 a = new CumSumProgram(e.shape, r, n);
                return this.compileAndRun(a, [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) {
                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) {
                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.where = function(e, t, r, n) {
                var a = new WhereProgram(e.rank, t.shape, t.rank),
                    o = this.makeOutputArray(a.outputShape, n);
                return this.compileAndRun(a, [e, t, r], o)
            }, e.prototype.topKValues = function(e, t) {
                throw new Error("topKValues GPU not yet implemented!")
            }, e.prototype.topKIndices = function(e, t) {
                throw new Error("topKIndices GPU not yet implemented!")
            }, e.prototype.min = function(e, t) {
                assertAxesAreInnerMostDims("min", t, e.rank);
                var r = computeOutAndReduceShapes(e.shape, t),
                    n = r[0],
                    a = sizeFromShape(r[1]),
                    o = e.as2D(-1, a);
                return this.reduce(o, "min", o.dtype).reshape(n)
            }, e.prototype.minimum = function(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);
                return this.compileAndRun(r, [e, t])
            }, e.prototype.max = function(e, t) {
                assertAxesAreInnerMostDims("max", t, e.rank);
                var r = computeOutAndReduceShapes(e.shape, t),
                    n = r[0],
                    a = sizeFromShape(r[1]),
                    o = e.as2D(-1, a);
                return this.reduce(o, "max", o.dtype).reshape(n)
            }, e.prototype.maximum = function(e, t) {
                var r = new BinaryOpProgram(MAX, e.shape, t.shape);
                return this.compileAndRun(r, [e, t])
            }, 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) {
                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.subtract = function(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 = this.makeOutputArray(r.outputShape, upcastType(e.dtype, t.dtype));
                return this.compileAndRun(r, [e, t], 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);
                return this.compileAndRun(t, [e])
            }, 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.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);
                return this.compileAndRun(t, [e])
            }, e.prototype.atanh = function(e) {
                var t = new UnaryOpProgram(e.shape, ATANH);
                return this.compileAndRun(t, [e])
            }, 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.conv2d = function(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 a = new Pool2DProgram(n, "max", !0),
                    o = this.compileAndRun(a, [t]),
                    i = new MaxPool2DBackpropProgram(n),
                    s = this.makeOutputArray(i.outputShape, t.dtype),
                    u = this.compileAndRun(i, [e, o], s);
                return o.dispose(), u
            }, e.prototype.avgPoolBackprop = function(e, t, r) {
                var n = new AvgPool2DBackpropProgram(r),
                    a = this.makeOutputArray(n.outputShape, t.dtype);
                return this.compileAndRun(n, [e], a)
            }, 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 a = new ResizeBilinearProgram(e.shape, t, r, n);
                return this.compileAndRun(a, [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 a = new ResizeNearestNeighborProgram(e.shape, t, r, n);
                return this.compileAndRun(a, [e])
            }, e.prototype.multinomial = function(e, t, r, n) {
                var a = t ? e : softmax(e),
                    o = a.shape[0],
                    i = a.shape[1],
                    s = new MultinomialProgram(o, i, r),
                    u = this.makeOutputArray(s.outputShape, "int32"),
                    l = s.getCustomSetupFunc(n);
                return this.compileAndRun(s, [a], u, l)
            }, e.prototype.oneHot = function(e, t, r, n) {
                var a = new OneHotProgram(e.size, t, r, n);
                return this.compileAndRun(a, [e])
            }, e.prototype.makeOutputArray = function(e, t) {
                return Tensor.make(e, {}, t)
            }, e.prototype.compileAndRun = function(e, t, r, n) {
                var a = this;
                null == r && (r = this.makeOutputArray(e.outputShape, t[0].dtype));
                var o = t.map(function(e) {
                    return a.uploadToGPU(e.dataId), {
                        tensor: e,
                        texData: a.texData.get(e.dataId)
                    }
                });
                this.uploadToGPU(r.dataId);
                var i, s = {
                        tensor: r,
                        texData: this.texData.get(r.dataId)
                    },
                    u = makeShaderKey(e, o, s),
                    l = this.getAndSaveBinary(u, function() {
                        return compileProgram(a.gpgpu, e, o, s)
                    }),
                    c = null != this.activeTimers;
                if (c && (i = this.startTimer()), runProgram(l, o, s, n), this.numBytesInGPU > this.NUM_BYTES_BEFORE_PAGING)
                    for (var p = this.numBytesInGPU - this.NUM_BYTES_BEFORE_PAGING; p > 0;) {
                        var d = this.lruDataGPU.shift(),
                            h = this.texData.get(d),
                            f = h.shape,
                            m = h.dtype;
                        p -= this.computeBytes(f, m), this.read(d)
                    }
                return c && (i = this.endTimer(i), this.activeTimers.push(this.getQueryTime(i))), 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.fromPixelsCanvas && this.fromPixelsCanvas.remove(), this.gpgpuCreatedLocally && this.gpgpu.dispose(), this.disposed = !0
                }
            }, e.prototype.throwIfNoData = function(e) {
                if (!this.texData.has(e)) throw new Error("WebGL backend: No data found for this tensor. Did you change your backend in the middle of the program? New backends can't use Tensors created with previous backends")
            }, e.prototype.uploadToGPU = function(e) {
                this.throwIfNoData(e);
                var t = this.texData.get(e),
                    r = t.shape,
                    n = t.values,
                    a = t.texture,
                    o = (t.dtype, t.texType);
                if (null != a) return this.lruDataGPU.splice(this.lruDataGPU.indexOf(e), 1), void this.lruDataGPU.push(e);
                var i, s = null != this.activeTimers;
                s && (i = performance.now());
                var u = getTextureShapeFromLogicalShape(this.gpgpu.gl, r);
                t.texShape = u;
                var l = this.acquireTexture(e, u, o);
                t.texture = l, null != n && (this.gpgpu.uploadMatrixToTexture(l, u[0], u[1], typedArrayToFloat32(n)), t.values = null, s && (this.uploadWaitMs += performance.now() - i))
            }, e.prototype.cacheOnCPU = function(e, t) {
                var r = this.delayedStorage,
                    n = this.texData.get(e),
                    a = n.texture,
                    o = n.texShape,
                    i = n.dtype,
                    s = n.texType;
                r && null != a && (this.releaseTexture(e, a, o, s), n.texture = null, n.texShape = null), null != t && (n.values = float32ToTypedArray(t, i))
            }, e.prototype.releaseTexture = function(e, t, r, n) {
                var a = this.texData.get(e),
                    o = a.shape,
                    i = a.dtype,
                    s = this.lruDataGPU.indexOf(e);
                s >= 0 && this.lruDataGPU.splice(s, 1), this.numBytesInGPU -= this.computeBytes(o, i), this.textureManager.releaseTexture(t, r, n)
            }, e.prototype.acquireTexture = function(e, t, r) {
                var n = this.texData.get(e),
                    a = n.shape,
                    o = n.dtype;
                return this.lruDataGPU.push(e), this.numBytesInGPU += this.computeBytes(a, o), this.textureManager.acquireTexture(t, r)
            }, e.prototype.computeBytes = function(e, t) {
                return sizeFromShape(e) * bytesPerElement(t)
            }, e
        }();
    ENV.get("IS_BROWSER") && ENV.registerBackend("webgl", function() {
        return new MathBackendWebGL
    }, 2);
    var __awaiter$6 = function(e, t, r, n) {
            return new(r || (r = Promise))(function(a, o) {
                function i(e) {
                    try {
                        u(n.next(e))
                    } catch (e) {
                        o(e)
                    }
                }

                function s(e) {
                    try {
                        u(n.throw(e))
                    } catch (e) {
                        o(e)
                    }
                }

                function u(e) {
                    e.done ? a(e.value) : new r(function(t) {
                        t(e.value)
                    }).then(i, s)
                }
                u((n = n.apply(e, t || [])).next())
            })
        },
        __generator$6 = function(e, t) {
            function r(e) {
                return function(t) {
                    return n([e, t])
                }
            }

            function n(r) {
                if (a) throw new TypeError("Generator is already executing.");
                for (; u;) try {
                    if (a = 1, o && (i = o[2 & r[0] ? "return" : r[0] ? "throw" : "next"]) && !(i = i.call(o, r[1])).done) return i;
                    switch (o = 0, i && (r = [0, i.value]), r[0]) {
                        case 0:
                        case 1:
                            i = r;
                            break;
                        case 4:
                            return u.label++, {
                                value: r[1],
                                done: !1
                            };
                        case 5:
                            u.label++, o = r[1], r = [0];
                            continue;
                        case 7:
                            r = u.ops.pop(), u.trys.pop();
                            continue;
                        default:
                            if (i = u.trys, !(i = i.length > 0 && i[i.length - 1]) && (6 === r[0] || 2 === r[0])) {
                                u = 0;
                                continue
                            }
                            if (3 === r[0] && (!i || r[1] > i[0] && r[1] < i[3])) {
                                u.label = r[1];
                                break
                            }
                            if (6 === r[0] && u.label < i[1]) {
                                u.label = i[1], i = r;
                                break
                            }
                            if (i && u.label < i[2]) {
                                u.label = i[2], u.ops.push(r);
                                break
                            }
                            i[2] && u.ops.pop(), u.trys.pop();
                            continue
                    }
                    r = t.call(e, u)
                } catch (e) {
                    r = [6, e], o = 0
                } finally {
                    a = i = 0
                }
                if (5 & r[0]) throw r[1];
                return {
                    value: r[0] ? r[1] : void 0,
                    done: !0
                }
            }
            var a, o, i, s, u = {
                label: 0,
                sent: function() {
                    if (1 & i[0]) throw i[1];
                    return i[1]
                },
                trys: [],
                ops: []
            };
            return s = {
                next: r(0),
                throw: r(1),
                return: r(2)
            }, "function" == typeof Symbol && (s[Symbol.iterator] = function() {
                return this
            }), s
        },
        MathBackendCPU = function() {
            function e() {
                this.data = new WeakMap, this.firstUse = !0, ENV.get("IS_BROWSER") && (this.canvas = document.createElement("canvas"))
            }
            return e.prototype.register = function(e, t, r) {
                if (this.firstUse && (this.firstUse = !1, ENV.get("IS_NODE") && console.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, null)
            }, e.prototype.write = function(e, t) {
                if (null == t) throw new Error("MathBackendCPU.write(): values can not be null");
                this.throwIfNoData(e), this.data.set(e, t)
            }, e.prototype.fromPixels = function(e, t) {
                if (null == e) throw new Error("MathBackendCPU.writePixels(): pixels can not be null");
                var r;
                if (e instanceof ImageData) r = e.data;
                else if (e instanceof HTMLCanvasElement) r = e.getContext("2d").getImageData(0, 0, e.width, e.height).data;
                else {
                    if (!(e instanceof HTMLImageElement || e instanceof HTMLVideoElement)) throw new Error("pixels is of unknown type: " + e.constructor.name);
                    if (null == this.canvas) throw new Error("Can't read pixels from HTMLImageElement outside the browser.");
                    this.canvas.width = e.width, this.canvas.height = e.height, this.canvas.getContext("2d").drawImage(e, 0, 0, e.width, e.height), r = this.canvas.getContext("2d").getImageData(0, 0, e.width, e.height).data
                }
                var n;
                if (4 === t) n = new Int32Array(r);
                else {
                    var a = e.width * e.height;
                    n = new Int32Array(a * t);
                    for (var o = 0; o < a; o++)
                        for (var i = 0; i < t; ++i) n[o * t + i] = r[4 * o + i]
                }
                var s = [e.height, e.width, t];
                return tensor3d(n, s, "int32")
            }, e.prototype.read = function(e) {
                return __awaiter$6(this, void 0, void 0, function() {
                    return __generator$6(this, function(t) {
                        return [2, this.readSync(e)]
                    })
                })
            }, e.prototype.readSync = function(e) {
                return this.throwIfNoData(e), this.data.get(e)
            }, e.prototype.disposeData = function(e) {
                this.data.has(e) && this.data.delete(e)
            }, e.prototype.time = function(e) {
                return __awaiter$6(this, void 0, void 0, function() {
                    var t, r;
                    return __generator$6(this, function(n) {
                        return t = performance.now(), e(), r = performance.now() - t, [2, {
                            kernelMs: r
                        }]
                    })
                })
            }, e.prototype.memory = function() {
                return {
                    unreliable: !0
                }
            }, e.prototype.throwIfNoData = function(e) {
                if (!this.data.has(e)) throw new Error("CPU backend: No data found for this tensor. Did you change your backend in the middle of the program? New backends can't use Tensors created with previous backends")
            }, e.prototype.slice = function(e, t, r) {
                for (var n = buffer(r, e.dtype), a = 0; a < n.size; ++a) {
                    var o = n.indexToLoc(a),
                        i = o.map(function(e, r) {
                            return e + t[r]
                        });
                    n.set.apply(n, [e.get.apply(e, i)].concat(o))
                }
                return n.toTensor()
            }, e.prototype.stridedSlice = function(e, t, r, n, a, o) {
                var i = getStridedSlicedInfo(e.shape, t, r, n, a, o),
                    s = i[0],
                    u = i[1];
                if (u.some(function(e) {
                        return 0 === e
                    })) return tensor([], u);
                for (var l = buffer(u, e.dtype), c = 0; c < l.size; c++) {
                    for (var p = l.indexToLoc(c), d = new Array(p.length), h = 0; h < d.length; h++) d[h] = p[h] * n[h] + s[h];
                    l.set.apply(l, [e.get.apply(e, d)].concat(p))
                }
                return l.toTensor()
            }, e.prototype.reverse = function(e, t) {
                for (var r = buffer(e.shape, e.dtype), n = e.buffer(), a = 0; a < r.size; a++) ! function(a) {
                    var o = r.indexToLoc(a),
                        i = o.slice();
                    t.forEach(function(t) {
                        return i[t] = e.shape[t] - 1 - i[t]
                    }), r.set.apply(r, [n.get.apply(n, i)].concat(o))
                }(a);
                return r.toTensor()
            }, e.prototype.concat = function(e, t) {
                var r = computeOutShape(e.shape, t.shape, 1),
                    n = buffer(r, e.dtype);
                if (1 === e.shape[0] && 1 === t.shape[0]) {
                    var a = e.dataSync(),
                        o = t.dataSync(),
                        i = n.values;
                    return i.set(a, 0), i.set(o, e.size), n.toTensor()
                }
                for (var s = 0; s < r[0]; ++s) {
                    for (u = 0; u < e.shape[1]; ++u) n.set(e.get(s, u), s, u);
                    for (var u = 0; u < t.shape[1]; ++u) n.set(t.get(s, u), s, u + e.shape[1])
                }
                return n.toTensor()
            }, e.prototype.neg = function(e) {
                return this.multiply(scalar(-1), e)
            }, e.prototype.add = function(e, t) {
                return this.broadcastedBinaryOp(e, t, upcastType(e.dtype, t.dtype), function(e, t) {
                    return e + t
                })
            }, e.prototype.subtract = function(e, t) {
                return this.broadcastedBinaryOp(e, t, upcastType(e.dtype, t.dtype), function(e, t) {
                    return e - t
                })
            }, e.prototype.pow = function(e, t) {
                return this.broadcastedBinaryOp(e, t, e.dtype, function(e, t) {
                    return Math.pow(e, t)
                })
            }, e.prototype.matMul = function(e, t, r, n) {
                for (var a = r ? e.shape[0] : e.shape[1], o = r ? e.shape[1] : e.shape[0], i = n ? t.shape[0] : t.shape[1], s = e.dataSync(), u = t.dataSync(), l = r ? [1, e.strides[0]] : [e.strides[0], 1], c = l[0], p = l[1], d = n ? [t.strides[0], 1] : [1, t.strides[0]], h = d[0], f = d[1], m = o * c, g = i * h, y = new Float32Array(o * i), v = 0, b = 0; b < m; b += c)
                    for (var x = 0; x < g; x += h) {
                        for (var w = b, _ = x, S = 0, N = 0; N < a; ++N) S += s[w] * u[_], w += p, _ += f;
                        y[v++] = S
                    }
                return tensor2d(y, [o, i])
            }, e.prototype.multiply = function(e, t) {
                return this.broadcastedBinaryOp(e, t, upcastType(e.dtype, t.dtype), function(e, t) {
                    return e * t
                })
            }, e.prototype.realDivide = function(e, t) {
                return this.broadcastedBinaryOp(e, t, "float32", function(e, t) {
                    return e / t
                })
            }, e.prototype.floorDiv = function(e, t) {
                return this.broadcastedBinaryOp(e, t, "int32", function(e, t) {
                    return Math.floor(e / t)
                })
            }, e.prototype.sum = function(e, t) {
                assertAxesAreInnerMostDims("sum", t, e.rank);
                for (var r = computeOutAndReduceShapes(e.shape, t), n = r[0], a = r[1], o = upcastType(e.dtype, "int32"), i = zeros(n, o), s = sizeFromShape(a), u = i.dataSync(), l = e.dataSync(), c = 0; c < u.length; ++c) {
                    for (var p = c * s, d = 0, h = 0; h < s; ++h) d += l[p + h];
                    u[c] = d
                }
                return i
            }, e.prototype.argMin = function(e, t) {
                var r = [t];
                assertAxesAreInnerMostDims("argMin", r, e.rank);
                for (var n = computeOutAndReduceShapes(e.shape, r), a = n[0], o = n[1], i = zeros(a, "int32"), s = sizeFromShape(o), 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) {
                var r = [t];
                assertAxesAreInnerMostDims("argMax", r, e.rank);
                for (var n = computeOutAndReduceShapes(e.shape, r), a = n[0], o = n[1], i = zeros(a, "int32"), s = sizeFromShape(o), 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 (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 a = upcastType(e.dtype, "int32"), o = zeros(e.shape, a), i = o.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 o
            }, e.prototype.equal = function(e, t) {
                return this.broadcastedBinaryOp(e, t, "bool", function(e, t) {
                    return e === t ? 1 : 0
                })
            }, e.prototype.notEqual = function(e, t) {
                return this.broadcastedBinaryOp(e, t, "bool", function(e, t) {
                    return e !== t ? 1 : 0
                })
            }, e.prototype.less = function(e, t) {
                return this.broadcastedBinaryOp(e, t, "bool", function(e, t) {
                    return e < t ? 1 : 0
                })
            }, e.prototype.lessEqual = function(e, t) {
                return this.broadcastedBinaryOp(e, t, "bool", function(e, t) {
                    return e <= t ? 1 : 0
                })
            }, e.prototype.greater = function(e, t) {
                return this.broadcastedBinaryOp(e, t, "bool", function(e, t) {
                    return e > t ? 1 : 0
                })
            }, e.prototype.greaterEqual = function(e, t) {
                return this.broadcastedBinaryOp(e, t, "bool", function(e, t) {
                    return e >= t ? 1 : 0
                })
            }, e.prototype.logicalNot = function(e) {
                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.broadcastedBinaryOp(e, t, "bool", function(e, t) {
                    return e && t
                })
            }, e.prototype.logicalOr = function(e, t) {
                return this.broadcastedBinaryOp(e, t, "bool", function(e, t) {
                    return e || t
                })
            }, e.prototype.where = function(e, t, r, n) {
                for (var a = e.dataSync(), o = t.dataSync(), i = r.dataSync(), s = zeros(t.shape, n), u = s.dataSync(), l = 0, c = 0 === e.rank || e.rank > 1 || 1 === t.rank ? 1 : t.shape[1], p = 0; p < a.length; p++)
                    for (var d = 0; d < c; d++) 1 === a[p] ? u[l++] = o[p] : u[l++] = i[p];
                return s
            }, e.prototype.topKValues = function(e, t) {
                return this.topK(e, t).values
            }, e.prototype.topKIndices = function(e, t) {
                return this.topK(e, t).indices
            }, e.prototype.topK = function(e, t) {
                for (var r = e.dataSync(), n = [], a = 0; a < r.length; a++) n.push({
                    value: r[a],
                    index: a
                });
                n.sort(function(e, t) {
                    return t.value - e.value
                });
                for (var o = getTypedArrayFromDType(e.dtype, t), i = new Int32Array(t), a = 0; a < t; a++) o[a] = n[a].value, i[a] = n[a].index;
                return {
                    values: tensor1d(o, e.dtype),
                    indices: tensor1d(i, "int32")
                }
            }, e.prototype.min = function(e, t) {
                assertAxesAreInnerMostDims("min", t, e.rank);
                for (var r = computeOutAndReduceShapes(e.shape, t), n = r[0], a = r[1], o = zeros(n, e.dtype), i = sizeFromShape(a), s = o.dataSync(), u = e.dataSync(), l = 0; l < s.length; ++l) {
                    for (var c = l * i, p = u[0], d = 0; d < i; ++d) {
                        var h = u[c + d];
                        h < p && (p = h)
                    }
                    s[l] = p
                }
                return o
            }, e.prototype.minimum = function(e, t) {
                return this.broadcastedBinaryOp(e, t, e.dtype, function(e, t) {
                    return Math.min(e, t)
                })
            }, e.prototype.mod = function(e, t) {
                return this.broadcastedBinaryOp(e, t, e.dtype, function(e, t) {
                    var r = e % t;
                    return e < 0 && t < 0 || e >= 0 && t >= 0 ? r : (r + t) % t
                })
            }, e.prototype.max = function(e, t) {
                assertAxesAreInnerMostDims("max", t, e.rank);
                for (var r = computeOutAndReduceShapes(e.shape, t), n = r[0], a = r[1], o = zeros(n, e.dtype), i = sizeFromShape(a), s = o.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 o
            }, e.prototype.maximum = function(e, t) {
                return this.broadcastedBinaryOp(e, t, e.dtype, function(e, t) {
                    return Math.max(e, t)
                })
            }, e.prototype.squaredDifference = function(e, t) {
                return this.broadcastedBinaryOp(e, t, e.dtype, function(e, t) {
                    var r = e - t;
                    return r * r
                })
            }, e.prototype.ceil = function(e) {
                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) {
                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) {
                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) {
                for (var t = e.dataSync(), r = new Float32Array(t.length), n = 0; n < t.length; ++n) {
                    var a = Math.floor(t[n]);
                    t[n] - a < .5 ? r[n] = Math.floor(t[n]) : t[n] - a > .5 ? r[n] = Math.ceil(t[n]) : r[n] = a % 2 == 0 ? a : a + 1
                }
                return Tensor.make(e.shape, {
                    values: r
                })
            }, e.prototype.exp = function(e) {
                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) {
                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) {
                for (var t = e.dataSync(), r = new Float32Array(t.length), n = 0; n < t.length; ++n) {
                    var a = t[n];
                    r[n] = Math.log(a)
                }
                return Tensor.make(e.shape, {
                    values: r
                })
            }, e.prototype.log1p = function(e) {
                for (var t = e.dataSync(), r = new Float32Array(t.length), n = 0; n < t.length; ++n) {
                    var a = t[n];
                    r[n] = Math.log1p(a)
                }
                return Tensor.make(e.shape, {
                    values: r
                })
            }, e.prototype.sqrt = function(e) {
                for (var t = e.dataSync(), r = new Float32Array(t.length), n = 0; n < t.length; ++n) {
                    var a = t[n];
                    r[n] = Math.sqrt(a)
                }
                return Tensor.make(e.shape, {
                    values: r
                })
            }, e.prototype.rsqrt = function(e) {
                for (var t = e.dataSync(), r = new Float32Array(t.length), n = 0; n < t.length; ++n) {
                    var a = t[n];
                    r[n] = 1 / Math.sqrt(a)
                }
                return Tensor.make(e.shape, {
                    values: r
                })
            }, e.prototype.square = function(e) {
                for (var t = e.dataSync(), r = new Float32Array(t.length), n = 0; n < t.length; ++n) {
                    var a = t[n];
                    r[n] = a * a
                }
                return Tensor.make(e.shape, {
                    values: r
                })
            }, e.prototype.reciprocal = function(e) {
                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) {
                for (var t = zeros(e.shape, e.dtype), r = t.dataSync(), n = e.dataSync(), a = 0; a < n.length; ++a) r[a] = Math.max(0, n[a]);
                return t
            }, e.prototype.elu = function(e) {
                for (var t = new Float32Array(e.size), r = e.dataSync(), n = 0; n < r.length; ++n) {
                    var a = r[n];
                    t[n] = a >= 0 ? a : Math.exp(a) - 1
                }
                return Tensor.make(e.shape, {
                    values: t
                })
            }, e.prototype.eluDer = function(e, t) {
                for (var r = new Float32Array(t.size), n = t.dataSync(), a = e.dataSync(), o = 0; o < n.length; ++o) {
                    var i = n[o];
                    r[o] = i >= 1 ? a[o] : a[o] * (i + 1)
                }
                return Tensor.make(t.shape, {
                    values: r
                })
            }, e.prototype.selu = function(e) {
                for (var t = SELU_SCALEALPHA, r = SELU_SCALE, n = new Float32Array(e.size), a = e.dataSync(), o = 0; o < a.length; ++o) {
                    var i = a[o];
                    n[o] = i >= 0 ? r * i : t * (Math.exp(i) - 1)
                }
                return Tensor.make(e.shape, {
                    values: n
                })
            }, e.prototype.clip = function(e, t, r) {
                for (var n = new Float32Array(e.size), a = e.dataSync(), o = 0; o < a.length; ++o) n[o] = Math.min(r, Math.max(t, a[o]));
                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.int = function(e) {
                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) {
                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) {
                for (var t = Math.log(1.1920928955078125e-7) + 2, r = new Float32Array(e.size), n = e.dataSync(), a = 0; a < n.length; ++a) {
                    var o = n[a] > -t,
                        i = n[a] < t,
                        s = Math.exp(n[a]),
                        u = void 0;
                    u = i ? s : o ? n[a] : Math.log(1 + s), r[a] = u
                }
                return Tensor.make(e.shape, {
                    values: r
                })
            }, e.prototype.sin = function(e) {
                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) {
                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) {
                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) {
                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) {
                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) {
                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.broadcastedBinaryOp(e, t, e.dtype, function(e, t) {
                    return Math.atan2(e, t)
                })
            }, e.prototype.sinh = function(e) {
                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) {
                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) {
                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) {
                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) {
                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) {
                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) {
                for (var t = new Float32Array(e.size), r = e.dataSync(), n = ERF_P, a = ERF_A1, o = 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 + o) * p + a) * p * Math.exp(-c * c)
                }
                return Tensor.make(e.shape, {
                    values: t
                })
            }, e.prototype.step = function(e, t) {
                void 0 === t && (t = 0);
                for (var r = new Float32Array(e.size), n = e.dataSync(), a = 0; a < n.length; ++a) {
                    var o = n[a];
                    isNaN(o) ? r[a] = NaN : r[a] = o > 0 ? 1 : t
                }
                return Tensor.make(e.shape, {
                    values: r
                })
            }, e.prototype.conv2d = function(e, t, r) {
                for (var n = r.filterHeight, a = r.filterWidth, o = r.dilationHeight, i = r.dilationWidth, s = r.padInfo.left, u = r.padInfo.top, l = buffer(r.outShape, e.dtype), c = 0; c < r.batchSize; ++c)
                    for (var p = 0; p < r.outChannels; ++p)
                        for (var d = 0; d < r.outHeight; ++d)
                            for (var h = d * r.strideHeight - s, f = 0; f < r.outWidth; ++f) {
                                for (var m = f * r.strideWidth - u, g = 0, y = 0; y < n; y++) {
                                    var v = h + y * o;
                                    if (!(v < 0 || v >= r.inHeight))
                                        for (var b = 0; b < a; b++) {
                                            var x = m + b * i;
                                            if (!(x < 0 || x >= r.inWidth))
                                                for (var w = 0; w < r.inChannels; ++w) g += e.get(c, v, x, w) * t.get(y, b, w, p)
                                        }
                                }
                                l.set(g, c, d, f, p)
                            }
                return l.toTensor()
            }, e.prototype.conv2dDerInput = function(e, t, r) {
                for (var n = buffer(r.inShape, "float32"), a = n.values, o = n.strides, i = o[0], s = o[1], u = o[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], y = m[1], v = m[2], b = r.batchSize, x = r.filterHeight, w = r.filterWidth, _ = r.inChannels, S = r.inHeight, N = r.inWidth, A = r.outChannels, E = r.outHeight, T = r.outWidth, I = r.strideHeight, O = r.strideWidth, P = x - 1 - r.padInfo.top, R = w - 1 - r.padInfo.left, C = 0; C < b; ++C)
                    for (var k = 0; k < _; ++k)
                        for (var D = 0; D < S; ++D)
                            for (var M = D - P, L = Math.max(0, Math.ceil(M / I)), z = Math.min(E, (x + M) / I), V = 0; V < N; ++V) {
                                for (var F = V - R, $ = Math.max(0, Math.ceil(F / O)), B = Math.min(T, (w + F) / O), U = 0, j = L; j < z; ++j)
                                    for (var G = j * I - M, W = $; W < B; ++W)
                                        for (var q = p * C + d * j + h * W, H = g * (x - 1 - G) + y * (w - 1 - (W * O - F)) + v * k, K = 0; K < A; ++K) U += l[q + K] * f[H + K];
                                a[i * C + s * D + u * V + k] = U
                            }
                return n.toTensor()
            }, e.prototype.conv2dDerFilter = function(e, t, r) {
                for (var n = r.strideHeight, a = r.strideWidth, o = r.filterHeight, i = r.filterWidth, s = buffer(r.filterShape, "float32"), u = r.padInfo.left, l = r.padInfo.top, c = 0; c < o; ++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) / a)), m = Math.min(r.outWidth, (r.inWidth + u - h) / a), g = 0; g < r.inChannels; ++g)
                            for (var y = 0; y < r.outChannels; ++y) {
                                for (var v = 0, b = 0; b < r.batchSize; ++b)
                                    for (var x = p; x < d; ++x)
                                        for (var w = c + x * n - l, _ = f; _ < m; ++_) {
                                            var S = h + _ * a - u;
                                            v += e.get(b, w, S, g) * t.get(b, x, _, y)
                                        }
                                s.set(v, c, h, g, y)
                            }
                return s.toTensor()
            }, e.prototype.depthwiseConv2D = function(e, t, r) {
                for (var n = r.filterHeight, a = r.filterWidth, o = 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 = 0; p < r.batchSize; ++p)
                    for (var d = 0; d < r.inChannels; ++d)
                        for (var h = 0; h < r.outHeight; ++h)
                            for (var f = h * r.strideHeight - s, m = 0; m < r.outWidth; ++m)
                                for (var g = m * r.strideWidth - u, y = 0; y < l; ++y) {
                                    for (var v = 0, b = 0; b < n; ++b) {
                                        var x = f + b * o;
                                        if (!(x < 0 || x >= r.inHeight))
                                            for (var w = 0; w < a; ++w) {
                                                var _ = g + w * i;
                                                _ < 0 || _ >= r.inWidth || (v += e.get(p, x, _, d) * t.get(b, w, d, y))
                                            }
                                    }
                                    c.set(v, p, h, m, d * l + y)
                                }
                return c.toTensor()
            }, e.prototype.depthwiseConv2DDerInput = function(e, t, r) {
                for (var n = buffer(r.inShape, "float32"), a = n.values, o = n.strides, i = o[0], s = o[1], u = o[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], y = m[1], v = m[2], b = r.batchSize, x = r.filterHeight, w = r.filterWidth, _ = r.inChannels, S = r.inHeight, N = r.inWidth, A = r.outChannels, E = r.outHeight, T = r.outWidth, I = r.strideHeight, O = r.strideWidth, P = x - 1 - r.padInfo.top, R = w - 1 - r.padInfo.left, C = A / _, k = 0; k < b; ++k)
                    for (var D = 0; D < _; ++D)
                        for (var M = 0; M < S; ++M)
                            for (var L = M - P, z = Math.max(0, Math.ceil(L / I)), V = Math.min(E, (x + L) / I), F = 0; F < N; ++F) {
                                for (var $ = F - R, B = Math.max(0, Math.ceil($ / O)), U = Math.min(T, (w + $) / O), j = 0, G = z; G < V; ++G)
                                    for (var W = G * I - L, q = B; q < U; ++q)
                                        for (var H = p * k + d * G + h * q, K = g * (x - 1 - W) + y * (w - 1 - (q * O - $)) + v * D, X = 0; X < C; ++X) j += l[H + (D * C + X)] * f[K + X];
                                a[i * k + s * M + u * F + D] = j
                            }
                return n.toTensor()
            }, e.prototype.depthwiseConv2DDerFilter = function(e, t, r) {
                for (var n = r.strideHeight, a = r.strideWidth, o = 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 < o; ++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) / a)), g = Math.min(r.outWidth, (r.inWidth + u - f) / a), y = 0; y < r.outChannels; ++y) {
                            for (var v = Math.trunc(y / c), b = y % c, x = 0, w = 0; w < r.batchSize; ++w)
                                for (var _ = d; _ < h; ++_)
                                    for (var S = p + _ * n - l, N = m; N < g; ++N) {
                                        var A = f + N * a - u;
                                        x += e.get(w, S, A, v) * t.get(w, _, N, y)
                                    }
                            s.set(x, p, f, v, b)
                        }
                return s.toTensor()
            }, e.prototype.tile = function(e, t) {
                for (var r = new Array(e.rank), n = 0; n < r.length; n++) r[n] = e.shape[n] * t[n];
                for (var a = buffer(r, e.dtype), o = e.buffer(), n = 0; n < a.values.length; ++n) {
                    for (var i = a.indexToLoc(n), s = new Array(e.rank), u = 0; u < s.length; u++) s[u] = i[u] % e.shape[u];
                    var l = o.locToIndex(s);
                    a.values[n] = o.values[l]
                }
                return a.toTensor()
            }, e.prototype.pad = function(e, t, r) {
                var n = t.map(function(t, r) {
                        return t[0] + e.shape[r] + t[1]
                    }),
                    a = t.map(function(e) {
                        return e[0]
                    }),
                    o = e.buffer(),
                    i = buffer(n, e.dtype);
                0 !== r && i.values.fill(r);
                for (var s = 0; s < e.size; s++) {
                    var u = o.indexToLoc(s),
                        l = u.map(function(e, t) {
                            return e + a[t]
                        });
                    i.set.apply(i, [e.get.apply(e, u)].concat(l))
                }
                return i.toTensor()
            }, e.prototype.transpose = function(e, t) {
                for (var r = new Array(e.rank), n = 0; n < r.length; n++) r[n] = e.shape[t[n]];
                for (var a = e.dataSync(), o = buffer(r, e.dtype), i = e.buffer(), 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 = o.locToIndex(u);
                    o.values[c] = a[n]
                }
                return o.toTensor()
            }, e.prototype.gather = function(e, t, r) {
                var n = e.shape.slice(),
                    a = t.dataSync();
                n[r] = a.length;
                for (var o = buffer(n, e.dtype), i = e.buffer(), s = 0; s < o.size; ++s) {
                    var u = o.indexToLoc(s),
                        l = u.slice();
                    l[r] = a[u[r]];
                    var c = i.locToIndex(l);
                    o.values[s] = i.values[c]
                }
                return o.toTensor()
            }, e.prototype.pool = function(e, t, r) {
                for (var n = t.strideHeight, a = t.strideWidth, o = t.filterHeight, i = t.filterWidth, s = buffer(t.outShape, "float32"), u = t.padInfo.top, l = t.padInfo.left, c = 0; c < t.batchSize; ++c)
                    for (var p = 0; p < t.inChannels; ++p)
                        for (var d = 0; d < t.outHeight; ++d)
                            for (var h = d * n - u, f = Math.max(0, h), m = Math.min(t.inHeight, o + h), g = 0; g < t.outWidth; ++g) {
                                for (var y = g * a - l, v = Math.max(0, y), b = Math.min(t.inWidth, i + y), x = "max" === r ? Number.NEGATIVE_INFINITY : Number.POSITIVE_INFINITY, w = 0, _ = 0, S = f; S < m; ++S) {
                                    for (var N = v; N < b; ++N) {
                                        var A = e.get(c, S, N, p);
                                        "max" === r && A > x ? x = A : "avg" === r && (w += A, _++)
                                    }
                                    if (isNaN(x)) break
                                }
                                s.set("avg" === r ? w / _ : x, c, d, g, p)
                            }
                return s.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, a = t.strideWidth, o = t.filterHeight, i = t.filterWidth, s = t.padInfo.top, u = t.padInfo.left, l = 0; l < t.batchSize; ++l)
                    for (var c = 0; c < t.inChannels; ++c)
                        for (var p = 0; p < t.outHeight; ++p)
                            for (var d = p * n - s, h = Math.max(0, d), f = Math.min(t.inHeight, o + d), m = 0; m < t.outWidth; ++m) {
                                for (var g = m * a - u, y = Math.max(0, g), v = Math.min(t.inWidth, i + g), b = Number.NEGATIVE_INFINITY, x = -1, w = h; w < f; ++w)
                                    for (var _ = w - d, S = y; S < v; ++S) {
                                        var N = S - g,
                                            A = e.get(l, w, S, c);
                                        A > b && (b = A, x = _ * i + N)
                                    }
                                r.set(x, l, p, m, c)
                            }
                return r.toTensor()
            }, e.prototype.maxPoolBackprop = function(e, t, r, n) {
                for (var a = this.maxPoolPositions(t, n), o = n.strideHeight, i = n.strideWidth, s = n.filterHeight, u = n.filterWidth, l = u - 1 - n.padInfo.left, c = s - 1 - n.padInfo.top, p = buffer(t.shape, "float32"), d = 0; d < n.batchSize; ++d)
                    for (var h = 0; h < n.inChannels; ++h)
                        for (var f = 0; f < n.inHeight; ++f)
                            for (var m = 0; m < n.inWidth; ++m) {
                                for (var g = f - c, y = m - l, v = 0, b = 0; b < s; ++b) {
                                    var x = (g + b) / o;
                                    if (!(x < 0 || x >= n.outHeight || Math.floor(x) !== x))
                                        for (var w = 0; w < u; ++w) {
                                            var _ = (y + w) / i;
                                            if (!(_ < 0 || _ >= n.outWidth || Math.floor(_) !== _)) {
                                                var S = s * u - 1 - a.get(d, x, _, h) === b * u + w ? 1 : 0;
                                                0 !== S && (v += e.get(d, x, _, h) * S)
                                            }
                                        }
                                }
                                p.set(v, d, f, m, h)
                            }
                return p.toTensor()
            }, e.prototype.avgPoolBackprop = function(e, t, r) {
                for (var n = r.strideHeight, a = r.strideWidth, o = r.filterHeight, i = r.filterWidth, s = i - 1 - r.padInfo.left, u = o - 1 - r.padInfo.top, l = buffer(t.shape, "float32"), c = 1 / (o * i), p = 0; p < r.batchSize; ++p)
                    for (var d = 0; d < r.inChannels; ++d)
                        for (var h = 0; h < r.inHeight; ++h)
                            for (var f = 0; f < r.inWidth; ++f) {
                                for (var m = h - u, g = f - s, y = 0, v = 0; v < o; ++v) {
                                    var b = (m + v) / n;
                                    if (!(b < 0 || b >= r.outHeight || Math.floor(b) !== b))
                                        for (var x = 0; x < i; ++x) {
                                            var w = (g + x) / a;
                                            w < 0 || w >= r.outWidth || Math.floor(w) !== w || (y += e.get(p, b, w, d))
                                        }
                                }
                                l.set(y * c, p, h, f, d)
                            }
                return l.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.pool(e, t, "avg").toFloat()
            }, e.prototype.resizeBilinear = function(e, t, r, n) {
                for (var a = e.shape, o = a[0], i = a[1], s = a[2], u = a[3], l = buffer([o, t, r, u], e.dtype), c = [n && t > 1 ? i - 1 : i, n && r > 1 ? s - 1 : s], p = [n && t > 1 ? t - 1 : t, n && r > 1 ? r - 1 : r], d = 0; d < o; d++)
                    for (var h = 0; h < t; h++)
                        for (var f = 0; f < r; f++)
                            for (var m = 0; m < u; m++) {
                                var g = c[0] * h / p[0],
                                    y = c[1] * f / p[1],
                                    v = Math.floor(g),
                                    b = Math.min(i - 1, Math.ceil(g)),
                                    x = Math.floor(y),
                                    w = Math.min(s - 1, Math.ceil(y)),
                                    _ = e.get(d, v, x, m),
                                    S = e.get(d, b, x, m),
                                    N = y - x,
                                    A = _ + (e.get(d, v, w, m) - _) * N,
                                    E = A + (S + (e.get(d, b, w, m) - S) * N - A) * (g - v);
                                l.set(E, d, h, f, m)
                            }
                return l.toTensor()
            }, e.prototype.resizeBilinearBackprop = function(e, t, r) {
                for (var n = t.shape, a = n[0], o = n[1], i = n[2], s = n[3], u = e.shape, l = u[1], c = u[2], p = buffer([a, o, i, s], t.dtype), d = [r && l > 1 ? o - 1 : o, 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 = 0; g < a; g++)
                    for (var y = 0; y < l; y++)
                        for (var v = y * f, b = Math.floor(v), x = Math.min(Math.ceil(v), o - 1), w = v - b, _ = 1 - w, S = 0; S < c; S++)
                            for (var N = S * m, A = Math.floor(N), E = Math.min(Math.ceil(N), i - 1), T = N - A, I = 1 - T, O = 0; O < s; O++) {
                                var P = e.get(g, y, S, O),
                                    R = p.get(g, b, A, O);
                                R += P * _ * I, p.set(R, g, b, A, O);
                                var C = p.get(g, b, E, O);
                                C += P * _ * T, p.set(C, g, b, E, O);
                                var k = p.get(g, x, A, O);
                                k += P * w * I, p.set(k, g, x, A, O);
                                var D = p.get(g, x, E, O);
                                D += P * w * T, p.set(D, g, x, E, O)
                            }
                return p.toTensor()
            }, e.prototype.resizeNearestNeighbor = function(e, t, r, n) {
                for (var a = e.shape, o = a[0], i = a[1], s = a[2], u = a[3], l = buffer([o, t, r, u], e.dtype), c = n ? [i - 1, s - 1] : [i, s], p = n ? [t - 1, r - 1] : [t, r], d = 0; d < o; d++)
                    for (var h = 0; h < t; h++)
                        for (var f = 0; f < r; f++)
                            for (var m = 0; m < u; m++) {
                                var g = c[0] * h / p[0],
                                    y = c[1] * f / p[1],
                                    v = Math.min(i - 1, n ? Math.round(g) : Math.floor(g)),
                                    b = Math.min(s - 1, n ? Math.round(y) : Math.floor(y)),
                                    x = e.get(d, v, b, m);
                                l.set(x, d, h, f, m)
                            }
                return l.toTensor()
            }, e.prototype.batchNormalization = function(e, t, r, n, a, o) {
                for (var i = e.dataSync(), s = t.dataSync(), u = r.dataSync(), l = a ? a.dataSync() : new Float32Array([1]), c = o ? o.dataSync() : new Float32Array([0]), p = new Float32Array(i.length), d = 0; d < i.length; d++) p[d] = c[d % c.length] + (i[d] - s[d % s.length]) * l[d % l.length] / Math.sqrt(u[d % u.length] + n);
                return tensor4d(p, e.shape)
            }, e.prototype.localResponseNormalization4D = function(e, t, r, n, a) {
                for (var o = buffer(e.shape, "float32"), i = t, s = o.shape[3] - 1, u = 0; u < o.shape[0]; u++)
                    for (var l = 0; l <= o.shape[1]; l++)
                        for (var c = 0; c < o.shape[2]; c++)
                            for (var p = 0; p < o.shape[3]; p++) {
                                var d = function(t, r, n, a) {
                                        for (var o = 0, u = Math.max(0, a - i); u <= Math.min(a + i, s); u++) {
                                            var l = e.get(t, r, n, u);
                                            o += l * l
                                        }
                                        return o
                                    }(u, l, c, p),
                                    h = e.get(u, l, c, p) * Math.pow(r + n * d, -a);
                                o.set(h, u, l, c, p)
                            }
                return o.toTensor()
            }, e.prototype.multinomial = function(e, t, r, n) {
                for (var a = t ? e : softmax(e), o = a.shape[0], i = a.shape[1], s = zeros([o, r], "int32"), u = s.dataSync(), l = a.dataSync(), c = 0; c < o; ++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 y = f();
                        u[m + g] = d.length;
                        for (var v = 0; v < d.length; v++)
                            if (y < d[v]) {
                                u[m + g] = v;
                                break
                            }
                    }
                }
                return s
            }, e.prototype.oneHot = function(e, t, r, n) {
                var a = new Float32Array(e.size * t);
                a.fill(n);
                for (var o = 0; o < e.size; ++o) a[o * t + e.get(o)] = r;
                return tensor2d(a, [e.size, t])
            }, e.prototype.broadcastedBinaryOp = function(e, t, r, n) {
                for (var a = assertAndGetBroadcastShape(e.shape, t.shape), o = buffer(a, r), i = e.dataSync(), s = t.dataSync(), u = getBroadcastDims(e.shape, a), l = getBroadcastDims(t.shape, a), c = e.buffer(), p = t.buffer(), d = 0; d < o.values.length; ++d) ! function(r) {
                    var a = o.indexToLoc(r),
                        d = a.slice(-e.rank);
                    u.forEach(function(e) {
                        return d[e] = 0
                    });
                    var h = c.locToIndex(d),
                        f = a.slice(-t.rank);
                    l.forEach(function(e) {
                        return f[e] = 0
                    });
                    var m = p.locToIndex(f);
                    o.values[r] = n(i[h], s[m])
                }(d);
                return o.toTensor()
            }, e.prototype.dispose = function() {}, e
        }();
    ENV.registerBackend("cpu", function() {
        return new MathBackendCPU
    }, 1);
    var __decorate$27 = function(e, t, r, n) {
            var a, o = arguments.length,
                i = o < 3 ? t : null === n ? n = Object.getOwnPropertyDescriptor(t, r) : n;
            if ("object" == typeof Reflect && "function" == typeof Reflect.decorate) i = Reflect.decorate(e, t, r, n);
            else
                for (var s = e.length - 1; s >= 0; s--)(a = e[s]) && (i = (o < 3 ? a(i) : o > 3 ? a(t, r, i) : a(t, r)) || i);
            return o > 3 && i && Object.defineProperty(t, r, i), i
        },
        BrowserUtil = function() {
            function e() {}
            return e.nextFrame = function() {
                return new Promise(function(e) {
                    return requestAnimationFrame(function() {
                        return e()
                    })
                })
            }, __decorate$27([doc()], e, "nextFrame", null), e
        }(),
        DTYPE_VALUE_SIZE_MAP = {
            float32: 4,
            int32: 4,
            uint16: 2,
            uint8: 1,
            bool: 1
        },
        __awaiter$7 = function(e, t, r, n) {
            return new(r || (r = Promise))(function(a, o) {
                function i(e) {
                    try {
                        u(n.next(e))
                    } catch (e) {
                        o(e)
                    }
                }

                function s(e) {
                    try {
                        u(n.throw(e))
                    } catch (e) {
                        o(e)
                    }
                }

                function u(e) {
                    e.done ? a(e.value) : new r(function(t) {
                        t(e.value)
                    }).then(i, s)
                }
                u((n = n.apply(e, t || [])).next())
            })
        },
        __generator$7 = function(e, t) {
            function r(e) {
                return function(t) {
                    return n([e, t])
                }
            }

            function n(r) {
                if (a) throw new TypeError("Generator is already executing.");
                for (; u;) try {
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                    switch (o = 0, i && (r = [0, i.value]), r[0]) {
                        case 0:
                        case 1:
                            i = r;
                            break;
                        case 4:
                            return u.label++, {
                                value: r[1],
                                done: !1
                            };
                        case 5:
                            u.label++, o = r[1], r = [0];
                            continue;
                        case 7:
                            r = u.ops.pop(), u.trys.pop();
                            continue;
                        default:
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                                u = 0;
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                            if (3 === r[0] && (!i || r[1] > i[0] && r[1] < i[3])) {
                                u.label = r[1];
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                            if (6 === r[0] && u.label < i[1]) {
                                u.label = i[1], i = r;
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                            if (i && u.label < i[2]) {
                                u.label = i[2], u.ops.push(r);
                                break
                            }
                            i[2] && u.ops.pop(), u.trys.pop();
                            continue
                    }
                    r = t.call(e, u)
                } catch (e) {
                    r = [6, e], o = 0
                } finally {
                    a = i = 0
                }
                if (5 & r[0]) throw r[1];
                return {
                    value: r[0] ? r[1] : void 0,
                    done: !0
                }
            }
            var a, o, i, s, u = {
                label: 0,
                sent: function() {
                    if (1 & i[0]) throw i[1];
                    return i[1]
                },
                trys: [],
                ops: []
            };
            return s = {
                next: r(0),
                throw: r(1),
                return: r(2)
            }, "function" == typeof Symbol && (s[Symbol.iterator] = function() {
                return this
            }), s
        },
        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
        }(),
        __decorate$28 = function(e, t, r, n) {
            var a, o = arguments.length,
                i = o < 3 ? t : null === n ? n = Object.getOwnPropertyDescriptor(t, r) : n;
            if ("object" == typeof Reflect && "function" == typeof Reflect.decorate) i = Reflect.decorate(e, t, r, n);
            else
                for (var s = e.length - 1; s >= 0; s--)(a = e[s]) && (i = (o < 3 ? a(i) : o > 3 ? a(t, r, i) : a(t, r)) || i);
            return o > 3 && i && Object.defineProperty(t, r, i), i
        },
        __awaiter$8 = function(e, t, r, n) {
            return new(r || (r = Promise))(function(a, o) {
                function i(e) {
                    try {
                        u(n.next(e))
                    } catch (e) {
                        o(e)
                    }
                }

                function s(e) {
                    try {
                        u(n.throw(e))
                    } catch (e) {
                        o(e)
                    }
                }

                function u(e) {
                    e.done ? a(e.value) : new r(function(t) {
                        t(e.value)
                    }).then(i, s)
                }
                u((n = n.apply(e, t || [])).next())
            })
        },
        __generator$8 = function(e, t) {
            function r(e) {
                return function(t) {
                    return n([e, t])
                }
            }

            function n(r) {
                if (a) throw new TypeError("Generator is already executing.");
                for (; u;) try {
                    if (a = 1, o && (i = o[2 & r[0] ? "return" : r[0] ? "throw" : "next"]) && !(i = i.call(o, r[1])).done) return i;
                    switch (o = 0, i && (r = [0, i.value]), r[0]) {
                        case 0:
                        case 1:
                            i = r;
                            break;
                        case 4:
                            return u.label++, {
                                value: r[1],
                                done: !1
                            };
                        case 5:
                            u.label++, o = r[1], r = [0];
                            continue;
                        case 7:
                            r = u.ops.pop(), u.trys.pop();
                            continue;
                        default:
                            if (i = u.trys, !(i = i.length > 0 && i[i.length - 1]) && (6 === r[0] || 2 === r[0])) {
                                u = 0;
                                continue
                            }
                            if (3 === r[0] && (!i || r[1] > i[0] && r[1] < i[3])) {
                                u.label = r[1];
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                            }
                            if (6 === r[0] && u.label < i[1]) {
                                u.label = i[1], i = r;
                                break
                            }
                            if (i && u.label < i[2]) {
                                u.label = i[2], u.ops.push(r);
                                break
                            }
                            i[2] && u.ops.pop(), u.trys.pop();
                            continue
                    }
                    r = t.call(e, u)
                } catch (e) {
                    r = [6, e], o = 0
                } finally {
                    a = i = 0
                }
                if (5 & r[0]) throw r[1];
                return {
                    value: r[0] ? r[1] : void 0,
                    done: !0
                }
            }
            var a, o, i, s, u = {
                label: 0,
                sent: function() {
                    if (1 & i[0]) throw i[1];
                    return i[1]
                },
                trys: [],
                ops: []
            };
            return s = {
                next: r(0),
                throw: r(1),
                return: r(2)
            }, "function" == typeof Symbol && (s[Symbol.iterator] = function() {
                return this
            }), s
        },
        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
        }(),
        ModelManagement = function() {
            function e() {}
            return e.listModels = function() {
                return __awaiter$8(this, void 0, void 0, function() {
                    var e, t, r, n, a, o, i, s;
                    return __generator$8(this, function(u) {
                        switch (u.label) {
                            case 0:
                                e = ModelStoreManagerRegistry.getSchemes(), t = {}, r = 0, n = e, u.label = 1;
                            case 1:
                                return r < n.length ? (a = n[r], [4, ModelStoreManagerRegistry.getManager(a).listModels()]) : [3, 4];
                            case 2:
                                o = u.sent();
                                for (i in o) t[s = a + URL_SCHEME_SUFFIX + i] = o[i];
                                u.label = 3;
                            case 3:
                                return r++, [3, 1];
                            case 4:
                                return [2, t]
                        }
                    })
                })
            }, e.removeModel = function(e) {
                return __awaiter$8(this, void 0, void 0, function() {
                    var t, r;
                    return __generator$8(this, function(n) {
                        switch (n.label) {
                            case 0:
                                return t = parseURL(e), r = ModelStoreManagerRegistry.getManager(t.scheme), [4, r.removeModel(t.path)];
                            case 1:
                                return [2, n.sent()]
                        }
                    })
                })
            }, e.copyModel = function(e, t) {
                return __awaiter$8(this, void 0, void 0, function() {
                    var r;
                    return __generator$8(this, function(n) {
                        switch (n.label) {
                            case 0:
                                return r = !1, [4, cloneModelInternal(e, t, r)];
                            case 1:
                                return [2, n.sent()]
                        }
                    })
                })
            }, e.moveModel = function(e, t) {
                return __awaiter$8(this, void 0, void 0, function() {
                    var r;
                    return __generator$8(this, function(n) {
                        switch (n.label) {
                            case 0:
                                return r = !0, [4, cloneModelInternal(e, t, r)];
                            case 1:
                                return [2, n.sent()]
                        }
                    })
                })
            }, __decorate$28([doc()], e, "listModels", null), __decorate$28([doc()], e, "removeModel", null), __decorate$28([doc()], e, "copyModel", null), __decorate$28([doc()], e, "moveModel", null), e
        }(),
        __awaiter$9 = function(e, t, r, n) {
            return new(r || (r = Promise))(function(a, o) {
                function i(e) {
                    try {
                        u(n.next(e))
                    } catch (e) {
                        o(e)
                    }
                }

                function s(e) {
                    try {
                        u(n.throw(e))
                    } catch (e) {
                        o(e)
                    }
                }

                function u(e) {
                    e.done ? a(e.value) : new r(function(t) {
                        t(e.value)
                    }).then(i, s)
                }
                u((n = n.apply(e, t || [])).next())
            })
        },
        __generator$9 = function(e, t) {
            function r(e) {
                return function(t) {
                    return n([e, t])
                }
            }

            function n(r) {
                if (a) throw new TypeError("Generator is already executing.");
                for (; u;) try {
                    if (a = 1, o && (i = o[2 & r[0] ? "return" : r[0] ? "throw" : "next"]) && !(i = i.call(o, r[1])).done) return i;
                    switch (o = 0, i && (r = [0, i.value]), r[0]) {
                        case 0:
                        case 1:
                            i = r;
                            break;
                        case 4:
                            return u.label++, {
                                value: r[1],
                                done: !1
                            };
                        case 5:
                            u.label++, o = r[1], r = [0];
                            continue;
                        case 7:
                            r = u.ops.pop(), u.trys.pop();
                            continue;
                        default:
                            if (i = u.trys, !(i = i.length > 0 && i[i.length - 1]) && (6 === r[0] || 2 === r[0])) {
                                u = 0;
                                continue
                            }
                            if (3 === r[0] && (!i || r[1] > i[0] && r[1] < i[3])) {
                                u.label = r[1];
                                break
                            }
                            if (6 === r[0] && u.label < i[1]) {
                                u.label = i[1], i = r;
                                break
                            }
                            if (i && u.label < i[2]) {
                                u.label = i[2], u.ops.push(r);
                                break
                            }
                            i[2] && u.ops.pop(), u.trys.pop();
                            continue
                    }
                    r = t.call(e, u)
                } catch (e) {
                    r = [6, e], o = 0
                } finally {
                    a = i = 0
                }
                if (5 & r[0]) throw r[1];
                return {
                    value: r[0] ? r[1] : void 0,
                    done: !0
                }
            }
            var a, o, i, s, u = {
                label: 0,
                sent: function() {
                    if (1 & i[0]) throw i[1];
                    return i[1]
                },
                trys: [],
                ops: []
            };
            return s = {
                next: r(0),
                throw: r(1),
                return: r(2)
            }, "function" == typeof Symbol && (s[Symbol.iterator] = function() {
                return this
            }), s
        },
        DATABASE_NAME = "tensorflowjs",
        DATABASE_VERSION = 1,
        MODEL_STORE_NAME = "models_store",
        INFO_STORE_NAME = "model_info_store",
        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$9(this, void 0, void 0, function() {
                    return __generator$9(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$9(this, void 0, void 0, function() {
                    return __generator$9(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 a = r.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);
                    a.onupgradeneeded = function() {
                        return setUpDatabase(a)
                    }, a.onsuccess = function() {
                        var o = a.result;
                        if (null == t) {
                            var i = o.transaction(MODEL_STORE_NAME, "readonly"),
                                s = i.objectStore(MODEL_STORE_NAME).get(r.modelPath);
                            s.onsuccess = function() {
                                if (null == s.result) return o.close(), n(new Error("Cannot find model with path '" + r.modelPath + "' in IndexedDB."));
                                e(s.result.modelArtifacts)
                            }, s.onerror = function(e) {
                                return o.close(), n(s.error)
                            }, i.oncomplete = function() {
                                return o.close()
                            }
                        } else {
                            var u, l = getModelArtifactsInfoForJSON(t),
                                c = o.transaction(INFO_STORE_NAME, "readwrite"),
                                p = c.objectStore(INFO_STORE_NAME),
                                d = p.put({
                                    modelPath: r.modelPath,
                                    modelArtifactsInfo: l
                                });
                            d.onsuccess = function() {
                                var a = (u = o.transaction(MODEL_STORE_NAME, "readwrite")).objectStore(MODEL_STORE_NAME).put({
                                    modelPath: r.modelPath,
                                    modelArtifacts: t,
                                    modelArtifactsInfo: l
                                });
                                a.onsuccess = function() {
                                    return e({
                                        modelArtifactsInfo: l
                                    })
                                }, a.onerror = function(e) {
                                    var t = (p = c.objectStore(INFO_STORE_NAME)).delete(r.modelPath);
                                    t.onsuccess = function() {
                                        return o.close(), n(a.error)
                                    }, t.onerror = function(e) {
                                        return o.close(), n(a.error)
                                    }
                                }
                            }, d.onerror = function(e) {
                                return o.close(), n(d.error)
                            }, c.oncomplete = function() {
                                null == u ? o.close() : u.oncomplete = function() {
                                    return o.close()
                                }
                            }
                        }
                    }, a.onerror = function(e) {
                        return n(a.error)
                    }
                })
            }, e.URL_SCHEME = "indexeddb://", e
        }(),
        indexedDBRouter = function(e) {
            return ENV.get("IS_BROWSER") && e.startsWith(BrowserIndexedDB.URL_SCHEME) ? browserIndexedDB(e.slice(BrowserIndexedDB.URL_SCHEME.length)) : null
        };
    IORouterRegistry.registerSaveRouter(indexedDBRouter), IORouterRegistry.registerLoadRouter(indexedDBRouter);
    var BrowserIndexedDBManager = function() {
        function e() {
            this.indexedDB = getIndexedDBFactory()
        }
        return e.prototype.listModels = function() {
            return __awaiter$9(this, void 0, void 0, function() {
                var e = this;
                return __generator$9(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,
                                a = e.transaction(INFO_STORE_NAME, "readonly"),
                                o = a.objectStore(INFO_STORE_NAME).getAll();
                            o.onsuccess = function() {
                                for (var e = {}, r = 0, n = o.result; r < n.length; r++) {
                                    var a = n[r];
                                    e[a.modelPath] = a.modelArtifactsInfo
                                }
                                t(e)
                            }, o.onerror = function(t) {
                                return e.close(), r(o.error)
                            }, a.oncomplete = function() {
                                return e.close()
                            }
                        }, n.onerror = function(e) {
                            return r(n.error)
                        }
                    })]
                })
            })
        }, e.prototype.removeModel = function(e) {
            return __awaiter$9(this, void 0, void 0, function() {
                var t = this;
                return __generator$9(this, function(r) {
                    return e = maybeStripScheme(e), [2, new Promise(function(r, n) {
                        var a = t.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);
                        a.onupgradeneeded = function() {
                            return setUpDatabase(a)
                        }, a.onsuccess = function() {
                            var t, o = a.result,
                                i = o.transaction(INFO_STORE_NAME, "readwrite"),
                                s = i.objectStore(INFO_STORE_NAME),
                                u = s.get(e);
                            u.onsuccess = function() {
                                if (null == u.result) return o.close(), n(new Error("Cannot find model with path '" + e + "' in IndexedDB."));
                                var a = s.delete(e),
                                    i = function() {
                                        var a = (t = o.transaction(MODEL_STORE_NAME, "readwrite")).objectStore(MODEL_STORE_NAME).delete(e);
                                        a.onsuccess = function() {
                                            return r(u.result.modelArtifactsInfo)
                                        }, a.onerror = function(e) {
                                            return n(u.error)
                                        }
                                    };
                                a.onsuccess = i, a.onerror = function(e) {
                                    return i(), o.close(), n(u.error)
                                }
                            }, u.onerror = function(e) {
                                return o.close(), n(u.error)
                            }, i.oncomplete = function() {
                                null == t ? o.close() : t.oncomplete = function() {
                                    return o.close()
                                }
                            }
                        }, a.onerror = function(e) {
                            return n(a.error)
                        }
                    })]
                })
            })
        }, e
    }();
    if (ENV.get("IS_BROWSER")) try {
        ModelStoreManagerRegistry.registerManager(BrowserIndexedDB.URL_SCHEME, new BrowserIndexedDBManager)
    } catch (e) {}
    var __awaiter$10 = function(e, t, r, n) {
            return new(r || (r = Promise))(function(a, o) {
                function i(e) {
                    try {
                        u(n.next(e))
                    } catch (e) {
                        o(e)
                    }
                }

                function s(e) {
                    try {
                        u(n.throw(e))
                    } catch (e) {
                        o(e)
                    }
                }

                function u(e) {
                    e.done ? a(e.value) : new r(function(t) {
                        t(e.value)
                    }).then(i, s)
                }
                u((n = n.apply(e, t || [])).next())
            })
        },
        __generator$10 = function(e, t) {
            function r(e) {
                return function(t) {
                    return n([e, t])
                }
            }

            function n(r) {
                if (a) throw new TypeError("Generator is already executing.");
                for (; u;) try {
                    if (a = 1, o && (i = o[2 & r[0] ? "return" : r[0] ? "throw" : "next"]) && !(i = i.call(o, r[1])).done) return i;
                    switch (o = 0, i && (r = [0, i.value]), r[0]) {
                        case 0:
                        case 1:
                            i = r;
                            break;
                        case 4:
                            return u.label++, {
                                value: r[1],
                                done: !1
                            };
                        case 5:
                            u.label++, o = r[1], r = [0];
                            continue;
                        case 7:
                            r = u.ops.pop(), u.trys.pop();
                            continue;
                        default:
                            if (i = u.trys, !(i = i.length > 0 && i[i.length - 1]) && (6 === r[0] || 2 === r[0])) {
                                u = 0;
                                continue
                            }
                            if (3 === r[0] && (!i || r[1] > i[0] && r[1] < i[3])) {
                                u.label = r[1];
                                break
                            }
                            if (6 === r[0] && u.label < i[1]) {
                                u.label = i[1], i = r;
                                break
                            }
                            if (i && u.label < i[2]) {
                                u.label = i[2], u.ops.push(r);
                                break
                            }
                            i[2] && u.ops.pop(), u.trys.pop();
                            continue
                    }
                    r = t.call(e, u)
                } catch (e) {
                    r = [6, e], o = 0
                } finally {
                    a = i = 0
                }
                if (5 & r[0]) throw r[1];
                return {
                    value: r[0] ? r[1] : void 0,
                    done: !0
                }
            }
            var a, o, i, s, u = {
                label: 0,
                sent: function() {
                    if (1 & i[0]) throw i[1];
                    return i[1]
                },
                trys: [],
                ops: []
            };
            return s = {
                next: r(0),
                throw: r(1),
                return: r(2)
            }, "function" == typeof Symbol && (s[Symbol.iterator] = function() {
                return this
            }), s
        },
        PATH_SEPARATOR = "/",
        PATH_PREFIX = "tensorflowjs_models",
        INFO_SUFFIX = "info",
        MODEL_TOPOLOGY_SUFFIX = "model_topology",
        WEIGHT_SPECS_SUFFIX = "weight_specs",
        WEIGHT_DATA_SUFFIX = "weight_data",
        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$10(this, void 0, void 0, function() {
                    var t, r, n, a;
                    return __generator$10(this, function(o) {
                        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 (a in this.keys) this.LS.removeItem(this.keys[a]);
                            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$10(this, void 0, void 0, function() {
                    var e, t, r, n, a;
                    return __generator$10(this, function(o) {
                        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 == (a = 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(a), [2, t]
                    })
                })
            }, e.URL_SCHEME = "localstorage://", e
        }(),
        localStorageRouter = function(e) {
            return ENV.get("IS_BROWSER") && e.startsWith(BrowserLocalStorage.URL_SCHEME) ? browserLocalStorage(e.slice(BrowserLocalStorage.URL_SCHEME.length)) : null
        };
    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$10(this, void 0, void 0, function() {
                var e, t, r, n, a, o;
                return __generator$10(this, function(i) {
                    for (e = {}, t = PATH_PREFIX + PATH_SEPARATOR, r = PATH_SEPARATOR + INFO_SUFFIX, n = 0; n < this.LS.length; ++n)(a = this.LS.key(n)).startsWith(t) && a.endsWith(r) && (o = getModelPathFromKey(a), e[o] = JSON.parse(this.LS.getItem(a)));
                    return [2, e]
                })
            })
        }, e.prototype.removeModel = function(e) {
            return __awaiter$10(this, void 0, void 0, function() {
                var t, r;
                return __generator$10(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 __awaiter$11 = function(e, t, r, n) {
            return new(r || (r = Promise))(function(a, o) {
                function i(e) {
                    try {
                        u(n.next(e))
                    } catch (e) {
                        o(e)
                    }
                }

                function s(e) {
                    try {
                        u(n.throw(e))
                    } catch (e) {
                        o(e)
                    }
                }

                function u(e) {
                    e.done ? a(e.value) : new r(function(t) {
                        t(e.value)
                    }).then(i, s)
                }
                u((n = n.apply(e, t || [])).next())
            })
        },
        __generator$11 = function(e, t) {
            function r(e) {
                return function(t) {
                    return n([e, t])
                }
            }

            function n(r) {
                if (a) throw new TypeError("Generator is already executing.");
                for (; u;) try {
                    if (a = 1, o && (i = o[2 & r[0] ? "return" : r[0] ? "throw" : "next"]) && !(i = i.call(o, r[1])).done) return i;
                    switch (o = 0, i && (r = [0, i.value]), r[0]) {
                        case 0:
                        case 1:
                            i = r;
                            break;
                        case 4:
                            return u.label++, {
                                value: r[1],
                                done: !1
                            };
                        case 5:
                            u.label++, o = r[1], r = [0];
                            continue;
                        case 7:
                            r = u.ops.pop(), u.trys.pop();
                            continue;
                        default:
                            if (i = u.trys, !(i = i.length > 0 && i[i.length - 1]) && (6 === r[0] || 2 === r[0])) {
                                u = 0;
                                continue
                            }
                            if (3 === r[0] && (!i || r[1] > i[0] && r[1] < i[3])) {
                                u.label = r[1];
                                break
                            }
                            if (6 === r[0] && u.label < i[1]) {
                                u.label = i[1], i = r;
                                break
                            }
                            if (i && u.label < i[2]) {
                                u.label = i[2], u.ops.push(r);
                                break
                            }
                            i[2] && u.ops.pop(), u.trys.pop();
                            continue
                    }
                    r = t.call(e, u)
                } catch (e) {
                    r = [6, e], o = 0
                } finally {
                    a = i = 0
                }
                if (5 & r[0]) throw r[1];
                return {
                    value: r[0] ? r[1] : void 0,
                    done: !0
                }
            }
            var a, o, i, s, u = {
                label: 0,
                sent: function() {
                    if (1 & i[0]) throw i[1];
                    return i[1]
                },
                trys: [],
                ops: []
            };
            return s = {
                next: r(0),
                throw: r(1),
                return: r(2)
            }, "function" == typeof Symbol && (s[Symbol.iterator] = function() {
                return this
            }), s
        },
        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$11(this, void 0, void 0, function() {
                    var t, r, n, a, o, i;
                    return __generator$11(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
                        }, a = window.URL.createObjectURL(new Blob([JSON.stringify(n)], {
                            type: "application/json"
                        })), o = null == this.jsonAnchor ? document.createElement("a") : this.jsonAnchor, o.download = this.modelTopologyFileName, o.href = a, o.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$11(this, void 0, void 0, function() {
                    var e, t, r = this;
                    return __generator$11(this, function(n) {
                        return e = this.files[0], t = this.files.slice(1), [2, new Promise(function(n, a) {
                            var o = new FileReader;
                            o.onload = function(o) {
                                var i = JSON.parse(o.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 a(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,
                                                        a = p.indexOf(e);
                                                    d[a] = r, -1 === d.indexOf(null) && n({
                                                        modelTopology: s,
                                                        weightSpecs: c,
                                                        weightData: concatenateArrayBuffers(d)
                                                    })
                                                }, t.onerror = function(t) {
                                                    a("Failed to weights data from file of path '" + e + "'.")
                                                }, t.readAsArrayBuffer(l[e])
                                            })
                                        })
                                    } else a(new Error("weightManifest field is missing from file " + e.name))
                                } else a(new Error("modelTopology field is missing from file " + e.name))
                            }, o.onerror = function(t) {
                                a("Failed to read model topology and weights manifest JSON from file '" + e.name + "'. BrowserFiles supports loading Keras-style tf.Model artifacts only.")
                            }, o.readAsText(e)
                        })]
                    })
                })
            }, e.prototype.checkManifestAndWeightFiles = function(e, t) {
                for (var r = [], n = t.map(function(e) {
                        return basename(e.name)
                    }), a = {}, o = 0, i = e; o < i.length; o++) i[o].paths.forEach(function(e) {
                    var o = basename(e);
                    if (-1 !== r.indexOf(o)) throw new Error("Duplicate file basename found in weights manifest: '" + o + "'");
                    if (r.push(o), -1 === n.indexOf(o)) throw new Error("Weight file with basename '" + o + "' is not provided.");
                    a[e] = t[n.indexOf(o)]
                });
                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 a
            }, e
        }(),
        browserDownloadsRouter = function(e) {
            return ENV.get("IS_BROWSER") && e.startsWith(BrowserDownloads.URL_SCHEME) ? browserDownloads(e.slice(BrowserDownloads.URL_SCHEME.length)) : null
        };
    IORouterRegistry.registerSaveRouter(browserDownloadsRouter);
    var __awaiter$12 = function(e, t, r, n) {
            return new(r || (r = Promise))(function(a, o) {
                function i(e) {
                    try {
                        u(n.next(e))
                    } catch (e) {
                        o(e)
                    }
                }

                function s(e) {
                    try {
                        u(n.throw(e))
                    } catch (e) {
                        o(e)
                    }
                }

                function u(e) {
                    e.done ? a(e.value) : new r(function(t) {
                        t(e.value)
                    }).then(i, s)
                }
                u((n = n.apply(e, t || [])).next())
            })
        },
        __generator$12 = function(e, t) {
            function r(e) {
                return function(t) {
                    return n([e, t])
                }
            }

            function n(r) {
                if (a) throw new TypeError("Generator is already executing.");
                for (; u;) try {
                    if (a = 1, o && (i = o[2 & r[0] ? "return" : r[0] ? "throw" : "next"]) && !(i = i.call(o, r[1])).done) return i;
                    switch (o = 0, i && (r = [0, i.value]), r[0]) {
                        case 0:
                        case 1:
                            i = r;
                            break;
                        case 4:
                            return u.label++, {
                                value: r[1],
                                done: !1
                            };
                        case 5:
                            u.label++, o = r[1], r = [0];
                            continue;
                        case 7:
                            r = u.ops.pop(), u.trys.pop();
                            continue;
                        default:
                            if (i = u.trys, !(i = i.length > 0 && i[i.length - 1]) && (6 === r[0] || 2 === r[0])) {
                                u = 0;
                                continue
                            }
                            if (3 === r[0] && (!i || r[1] > i[0] && r[1] < i[3])) {
                                u.label = r[1];
                                break
                            }
                            if (6 === r[0] && u.label < i[1]) {
                                u.label = i[1], i = r;
                                break
                            }
                            if (i && u.label < i[2]) {
                                u.label = i[2], u.ops.push(r);
                                break
                            }
                            i[2] && u.ops.pop(), u.trys.pop();
                            continue
                    }
                    r = t.call(e, u)
                } catch (e) {
                    r = [6, e], o = 0
                } finally {
                    a = i = 0
                }
                if (5 & r[0]) throw r[1];
                return {
                    value: r[0] ? r[1] : void 0,
                    done: !0
                }
            }
            var a, o, i, s, u = {
                label: 0,
                sent: function() {
                    if (1 & i[0]) throw i[1];
                    return i[1]
                },
                trys: [],
                ops: []
            };
            return s = {
                next: r(0),
                throw: r(1),
                return: r(2)
            }, "function" == typeof Symbol && (s[Symbol.iterator] = function() {
                return this
            }), s
        },
        __awaiter$13 = function(e, t, r, n) {
            return new(r || (r = Promise))(function(a, o) {
                function i(e) {
                    try {
                        u(n.next(e))
                    } catch (e) {
                        o(e)
                    }
                }

                function s(e) {
                    try {
                        u(n.throw(e))
                    } catch (e) {
                        o(e)
                    }
                }

                function u(e) {
                    e.done ? a(e.value) : new r(function(t) {
                        t(e.value)
                    }).then(i, s)
                }
                u((n = n.apply(e, t || [])).next())
            })
        },
        __generator$13 = function(e, t) {
            function r(e) {
                return function(t) {
                    return n([e, t])
                }
            }

            function n(r) {
                if (a) throw new TypeError("Generator is already executing.");
                for (; u;) try {
                    if (a = 1, o && (i = o[2 & r[0] ? "return" : r[0] ? "throw" : "next"]) && !(i = i.call(o, r[1])).done) return i;
                    switch (o = 0, i && (r = [0, i.value]), r[0]) {
                        case 0:
                        case 1:
                            i = r;
                            break;
                        case 4:
                            return u.label++, {
                                value: r[1],
                                done: !1
                            };
                        case 5:
                            u.label++, o = r[1], r = [0];
                            continue;
                        case 7:
                            r = u.ops.pop(), u.trys.pop();
                            continue;
                        default:
                            if (i = u.trys, !(i = i.length > 0 && i[i.length - 1]) && (6 === r[0] || 2 === r[0])) {
                                u = 0;
                                continue
                            }
                            if (3 === r[0] && (!i || r[1] > i[0] && r[1] < i[3])) {
                                u.label = r[1];
                                break
                            }
                            if (6 === r[0] && u.label < i[1]) {
                                u.label = i[1], i = r;
                                break
                            }
                            if (i && u.label < i[2]) {
                                u.label = i[2], u.ops.push(r);
                                break
                            }
                            i[2] && u.ops.pop(), u.trys.pop();
                            continue
                    }
                    r = t.call(e, u)
                } catch (e) {
                    r = [6, e], o = 0
                } finally {
                    a = i = 0
                }
                if (5 & r[0]) throw r[1];
                return {
                    value: r[0] ? r[1] : void 0,
                    done: !0
                }
            }
            var a, o, i, s, u = {
                label: 0,
                sent: function() {
                    if (1 & i[0]) throw i[1];
                    return i[1]
                },
                trys: [],
                ops: []
            };
            return s = {
                next: r(0),
                throw: r(1),
                return: r(2)
            }, "function" == typeof Symbol && (s[Symbol.iterator] = function() {
                return this
            }), s
        },
        BrowserHTTPRequest = function() {
            function e(e, t) {
                if (this.DEFAULT_METHOD = "POST", !ENV.get("IS_BROWSER")) throw new Error("browserHTTPRequest is not supported outside the web browser.");
                if (assert(null != e && e.length > 0, "URL path for browserHTTPRequest must not be null, undefined or empty."), 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$13(this, void 0, void 0, function() {
                    var t, r, n, a;
                    return __generator$13(this, function(o) {
                        switch (o.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), t.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 === (a = o.sent()).status) return [2, {
                                    modelArtifactsInfo: getModelArtifactsInfoForJSON(e),
                                    responses: [a]
                                }];
                                throw new Error("BrowserHTTPRequest.save() failed due to HTTP response status " + a.status + ".")
                        }
                    })
                })
            }, e.prototype.load = function() {
                return __awaiter$13(this, void 0, void 0, function() {
                    var e, t, r, n, a, o, i, s, u, l, c, p, d;
                    return __generator$13(this, function(h) {
                        switch (h.label) {
                            case 0:
                                return [4, fetch(this.path, this.requestInit)];
                            case 1:
                                return e = h.sent(), [4, e.json()];
                            case 2:
                                if (t = h.sent(), r = t.modelTopology, n = t.weightsManifest, null == r && null == n) throw new Error("The JSON from HTTP path " + this.path + " contains neither model topology or manifest for weights.");
                                if (null == n) return [3, 4];
                                for (i = t.weightsManifest, a = [], s = 0, u = i; s < u.length; s++) l = u[s], a.push.apply(a, l.weights);
                                return (c = this.path.substring(0, this.path.lastIndexOf("/"))).endsWith("/") || (c += "/"), p = [], i.forEach(function(e) {
                                    e.paths.forEach(function(e) {
                                        p.push(c + e)
                                    })
                                }), d = concatenateArrayBuffers, [4, loadWeightsAsArrayBuffer(p, this.requestInit)];
                            case 3:
                                o = d.apply(void 0, [h.sent()]), h.label = 4;
                            case 4:
                                return [2, {
                                    modelTopology: r,
                                    weightSpecs: a,
                                    weightData: o
                                }]
                        }
                    })
                })
            }, e.URL_SCHEMES = ["http://", "https://"], e
        }(),
        httpRequestRouter = function(e) {
            if (ENV.get("IS_BROWSER")) {
                for (var t = 0, r = BrowserHTTPRequest.URL_SCHEMES; t < r.length; t++) {
                    var n = r[t];
                    if (e.startsWith(n)) return browserHTTPRequest(e)
                }
                return null
            }
            return null
        };
    IORouterRegistry.registerSaveRouter(httpRequestRouter), IORouterRegistry.registerLoadRouter(httpRequestRouter);
    var registerSaveRouter = IORouterRegistry.registerSaveRouter,
        registerLoadRouter = IORouterRegistry.registerLoadRouter,
        getSaveHandlers = IORouterRegistry.getSaveHandlers,
        getLoadHandlers = IORouterRegistry.getLoadHandlers,
        copyModel = ModelManagement.copyModel,
        listModels = ModelManagement.listModels,
        moveModel = ModelManagement.moveModel,
        removeModel = ModelManagement.removeModel,
        io = Object.freeze({
            browserFiles: browserFiles,
            browserHTTPRequest: browserHTTPRequest,
            copyModel: copyModel,
            decodeWeights: decodeWeights,
            encodeWeights: encodeWeights,
            getLoadHandlers: getLoadHandlers,
            getModelArtifactsInfoForJSON: getModelArtifactsInfoForJSON,
            getSaveHandlers: getSaveHandlers,
            listModels: listModels,
            loadWeights: loadWeights,
            moveModel: moveModel,
            registerLoadRouter: registerLoadRouter,
            registerSaveRouter: registerSaveRouter,
            removeModel: removeModel
        }),
        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(e) {
                this.getMap().classNameMap[e.className] = [e, e.fromConfig]
            }, e
        }(),
        serialization = Object.freeze({
            Serializable: Serializable,
            SerializationMap: SerializationMap
        }),
        WEBGL_ENVS = {
            BACKEND: "test-webgl"
        },
        CPU_ENVS = {
            BACKEND: "test-cpu"
        },
        ALL_ENVS = {},
        TEST_EPSILON = .001,
        test_util = Object.freeze({
            WEBGL_ENVS: WEBGL_ENVS,
            CPU_ENVS: CPU_ENVS,
            ALL_ENVS: ALL_ENVS,
            TEST_EPSILON: TEST_EPSILON,
            expectArraysClose: expectArraysClose,
            expectPromiseToFail: expectPromiseToFail,
            expectArraysEqual: expectArraysEqual,
            expectNumbersClose: expectNumbersClose,
            expectValuesInRange: expectValuesInRange
        }),
        version = "0.11.6",
        webgl = Object.freeze({
            gpgpu_util: gpgpu_util,
            webgl_util: webgl_util,
            MathBackendWebGL: MathBackendWebGL,
            GPGPUContext: GPGPUContext
        }),
        __extends$1 = function() {
            var e = 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])
            };
            return function(t, r) {
                function n() {
                    this.constructor = t
                }
                e(t, r), t.prototype = null === r ? Object.create(r) : (n.prototype = r.prototype, new n)
            }
        }(),
        __decorate$29 = function(e, t, r, n) {
            var a, o = arguments.length,
                i = o < 3 ? t : null === n ? n = Object.getOwnPropertyDescriptor(t, r) : n;
            if ("object" == typeof Reflect && "function" == typeof Reflect.decorate) i = Reflect.decorate(e, t, r, n);
            else
                for (var s = e.length - 1; s >= 0; s--)(a = e[s]) && (i = (o < 3 ? a(i) : o > 3 ? a(t, r, i) : a(t, r)) || i);
            return o > 3 && i && Object.defineProperty(t, r, i), i
        },
        Optimizer = function(e) {
            function t() {
                return null !== e && e.apply(this, arguments) || this
            }
            return __extends$1(t, e), t.prototype.minimize = function(e, t, r) {
                void 0 === t && (t = !1);
                var n = this.computeGradients(e, r),
                    a = n.value,
                    o = n.grads;
                return this.applyGradients(o), Object.keys(o).forEach(function(e) {
                    return o[e].dispose()
                }), t ? a : (a.dispose(), null)
            }, t.prototype.computeGradients = function(e, t) {
                return variableGrads(e, t)
            }, __decorate$29([doc()], t.prototype, "minimize", null), t = __decorate$29([doc()], t)
        }(Serializable),
        __extends$2 = function() {
            var e = 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])
            };
            return function(t, r) {
                function n() {
                    this.constructor = t
                }
                e(t, r), t.prototype = null === r ? Object.create(r) : (n.prototype = r.prototype, new n)
            }
        }(),
        AdadeltaOptimizer = function(e) {
            function t(t, r, n) {
                void 0 === n && (n = 1e-8);
                var a = e.call(this) || this;
                return a.learningRate = t, a.rho = r, a.epsilon = n, a.accumulatedGrads = {}, a.accumulatedUpdates = {}, a.c = keep(scalar(-t)), a.epsilonScalar = keep(scalar(n)), a.rhoScalar = keep(scalar(r)), a.oneMinusRho = keep(scalar(1 - r)), a
            }
            return __extends$2(t, e), t.prototype.applyGradients = function(e) {
                var t = this,
                    r = this;
                for (var n in e) ! function(n) {
                    var a = ENV.engine.registeredVariables[n];
                    null == r.accumulatedGrads[n] && tidy(function() {
                        t.accumulatedGrads[n] = zerosLike(a).variable(!1)
                    });
                    null == r.accumulatedUpdates[n] && tidy(function() {
                        t.accumulatedUpdates[n] = zerosLike(a).variable(!1)
                    });
                    var o = e[n],
                        i = r.accumulatedGrads[n],
                        s = r.accumulatedUpdates[n];
                    tidy(function() {
                        var e = t.rhoScalar.mul(i).add(t.oneMinusRho.mul(o.square())),
                            r = s.add(t.epsilonScalar).sqrt().div(i.add(t.epsilonScalar).sqrt()).mul(o),
                            u = t.rhoScalar.mul(s).add(t.oneMinusRho.mul(r.square()));
                        t.accumulatedGrads[n].assign(e), t.accumulatedUpdates[n].assign(u);
                        var l = t.c.mul(r).add(a);
                        a.assign(l)
                    })
                }(n)
            }, 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);
    SerializationMap.register(AdadeltaOptimizer);
    var __extends$3 = function() {
            var e = 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])
            };
            return function(t, r) {
                function n() {
                    this.constructor = t
                }
                e(t, r), t.prototype = null === r ? Object.create(r) : (n.prototype = r.prototype, new n)
            }
        }(),
        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(1e-8)), n
            }
            return __extends$3(t, e), t.prototype.applyGradients = function(e) {
                var t = this,
                    r = this;
                for (var n in e) ! function(n) {
                    var a = ENV.engine.registeredVariables[n];
                    null == r.accumulatedGrads[n] && tidy(function() {
                        t.accumulatedGrads[n] = fill(a.shape, t.initialAccumulatorValue).variable(!1)
                    });
                    var o = e[n],
                        i = r.accumulatedGrads[n];
                    tidy(function() {
                        var e = i.add(o.square());
                        t.accumulatedGrads[n].assign(e);
                        var r = t.c.mul(o.div(e.add(t.epsilon).sqrt())).add(a);
                        a.assign(r)
                    })
                }(n)
            }, 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);
    SerializationMap.register(AdagradOptimizer);
    var __extends$4 = function() {
            var e = 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])
            };
            return function(t, r) {
                function n() {
                    this.constructor = t
                }
                e(t, r), t.prototype = null === r ? Object.create(r) : (n.prototype = r.prototype, new n)
            }
        }(),
        AdamOptimizer = function(e) {
            function t(t, r, n, a) {
                void 0 === a && (a = 1e-8);
                var o = e.call(this) || this;
                return o.learningRate = t, o.beta1 = r, o.beta2 = n, o.epsilon = a, o.accumulatedFirstMoment = {}, o.accumulatedSecondMoment = {}, o.c = keep(scalar(-t)), o.epsScalar = keep(scalar(a)), o.beta1Scalar = keep(scalar(r)), o.beta2Scalar = keep(scalar(n)), tidy(function() {
                    o.accBeta1 = scalar(r).variable(), o.accBeta2 = scalar(n).variable()
                }), o.oneMinusBeta1 = keep(scalar(1 - r)), o.oneMinusBeta2 = keep(scalar(1 - n)), o.one = keep(scalar(1)), o
            }
            return __extends$4(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 a in e) {
                        var o = ENV.engine.registeredVariables[a];
                        if (null == t.accumulatedFirstMoment[a]) {
                            i = !1;
                            t.accumulatedFirstMoment[a] = zerosLike(o).variable(i)
                        }
                        if (null == t.accumulatedSecondMoment[a]) {
                            var i = !1;
                            t.accumulatedSecondMoment[a] = zerosLike(o).variable(i)
                        }
                        var s = e[a],
                            u = t.accumulatedFirstMoment[a],
                            l = t.accumulatedSecondMoment[a],
                            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[a].assign(c), t.accumulatedSecondMoment[a].assign(p);
                        var f = t.c.mul(d.div(t.epsScalar.add(h.sqrt()))).add(o);
                        o.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);
    SerializationMap.register(AdamOptimizer);
    var __extends$5 = function() {
            var e = 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])
            };
            return function(t, r) {
                function n() {
                    this.constructor = t
                }
                e(t, r), t.prototype = null === r ? Object.create(r) : (n.prototype = r.prototype, new n)
            }
        }(),
        AdamaxOptimizer = function(e) {
            function t(t, r, n, a, o) {
                void 0 === a && (a = 1e-8), void 0 === o && (o = 0);
                var i = e.call(this) || this;
                return i.learningRate = t, i.beta1 = r, i.beta2 = n, i.epsilon = a, i.decay = o, i.accumulatedFirstMoment = {}, i.accumulatedWeightedInfNorm = {}, i.c = keep(scalar(-t)), i.epsScalar = keep(scalar(a)), i.beta1Scalar = keep(scalar(r)), i.beta2Scalar = keep(scalar(n)), i.decayScalar = keep(scalar(o)), tidy(function() {
                    i.iteration = scalar(0).variable(), i.accBeta1 = scalar(r).variable()
                }), i.oneMinusBeta1 = keep(scalar(1 - r)), i.one = keep(scalar(1)), i
            }
            return __extends$5(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 a in e) {
                        var o = ENV.engine.registeredVariables[a];
                        if (null == t.accumulatedFirstMoment[a]) {
                            i = !1;
                            t.accumulatedFirstMoment[a] = zerosLike(o).variable(i)
                        }
                        if (null == t.accumulatedWeightedInfNorm[a]) {
                            var i = !1;
                            t.accumulatedWeightedInfNorm[a] = zerosLike(o).variable(i)
                        }
                        var s = e[a],
                            u = t.accumulatedFirstMoment[a],
                            l = t.accumulatedWeightedInfNorm[a],
                            c = t.beta1Scalar.mul(u).add(t.oneMinusBeta1.mul(s)),
                            p = t.beta2Scalar.mul(l),
                            d = s.abs(),
                            h = p.maximum(d);
                        t.accumulatedFirstMoment[a].assign(c), t.accumulatedWeightedInfNorm[a].assign(h);
                        var f = n.div(r).mul(c.div(t.epsScalar.add(h))).add(o);
                        o.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);
    SerializationMap.register(AdamaxOptimizer);
    var __extends$6 = function() {
            var e = 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])
            };
            return function(t, r) {
                function n() {
                    this.constructor = t
                }
                e(t, r), t.prototype = null === r ? Object.create(r) : (n.prototype = r.prototype, new n)
            }
        }(),
        SGDOptimizer = function(e) {
            function t(t) {
                var r = e.call(this) || this;
                return r.learningRate = t, r.setLearningRate(t), r
            }
            return __extends$6(t, e), t.prototype.applyGradients = function(e) {
                var t = this;
                Object.keys(e).forEach(function(r) {
                    var n = e[r],
                        a = ENV.engine.registeredVariables[r];
                    tidy(function() {
                        var e = t.c.mul(n).add(a);
                        a.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);
    SerializationMap.register(SGDOptimizer);
    var __extends$7 = function() {
            var e = 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])
            };
            return function(t, r) {
                function n() {
                    this.constructor = t
                }
                e(t, r), t.prototype = null === r ? Object.create(r) : (n.prototype = r.prototype, new n)
            }
        }(),
        MomentumOptimizer = function(e) {
            function t(t, r, n) {
                void 0 === n && (n = !1);
                var a = e.call(this, t) || this;
                return a.learningRate = t, a.momentum = r, a.useNesterov = n, a.m = scalar(a.momentum), a.accumulations = {}, a
            }
            return __extends$7(t, e), t.prototype.applyGradients = function(e) {
                var t = this,
                    r = this;
                for (var n in e) ! function(n) {
                    var a = ENV.engine.registeredVariables[n];
                    null == r.accumulations[n] && tidy(function() {
                        t.accumulations[n] = zerosLike(a).variable(!1)
                    });
                    var o = r.accumulations[n],
                        i = e[n];
                    tidy(function() {
                        var e, r = t.m.mul(o).add(i);
                        e = t.useNesterov ? t.c.mul(i.add(r.mul(t.m))).add(a) : t.c.mul(r).add(a), t.accumulations[n].assign(r), a.assign(e)
                    })
                }(n)
            }, 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);
    SerializationMap.register(MomentumOptimizer);
    var __extends$8 = function() {
            var e = 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])
            };
            return function(t, r) {
                function n() {
                    this.constructor = t
                }
                e(t, r), t.prototype = null === r ? Object.create(r) : (n.prototype = r.prototype, new n)
            }
        }(),
        RMSPropOptimizer = function(e) {
            function t(t, r, n, a, o) {
                void 0 === r && (r = .9), void 0 === n && (n = 0), void 0 === a && (a = 1e-8), void 0 === o && (o = !1);
                var i = e.call(this) || this;
                return i.learningRate = t, i.decay = r, i.momentum = n, i.epsilon = a, i.accumulatedMeanSquares = {}, i.accumulatedMeanGrads = {}, i.accumulatedMoments = {}, i.c = keep(scalar(t)), i.epsilonScalar = keep(scalar(a)), i.decayScalar = keep(scalar(r)), i.momentumScalar = keep(scalar(n)), i.oneMinusDecay = keep(scalar(1 - r)), i.centered = o, i
            }
            return __extends$8(t, e), t.prototype.applyGradients = function(e) {
                var t = this,
                    r = this;
                for (var n in e) ! function(n) {
                    var a = ENV.engine.registeredVariables[n];
                    null == r.accumulatedMeanSquares[n] && tidy(function() {
                        t.accumulatedMeanSquares[n] = zerosLike(a).variable(!1)
                    });
                    null == r.accumulatedMeanGrads[n] && r.centered && tidy(function() {
                        t.accumulatedMeanGrads[n] = zerosLike(a).variable(!1)
                    });
                    null == r.accumulatedMoments[n] && tidy(function() {
                        t.accumulatedMoments[n] = zerosLike(a).variable(!1)
                    });
                    var o = r.accumulatedMeanSquares[n],
                        i = r.accumulatedMeanGrads[n],
                        s = r.accumulatedMoments[n],
                        u = e[n];
                    tidy(function() {
                        var e = t.decayScalar.mul(o).add(t.oneMinusDecay.mul(u.square()));
                        if (t.centered) {
                            var r = t.decayScalar.mul(i).add(t.oneMinusDecay.mul(u)),
                                l = t.momentumScalar.mul(s).add(t.c.mul(u).div(e.sub(r.square().add(t.epsilonScalar)).sqrt()));
                            t.accumulatedMeanSquares[n].assign(e), t.accumulatedMeanGrads[n].assign(r), t.accumulatedMoments[n].assign(l), p = a.sub(l), a.assign(p)
                        } else {
                            var c = t.decayScalar.mul(o).add(t.oneMinusDecay.mul(u.square())),
                                l = t.momentumScalar.mul(s).add(t.c.mul(u).div(c.add(t.epsilonScalar).sqrt()));
                            t.accumulatedMeanSquares[n].assign(c), t.accumulatedMoments[n].assign(l);
                            var p = a.sub(l);
                            a.assign(p)
                        }
                    })
                }(n)
            }, 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);
    SerializationMap.register(RMSPropOptimizer);
    var __decorate$30 = function(e, t, r, n) {
            var a, o = arguments.length,
                i = o < 3 ? t : null === n ? n = Object.getOwnPropertyDescriptor(t, r) : n;
            if ("object" == typeof Reflect && "function" == typeof Reflect.decorate) i = Reflect.decorate(e, t, r, n);
            else
                for (var s = e.length - 1; s >= 0; s--)(a = e[s]) && (i = (o < 3 ? a(i) : o > 3 ? a(t, r, i) : a(t, r)) || i);
            return o > 3 && i && Object.defineProperty(t, r, i), i
        },
        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, a) {
                return void 0 === t && (t = .9), void 0 === r && (r = 0), void 0 === n && (n = 1e-8), void 0 === a && (a = !1), new RMSPropOptimizer(e, t, r, n, a)
            }, 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 = 1e-8), 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 = 1e-8), new AdadeltaOptimizer(e, t, r)
            }, e.adamax = function(e, t, r, n, a) {
                return void 0 === e && (e = .002), void 0 === t && (t = .9), void 0 === r && (r = .999), void 0 === n && (n = 1e-8), void 0 === a && (a = 0), new AdamaxOptimizer(e, t, r, n, a)
            }, e.adagrad = function(e, t) {
                return void 0 === t && (t = .1), new AdagradOptimizer(e, t)
            }, __decorate$30([doc()], e, "sgd", null), __decorate$30([doc()], e, "momentum", null), __decorate$30([doc()], e, "rmsprop", null), __decorate$30([doc()], e, "adam", null), __decorate$30([doc()], e, "adadelta", null), __decorate$30([doc()], e, "adamax", null), __decorate$30([doc()], e, "adagrad", null), 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,
        nextFrame = BrowserUtil.nextFrame,
        __extends$9 = function() {
            var e = 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])
            };
            return function(t, r) {
                function n() {
                    this.constructor = t
                }
                e(t, r), t.prototype = null === r ? Object.create(r) : (n.prototype = r.prototype, new n)
            }
        }(),
        AttributeError = function(e) {
            function t(r) {
                var n = e.call(this, r) || this;
                return Object.setPrototypeOf(n, t.prototype), n
            }
            return __extends$9(t, e), t
        }(Error),
        RuntimeError = function(e) {
            function t(r) {
                var n = e.call(this, r) || this;
                return Object.setPrototypeOf(n, t.prototype), n
            }
            return __extends$9(t, e), t
        }(Error),
        ValueError = function(e) {
            function t(r) {
                var n = e.call(this, r) || this;
                return Object.setPrototypeOf(n, t.prototype), n
            }
            return __extends$9(t, e), t
        }(Error),
        NotImplementedError = function(e) {
            function t(r) {
                var n = e.call(this, r) || this;
                return Object.setPrototypeOf(n, t.prototype), n
            }
            return __extends$9(t, e), t
        }(Error),
        AssertionError = function(e) {
            function t(r) {
                var n = e.call(this, r) || this;
                return Object.setPrototypeOf(n, t.prototype), n
            }
            return __extends$9(t, e), t
        }(Error),
        IndexError = function(e) {
            function t(r) {
                var n = e.call(this, r) || this;
                return Object.setPrototypeOf(n, t.prototype), n
            }
            return __extends$9(t, e), t
        }(Error),
        __assign = Object.assign || function(e) {
            for (var t, r = 1, n = arguments.length; r < n; r++) {
                t = arguments[r];
                for (var a in t) Object.prototype.hasOwnProperty.call(t, a) && (e[a] = t[a])
            }
            return e
        },
        _GLOBAL_CUSTOM_OBJECTS = {},
        nameMap = new Map,
        VALID_DATA_FORMAT_VALUES = ["channelsFirst", "channelsLast"],
        VALID_PADDING_MODE_VALUES = ["valid", "same", "causal"],
        VALID_POOL_MODE_VALUES = ["max", "avg"],
        _nameScopeStack = [],
        _nameScopeDivider = "/",
        tensorNameRegex = new RegExp(/^[A-Za-z][A-Za-z0-9\._\/]*$/),
        __decorate$31 = function(e, t, r, n) {
            var a, o = arguments.length,
                i = o < 3 ? t : null === n ? n = Object.getOwnPropertyDescriptor(t, r) : n;
            if ("object" == typeof Reflect && "function" == typeof Reflect.decorate) i = Reflect.decorate(e, t, r, n);
            else
                for (var s = e.length - 1; s >= 0; s--)(a = e[s]) && (i = (o < 3 ? a(i) : o > 3 ? a(t, r, i) : a(t, r)) || i);
            return o > 3 && i && Object.defineProperty(t, r, i), i
        },
        _nextUniqueTensorId = 0,
        SymbolicTensor = function() {
            function e(e, t, r, n, a, o, i) {
                this.dtype = e, this.shape = t, this.sourceLayer = r, this.inputs = n, this.callArgs = a, this.outputTensorIndex = i, this.id = getNextUniqueTensorId(), null != o && (this.originalName = getScopedTensorName(o), this.name = getUniqueTensorName(this.originalName)), this.rank = t.length
            }
            return e = __decorate$31([doc()], e)
        }(),
        DEFAULT_VARIABLE_NAME_PREFIX = "Variable",
        LayerVariable = function() {
            function e(e, t, r, n, a) {
                void 0 === t && (t = "float32"), void 0 === r && (r = DEFAULT_VARIABLE_NAME_PREFIX), void 0 === n && (n = !0), void 0 === a && (a = null), this.dtype = null == t ? "float32" : t, this.shape = e.shape, this.id = getNextUniqueTensorId(), r = null == r ? DEFAULT_VARIABLE_NAME_PREFIX : r, this.originalName = getScopedTensorName(r), this.name = getUniqueTensorName(this.originalName), this.trainable = n, this.constraint = a, this.val = variable(e, this.trainable, this.name, this.dtype)
            }
            return e.prototype.read = function() {
                return this.val
            }, e.prototype.write = function(e) {
                return checkShapesMatch(this.val, e), this.val.assign(e), null != this.constraint && this.val.assign(this.constraint.apply(this.val)), this
            }, e
        }(),
        _epsilon = 1e-7,
        DEFAULT_DTYPE = "float32",
        scalarCache = {
            float32: {},
            int32: {}
        },
        epsilon$1 = epsilon,
        _uidPrefixes = {},
        __extends$10 = function() {
            var e = 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])
            };
            return function(t, r) {
                function n() {
                    this.constructor = t
                }
                e(t, r), t.prototype = null === r ? Object.create(r) : (n.prototype = r.prototype, new n)
            }
        }(),
        __decorate$32 = function(e, t, r, n) {
            var a, o = arguments.length,
                i = o < 3 ? t : null === n ? n = Object.getOwnPropertyDescriptor(t, r) : n;
            if ("object" == typeof Reflect && "function" == typeof Reflect.decorate) i = Reflect.decorate(e, t, r, n);
            else
                for (var s = e.length - 1; s >= 0; s--)(a = e[s]) && (i = (o < 3 ? a(i) : o > 3 ? a(t, r, i) : a(t, r)) || i);
            return o > 3 && i && Object.defineProperty(t, r, i), i
        },
        Constraint = function(e) {
            function t() {
                return null !== e && e.apply(this, arguments) || this
            }
            return __extends$10(t, e), t.prototype.getConfig = function() {
                return {}
            }, t = __decorate$32([doc()], t)
        }(Serializable),
        MaxNorm = function(e) {
            function t(t) {
                var r = e.call(this) || this;
                return r.defaultMaxValue = 2, r.defaultAxis = 0, r.maxValue = null != t.maxValue ? t.maxValue : r.defaultMaxValue, r.axis = null != t.axis ? t.axis : r.defaultAxis, r
            }
            return __extends$10(t, e), t.prototype.apply = function(e) {
                var t = this;
                return tidy(function() {
                    var r = calcL2Norms(e, t.axis),
                        n = clipByValue(r, 0, t.maxValue);
                    return mul(e, div(n, scalarPlusArray(getScalar(epsilon$1()), r)))
                })
            }, t.prototype.getConfig = function() {
                return {
                    maxValue: this.maxValue,
                    axis: this.axis
                }
            }, t.className = "MaxNorm", t
        }(Constraint);
    SerializationMap.register(MaxNorm);
    var UnitNorm = function(e) {
        function t(t) {
            var r = e.call(this) || this;
            return r.defaultAxis = 0, r.axis = null != t.axis ? t.axis : r.defaultAxis, r
        }
        return __extends$10(t, e), t.prototype.apply = function(e) {
            var t = this;
            return tidy(function() {
                return div(e, scalarPlusArray(getScalar(epsilon$1()), calcL2Norms(e, t.axis)))
            })
        }, t.prototype.getConfig = function() {
            return {
                axis: this.axis
            }
        }, t.className = "UnitNorm", t
    }(Constraint);
    SerializationMap.register(UnitNorm);
    var NonNeg = function(e) {
        function t() {
            return null !== e && e.apply(this, arguments) || this
        }
        return __extends$10(t, e), t.prototype.apply = function(e) {
            return relu(e)
        }, t.className = "NonNeg", t
    }(Constraint);
    SerializationMap.register(NonNeg);
    var MinMaxNorm = function(e) {
        function t(t) {
            var r = e.call(this) || this;
            return r.defaultMinValue = 0, r.defaultMaxValue = 1, r.defaultRate = 1, r.defaultAxis = 0, r.minValue = null != t.minValue ? t.minValue : r.defaultMinValue, r.maxValue = null != t.maxValue ? t.maxValue : r.defaultMaxValue, r.rate = null != t.rate ? t.rate : r.defaultRate, r.axis = null != t.axis ? t.axis : r.defaultAxis, r
        }
        return __extends$10(t, e), t.prototype.apply = function(e) {
            var t = this;
            return tidy(function() {
                var r = calcL2Norms(e, t.axis),
                    n = add(scalarTimesArray(getScalar(t.rate), clipByValue(r, t.minValue, t.maxValue)), scalarTimesArray(getScalar(1 - t.rate), r));
                return mul(e, div(n, scalarPlusArray(getScalar(epsilon$1()), r)))
            })
        }, t.prototype.getConfig = function() {
            return {
                minValue: this.minValue,
                maxValue: this.maxValue,
                rate: this.rate,
                axis: this.axis
            }
        }, t.className = "MinMaxNorm", t
    }(Constraint);
    SerializationMap.register(MinMaxNorm);
    var CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP = {
            maxNorm: "MaxNorm",
            minMaxNorm: "MinMaxNorm",
            nonNeg: "NonNeg",
            unitNorm: "UnitNorm"
        },
        version$1 = "0.6.6",
        __extends$11 = function() {
            var e = 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])
            };
            return function(t, r) {
                function n() {
                    this.constructor = t
                }
                e(t, r), t.prototype = null === r ? Object.create(r) : (n.prototype = r.prototype, new n)
            }
        }(),
        __decorate$33 = function(e, t, r, n) {
            var a, o = arguments.length,
                i = o < 3 ? t : null === n ? n = Object.getOwnPropertyDescriptor(t, r) : n;
            if ("object" == typeof Reflect && "function" == typeof Reflect.decorate) i = Reflect.decorate(e, t, r, n);
            else
                for (var s = e.length - 1; s >= 0; s--)(a = e[s]) && (i = (o < 3 ? a(i) : o > 3 ? a(t, r, i) : a(t, r)) || i);
            return o > 3 && i && Object.defineProperty(t, r, i), i
        },
        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 || {}
            }
        }(),
        _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 r = 0, n = e.inboundLayers; r < n.length; r++) {
                    var a = n[r];
                    null != a && a.outboundNodes.push(this)
                }
                e.outboundLayer.inboundNodes.push(this)
            }
            return e.prototype.getConfig = function() {
                for (var e = [], t = 0, r = this.inboundLayers; t < r.length; t++) {
                    var n = r[t];
                    null != n ? e.push(n.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 r = e.call(this) || this;
                r._callHook = null, r._addedWeightNames = [], r._stateful = !1, r.id = _nextLayerID++, r.activityRegularizer = null, r.inputSpec = null, r.supportsMasking = !1, r._trainableWeights = [], r._nonTrainableWeights = [], r._losses = [], r._updates = [], r._built = !1, r.inboundNodes = [], r.outboundNodes = [];
                var n = t.name;
                if (!n) {
                    var a = r.getClassName();
                    n = toSnakeCase(a) + "_" + getUid(a)
                }
                if (r.name = n, r.trainable = null == t.trainable || t.trainable, r.updatable = null == t.updatable || t.updatable, null != t.inputShape || null != t.batchInputShape) {
                    var o = void 0;
                    if (null != t.batchInputShape) o = t.batchInputShape;
                    else if (null != t.inputShape) {
                        var i = null;
                        null != t.batchSize && (i = t.batchSize), o = [i].concat(t.inputShape)
                    }
                    r.batchInputShape = o;
                    var s = t.dtype;
                    null == s && (s = t.inputDType), null == s && (s = floatx()), r.dtype = s
                }
                return null != t.weights ? r.initialWeights = t.weights : r.initialWeights = null, r
            }
            return __extends$11(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.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 r = 0; r < e.length; r++) {
                        var n = e[r],
                            a = t[r];
                        if (null != a) {
                            var o = n.rank;
                            if (null != a.ndim && o !== a.ndim) throw new ValueError("Input " + r + " is incompatible with layer " + this.name + ": expected ndim=" + a.ndim + ", found ndim=" + o);
                            if (null != a.maxNDim && o > a.maxNDim) throw new ValueError("Input " + r + " is incompatible with layer " + this.name + ": expected max_ndim=" + a.maxNDim + ", found ndim=" + o);
                            if (null != a.minNDim && o < a.minNDim) throw new ValueError("Input " + r + " is incompatible with layer " + this.name + ": expected min_ndim=" + a.minNDim + ", found ndim=" + o + ".");
                            if (null != a.dtype && dtype(n) !== a.dtype) {
                                var i = dtype(n);
                                throw new ValueError("Input " + r + " is incompatible with layer " + this.name + " : expected dtype=" + a.dtype + ", found dtype=" + i + ".")
                            }
                            if (a.axes) {
                                p = intShape(n);
                                for (var s in a.axes) {
                                    var u = Number(s),
                                        l = a.axes[s],
                                        c = u >= 0 ? p[u] : p[p.length + u];
                                    if (null != l && -1 === [l, null].indexOf(c)) throw new ValueError("Input " + r + " is incompatible with layer " + this.name + ": expected axis " + u + " of input shape to have value " + l + " but got shape " + p + ".")
                                }
                            }
                            if (null != a.shape)
                                for (var p = intShape(n), d = 0; d < a.shape.length; ++d) {
                                    var h = a.shape[d],
                                        f = p[d];
                                    if (null != h && null != f && h !== f) throw new ValueError("Input " + r + " is incompatible with layer " + this.name + ": expected shape=" + a.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 r = this;
                t = t || {};
                for (var n = toList(e), a = !0, o = 0, i = n; o < i.length; o++)
                    if (!((c = i[o]) instanceof SymbolicTensor)) {
                        a = !1;
                        break
                    }
                for (var s = !0, u = 0, l = n; u < l.length; u++) {
                    var c = l[u];
                    if (c instanceof SymbolicTensor) {
                        s = !1;
                        break
                    }
                }
                if (a === s) throw new ValueError("Arguments to apply() must be all SymbolicTensors or all Tensors");
                return nameScope$1(this.name, function() {
                    if (!r.built) {
                        r.assertInputCompatibility(e);
                        for (var a = [], o = 0, i = toList(e); o < i.length; o++) {
                            var u = i[o];
                            a.push(intShape(u))
                        }
                        r.build(singletonOrArray(a)), r.built = !0, r.initialWeights && r.setWeights(r.initialWeights)
                    }
                    if (r.assertInputCompatibility(e), s) {
                        for (var l = [], c = 0, p = toList(m = r.call(e, t)); c < p.length; c++) {
                            var d = p[c]; - 1 !== n.indexOf(d) && (d = identity(d)), l.push(d)
                        }
                        if (m = singletonOrArray(l), null != r.activityRegularizer) throw new NotImplementedError("Layer invocation in the presence of activity regularizer(s) is not supported yet.");
                        return m
                    }
                    var h = collectInputShape(e),
                        f = r.computeOutputShape(h),
                        m = void 0;
                    if (m = null != f && f.length > 0 && Array.isArray(f[0]) ? f.map(function(n, a) {
                            return new SymbolicTensor("float32", n, r, toList(e), t, r.name, a)
                        }) : new SymbolicTensor("float32", f, r, toList(e), t, r.name), r.addInboundNode(e, m, null, null, h, f, t), null != r.activityRegularizer) throw new NotImplementedError("Layer invocation in the presence of activity regularizer(s) is not supported yet.");
                    return m
                })
            }, 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 r = t.weights;
                    if (r.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 " + r.length + " weights. Provided weights: " + e + "...");
                    if (0 !== r.length) {
                        for (var n = [], a = batchGetValue(r), o = 0; o < a.length; ++o) {
                            var i = a[o],
                                s = r[o],
                                u = e[o];
                            if (!arraysEqual(i.shape, u.shape)) throw new ValueError("Layer weight shape " + i.shape + " not compatible with provided weight shape " + u.shape);
                            n.push([s, u])
                        }
                        batchSetValue(n)
                    }
                })
            }, t.prototype.addWeight = function(e, t, r, n, a, o, i) {
                if (-1 !== this._addedWeightNames.indexOf(e)) throw new ValueError("Duplicate weight name " + e + " for layer " + this.name);
                this._addedWeightNames.push(e), null == r && (r = floatx());
                var s = new LayerVariable(n.apply(t, r), r, e, o, i);
                return null != a && this.addLoss(function() {
                    return a.apply(s.read())
                }), null == o && (o = !0), o ? this._trainableWeights.push(s) : this._nonTrainableWeights.push(s), s
            }, t.prototype.addLoss = function(e) {
                if (!(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);
                    var t
                }
            }, t.prototype.computeOutputShape = function(e) {
                return e
            }, t.prototype.computeMask = function(e, t) {
                var r = 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 " + r.name + " does not support masking,but was passed an inputMask.")
                        })
                    }
                    return null
                }
                return t
            }, t.prototype.addInboundNode = function(e, t, r, n, a, o, i) {
                void 0 === i && (i = null);
                var s = toList(e);
                t = toList(t), r = toList(r), n = toList(n), a = normalizeShapeList(a), o = normalizeShapeList(o);
                for (var u = [], l = [], c = [], p = 0, d = s; p < d.length; p++) {
                    var h = d[p];
                    u.push(h.sourceLayer), l.push(h.nodeIndex), c.push(h.tensorIndex)
                }
                new Node({
                    outboundLayer: this,
                    inboundLayers: u,
                    nodeIndices: l,
                    tensorIndices: c,
                    inputTensors: s,
                    outputTensors: t,
                    inputMasks: r,
                    outputMasks: n,
                    inputShapes: a,
                    outputShapes: o
                }, i);
                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
            }, __decorate$33([doc()], t.prototype, "apply", null), t = __decorate$33([doc()], t)
        }(Serializable),
        InputLayer = function(e) {
            function t(t) {
                var r = 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), r.trainable = !1, r.built = !0, r.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 n = t.batchInputShape;
                if (null == n) {
                    if (null == t.inputShape) throw new ValueError("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");
                    n = [t.batchSize].concat(t.inputShape)
                } else if (null != t.batchSize) throw new ValueError("Cannot specify batchSize if batchInputShape isspecified when creating an InputLayer.");
                var a = t.dtype || floatx();
                r.batchInputShape = n, r.dtype = a, r.inputSpec = [{
                    shape: n
                }];
                var o = new SymbolicTensor(r.dtype, r.batchInputShape, r, [], {}, r.name);
                return o.nodeIndex = 0, o.tensorIndex = 0, new Node({
                    outboundLayer: r,
                    inboundLayers: [],
                    nodeIndices: [],
                    tensorIndices: [],
                    inputTensors: [o],
                    outputTensors: [o],
                    inputMasks: [null],
                    outputMasks: [null],
                    inputShapes: [n],
                    outputShapes: [n]
                }), r
            }
            return __extends$11(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.getConfig = function() {
                return {
                    batchInputShape: this.batchInputShape,
                    dtype: this.dtype,
                    sparse: this.sparse,
                    name: this.name
                }
            }, t.className = "InputLayer", t
        }(Layer);
    SerializationMap.register(InputLayer);
    var Container = function(e) {
            function t(r) {
                var n = e.call(this, {}) || this;
                if (n.containerNodes = new Set, n.name = r.name, null == n.name) {
                    var a = n.getClassName().toLowerCase();
                    n.name = getUid(a)
                }
                if (n.supportsMasking = !1, n.trainable = !0, n.updatable = !0, Array.isArray(r.inputs) ? n.inputs = r.inputs.slice() : n.inputs = [r.inputs], Array.isArray(r.outputs) ? n.outputs = r.outputs.slice() : n.outputs = [r.outputs], unique(n.inputs).length !== n.inputs.length) throw new ValueError("The list of inputs passed to the model is redundant. All inputs should only appear once. Found: " + n.inputs.map(function(e) {
                    return e.name
                }));
                unique(n.outputs).length !== n.outputs.length && console.warn("The list of outputs passed to the model is redundant. All outputs should only appear once. Found: " + n.outputs.map(function(e) {
                    return e.name
                })), n.inputLayers = [], n.inputLayersNodeIndices = [], n.inputLayersTensorIndices = [], n.outputLayers = [], n.outputLayersNodeIndices = [], n.outputLayersTensorIndices = [], n.layers = [];
                for (var o = 0, i = n.outputs; o < i.length; o++) {
                    var s = (ee = i[o]).sourceLayer,
                        u = ee.nodeIndex,
                        l = ee.tensorIndex;
                    n.outputLayers.push(s), n.outputLayersNodeIndices.push(u), n.outputLayersTensorIndices.push(l)
                }
                for (var c = 0, p = n.inputs; c < p.length; c++) {
                    var s = (ee = p[c]).sourceLayer,
                        u = ee.nodeIndex,
                        l = ee.tensorIndex;
                    assert$1(0 === u, "input layer has >1 nodes"), assert$1(0 === l, "input layer has >1 tensors"), n.inputLayers.push(s), n.inputLayersNodeIndices.push(u), n.inputLayersTensorIndices.push(l)
                }
                n.inputNames = [], n.outputNames = [], n.feedInputShapes = [], n.feedInputNames = [], n.feedOutputNames = [];
                for (O = 0; O < n.inputLayers.length; O++) {
                    if (!((s = n.inputLayers[O]) instanceof InputLayer)) throw new TypeError("Input layers to a Model must be InputLayer objects. Received inputs: " + r.inputs + ". Input " + O + " (0-based) originates from layer type " + s.getClassName() + ".");
                    n.inputNames.push(s.name), n.feedInputShapes.push(s.batchInputShape), n.feedInputNames.push(s.name)
                }
                for (var d = 0, h = n.outputLayers; d < h.length; d++) {
                    s = h[d];
                    n.outputNames.push(s.name)
                }
                n.internalInputShapes = n.inputs.map(function(e) {
                    return e.shape
                }), n.internalOutputShapes = n.outputs.map(function(e) {
                    return e.shape
                });
                for (var f = {}, m = {}, g = {}, y = {}, v = {}, b = [], x = function(e, r, a, o, i, s) {
                        null != o && null != i && null != s || (o = e.sourceLayer, i = e.nodeIndex, s = e.tensorIndex);
                        var u = o.inboundNodes[i];
                        if (-1 !== a.indexOf(u)) throw new RuntimeError("The tensor " + e.name + ' at layer "' + o.name + '" is part of a cycle.');
                        if (-1 === r.indexOf(u)) {
                            n.containerNodes.add(t.nodeKey(o, i)), o.id in v || (v[o.id] = Object.keys(v).length), -1 === a.indexOf(u) && a.push(u);
                            for (var l = u.inboundLayers.length, c = 0; c < l; c++) {
                                var p = u.inputTensors[c],
                                    d = u.inboundLayers[c],
                                    h = u.nodeIndices[c],
                                    f = u.tensorIndices[c];
                                x(p, r, a, d, h, f)
                            }
                            for (r.push(u); a.indexOf(u) >= 0;) a.splice(a.indexOf(u), 1);
                            b.push(u)
                        }
                    }, w = [], _ = [], S = 0, N = n.outputs; S < N.length; S++) {
                    ee = N[S];
                    x(ee, w, _)
                }
                for (var A = 0, E = b.slice().reverse(); A < E.length; A++) {
                    m[(X = E[A]).id] = X, X.id in f || (f[X.id] = 0);
                    var T = f[X.id],
                        I = null == g[X.outboundLayer.id] ? 0 : g[X.outboundLayer.id];
                    T = Math.max(T, I), g[X.outboundLayer.id] = T, y[X.outboundLayer.id] = X.outboundLayer, f[X.id] = T;
                    for (var O = 0; O < X.inboundLayers.length; O++) {
                        var P = X.inboundLayers[O],
                            u = X.nodeIndices[O],
                            R = P.inboundNodes[u],
                            C = null == f[R.id] ? 0 : f[R.id];
                        f[R.id] = Math.max(T + 1, C), m[R.id] = R
                    }
                }
                var k = {};
                for (var D in f)(T = f[D]) in k || (k[T] = []), k[T].push(m[D]);
                var M = {};
                for (var L in g)(T = g[L]) in M || (M[T] = []), M[T].push(y[L]);
                var z = Object.keys(M).map(function(e) {
                    return parseInt(e, 10)
                }).sort(reverseNumberCompare);
                n.layers = [];
                for (var V = 0, F = z; V < F.length; V++) {
                    var $ = M[T = F[V]];
                    $.sort(function(e, t) {
                        var r = v[e.id],
                            n = v[t.id];
                        return r < n ? -1 : r > n ? 1 : 0
                    });
                    for (var B = 0, U = $; B < U.length; B++) {
                        s = U[B];
                        n.layers.push(s)
                    }
                }
                n.layersByDepth = M, z = Object.keys(k).map(function(e) {
                    return parseInt(e, 10)
                }).sort(reverseNumberCompare);
                for (var j = n.inputs.slice(), G = [], W = 0, q = z; W < q.length; W++)
                    for (var H = 0, K = k[T = q[W]]; H < K.length; H++) {
                        var X = K[H];
                        if (null != (s = X.outboundLayer)) {
                            for (var J = 0, Y = X.inputTensors; J < Y.length; J++) {
                                ee = Y[J];
                                if (-1 === j.indexOf(ee)) throw new RuntimeError("Graph disconnected: cannot obtain value for tensor " + ee + ' at layer "' + s.name + '". The following previous layers were accessed without issue: ' + G)
                            }
                            for (var Z = 0, Q = X.outputTensors; Z < Q.length; Z++) {
                                var ee = Q[Z];
                                j.push(ee)
                            }
                            G.push(s.name)
                        }
                    }
                n.nodesByDepth = k;
                for (var te = n.layers.map(function(e) {
                        return e.name
                    }), re = 0, ne = te; re < ne.length; re++) ! 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))
                }(ne[re]);
                return n.outboundNodes = [], n.inboundNodes = [], new Node({
                    outboundLayer: n,
                    inboundLayers: [],
                    nodeIndices: [],
                    tensorIndices: [],
                    inputTensors: n.inputs,
                    outputTensors: n.outputs,
                    inputMasks: n.inputs.map(function(e) {
                        return null
                    }),
                    outputMasks: n.outputs.map(function(e) {
                        return null
                    }),
                    inputShapes: n.inputs.map(function(e) {
                        return e.shape
                    }),
                    outputShapes: n.outputs.map(function(e) {
                        return e.shape
                    })
                }), n.built = !0, n
            }
            return __extends$11(t, 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, r = this.layers; t < r.length; t++) {
                        var n = r[t];
                        e = e.concat(n.trainableWeights)
                    }
                    return e
                },
                enumerable: !0,
                configurable: !0
            }), Object.defineProperty(t.prototype, "nonTrainableWeights", {
                get: function() {
                    for (var e = [], t = 0, r = this.layers; t < r.length; t++) {
                        i = r[t];
                        e.push.apply(e, i.nonTrainableWeights)
                    }
                    if (!this.trainable) {
                        for (var n = [], a = 0, o = this.layers; a < o.length; a++) {
                            var i = o[a];
                            n.push.apply(n, i.trainableWeights)
                        }
                        return n.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, r) {
                void 0 === t && (t = !1), void 0 === r && (r = !1), r ? loadWeightsFromNamedTensorMap(e, this.layers) : loadWeightsFromJson(e, this.layers, t)
            }, t.prototype.updatedConfig = function() {
                var e = this.getConfig();
                return {
                    className: this.getClassName(),
                    config: e,
                    kerasVersion: "tfjs-layers " + version$1,
                    backend: "TensorFlow.js"
                }
            }, t.prototype.toJSON = function(e, t) {
                void 0 === t && (t = !0);
                var r = convertTsToPythonic(this.updatedConfig());
                return t ? JSON.stringify(r) : r
            }, t.prototype.call = function(e, t) {
                var r = this;
                return tidy(function() {
                    e = toList(e);
                    var n;
                    return n = "mask" in t ? toList(t.mask) : pyListRepeat(null, e.length), r.runInternalGraph(e, n)[0]
                })
            }, t.prototype.computeMask = function(e, t) {
                var r = this;
                return tidy(function() {
                    e = toList(e);
                    var n;
                    return n = null == t ? pyListRepeat(null, e.length) : toList(t), r.runInternalGraph(e, n)[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 r = {}, n = 0; n < t.length; n++) {
                    var a = this.inputLayers[n],
                        o = t[n];
                    r[S = a.name + "_0_0"] = o
                }
                var i = Object.keys(this.nodesByDepth).map(function(e) {
                    return parseInt(e, 10)
                }).sort(reverseNumberCompare);
                if (i.length > 1)
                    for (var s = 0, u = i; s < u.length; s++)
                        for (var l = u[s], c = 0, p = this.nodesByDepth[l]; c < p.length; c++) {
                            var d = p[c],
                                a = d.outboundLayer;
                            if (-1 === this.inputLayers.map(function(e) {
                                    return e.id
                                }).indexOf(a.id)) {
                                for (var h = [], f = 0; f < d.inboundLayers.length; f++) {
                                    var m = d.inboundLayers[f],
                                        g = d.nodeIndices[f],
                                        y = d.tensorIndices[f],
                                        v = r[S = m.name + "_" + g + "_" + y];
                                    h.push(v)
                                }
                                for (var b = normalizeShapeList(a.computeOutputShape(singletonOrArray(h))), x = a.inboundNodes.indexOf(d), f = 0; f < b.length; f++) r[S = a.name + "_" + x + "_" + f] = b[f]
                            }
                        }
                for (var w = [], _ = [], n = 0; n < this.outputLayers.length; n++) {
                    var a = this.outputLayers[n],
                        x = this.outputLayersNodeIndices[n],
                        y = this.outputLayersTensorIndices[n],
                        S = a.name + "_" + x + "_" + y;
                    _.push(S)
                }
                for (n = 0; n < _.length; n++) {
                    var N = _[n];
                    assert$1(N in r), w.push(r[N])
                }
                return singletonOrArray(w)
            }, t.prototype.runInternalGraph = function(e, t) {
                null == t && (t = pyListRepeat(null, e.length));
                for (var r = {}, n = 0; n < this.inputs.length; ++n) {
                    var a = this.inputs[n],
                        o = e[n],
                        i = t[n];
                    r[a.id] = [o, i]
                }
                for (var s = 0, u = Object.keys(this.nodesByDepth).map(function(e) {
                        return parseInt(e, 10)
                    }).sort(reverseNumberCompare); s < u.length; s++)
                    for (var l = u[s], c = 0, p = this.nodesByDepth[l]; c < p.length; c++) {
                        for (var d = p[c], h = d.outboundLayer, f = d.inputTensors, m = d.outputTensors, g = new Array, y = 0, v = f; y < v.length; y++)(a = v[y]).id in r && g.push(r[a.id]);
                        if (g.length === f.length) {
                            var b = {},
                                x = void 0,
                                w = void 0,
                                _ = void 0,
                                S = void 0;
                            if (null != d.callArgs && (b = d.callArgs), 1 === g.length) {
                                var N = g[0],
                                    A = N[0],
                                    E = N[1];
                                null == b.mask && (b.mask = E), _ = toList(h.call(A, b)), S = toList(h.computeMask(A, E)), x = [A], w = [E]
                            } else x = g.map(function(e) {
                                return e[0]
                            }), w = g.map(function(e) {
                                return e[1]
                            }), null == b.mask && (b.mask = w), _ = toList(h.call(x, b)), S = toList(h.computeMask(x, w));
                            if (h.activityRegularizer) throw new NotImplementedError("Model invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");
                            for (n = 0; n < m.length; ++n) {
                                var a = m[n],
                                    o = _[n],
                                    i = S[n];
                                r[a.id] = [o, i]
                            }
                        }
                    }
                for (var T = [], I = [], O = [], P = 0, R = this.outputs; P < R.length; P++) {
                    assert$1((a = R[P]).id in r, "Could not compute output " + a.name + " : " + a.id);
                    var C = r[a.id],
                        k = C[0],
                        i = C[1];
                    O.push(k.shape), T.push(k), I.push(i)
                }
                return [T, I, O]
            }, t.prototype.buildNodeConversionMap = function(e) {
                for (var r, n = {}, a = 0, o = this.layers; a < o.length; a++) {
                    var i = o[a];
                    r = i instanceof t ? 1 : 0;
                    for (var s = 0; s < i.inboundNodes.length; s++) {
                        var u = t.nodeKey(i, s);
                        u in this.containerNodes && (n[u] = r, r += 1)
                    }
                }
                return n
            }, 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 r = 0, n = this.layers; r < n.length; r++) {
                    var a = n[r];
                    if (a.name === e) return a
                }
                throw new ValueError("No such layer: " + e)
            }, t.prototype.calculateLosses = function() {
                var e = this;
                return tidy(function() {
                    for (var r = [], n = 0, a = e.layers; n < a.length; n++)
                        for (var o = a[n], i = 0; i < o.inboundNodes.length; ++i) {
                            var s = t.nodeKey(o, i);
                            e.containerNodes.has(s) && r.push.apply(r, o.calculateLosses())
                        }
                    return r
                })
            }, t.prototype.getConfig = function() {
                for (var e = {
                        name: this.name
                    }, r = this.buildNodeConversionMap(this.layers), n = [], a = 0, o = this.layers; a < o.length; a++) {
                    for (var i = (b = o[a]).getClassName(), s = b.getConfig(), u = [], l = 0; l < b.inboundNodes.length; l++) {
                        var c = b.inboundNodes[l],
                            p = t.nodeKey(b, l),
                            d = {};
                        if (this.containerNodes.has(p) && (c.callArgs && (-1 === JSON.stringify(c.callArgs).indexOf("undefined") ? d = c.callArgs : (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)."), d = {})), c.inboundLayers.length > 0)) {
                            for (var h = [], f = 0; f < c.inboundLayers.length; f++) {
                                var m = c.inboundLayers[f],
                                    g = c.nodeIndices[f],
                                    y = c.tensorIndices[f];
                                null !== (w = r[t.nodeKey(m, g)]) && void 0 !== w || (w = 0), h.push([m.name, w, y, d])
                            }
                            u.push(h)
                        }
                    }
                    n.push({
                        name: b.name,
                        className: i,
                        config: s,
                        inboundNodes: u
                    })
                }
                e.layers = n;
                for (var v = [], f = 0; f < this.inputLayers.length; f++) {
                    var b = this.inputLayers[f],
                        g = this.inputLayersNodeIndices[f],
                        p = t.nodeKey(b, g);
                    if (this.containerNodes.has(p)) {
                        null !== (w = r[p]) && void 0 !== w || (w = 0);
                        y = this.inputLayersTensorIndices[f];
                        v.push([b.name, w, y])
                    }
                }
                e.inputLayers = v;
                for (var x = [], f = 0; f < this.outputLayers.length; f++) {
                    var b = this.outputLayers[f],
                        g = this.outputLayersNodeIndices[f],
                        p = t.nodeKey(b, g);
                    if (this.containerNodes.has(p)) {
                        var w = r[p];
                        null !== w && void 0 !== w || (w = 0);
                        y = this.outputLayersTensorIndices[f];
                        x.push([b.name, w, y])
                    }
                }
                return e.outputLayers = x, e
            }, t.fromConfig = function(e, t) {
                function r(e, t) {
                    e.name in a ? a[e.name].push(t) : a[e.name] = [t]
                }
                for (var n = {}, a = {}, o = t.name, i = t.layers, s = 0, u = i; s < u.length; s++) ! function(e) {
                    var a = e.name,
                        o = deserialize(e, null != t.customObjects ? t.customObjects : {});
                    n[a] = o;
                    for (var i = 0, s = e.inboundNodes; i < s.length; i++) {
                        var u = s[i];
                        if (!(u instanceof Array)) throw new ValueError("Corrupted configuration, expected array for nodeData: " + u);
                        r(o, u)
                    }
                }(p = u[s]);
                for (; !isObjectEmpty(a);)
                    for (var l = 0, c = i; l < c.length; l++) {
                        var p = c[l];
                        if ((S = n[p.name]).name in a) {
                            for (var d = 0, h = a[S.name]; d < h.length; d++) ! function(e, t) {
                                for (var a, o = [], i = 0, s = t; i < s.length; i++) {
                                    var u = s[i],
                                        l = u[0],
                                        c = u[1],
                                        p = u[2];
                                    if (3 === u.length) a = {};
                                    else {
                                        if (4 !== u.length) throw new ValueError("Improperly formatted model config for layer " + JSON.stringify(e) + ": " + JSON.stringify(u));
                                        a = u[3]
                                    }
                                    if (!(l in n)) return void r(e, t);
                                    var d = n[l];
                                    if (d.inboundNodes.length <= c) return void r(e, t);
                                    var h = d.inboundNodes[c];
                                    o.push(h.outputTensors[p])
                                }
                                o.length > 0 && e.apply(singletonOrArray(o), a)
                            }(S, h[d]);
                            delete a[S.name]
                        }
                    }
                for (var f = [], m = [], g = 0, y = t.inputLayers; g < y.length; g++) {
                    var v = (p = y[g])[0],
                        b = p[1],
                        x = p[2];
                    assert$1(v in n);
                    N = (S = n[v]).inboundNodes[b].outputTensors;
                    f.push(N[x])
                }
                for (var w = 0, _ = t.outputLayers; w < _.length; w++) {
                    var v = (p = _[w])[0],
                        b = p[1],
                        x = p[2];
                    assert$1(v in n);
                    var S = n[v],
                        N = S.inboundNodes[b].outputTensors;
                    m.push(N[x])
                }
                return new e({
                    inputs: f,
                    outputs: m,
                    name: o
                })
            }, 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
            }), __decorate$33([doc()], t.prototype, "getLayer", null), t
        }(Layer),
        __extends$12 = function() {
            var e = 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])
            };
            return function(t, r) {
                function n() {
                    this.constructor = t
                }
                e(t, r), t.prototype = null === r ? Object.create(r) : (n.prototype = r.prototype, new n)
            }
        }(),
        __awaiter$14 = function(e, t, r, n) {
            return new(r || (r = Promise))(function(a, o) {
                function i(e) {
                    try {
                        u(n.next(e))
                    } catch (e) {
                        o(e)
                    }
                }

                function s(e) {
                    try {
                        u(n.throw(e))
                    } catch (e) {
                        o(e)
                    }
                }

                function u(e) {
                    e.done ? a(e.value) : new r(function(t) {
                        t(e.value)
                    }).then(i, s)
                }
                u((n = n.apply(e, t || [])).next())
            })
        },
        __generator$14 = function(e, t) {
            function r(e) {
                return function(t) {
                    return n([e, t])
                }
            }

            function n(r) {
                if (a) throw new TypeError("Generator is already executing.");
                for (; u;) try {
                    if (a = 1, o && (i = o[2 & r[0] ? "return" : r[0] ? "throw" : "next"]) && !(i = i.call(o, r[1])).done) return i;
                    switch (o = 0, i && (r = [0, i.value]), r[0]) {
                        case 0:
                        case 1:
                            i = r;
                            break;
                        case 4:
                            return u.label++, {
                                value: r[1],
                                done: !1
                            };
                        case 5:
                            u.label++, o = r[1], r = [0];
                            continue;
                        case 7:
                            r = u.ops.pop(), u.trys.pop();
                            continue;
                        default:
                            if (i = u.trys, !(i = i.length > 0 && i[i.length - 1]) && (6 === r[0] || 2 === r[0])) {
                                u = 0;
                                continue
                            }
                            if (3 === r[0] && (!i || r[1] > i[0] && r[1] < i[3])) {
                                u.label = r[1];
                                break
                            }
                            if (6 === r[0] && u.label < i[1]) {
                                u.label = i[1], i = r;
                                break
                            }
                            if (i && u.label < i[2]) {
                                u.label = i[2], u.ops.push(r);
                                break
                            }
                            i[2] && u.ops.pop(), u.trys.pop();
                            continue
                    }
                    r = t.call(e, u)
                } catch (e) {
                    r = [6, e], o = 0
                } finally {
                    a = i = 0
                }
                if (5 & r[0]) throw r[1];
                return {
                    value: r[0] ? r[1] : void 0,
                    done: !0
                }
            }
            var a, o, i, s, u = {
                label: 0,
                sent: function() {
                    if (1 & i[0]) throw i[1];
                    return i[1]
                },
                trys: [],
                ops: []
            };
            return s = {
                next: r(0),
                throw: r(1),
                return: r(2)
            }, "function" == typeof Symbol && (s[Symbol.iterator] = function() {
                return this
            }), s
        },
        Callback = function() {
            function e() {
                this.validationData = null, this.model = null
            }
            return e.prototype.setParams = function(e) {
                this.params = e
            }, e.prototype.setModel = function(e) {
                this.model = e
            }, e.prototype.onEpochBegin = function(e, t) {
                return __awaiter$14(this, void 0, void 0, function() {
                    return __generator$14(this, function(e) {
                        return [2]
                    })
                })
            }, e.prototype.onEpochEnd = function(e, t) {
                return __awaiter$14(this, void 0, void 0, function() {
                    return __generator$14(this, function(e) {
                        return [2]
                    })
                })
            }, e.prototype.onBatchBegin = function(e, t) {
                return __awaiter$14(this, void 0, void 0, function() {
                    return __generator$14(this, function(e) {
                        return [2]
                    })
                })
            }, e.prototype.onBatchEnd = function(e, t) {
                return __awaiter$14(this, void 0, void 0, function() {
                    return __generator$14(this, function(e) {
                        return [2]
                    })
                })
            }, e.prototype.onTrainBegin = function(e) {
                return __awaiter$14(this, void 0, void 0, function() {
                    return __generator$14(this, function(e) {
                        return [2]
                    })
                })
            }, e.prototype.onTrainEnd = function(e) {
                return __awaiter$14(this, void 0, void 0, function() {
                    return __generator$14(this, function(e) {
                        return [2]
                    })
                })
            }, 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, r = this.callbacks; t < r.length; t++) r[t].setParams(e)
            }, e.prototype.setModel = function(e) {
                for (var t = 0, r = this.callbacks; t < r.length; t++) r[t].setModel(e)
            }, e.prototype.onEpochBegin = function(e, t) {
                return __awaiter$14(this, void 0, void 0, function() {
                    var r, n, a;
                    return __generator$14(this, function(o) {
                        switch (o.label) {
                            case 0:
                                null == t && (t = {}), r = 0, n = this.callbacks, o.label = 1;
                            case 1:
                                return r < n.length ? (a = n[r], [4, a.onEpochBegin(e, t)]) : [3, 4];
                            case 2:
                                o.sent(), o.label = 3;
                            case 3:
                                return r++, [3, 1];
                            case 4:
                                return [2]
                        }
                    })
                })
            }, e.prototype.onEpochEnd = function(e, t) {
                return __awaiter$14(this, void 0, void 0, function() {
                    var r, n, a;
                    return __generator$14(this, function(o) {
                        switch (o.label) {
                            case 0:
                                null == t && (t = {}), r = 0, n = this.callbacks, o.label = 1;
                            case 1:
                                return r < n.length ? (a = n[r], [4, a.onEpochEnd(e, t)]) : [3, 4];
                            case 2:
                                o.sent(), o.label = 3;
                            case 3:
                                return r++, [3, 1];
                            case 4:
                                return [2]
                        }
                    })
                })
            }, e.prototype.onBatchBegin = function(e, t) {
                return __awaiter$14(this, void 0, void 0, function() {
                    var r, n, a;
                    return __generator$14(this, function(o) {
                        switch (o.label) {
                            case 0:
                                null == t && (t = {}), r = 0, n = this.callbacks, o.label = 1;
                            case 1:
                                return r < n.length ? (a = n[r], [4, a.onBatchBegin(e, t)]) : [3, 4];
                            case 2:
                                o.sent(), o.label = 3;
                            case 3:
                                return r++, [3, 1];
                            case 4:
                                return [2]
                        }
                    })
                })
            }, e.prototype.onBatchEnd = function(e, t) {
                return __awaiter$14(this, void 0, void 0, function() {
                    var r, n, a;
                    return __generator$14(this, function(o) {
                        switch (o.label) {
                            case 0:
                                null == t && (t = {}), r = 0, n = this.callbacks, o.label = 1;
                            case 1:
                                return r < n.length ? (a = n[r], [4, a.onBatchEnd(e, t)]) : [3, 4];
                            case 2:
                                o.sent(), o.label = 3;
                            case 3:
                                return r++, [3, 1];
                            case 4:
                                return [2]
                        }
                    })
                })
            }, e.prototype.onTrainBegin = function(e) {
                return __awaiter$14(this, void 0, void 0, function() {
                    var t, r, n;
                    return __generator$14(this, function(a) {
                        switch (a.label) {
                            case 0:
                                null == e && (e = {}), t = 0, r = this.callbacks, a.label = 1;
                            case 1:
                                return t < r.length ? (n = r[t], [4, n.onTrainBegin(e)]) : [3, 4];
                            case 2:
                                a.sent(), a.label = 3;
                            case 3:
                                return t++, [3, 1];
                            case 4:
                                return [2]
                        }
                    })
                })
            }, e.prototype.onTrainEnd = function(e) {
                return __awaiter$14(this, void 0, void 0, function() {
                    var t, r, n;
                    return __generator$14(this, function(a) {
                        switch (a.label) {
                            case 0:
                                null == e && (e = {}), t = 0, r = this.callbacks, a.label = 1;
                            case 1:
                                return t < r.length ? (n = r[t], [4, n.onTrainEnd(e)]) : [3, 4];
                            case 2:
                                a.sent(), a.label = 3;
                            case 3:
                                return t++, [3, 1];
                            case 4:
                                return [2]
                        }
                    })
                })
            }, e
        }(),
        BaseLogger = function(e) {
            function t() {
                return e.call(this) || this
            }
            return __extends$12(t, e), t.prototype.onEpochBegin = function(e, t) {
                return __awaiter$14(this, void 0, void 0, function() {
                    return __generator$14(this, function(e) {
                        return this.seen = 0, this.totals = {}, [2]
                    })
                })
            }, t.prototype.onBatchEnd = function(e, t) {
                return __awaiter$14(this, void 0, void 0, function() {
                    var e, r, n, a, o = this;
                    return __generator$14(this, function(i) {
                        null == t && (t = {}), e = null == t.size ? 0 : t.size, this.seen += e, r = function(r) {
                            var a = t[r];
                            if ("number" == typeof a) n.totals.hasOwnProperty(r) || (n.totals[r] = 0), n.totals[r] = n.totals[r] + a * e;
                            else {
                                var i = void 0;
                                r in n.totals ? i = n.totals[r] : n.totals[r] = getScalar(0), n.totals[r] = tidy(function() {
                                    return scalarPlusArray(o.totals[r], mul(a, getScalar(e)))
                                }), null != i && i.dispose()
                            }
                        }, n = this;
                        for (a in t) r(a);
                        return [2]
                    })
                })
            }, t.prototype.onEpochEnd = function(e, t) {
                return __awaiter$14(this, void 0, void 0, function() {
                    var e, r, n, a, o, i = this;
                    return __generator$14(this, function(s) {
                        if (null != t)
                            for (e = function(e) {
                                    if (null == r.totals[e]) return "continue";
                                    "number" == typeof r.totals[e] ? t[e] = r.totals[e] / r.seen : tidy(function() {
                                        t[e] = scalarTimesArray(div(getScalar(1), getScalar(i.seen)), i.totals[e]), i.totals[e].dispose(), keep(t[e])
                                    })
                                }, r = this, n = 0, a = this.params.metrics; n < a.length; n++) o = a[n], e(o);
                        return [2]
                    })
                })
            }, t
        }(Callback),
        History = function(e) {
            function t() {
                return null !== e && e.apply(this, arguments) || this
            }
            return __extends$12(t, e), t.prototype.onTrainBegin = function(e) {
                return __awaiter$14(this, void 0, void 0, function() {
                    return __generator$14(this, function(e) {
                        return this.epoch = [], this.history = {}, [2]
                    })
                })
            }, t.prototype.onEpochEnd = function(e, t) {
                return __awaiter$14(this, void 0, void 0, function() {
                    var r;
                    return __generator$14(this, function(n) {
                        null == t && (t = {}), this.epoch.push(e);
                        for (r in t) null == this.history[r] && (this.history[r] = []), this.history[r].push(t[r]);
                        return [2]
                    })
                })
            }, t.prototype.syncData = function() {
                return __awaiter$14(this, void 0, void 0, function() {
                    var e, t, r, n, a, o, i, s, u;
                    return __generator$14(this, function(l) {
                        switch (l.label) {
                            case 0:
                                e = [], t = [], r = [];
                                for (n in this.history)
                                    for (a = this.history[n], o = 0; o < a.length; ++o) "number" != typeof a[o] && (i = a[o], e.push(i.data()), t.push(n), r.push(o));
                                return [4, Promise.all(e)];
                            case 1:
                                for (s = l.sent(), u = 0; u < s.length; ++u) this.history[t[u]][r[u]].dispose(), this.history[t[u]][r[u]] = s[u][0];
                                return [2]
                        }
                    })
                })
            }, t
        }(Callback),
        CustomCallback = function(e) {
            function t(t) {
                var r = e.call(this) || this;
                return r.trainBegin = t.onTrainBegin, r.trainEnd = t.onTrainEnd, r.epochBegin = t.onEpochBegin, r.epochEnd = t.onEpochEnd, r.batchBegin = t.onBatchBegin, r.batchEnd = t.onBatchEnd, r
            }
            return __extends$12(t, e), t.prototype.onEpochBegin = function(e, t) {
                return __awaiter$14(this, void 0, void 0, function() {
                    return __generator$14(this, function(r) {
                        switch (r.label) {
                            case 0:
                                return null == this.epochBegin ? [3, 3] : [4, resolveScalarsInLogs(t)];
                            case 1:
                                return r.sent(), [4, this.epochBegin(e, t)];
                            case 2:
                                r.sent(), r.label = 3;
                            case 3:
                                return [2]
                        }
                    })
                })
            }, t.prototype.onEpochEnd = function(e, t) {
                return __awaiter$14(this, void 0, void 0, function() {
                    return __generator$14(this, function(r) {
                        switch (r.label) {
                            case 0:
                                return null == this.epochEnd ? [3, 3] : [4, resolveScalarsInLogs(t)];
                            case 1:
                                return r.sent(), [4, this.epochEnd(e, t)];
                            case 2:
                                r.sent(), r.label = 3;
                            case 3:
                                return [2]
                        }
                    })
                })
            }, t.prototype.onBatchBegin = function(e, t) {
                return __awaiter$14(this, void 0, void 0, function() {
                    return __generator$14(this, function(r) {
                        switch (r.label) {
                            case 0:
                                return null == this.batchBegin ? [3, 3] : [4, resolveScalarsInLogs(t)];
                            case 1:
                                return r.sent(), [4, this.batchBegin(e, t)];
                            case 2:
                                r.sent(), r.label = 3;
                            case 3:
                                return [2]
                        }
                    })
                })
            }, t.prototype.onBatchEnd = function(e, t) {
                return __awaiter$14(this, void 0, void 0, function() {
                    return __generator$14(this, function(r) {
                        switch (r.label) {
                            case 0:
                                return null == this.batchEnd ? [3, 3] : [4, resolveScalarsInLogs(t)];
                            case 1:
                                return r.sent(), [4, this.batchEnd(e, t)];
                            case 2:
                                r.sent(), r.label = 3;
                            case 3:
                                return [2]
                        }
                    })
                })
            }, t.prototype.onTrainBegin = function(e) {
                return __awaiter$14(this, void 0, void 0, function() {
                    return __generator$14(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$14(this, void 0, void 0, function() {
                    return __generator$14(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
        }(Callback),
        mse$1 = meanSquaredError,
        MSE$1 = meanSquaredError,
        mae$1 = meanAbsoluteError,
        MAE$1 = meanAbsoluteError,
        mape$1 = meanAbsolutePercentageError,
        MAPE$1 = meanAbsolutePercentageError,
        categoricalCrossentropy$1 = categoricalCrossentropy,
        cosine$1 = cosineProximity,
        sparseCategoricalCrossentropy$1 = sparseCategoricalCrossentropy,
        FeedDict = function() {
            function e(t) {
                if (this.id2Value = {}, t instanceof e)
                    for (var r in t.id2Value) this.id2Value[r] = t.id2Value[r];
                else {
                    if (null == t) return;
                    for (var n = 0, a = t; n < a.length; n++) {
                        var o = a[n];
                        this.add(o.key, o.value)
                    }
                }
            }
            return e.prototype.add = function(e, t) {
                if (assertFeedCompatibility(e, t), null != this.id2Value[e.id]) throw new ValueError("Duplicate key: name=" + e.name + ", id=" + e.id);
                return this.id2Value[e.id] = 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
        }(),
        __extends$13 = function() {
            var e = 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])
            };
            return function(t, r) {
                function n() {
                    this.constructor = t
                }
                e(t, r), t.prototype = null === r ? Object.create(r) : (n.prototype = r.prototype, new n)
            }
        }(),
        __decorate$34 = function(e, t, r, n) {
            var a, o = arguments.length,
                i = o < 3 ? t : null === n ? n = Object.getOwnPropertyDescriptor(t, r) : n;
            if ("object" == typeof Reflect && "function" == typeof Reflect.decorate) i = Reflect.decorate(e, t, r, n);
            else
                for (var s = e.length - 1; s >= 0; s--)(a = e[s]) && (i = (o < 3 ? a(i) : o > 3 ? a(t, r, i) : a(t, r)) || i);
            return o > 3 && i && Object.defineProperty(t, r, i), i
        },
        __awaiter$15 = function(e, t, r, n) {
            return new(r || (r = Promise))(function(a, o) {
                function i(e) {
                    try {
                        u(n.next(e))
                    } catch (e) {
                        o(e)
                    }
                }

                function s(e) {
                    try {
                        u(n.throw(e))
                    } catch (e) {
                        o(e)
                    }
                }

                function u(e) {
                    e.done ? a(e.value) : new r(function(t) {
                        t(e.value)
                    }).then(i, s)
                }
                u((n = n.apply(e, t || [])).next())
            })
        },
        __generator$15 = function(e, t) {
            function r(e) {
                return function(t) {
                    return n([e, t])
                }
            }

            function n(r) {
                if (a) throw new TypeError("Generator is already executing.");
                for (; u;) try {
                    if (a = 1, o && (i = o[2 & r[0] ? "return" : r[0] ? "throw" : "next"]) && !(i = i.call(o, r[1])).done) return i;
                    switch (o = 0, i && (r = [0, i.value]), r[0]) {
                        case 0:
                        case 1:
                            i = r;
                            break;
                        case 4:
                            return u.label++, {
                                value: r[1],
                                done: !1
                            };
                        case 5:
                            u.label++, o = r[1], r = [0];
                            continue;
                        case 7:
                            r = u.ops.pop(), u.trys.pop();
                            continue;
                        default:
                            if (i = u.trys, !(i = i.length > 0 && i[i.length - 1]) && (6 === r[0] || 2 === r[0])) {
                                u = 0;
                                continue
                            }
                            if (3 === r[0] && (!i || r[1] > i[0] && r[1] < i[3])) {
                                u.label = r[1];
                                break
                            }
                            if (6 === r[0] && u.label < i[1]) {
                                u.label = i[1], i = r;
                                break
                            }
                            if (i && u.label < i[2]) {
                                u.label = i[2], u.ops.push(r);
                                break
                            }
                            i[2] && u.ops.pop(), u.trys.pop();
                            continue
                    }
                    r = t.call(e, u)
                } catch (e) {
                    r = [6, e], o = 0
                } finally {
                    a = i = 0
                }
                if (5 & r[0]) throw r[1];
                return {
                    value: r[0] ? r[1] : void 0,
                    done: !0
                }
            }
            var a, o, i, s, u = {
                label: 0,
                sent: function() {
                    if (1 & i[0]) throw i[1];
                    return i[1]
                },
                trys: [],
                ops: []
            };
            return s = {
                next: r(0),
                throw: r(1),
                return: r(2)
            }, "function" == typeof Symbol && (s[Symbol.iterator] = function() {
                return this
            }), s
        },
        ModelLoggingVerbosity;
    ! function(e) {
        e[e.SILENT = 0] = "SILENT", e[e.VERBOSE = 1] = "VERBOSE"
    }(ModelLoggingVerbosity || (ModelLoggingVerbosity = {}));
    var Model = function(e) {
        function t(t) {
            return e.call(this, t) || this
        }
        return __extends$13(t, e), 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 r = [];
            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 n = e.loss;
                    r = n.map(function(e) {
                        return get(e)
                    })
                } else {
                    var a = get(e.loss);
                    this.outputs.map(function(e) {
                        r.push(a)
                    })
                } else {
                e.loss = e.loss;
                for (var o in e.loss)
                    if (-1 === this.outputNames.indexOf(o)) throw new ValueError('Unknown entry in loss dictionary: "' + o + '". Only expect the following keys: ' + this.outputNames);
                for (var i in this.outputNames) null == e.loss[i] && console.warn('Output "' + i + '" is missing from loss dictionary. We assume this was done on purpose, and we will not be expecting data to be passed to ' + i + " during training"), r.push(get(e.loss[i]))
            }
            this.lossFunctions = r, this.feedOutputNames = [], this.feedOutputShapes = [], this.feedLossFns = [];
            for (var s = 0; s < this.outputs.length; ++s) {
                var u = this.internalOutputShapes[s],
                    l = this.outputNames[s];
                this.feedOutputNames.push(l), this.feedOutputShapes.push(u), this.feedLossFns.push(this.lossFunctions[s])
            }
            var c = [];
            this.metrics = e.metrics, this.metricsNames = ["loss"], this.metricsTensors = [], nameScope$1("loss", function() {
                for (var e = 0; e < t.outputs.length; ++e)
                    if (-1 === c.indexOf(e)) {
                        var r = t.lossFunctions[e];
                        t.outputs.length > 1 && (t.metricsTensors.push([r, e]), t.metricsNames.push(t.outputNames[e] + "_loss"))
                    }
            });
            var p = collectMetrics(e.metrics, this.outputNames),
                d = function(e, r, n) {
                    t.outputNames.length > 1 && (r = t.outputNames[e] + "_" + r), t.metricsNames.push(r), t.metricsTensors.push([n, e])
                };
            nameScope$1("metric", function() {
                for (var e = 0; e < t.outputs.length; ++e) ! function(e) {
                    if (-1 !== c.indexOf(e)) return "continue";
                    ! function(r) {
                        for (var n, a, o, i = 0, s = p[e]; i < s.length; i++) ! function(r) {
                            if (-1 !== ["accuracy", "acc", "crossentropy", "ce"].indexOf(r)) {
                                var i = t.internalOutputShapes[e];
                                1 === i[i.length - 1] || t.lossFunctions[e] === binaryCrossentropy ? -1 !== ["accuracy", "acc"].indexOf(r) ? a = binaryAccuracy : -1 !== ["crossentropy", "ce"].indexOf(r) && (a = binaryCrossentropy$1) : t.lossFunctions[e] === sparseCategoricalCrossentropy ? -1 !== ["accuracy", "acc"].indexOf(r) ? a = sparseCategoricalAccuracy : -1 !== ["crossentropy", "ce"].indexOf(r) && (a = sparseCategoricalCrossentropy$1) : -1 !== ["accuracy", "acc"].indexOf(r) ? a = categoricalAccuracy : -1 !== ["crossentropy", "ce"].indexOf(r) && (a = categoricalCrossentropy$1);
                                var s = void 0; - 1 !== ["accuracy", "acc"].indexOf(r) ? s = "acc" : -1 !== ["crossentropy", "ce"].indexOf(r) && (s = "ce"), o = a, n = "" + s
                            } else {
                                var u = get$1(r);
                                o = u, n = "" + r
                            }
                            var l;
                            nameScope$1(n, function() {
                                l = o
                            }), d(e, n, l)
                        }(s[i])
                    }()
                }(e)
            }), 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, r) {
            void 0 === r && (r = {});
            var n = null == r.batchSize ? 32 : r.batchSize,
                a = this.standardizeUserData(e, t, !0, n),
                o = a[0].concat(a[1]);
            this.makeTestFunction();
            var i = this.testFunction;
            return singletonOrArray(this.testLoop(i, o, n, r.verbose, r.steps))
        }, t.prototype.checkNumSamples = function(e, t, r, n) {
            void 0 === n && (n = "steps");
            var a;
            if (null != r) {
                if (a = null, null != t) throw new ValueError("If " + n + " 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 " + n + " shoud be specified.");
                a = Array.isArray(e) ? e[0].shape[0] : e.shape[0]
            }
            return a
        }, t.prototype.predictLoop = function(e, t, r) {
            var n = this;
            void 0 === t && (t = 32), void 0 === r && (r = !1);
            var a = this.checkNumSamples(e);
            if (r) throw new NotImplementedError("Verbose predictLoop() is not implemented yet.");
            for (var o = makeBatches(a, t), i = [], s = 0; s < o.length; ++s) ! function(t) {
                var r = tidy(function() {
                    var r = o[t][0],
                        a = o[t][1],
                        i = sliceArrays(e, r, a),
                        s = [];
                    if (Array.isArray(i))
                        for (var u = 0; u < i.length; ++u) s.push({
                            key: n.inputs[u],
                            value: i[u]
                        });
                    else s.push({
                        key: n.inputs[0],
                        value: i
                    });
                    var l = new FeedDict(s);
                    return execute(n.outputs, l)
                });
                if (0 === t)
                    for (var a = 0, s = r; a < s.length; a++) {
                        var u = s[a];
                        i.push(u)
                    } else
                        for (var l = 0; l < r.length; ++l) i[l] = concatAlongFirstAxis(i[l], r[l])
            }(s);
            return singletonOrArray(i)
        }, t.prototype.predict = function(e, t) {
            void 0 === t && (t = {}), checkInputData(e, this.inputNames, this.feedInputShapes, !1);
            var r = null == t.batchSize ? 32 : t.batchSize;
            return this.predictLoop(e, r)
        }, 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, r, n) {
            if (void 0 === r && (r = !0), null == this.optimizer) throw new RuntimeError("You must compile a model before training/testing. Use Model.compile(modelCompileConfig).");
            for (var a = [], o = 0; o < this.feedOutputShapes.length; ++o) {
                var i = this.feedOutputShapes[o];
                this.feedLossFns[o] === sparseCategoricalCrossentropy ? a.push(i.slice(0, i.length - 1).concat([1])) : a.push(i)
            }
            if (e = standardizeInputData(e, this.feedInputNames, this.feedInputShapes, !1, "input"), t = standardizeInputData(t, this.feedOutputNames, a, !1, "target"), checkArrayLengths(e, t), checkLossAndTargetCompatibility(t, this.feedLossFns, this.feedOutputShapes), this.stateful && null != n && n > 0 && e[0].shape[0] % n != 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 " + n + ". Found: " + e[0].shape[0] + " sample(s).");
            return [e, t, null]
        }, t.prototype.fitLoop = function(e, t, r, n, a, o, i, s, u, l, c, p, d, h) {
            return void 0 === p && (p = 0), __awaiter$15(this, void 0, void 0, function() {
                var f, m, g, y, v, b, x, w, _ = this;
                return __generator$15(this, function(S) {
                    switch (S.label) {
                        case 0:
                            if (null == n && (n = 32), null == a && (a = 1), null == l && (l = !0), null == p && (p = 0), f = !1, null != s && null != u && (f = !0), null != h && (f = !0, null == d)) throw new ValueError("Can only use `validationSteps` when doing step-wise training, i.e., `stepsPerEpoch` must be set.");
                            if (null != (m = this.checkNumSamples(t, n, d, "steps_per_epoch")) && (g = range$1(0, m)), this.history = new History, i = null == i ? [new BaseLogger] : [new BaseLogger].concat(i), i = i.concat([this.history]), o > 0) throw new NotImplementedError("Verbose mode is not implemented yet.");
                            return (y = new CallbackList(i)).setModel(this), y.setParams({
                                epochs: a,
                                steps: d,
                                verbose: o,
                                doValidation: f,
                                metrics: c
                            }), [4, y.onTrainBegin()];
                        case 1:
                            S.sent(), this.stopTraining = !1, v = function(a) {
                                var o, i, c, p, h, v;
                                return __generator$15(this, function(x) {
                                    switch (x.label) {
                                        case 0:
                                            return [4, y.onEpochBegin(a)];
                                        case 1:
                                            if (x.sent(), o = {}, null == d) return [3, 2];
                                            throw new NotImplementedError("stepsPerEpoch mode is not implemented yet.");
                                        case 2:
                                            if ("batch" === l) throw new NotImplementedError("batch shuffling is not implemneted yet");
                                            l && shuffle(g), i = tensor1d(g), c = makeBatches(m, n), p = function(a) {
                                                var l;
                                                return __generator$15(this, function(p) {
                                                    switch (p.label) {
                                                        case 0:
                                                            return l = {}, [4, y.onBatchBegin(a, l)];
                                                        case 1:
                                                            return p.sent(), tidy(function() {
                                                                var p = c[a][0],
                                                                    d = c[a][1],
                                                                    h = sliceAlongFirstAxis(i, p, d - p);
                                                                l.batch = a, l.size = d - p;
                                                                for (var m = sliceArraysByIndices(t, h), g = e(m), y = 0; y < r.length; ++y) {
                                                                    var v = r[y],
                                                                        b = g[y];
                                                                    l[v] = b, keep(b)
                                                                }
                                                                if (a === c.length - 1 && f)
                                                                    for (var x = _.testLoop(s, u, n), y = 0; y < r.length; ++y) {
                                                                        var v = r[y],
                                                                            b = x[y];
                                                                        keep(b), o["val_" + v] = b
                                                                    }
                                                            }), [4, y.onBatchEnd(a, l)];
                                                        case 2:
                                                            return p.sent(), disposeTensorsInLogs(l), b.stopTraining ? [2, "break"] : [2]
                                                    }
                                                })
                                            }, h = 0, x.label = 3;
                                        case 3:
                                            return h < c.length ? [5, p(h)] : [3, 6];
                                        case 4:
                                            if ("break" === (v = x.sent())) return [3, 6];
                                            x.label = 5;
                                        case 5:
                                            return ++h, [3, 3];
                                        case 6:
                                            i.dispose(), x.label = 7;
                                        case 7:
                                            return [4, y.onEpochEnd(a, o)];
                                        case 8:
                                            return x.sent(), b.stopTraining ? [2, "break"] : [2]
                                    }
                                })
                            }, b = this, x = p, S.label = 2;
                        case 2:
                            return x < a ? [5, v(x)] : [3, 5];
                        case 3:
                            if ("break" === (w = S.sent())) return [3, 5];
                            S.label = 4;
                        case 4:
                            return ++x, [3, 2];
                        case 5:
                            return [4, y.onTrainEnd()];
                        case 6:
                            return S.sent(), [4, this.history.syncData()];
                        case 7:
                            return S.sent(), [2, this.history]
                    }
                })
            })
        }, t.prototype.testLoop = function(e, t, r, n, a) {
            void 0 === n && (n = 0);
            var o = this.checkNumSamples(t, r, a, "steps"),
                i = [];
            if (1 === n) throw new NotImplementedError("Verbose mode is not implemented yet.");
            if (null != a) throw new NotImplementedError("steps mode in testLoop() is not implemented yet");
            for (var s = makeBatches(o, r), u = tensor1d(range$1(0, o)), l = 0; l < s.length; ++l) {
                var c = s[l][0],
                    p = s[l][1],
                    d = e(sliceArraysByIndices(t, sliceAlongFirstAxis(u, c, p - c)));
                if (0 === l)
                    for (f = 0; f < d.length; ++f) i.push(getScalar(0));
                for (f = 0; f < d.length; ++f) {
                    var h = d[f];
                    i[f] = add(i[f], scalarTimesArray(getScalar(p - c), h))
                }
            }
            for (var f = 0; f < i.length; ++f) i[f] = div(i[f], getScalar(o));
            return i
        }, t.prototype.getDedupedMetricsNames = function() {
            for (var e = this.metricsNames, t = [], r = 0; r < e.length; ++r) {
                var n = e[r],
                    a = n;
                count(e, n) > 1 && (a += "_" + count(e.slice(0, r), n)), t.push(a)
            }
            return t
        }, t.prototype.makeTestFunction = function() {
            var e = this;
            this.testFunction = function(t) {
                return tidy(function() {
                    for (var r, n = [], a = t.slice(0, e.inputs.length), o = t.slice(e.inputs.length, e.inputs.length + e.outputs.length), i = [], s = 0; s < e.inputs.length; ++s) i.push({
                        key: e.inputs[s],
                        value: a[s]
                    });
                    for (var u = new FeedDict(i), l = execute(e.outputs, u), s = 0; s < e.lossFunctions.length; ++s) {
                        var c = e.lossFunctions[s],
                            p = mean(c(o[s], l[s]));
                        r = 0 === s ? p : add(r, p), n.push(r)
                    }
                    for (s = 0; s < e.metricsTensors.length; ++s) {
                        var d = e.metricsTensors[s][0],
                            h = e.metricsTensors[s][1],
                            f = mean(d(o[h], l[h]));
                        n.push(f)
                    }
                    return n
                })
            }
        }, t.prototype.fit = function(e, t, r) {
            return void 0 === r && (r = {}), __awaiter$15(this, void 0, void 0, function() {
                var n, a, o, i, s, u, l, c, p, d, h, f, m, g, y, v, b, x, w, _ = this;
                return __generator$15(this, function(S) {
                    switch (S.label) {
                        case 0:
                            if (n = null == r.batchSize ? 32 : r.batchSize, a = this.standardizeUserData(e, t, !1, n), o = a[0], i = a[1], s = !1, p = !1, null != r.validationData && r.validationData.length > 0) {
                                if (s = !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], l = r.validationData[1], d = this.standardizeUserData(u, l, !0, n), u = d[0], l = d[1], c = u.concat(l)
                            } else null != r.validationSplit && r.validationSplit > 0 && r.validationSplit < 1 ? (s = !0, h = Math.floor(o[0].shape[0] * (1 - r.validationSplit)), f = o[0].shape[0], u = sliceArrays(o, h, f), o = sliceArrays(o, 0, h), l = sliceArrays(i, h, f), i = sliceArrays(i, 0, h), p = !0, c = u.concat(l)) : null != r.validationSteps && (s = !0);
                            return m = o.concat(i), this.checkTrainableWeightsConsistency(), g = function(e) {
                                var t = e.slice(0, _.inputs.length),
                                    r = e.slice(_.inputs.length, _.inputs.length + _.outputs.length),
                                    n = [],
                                    a = _.collectedTrainableWeights.map(function(e) {
                                        return e.read()
                                    });
                                return [_.optimizer.minimize(function() {
                                    for (var e = [], a = 0; a < _.inputs.length; ++a) e.push({
                                        key: _.inputs[a],
                                        value: t[a]
                                    });
                                    for (var o, i = new FeedDict(e), s = execute(_.outputs, i, {
                                            training: !0
                                        }), a = 0; a < _.lossFunctions.length; ++a) {
                                        var u = (0, _.lossFunctions[a])(r[a], s[a]);
                                        mean(u), o = 0 === a ? u : add(o, u)
                                    }
                                    for (a = 0; a < _.metricsTensors.length; ++a) {
                                        var l = _.metricsTensors[a][0],
                                            c = _.metricsTensors[a][1],
                                            p = mean(l(r[c], s[c]));
                                        keep(p), n.push(p)
                                    }
                                    return o = mean(o), _.calculateLosses().forEach(function(e) {
                                        o = add(o, e)
                                    }), o
                                }, !0, a)].concat(n)
                            }, y = this.getDedupedMetricsNames(), s ? (this.makeTestFunction(), v = this.testFunction, b = y.slice().concat(y.map(function(e) {
                                return "val_" + e
                            }))) : (v = null, c = [], b = y.slice()), x = standardizeCallbacks(r.callbacks), [4, this.fitLoop(g, m, y, n, r.epochs, r.verbose, x, v, c, r.shuffle, b, null, null, null)];
                        case 1:
                            return w = S.sent(), p && (c.forEach(function(e) {
                                return e.dispose()
                            }), o.forEach(function(e) {
                                return e.dispose()
                            }), i.forEach(function(e) {
                                return e.dispose()
                            })), [2, w]
                    }
                })
            })
        }, t.prototype.getNamedWeights = function(e) {
            for (var t = {}, r = null != e && e.trainableOnly, n = r ? this.trainableWeights : this.weights, a = this.getWeights(r), o = 0; o < n.length; ++o) r && !n[o].trainable || (t[n[o].originalName] = a[o]);
            return t
        }, t.prototype.save = function(e, t) {
            return __awaiter$15(this, void 0, void 0, function() {
                var r, n, a, o, i;
                return __generator$15(this, function(s) {
                    switch (s.label) {
                        case 0:
                            if ("string" == typeof e) {
                                if (0 === (r = getSaveHandlers(e)).length) throw new ValueError("Cannot find any save handlers for URL '" + e + "'");
                                if (r.length > 1) throw new ValueError("Found more than one (" + r.length + ") save handlers for URL '" + e + "'");
                                e = r[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, encodeWeights(this.getNamedWeights(t))];
                        case 1:
                            return n = s.sent(), a = !1, o = null, i = this.toJSON(o, a), [2, e.save({
                                modelTopology: i,
                                weightData: n.data,
                                weightSpecs: n.specs
                            })]
                    }
                })
            })
        }, t.className = "Model", __decorate$34([doc()], t.prototype, "compile", null), __decorate$34([doc()], t.prototype, "evaluate", null), __decorate$34([doc()], t.prototype, "predict", null), __decorate$34([doc()], t.prototype, "predictOnBatch", null), __decorate$34([doc()], t.prototype, "fit", null), t = __decorate$34([doc()], t)
    }(Container);
    SerializationMap.register(Model);
    var __extends$14 = function() {
            var e = 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])
            };
            return function(t, r) {
                function n() {
                    this.constructor = t
                }
                e(t, r), t.prototype = null === r ? Object.create(r) : (n.prototype = r.prototype, new n)
            }
        }(),
        __decorate$35 = function(e, t, r, n) {
            var a, o = arguments.length,
                i = o < 3 ? t : null === n ? n = Object.getOwnPropertyDescriptor(t, r) : n;
            if ("object" == typeof Reflect && "function" == typeof Reflect.decorate) i = Reflect.decorate(e, t, r, n);
            else
                for (var s = e.length - 1; s >= 0; s--)(a = e[s]) && (i = (o < 3 ? a(i) : o > 3 ? a(t, r, i) : a(t, r)) || i);
            return o > 3 && i && Object.defineProperty(t, r, i), i
        },
        VALID_FAN_MODE_VALUES = ["fanIn", "fanOut", "fanAvg"],
        VALID_DISTRIBUTION_VALUES = ["normal", "uniform"],
        Initializer = function(e) {
            function t() {
                return null !== e && e.apply(this, arguments) || this
            }
            return __extends$14(t, e), t.prototype.fromConfigUsesCustomObjects = function() {
                return !1
            }, t.prototype.getConfig = function() {
                return {}
            }, t = __decorate$35([doc()], t)
        }(Serializable),
        Zeros = function(e) {
            function t() {
                return null !== e && e.apply(this, arguments) || this
            }
            return __extends$14(t, e), t.prototype.apply = function(e, t) {
                return zeros(e, t)
            }, t.className = "Zeros", t
        }(Initializer);
    SerializationMap.register(Zeros);
    var Ones = function(e) {
        function t() {
            return null !== e && e.apply(this, arguments) || this
        }
        return __extends$14(t, e), t.prototype.apply = function(e, t) {
            return ones(e, t)
        }, t.className = "Ones", t
    }(Initializer);
    SerializationMap.register(Ones);
    var Constant = function(e) {
        function t(t) {
            var r = e.call(this) || this;
            return r.value = t.value, r
        }
        return __extends$14(t, e), t.prototype.apply = function(e, t) {
            var r = this;
            return tidy(function() {
                return scalarTimesArray(scalar(r.value), ones(e, t))
            })
        }, t.prototype.getConfig = function() {
            return {
                value: this.value
            }
        }, t.className = "Constant", t
    }(Initializer);
    SerializationMap.register(Constant);
    var RandomUniform = function(e) {
        function t(t) {
            var r = e.call(this) || this;
            return r.DEFAULT_MINVAL = -.05, r.DEFAULT_MAXVAL = .05, r.minval = t.minval || r.DEFAULT_MINVAL, r.maxval = t.maxval || r.DEFAULT_MAXVAL, r.seed = t.seed, r
        }
        return __extends$14(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);
    SerializationMap.register(RandomUniform);
    var RandomNormal = function(e) {
        function t(t) {
            var r = e.call(this) || this;
            return r.DEFAULT_MEAN = 0, r.DEFAULT_STDDEV = .05, r.mean = t.mean || r.DEFAULT_MEAN, r.stddev = t.stddev || r.DEFAULT_STDDEV, r.seed = t.seed, r
        }
        return __extends$14(t, e), t.prototype.apply = function(e, t) {
            if ("bool" === t) throw new NotImplementedError("randomNormal does not support dType bool.");
            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);
    SerializationMap.register(RandomNormal);
    var TruncatedNormal = function(e) {
        function t(t) {
            var r = e.call(this) || this;
            return r.DEFAULT_MEAN = 0, r.DEFAULT_STDDEV = .05, r.mean = t.mean || r.DEFAULT_MEAN, r.stddev = t.stddev || r.DEFAULT_STDDEV, r.seed = t.seed, r
        }
        return __extends$14(t, e), t.prototype.apply = function(e, t) {
            if ("bool" === t) throw new NotImplementedError("truncatedNormal does not support dType bool.");
            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);
    SerializationMap.register(TruncatedNormal);
    var Identity = function(e) {
        function t(t) {
            var r = e.call(this) || this;
            return r.gain = null != t.gain ? scalar(t.gain) : getScalar(1), r
        }
        return __extends$14(t, e), t.prototype.apply = function(e, t) {
            var r = 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 scalarTimesArray(r.gain, eye(e[0]))
            })
        }, t.prototype.getConfig = function() {
            return {
                gain: this.gain.get()
            }
        }, t.className = "Identity", t
    }(Initializer);
    SerializationMap.register(Identity);
    var VarianceScaling = function(e) {
        function t(t) {
            var r = e.call(this) || this;
            if (t.scale < 0) throw new ValueError("scale must be a positive float. Got: " + t.scale);
            return r.scale = null == t.scale ? 1 : t.scale, r.mode = t.mode, checkFanMode(r.mode), r.distribution = t.distribution, checkDistribution(r.distribution), r.seed = t.seed, r
        }
        return __extends$14(t, e), t.prototype.apply = function(e, t) {
            var r = computeFans(e),
                n = r[0],
                a = r[1],
                o = this.scale;
            if ("fanIn" === this.mode ? o /= Math.max(1, n) : "fanOut" === this.mode ? o /= Math.max(1, a) : o /= Math.max(1, (n + a) / 2), "normal" === this.distribution) {
                var i = Math.sqrt(o);
                if ("bool" === t) throw new NotImplementedError(this.getClassName() + " does not support dType bool.");
                return truncatedNormal(e, 0, i, t, this.seed)
            }
            var s = Math.sqrt(3 * o);
            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);
    SerializationMap.register(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$14(t, e), t.prototype.getClassName = function() {
                return VarianceScaling.className
            }, t
        }(VarianceScaling),
        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$14(t, e), t.prototype.getClassName = function() {
                return VarianceScaling.className
            }, t
        }(VarianceScaling),
        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$14(t, e), t.prototype.getClassName = function() {
                return VarianceScaling.className
            }, t
        }(VarianceScaling),
        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$14(t, e), t.prototype.getClassName = function() {
                return VarianceScaling.className
            }, t
        }(VarianceScaling),
        Orthogonal = function(e) {
            function t(t) {
                var r = e.call(this) || this;
                if (r.DEFAULT_GAIN = 1, r.gain = null == t.gain ? r.DEFAULT_GAIN : t.gain, r.seed = t.seed, null != r.seed) throw new NotImplementedError("Random seed is not implemented for Orthogonal Initializer yet.");
                return r
            }
            return __extends$14(t, e), t.prototype.apply = function(e, t) {
                var r = 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"),
                        n = linalg.gramSchmidt(t);
                    return e[0] > e[1] && (n = n.transpose()), scalarTimesArray(getScalar(r.gain), n)
                })
            }, t.prototype.getConfig = function() {
                return {
                    gain: this.gain,
                    seed: this.seed
                }
            }, t.className = "Orthogonal", t
        }(Initializer);
    SerializationMap.register(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"
        },
        __extends$15 = function() {
            var e = 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])
            };
            return function(t, r) {
                function n() {
                    this.constructor = t
                }
                e(t, r), t.prototype = null === r ? Object.create(r) : (n.prototype = r.prototype, new n)
            }
        }(),
        Activation = function(e) {
            function t() {
                return null !== e && e.apply(this, arguments) || this
            }
            return __extends$15(t, e), t.prototype.getConfig = function() {
                return {}
            }, t
        }(Serializable),
        Elu = function(e) {
            function t() {
                return null !== e && e.apply(this, arguments) || this
            }
            return __extends$15(t, e), t.prototype.apply = function(e, t) {
                return void 0 === t && (t = 1), elu$1(e, t)
            }, t.className = "elu", t
        }(Activation);
    SerializationMap.register(Elu);
    var Selu = function(e) {
        function t() {
            return null !== e && e.apply(this, arguments) || this
        }
        return __extends$15(t, e), t.prototype.apply = function(e) {
            return selu(e)
        }, t.className = "selu", t
    }(Activation);
    SerializationMap.register(Selu);
    var Relu = function(e) {
        function t() {
            return null !== e && e.apply(this, arguments) || this
        }
        return __extends$15(t, e), t.prototype.apply = function(e) {
            return relu(e)
        }, t.className = "relu", t
    }(Activation);
    SerializationMap.register(Relu);
    var Relu6 = function(e) {
        function t() {
            return null !== e && e.apply(this, arguments) || this
        }
        return __extends$15(t, e), t.prototype.apply = function(e) {
            return tidy(function() {
                return minimum(getScalar(6), relu(e))
            })
        }, t.className = "relu6", t
    }(Activation);
    SerializationMap.register(Relu6);
    var Linear = function(e) {
        function t() {
            return null !== e && e.apply(this, arguments) || this
        }
        return __extends$15(t, e), t.prototype.apply = function(e) {
            return e
        }, t.className = "linear", t
    }(Activation);
    SerializationMap.register(Linear);
    var Sigmoid = function(e) {
        function t() {
            return null !== e && e.apply(this, arguments) || this
        }
        return __extends$15(t, e), t.prototype.apply = function(e) {
            return sigmoid(e)
        }, t.className = "sigmoid", t
    }(Activation);
    SerializationMap.register(Sigmoid);
    var HardSigmoid = function(e) {
        function t() {
            return null !== e && e.apply(this, arguments) || this
        }
        return __extends$15(t, e), t.prototype.apply = function(e) {
            return hardSigmoid(e)
        }, t.className = "hardSigmoid", t
    }(Activation);
    SerializationMap.register(HardSigmoid);
    var Softplus = function(e) {
        function t() {
            return null !== e && e.apply(this, arguments) || this
        }
        return __extends$15(t, e), t.prototype.apply = function(e) {
            return softplus(e)
        }, t.className = "softplus", t
    }(Activation);
    SerializationMap.register(Softplus);
    var Softsign = function(e) {
        function t() {
            return null !== e && e.apply(this, arguments) || this
        }
        return __extends$15(t, e), t.prototype.apply = function(e) {
            return softsign(e)
        }, t.className = "softsign", t
    }(Activation);
    SerializationMap.register(Softsign);
    var Tanh = function(e) {
        function t() {
            return null !== e && e.apply(this, arguments) || this
        }
        return __extends$15(t, e), t.prototype.apply = function(e) {
            return tanh$1(e)
        }, t.className = "tanh", t
    }(Activation);
    SerializationMap.register(Tanh);
    var Softmax = function(e) {
        function t() {
            return null !== e && e.apply(this, arguments) || this
        }
        return __extends$15(t, e), t.prototype.apply = function(e, t) {
            return void 0 === t && (t = -1), softmax(e, t)
        }, t.className = "softmax", t
    }(Activation);
    SerializationMap.register(Softmax);
    var __extends$16 = function() {
            var e = 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])
            };
            return function(t, r) {
                function n() {
                    this.constructor = t
                }
                e(t, r), t.prototype = null === r ? Object.create(r) : (n.prototype = r.prototype, new n)
            }
        }(),
        LeakyReLU = function(e) {
            function t(t) {
                var r = e.call(this, null == t ? {} : t) || this;
                return r.DEFAULT_ALPHA = .3, null == t && (t = {}), r.alpha = null == t.alpha ? r.DEFAULT_ALPHA : t.alpha, r
            }
            return __extends$16(t, e), t.prototype.call = function(e, t) {
                var r = getExactlyOneTensor(e);
                return leakyRelu(r, this.alpha)
            }, t.prototype.computeOutputShape = function(e) {
                return e
            }, t.prototype.getConfig = function() {
                var t = {
                        alpha: this.alpha
                    },
                    r = e.prototype.getConfig.call(this);
                return Object.assign(t, r), t
            }, t.className = "LeakyReLU", t
        }(Layer);
    SerializationMap.register(LeakyReLU);
    var ELU$1 = function(e) {
        function t(t) {
            var r = e.call(this, null == t ? {} : t) || this;
            if (r.DEFAULT_ALPHA = 1, null == t && (t = {}), null != t.alpha && t.alpha !== r.DEFAULT_ALPHA) throw new NotImplementedError("Non-default alpha value (" + t.alpha + ") is not supported by the ELU layer yet.");
            return r.alpha = null == t.alpha ? r.DEFAULT_ALPHA : t.alpha, r
        }
        return __extends$16(t, e), t.prototype.call = function(e, t) {
            var r = getExactlyOneTensor(e);
            return elu(r)
        }, t.prototype.computeOutputShape = function(e) {
            return e
        }, t.prototype.getConfig = function() {
            var t = {
                    alpha: this.alpha
                },
                r = e.prototype.getConfig.call(this);
            return Object.assign(t, r), t
        }, t.className = "ELU", t
    }(Layer);
    SerializationMap.register(ELU$1);
    var ThresholdedReLU = function(e) {
        function t(t) {
            var r = e.call(this, null == t ? {} : t) || this;
            return r.DEFAULT_THETA = 1, null == t && (t = {}), r.theta = null == t.theta ? r.DEFAULT_THETA : t.theta, r.thetaTensor = getScalar(r.theta), r
        }
        return __extends$16(t, e), t.prototype.call = function(e, t) {
            var r = getExactlyOneTensor(e);
            return r.mul(cast$1(r.greater(this.thetaTensor), "float32"))
        }, t.prototype.computeOutputShape = function(e) {
            return e
        }, t.prototype.getConfig = function() {
            var t = {
                    theta: this.theta
                },
                r = e.prototype.getConfig.call(this);
            return Object.assign(t, r), t
        }, t.className = "ThresholdedReLU", t
    }(Layer);
    SerializationMap.register(ThresholdedReLU);
    var Softmax$1 = function(e) {
        function t(t) {
            var r = e.call(this, null == t ? {} : t) || this;
            return r.DEFAULT_AXIS = 1, null == t && (t = {}), r.softmax = (new Softmax).apply, r.axis = null == t.axis ? r.DEFAULT_AXIS : t.axis, r
        }
        return __extends$16(t, e), t.prototype.call = function(e, t) {
            var r = getExactlyOneTensor(e);
            return this.softmax(r, this.axis)
        }, t.prototype.computeOutputShape = function(e) {
            return e
        }, t.prototype.getConfig = function() {
            var t = {
                    axis: this.axis
                },
                r = e.prototype.getConfig.call(this);
            return Object.assign(t, r), t
        }, t.className = "Softmax", t
    }(Layer);
    SerializationMap.register(Softmax$1);
    var __extends$17 = function() {
            var e = 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])
            };
            return function(t, r) {
                function n() {
                    this.constructor = t
                }
                e(t, r), t.prototype = null === r ? Object.create(r) : (n.prototype = r.prototype, new n)
            }
        }(),
        __decorate$36 = function(e, t, r, n) {
            var a, o = arguments.length,
                i = o < 3 ? t : null === n ? n = Object.getOwnPropertyDescriptor(t, r) : n;
            if ("object" == typeof Reflect && "function" == typeof Reflect.decorate) i = Reflect.decorate(e, t, r, n);
            else
                for (var s = e.length - 1; s >= 0; s--)(a = e[s]) && (i = (o < 3 ? a(i) : o > 3 ? a(t, r, i) : a(t, r)) || i);
            return o > 3 && i && Object.defineProperty(t, r, i), i
        },
        Regularizer = function(e) {
            function t() {
                return null !== e && e.apply(this, arguments) || this
            }
            return __extends$17(t, e), t
        }(Serializable),
        L1L2 = function(e) {
            function t(t) {
                var r = e.call(this) || this,
                    n = null == t || null == t.l1 ? .01 : t.l1,
                    a = null == t || null == t.l2 ? .01 : t.l2;
                return r.hasL1 = 0 !== n, r.hasL2 = 0 !== a, r.l1 = getScalar(n), r.l2 = getScalar(a), r
            }
            return __extends$17(t, e), t.prototype.apply = function(e) {
                var t = this;
                return tidy(function() {
                    var r = zeros([1]);
                    return t.hasL1 && (r = add(r, sum(scalarTimesArray(t.l1, abs(e))))), t.hasL2 && (r = add(r, sum(scalarTimesArray(t.l2, square$1(e))))), r.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 = __decorate$36([doc()], t)
        }(Regularizer);
    SerializationMap.register(L1L2);
    var REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP = {
            l1l2: "L1L2"
        },
        __extends$18 = function() {
            var e = 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])
            };
            return function(t, r) {
                function n() {
                    this.constructor = t
                }
                e(t, r), t.prototype = null === r ? Object.create(r) : (n.prototype = r.prototype, new n)
            }
        }(),
        Conv = function(e) {
            function t(t, r) {
                var n = e.call(this, r) || this;
                if (n.kernel = null, n.bias = null, n.DEFAULT_KERNEL_INITIALIZER = "glorotNormal", n.DEFAULT_BIAS_INITIALIZER = "zeros", n.rank = t, 1 !== n.rank && 2 !== n.rank) throw new NotImplementedError("Convolution layer for rank other than 1 or 2 (" + n.rank + ") is not implemented yet.");
                if (n.filters = r.filters, n.kernelSize = normalizeArray(r.kernelSize, t, "kernelSize"), n.strides = normalizeArray(null == r.strides ? 1 : r.strides, t, "strides"), n.padding = null == r.padding ? "valid" : r.padding, checkPaddingMode(n.padding), n.dataFormat = null == r.dataFormat ? "channelsLast" : r.dataFormat, checkDataFormat(n.dataFormat), n.dilationRate = null == r.dilationRate ? 1 : r.dilationRate, 1 === n.rank && Array.isArray(n.dilationRate) && 1 !== n.dilationRate.length) throw new ValueError("dilationRate must be a number or an array of a single number for 1D convolution, but received " + JSON.stringify(n.dilationRate));
                if (2 === n.rank)
                    if ("number" == typeof n.dilationRate) n.dilationRate = [n.dilationRate, n.dilationRate];
                    else if (2 !== n.dilationRate.length) throw new ValueError("dilationRate must be a number or array of two numbers for 2D convolution, but received " + JSON.stringify(n.dilationRate));
                return n.activation = getActivation(r.activation), n.useBias = null == r.useBias || r.useBias, n.kernelInitializer = getInitializer(r.kernelInitializer || n.DEFAULT_KERNEL_INITIALIZER), n.biasInitializer = getInitializer(r.biasInitializer || n.DEFAULT_BIAS_INITIALIZER), n.kernelConstraint = getConstraint(r.kernelConstraint), n.biasConstraint = getConstraint(r.biasConstraint), n.kernelRegularizer = getRegularizer(r.kernelRegularizer), n.biasRegularizer = getRegularizer(r.biasRegularizer), n.activityRegularizer = getRegularizer(r.activityRegularizer), n
            }
            return __extends$18(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 r = e[t],
                    n = this.kernelSize.concat([r, this.filters]);
                this.kernel = this.addWeight("kernel", n, 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: (a = {}, a[t] = r, a)
                }], this.built = !0;
                var a
            }, t.prototype.call = function(e, t) {
                var r = this;
                return tidy(function() {
                    e = getExactlyOneTensor(e);
                    var t, n = null == r.bias ? null : r.bias.read();
                    if (1 === r.rank) t = conv1dWithBias(e, r.kernel.read(), n, r.strides[0], r.padding, r.dataFormat, r.dilationRate);
                    else if (2 === r.rank) t = conv2dWithBias(e, r.kernel.read(), n, r.strides, r.padding, r.dataFormat, r.dilationRate);
                    else if (3 === r.rank) throw new NotImplementedError("3D convolution is not implemented yet.");
                    return null != r.activation && (t = r.activation.apply(t)), t
                })
            }, t.prototype.computeOutputShape = function(e) {
                e = getExactlyOneShape(e);
                for (var t = [], r = "channelsLast" === this.dataFormat ? e.slice(1, e.length - 1) : e.slice(2), n = 0; n < r.length; ++n) {
                    var a = convOutputLength(r[n], this.kernelSize[n], this.padding, this.strides[n], "number" == typeof this.dilationRate ? this.dilationRate : this.dilationRate[n]);
                    t.push(a)
                }
                var o = [e[0]];
                return "channelsLast" === this.dataFormat ? (o = o.concat(t)).push(this.filters) : (o.push(this.filters), o = o.concat(t)), o
            }, t.prototype.getConfig = function() {
                var t = {
                        rank: this.rank,
                        filters: this.filters,
                        kernelSize: this.kernelSize,
                        strides: this.strides,
                        padding: this.padding,
                        dataFormat: this.dataFormat,
                        dilationRate: this.dilationRate,
                        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)
                    },
                    r = e.prototype.getConfig.call(this);
                return Object.assign(t, r), t
            }, t
        }(Layer),
        Conv2D = function(e) {
            function t(t) {
                return e.call(this, 2, t) || this
            }
            return __extends$18(t, e), t.prototype.getConfig = function() {
                var t = e.prototype.getConfig.call(this);
                return delete t.rank, t
            }, t.className = "Conv2D", t
        }(Conv);
    SerializationMap.register(Conv2D);
    var Conv2DTranspose = function(e) {
        function t(t) {
            var r = e.call(this, t) || this;
            if (r.inputSpec = [new InputSpec({
                    ndim: 4
                })], "same" !== r.padding && "valid" !== r.padding) throw new ValueError("Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode " + r.padding);
            return r
        }
        return __extends$18(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 r = e[t],
                n = this.kernelSize.concat([this.filters, r]);
            this.kernel = this.addWeight("kernel", n, "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: (a = {}, a[t] = r, a)
            })], this.built = !0;
            var a
        }, t.prototype.call = function(e, t) {
            var r = 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 n, a, o = t.shape,
                    i = o[0];
                "channelsFirst" === r.dataFormat ? (n = 2, a = 3) : (n = 1, a = 2);
                var s = o[n],
                    u = o[a],
                    l = r.kernelSize[0],
                    c = r.kernelSize[1],
                    p = r.strides[0],
                    d = r.strides[1],
                    h = [i, deconvLength(s, p, l, r.padding), deconvLength(u, d, c, r.padding), r.filters];
                "channelsLast" !== r.dataFormat && (t = transpose(t, [0, 2, 3, 1]));
                var f = conv2dTranspose(t, r.kernel.read(), h, r.strides, r.padding);
                return "channelsLast" !== r.dataFormat && (f = transpose(f, [0, 3, 1, 2])), null != r.bias && (f = biasAdd(f, r.bias.read(), r.dataFormat)), null != r.activation && (f = r.activation.apply(f)), f
            })
        }, t.prototype.computeOutputShape = function(e) {
            var t, r, n, a = (e = getExactlyOneShape(e)).slice();
            "channelsFirst" === this.dataFormat ? (t = 1, r = 2, n = 3) : (t = 3, r = 1, n = 2);
            var o = this.kernelSize[0],
                i = this.kernelSize[1],
                s = this.strides[0],
                u = this.strides[1];
            return a[t] = this.filters, a[r] = deconvLength(a[r], s, o, this.padding), a[n] = deconvLength(a[n], u, i, this.padding), a
        }, t.prototype.getConfig = function() {
            var t = e.prototype.getConfig.call(this);
            return delete t.dilationRate, t
        }, t.className = "Conv2DTranspose", t
    }(Conv2D);
    SerializationMap.register(Conv2DTranspose);
    var SeparableConv = function(e) {
            function t(t, r) {
                var n = e.call(this, t, r) || this;
                if (n.DEFAULT_DEPTHWISE_INITIALIZER = "glorotUniform", n.DEFAULT_POINTWISE_INITIALIZER = "glorotUniform", n.depthwiseKernel = null, n.pointwiseKernel = null, null == r.filters) throw new ValueError("The `filters` configuration field is required by SeparableConv, but is unspecified.");
                if (null != r.kernelInitializer || null != r.kernelRegularizer || null != r.kernelConstraint) throw new ValueError("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");
                if (null != r.padding && "same" !== r.padding && "valid" !== r.padding) throw new ValueError("SeparableConv" + n.rank + "D supports only padding modes: 'same' and 'valid', but received " + JSON.stringify(r.padding));
                return n.depthMultiplier = null == r.depthMultiplier ? 1 : r.depthMultiplier, n.depthwiseInitializer = getInitializer(r.depthwiseInitializer || n.DEFAULT_DEPTHWISE_INITIALIZER), n.depthwiseRegularizer = getRegularizer(r.depthwiseRegularizer), n.depthwiseConstraint = getConstraint(r.depthwiseConstraint), n.pointwiseInitializer = getInitializer(r.depthwiseInitializer || n.DEFAULT_POINTWISE_INITIALIZER), n.pointwiseRegularizer = getRegularizer(r.pointwiseRegularizer), n.pointwiseConstraint = getConstraint(r.pointwiseConstraint), n
            }
            return __extends$18(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 r = e[t], n = this.kernelSize.concat([r, this.depthMultiplier]), a = [], o = 0; o < this.rank; ++o) a.push(1);
                a.push(r * this.depthMultiplier, this.filters);
                this.depthwiseKernel = this.addWeight("depthwise_kernel", n, "float32", this.depthwiseInitializer, this.depthwiseRegularizer, !0, this.depthwiseConstraint), this.pointwiseKernel = this.addWeight("pointwise_kernel", a, "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: (i = {}, i[t] = r, i)
                })], this.built = !0;
                var i
            }, t.prototype.call = function(e, t) {
                var r = this;
                return tidy(function() {
                    e = getExactlyOneTensor(e);
                    var t;
                    if (1 === r.rank) throw new NotImplementedError("1D separable convolution is not implemented yet.");
                    return 2 === r.rank && ("channelsFirst" === r.dataFormat && (e = transpose(e, [0, 2, 3, 1])), t = separableConv2d(e, r.depthwiseKernel.read(), r.pointwiseKernel.read(), r.strides, r.padding, r.dilationRate, "NHWC")), r.useBias && (t = biasAdd(t, r.bias.read(), r.dataFormat)), null != r.activation && (t = r.activation.apply(t)), "channelsFirst" === r.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$18(t, e), t.className = "SeparableConv2D", t
        }(SeparableConv);
    SerializationMap.register(SeparableConv2D);
    var Conv1D = function(e) {
        function t(t) {
            var r = e.call(this, 1, t) || this;
            return r.inputSpec = [{
                ndim: 3
            }], r
        }
        return __extends$18(t, e), t.prototype.getConfig = function() {
            var t = e.prototype.getConfig.call(this);
            return delete t.rank, delete t.dataFormat, t
        }, t.className = "Conv1D", t
    }(Conv);
    SerializationMap.register(Conv1D);
    var Cropping2D = function(e) {
        function t(t) {
            var r = e.call(this, t) || this;
            return "number" == typeof t.cropping ? r.cropping = [
                [t.cropping, t.cropping],
                [t.cropping, t.cropping]
            ] : "number" == typeof t.cropping[0] ? r.cropping = [
                [t.cropping[0], t.cropping[0]],
                [t.cropping[1], t.cropping[1]]
            ] : r.cropping = t.cropping, r.dataFormat = void 0 === t.dataFormat ? "channelsLast" : t.dataFormat, r.inputSpec = [{
                ndim: 4
            }], r
        }
        return __extends$18(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[2] - 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 r = this;
            return tidy(function() {
                if (e = getExactlyOneTensor(e), "channelsLast" === r.dataFormat) return sliceAlongAxis(t = sliceAlongAxis(e, r.cropping[0][0], e.shape[1] - r.cropping[0][0] - r.cropping[0][1], 2), r.cropping[1][0], e.shape[2] - r.cropping[1][1] - r.cropping[1][0], 3);
                var t = sliceAlongAxis(e, r.cropping[0][0], e.shape[2] - r.cropping[0][0] - r.cropping[0][1], 3);
                return sliceAlongAxis(t, r.cropping[1][0], e.shape[3] - r.cropping[1][1] - r.cropping[1][0], 4)
            })
        }, t.prototype.getConfig = function() {
            var t = {
                    cropping: this.cropping,
                    dataFormat: this.dataFormat
                },
                r = e.prototype.getConfig.call(this);
            return Object.assign(t, r), t
        }, t.className = "Cropping2D", t
    }(Layer);
    SerializationMap.register(Cropping2D);
    var UpSampling2D = function(e) {
        function t(t) {
            var r = e.call(this, t) || this;
            return r.DEFAULT_SIZE = [2, 2], r.inputSpec = [{
                ndim: 4
            }], r.size = void 0 === t.size ? r.DEFAULT_SIZE : t.size, r.dataFormat = void 0 === t.dataFormat ? "channelsLast" : t.dataFormat, r
        }
        return __extends$18(t, e), t.prototype.computeOutputShape = function(e) {
            if ("channelsFirst" === this.dataFormat) {
                var t = this.size[0] * e[2],
                    r = this.size[1] * e[3];
                return [e[0], e[1], t, r]
            }
            var t = this.size[0] * e[1],
                r = this.size[1] * e[2];
            return [e[0], t, r, e[3]]
        }, t.prototype.call = function(e, t) {
            var r = this;
            return tidy(function() {
                var t = getExactlyOneTensor(e),
                    n = t.shape;
                if ("channelsFirst" === r.dataFormat) {
                    t = transpose(t, [0, 2, 3, 1]);
                    var a = r.size[0] * n[2],
                        o = r.size[1] * n[3],
                        i = t.resizeNearestNeighbor([a, o]);
                    return transpose(i, [0, 3, 1, 2])
                }
                var a = r.size[0] * n[1],
                    o = r.size[1] * n[2];
                return t.resizeNearestNeighbor([a, o])
            })
        }, t.prototype.getConfig = function() {
            var t = {
                    size: this.size,
                    dataFormat: this.dataFormat
                },
                r = e.prototype.getConfig.call(this);
            return Object.assign(t, r), t
        }, t.className = "UpSampling2D", t
    }(Layer);
    SerializationMap.register(UpSampling2D);
    var __extends$19 = function() {
            var e = 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])
            };
            return function(t, r) {
                function n() {
                    this.constructor = t
                }
                e(t, r), t.prototype = null === r ? Object.create(r) : (n.prototype = r.prototype, new n)
            }
        }(),
        DepthwiseConv2D = function(e) {
            function t(t) {
                var r = e.call(this, t) || this;
                return r.depthwiseKernel = null, r.depthMultiplier = null == t.depthMultiplier ? 1 : t.depthMultiplier, r.depthwiseInitializer = getInitializer(t.depthwiseInitializer || r.DEFAULT_KERNEL_INITIALIZER), r.depthwiseConstraint = getConstraint(t.depthwiseConstraint), r.depthwiseRegularizer = getRegularizer(t.depthwiseRegularizer), r
            }
            return __extends$19(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 r = e[t],
                    n = [this.kernelSize[0], this.kernelSize[1], r, this.depthMultiplier];
                this.depthwiseKernel = this.addWeight("depthwise_kernel", n, null, this.depthwiseInitializer, this.depthwiseRegularizer, !0, this.depthwiseConstraint), this.useBias ? this.bias = this.addWeight("bias", [r * this.depthMultiplier], null, this.biasInitializer, this.biasRegularizer, !0, this.biasConstraint) : this.bias = null, this.built = !0
            }, t.prototype.call = function(e, t) {
                var r = this;
                return tidy(function() {
                    var t = depthwiseConv2d$1(e = getExactlyOneTensor(e), r.depthwiseKernel.read(), r.strides, r.padding, r.dataFormat, null);
                    return r.useBias && (t = biasAdd(t, r.bias.read(), r.dataFormat)), null != r.activation && (t = r.activation.apply(t)), t
                })
            }, t.prototype.computeOutputShape = function(e) {
                e = getExactlyOneShape(e);
                var t = "channelsFirst" === this.dataFormat ? e[2] : e[1],
                    r = "channelsFirst" === this.dataFormat ? e[3] : e[2],
                    n = "channelsFirst" === this.dataFormat ? e[1] * this.depthMultiplier : e[3] * this.depthMultiplier,
                    a = convOutputLength(t, this.kernelSize[0], this.padding, this.strides[0]),
                    o = convOutputLength(r, this.kernelSize[1], this.padding, this.strides[1]);
                return "channelsFirst" === this.dataFormat ? [e[0], n, a, o] : [e[0], a, o, n]
            }, t.className = "DepthwiseConv2D", t
        }(Conv2D);
    SerializationMap.register(DepthwiseConv2D);
    var __extends$20 = function() {
            var e = 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])
            };
            return function(t, r) {
                function n() {
                    this.constructor = t
                }
                e(t, r), t.prototype = null === r ? Object.create(r) : (n.prototype = r.prototype, new n)
            }
        }(),
        Dropout = function(e) {
            function t(t) {
                var r = e.call(this, t) || this;
                if (r.rate = Math.max(Math.min(t.rate, 1), 0), r.rateScalar = getScalar(r.rate), r.noiseShape = t.noiseShape, r.seed = t.seed, null != r.seed) throw new NotImplementedError("Non-default seed is not implemented in Dropout layer yet: " + r.seed);
                return r.supportsMasking = !0, r
            }
            return __extends$20(t, e), t.prototype.getNoiseShape = function(e) {
                if (null == this.noiseShape) return this.noiseShape;
                for (var t = e.shape, r = [], n = 0; n < this.noiseShape.length; ++n) r.push(null == this.noiseShape[n] ? t[n] : this.noiseShape[n]);
                return r
            }, t.prototype.call = function(e, t) {
                var r = this;
                return tidy(function() {
                    r.invokeCallHook(e, t);
                    var n = getExactlyOneTensor(e);
                    if (null != r.noiseShape && !arraysEqual(n.shape, r.noiseShape)) throw new NotImplementedError("Non-default noise shape is not implemented in Dropout layer yet: " + JSON.stringify(r.noiseShape));
                    if (0 < r.rate && r.rate < 1) {
                        var a = null != t.training && t.training,
                            o = r.getNoiseShape(n);
                        return inTrainPhase(function() {
                            return dropout(n, r.rateScalar, o, r.seed)
                        }, function() {
                            return n
                        }, a)
                    }
                    return e
                })
            }, t.prototype.getConfig = function() {
                var t = {
                        rate: this.rate,
                        noiseShape: this.noiseShape,
                        seed: this.seed
                    },
                    r = e.prototype.getConfig.call(this);
                return Object.assign(t, r), t
            }, t.className = "Dropout", t
        }(Layer);
    SerializationMap.register(Dropout);
    var Dense = function(e) {
        function t(t) {
            var r = e.call(this, t) || this;
            if (r.activation = null, r.useBias = !0, r.kernel = null, r.bias = null, r.DEFAULT_KERNEL_INITIALIZER = "glorotNormal", r.DEFAULT_BIAS_INITIALIZER = "zeros", null == t.batchInputShape && null == t.inputShape && null != t.inputDim) {
                var n = null;
                null != t.batchSize && (n = t.batchSize), r.batchInputShape = [n, t.inputDim]
            }
            return r.units = t.units, r.activation = getActivation(t.activation), null != t.useBias && (r.useBias = t.useBias), r.kernelInitializer = getInitializer(t.kernelInitializer || r.DEFAULT_KERNEL_INITIALIZER), r.biasInitializer = getInitializer(t.biasInitializer || r.DEFAULT_BIAS_INITIALIZER), r.kernelConstraint = getConstraint(t.kernelConstraint), r.biasConstraint = getConstraint(t.biasConstraint), r.kernelRegularizer = getRegularizer(t.kernelRegularizer), r.biasRegularizer = getRegularizer(t.biasRegularizer), r.activityRegularizer = getRegularizer(t.activityRegularizer), r.inputSpec = [{
                minNDim: 2
            }], r
        }
        return __extends$20(t, e), t.prototype.build = function(e) {
            var t = (e = getExactlyOneShape(e))[e.length - 1];
            null == this.kernel && (this.kernel = this.addWeight("kernel", [t, 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: (r = {}, r[-1] = t, r)
            }], this.built = !0;
            var r
        }, 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 r = this;
            return tidy(function() {
                r.invokeCallHook(e, t);
                var n = dot$1(getExactlyOneTensor(e), r.kernel.read());
                return null != r.bias && (n = biasAdd(n, r.bias.read())), null != r.activation && (n = r.activation.apply(n)), n
            })
        }, 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)
                },
                r = e.prototype.getConfig.call(this);
            return Object.assign(t, r), t
        }, t.className = "Dense", t
    }(Layer);
    SerializationMap.register(Dense);
    var Flatten = function(e) {
        function t(t) {
            var r = e.call(this, t || {}) || this;
            return r.inputSpec = [{
                minNDim: 3
            }], r
        }
        return __extends$20(t, e), t.prototype.computeOutputShape = function(e) {
            for (var t = 0, r = (e = getExactlyOneShape(e)).slice(1); t < r.length; t++)
                if (null == r[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 r = this;
            return tidy(function() {
                return r.invokeCallHook(e, t), batchFlatten(getExactlyOneTensor(e))
            })
        }, t.className = "Flatten", t
    }(Layer);
    SerializationMap.register(Flatten);
    var Activation$1 = function(e) {
        function t(t) {
            var r = e.call(this, t) || this;
            return r.supportsMasking = !0, r.activation = getActivation(t.activation), r
        }
        return __extends$20(t, e), t.prototype.call = function(e, t) {
            var r = this;
            return tidy(function() {
                r.invokeCallHook(e, t);
                var n = getExactlyOneTensor(e);
                return r.activation.apply(n)
            })
        }, t.prototype.getConfig = function() {
            var t = {
                    activation: serializeActivation(this.activation)
                },
                r = e.prototype.getConfig.call(this);
            return Object.assign(t, r), t
        }, t.className = "Activation", t
    }(Layer);
    SerializationMap.register(Activation$1);
    var RepeatVector = function(e) {
        function t(t) {
            var r = e.call(this, t) || this;
            return r.n = t.n, r.inputSpec = [{
                ndim: 2
            }], r
        }
        return __extends$20(t, e), t.prototype.computeOutputShape = function(e) {
            return [e[0], this.n, e[1]]
        }, t.prototype.call = function(e, t) {
            var r = this;
            return tidy(function() {
                return e = getExactlyOneTensor(e), repeat(e, r.n)
            })
        }, t.prototype.getConfig = function() {
            var t = {
                    n: this.n
                },
                r = e.prototype.getConfig.call(this);
            return Object.assign(t, r), t
        }, t.className = "RepeatVector", t
    }(Layer);
    SerializationMap.register(RepeatVector);
    var Reshape = function(e) {
        function t(t) {
            var r = e.call(this, t) || this;
            r.targetShape = t.targetShape;
            for (var n = 0; n < r.targetShape.length; ++n) r.isUnknown(r.targetShape[n]) && (r.targetShape[n] = null);
            return r
        }
        return __extends$20(t, e), t.prototype.isUnknown = function(e) {
            return e < 0 || null == e
        }, t.prototype.fixUnknownDimension = function(e, t) {
            for (var r = "Total size of new array must be unchanged.", n = t.slice(), a = 1, o = null, i = 0; i < n.length; ++i) {
                var s = n[i];
                if (this.isUnknown(s)) {
                    if (null !== o) throw new ValueError("Can only specifiy one unknown dimension.");
                    o = i
                } else a *= s
            }
            var u = arrayProd(e);
            if (null !== o) {
                if (0 === a || u % a != 0) throw new ValueError(r);
                n[o] = u / a
            } else if (u !== a) throw new ValueError(r);
            return n
        }, t.prototype.computeOutputShape = function(e) {
            for (var t = !1, r = 0; r < e.length; ++r)
                if (this.isUnknown(e[r])) {
                    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 r = this;
            return tidy(function() {
                r.invokeCallHook(e, t);
                var n = getExactlyOneTensor(e),
                    a = shape(n),
                    o = a.slice(0, 1).concat(r.fixUnknownDimension(a.slice(1), r.targetShape));
                return n.reshape(o)
            })
        }, t.prototype.getConfig = function() {
            var t = {
                    targetShape: this.targetShape
                },
                r = e.prototype.getConfig.call(this);
            return Object.assign(t, r), t
        }, t.className = "Reshape", t
    }(Layer);
    SerializationMap.register(Reshape);
    var __extends$21 = function() {
            var e = 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])
            };
            return function(t, r) {
                function n() {
                    this.constructor = t
                }
                e(t, r), t.prototype = null === r ? Object.create(r) : (n.prototype = r.prototype, new n)
            }
        }(),
        Embedding = function(e) {
            function t(t) {
                var r = e.call(this, t) || this;
                if (r.embeddings = null, r.DEFAULT_EMBEDDINGS_INITIALIZER = "randomUniform", null == t.batchInputShape && null == t.inputShape) {
                    var n = null;
                    null != t.batchSize && (n = t.batchSize), null == t.inputLength ? r.batchInputShape = [n, null] : r.batchInputShape = [n].concat(toList(t.inputLength))
                }
                return r.inputDim = t.inputDim, r.outputDim = t.outputDim, r.embeddingsInitializer = getInitializer(t.embeddingsInitializer || r.DEFAULT_EMBEDDINGS_INITIALIZER), r.embeddingsRegularizer = getRegularizer(t.embeddingsRegularizer), r.activityRegularizer = getRegularizer(t.activityRegularizer), r.embeddingsConstraint = getConstraint(t.embeddingsConstraint), r.maskZero = t.maskZero, r.inputLength = t.inputLength, r
            }
            return __extends$21(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.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 r = 0, n = 0; n < t.length; ++n) {
                    var a = t[n],
                        o = e[n + 1];
                    if (null != a && null != o && a !== o) throw new ValueError('"inputLength" is ' + this.inputLength + ", but received input shape has shape " + e);
                    null == a && (t[r] = o), r++
                }
                return [e[0]].concat(t, [this.outputDim])
            }, t.prototype.call = function(e, t) {
                var r = this;
                return tidy(function() {
                    r.invokeCallHook(e, t);
                    var n = getExactlyOneTensor(e);
                    return "int32" !== dtype(n) && (n = cast$1(n, "int32")), gather$1(r.embeddings.read(), n.as1D()).reshape(getExactlyOneShape(r.computeOutputShape(n.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
                    },
                    r = e.prototype.getConfig.call(this);
                return Object.assign(t, r), t
            }, t.className = "Embedding", t
        }(Layer);
    SerializationMap.register(Embedding);
    var __extends$22 = function() {
            var e = 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])
            };
            return function(t, r) {
                function n() {
                    this.constructor = t
                }
                e(t, r), t.prototype = null === r ? Object.create(r) : (n.prototype = r.prototype, new n)
            }
        }(),
        Merge = function(e) {
            function t(t) {
                var r = e.call(this, t || {}) || this;
                return r.supportsMasking = !0, r
            }
            return __extends$22(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 r = e.slice(0, e.length - t.length), n = 0; n < t.length; ++n) {
                    var a = e[e.length - t.length + n],
                        o = t[n];
                    if (null == a || null == o || a < 0 || o < 0) r.push(null);
                    else if (1 === a) r.push(o);
                    else if (1 === o) r.push(a);
                    else {
                        if (a !== o) throw new ValueError("Operands could not be broadcast together with shapes " + JSON.stringify(e) + " " + JSON.stringify(t));
                        r.push(a)
                    }
                }
                return r
            }, 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 = [], r = 0, n = e; r < n.length; r++) null != (i = n[r]) && null !== i[0] && t.push(i[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 a = null == e[0] ? null : e[0].slice(1), o = 1; o < e.length; ++o) {
                    var i = null == e[o] ? null : e[o].slice(1);
                    a = this.computeElementwiseOpOutputShape(a, i)
                }
                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 r = this;
                return tidy(function() {
                    if (e = e, r.reshapeRequired) {
                        var t = [],
                            n = e.map(function(e) {
                                return e.rank
                            });
                        if (-1 === n.indexOf(null)) {
                            for (var a = max$1(n), o = 0, i = e; o < i.length; o++) {
                                for (var s = (d = i[o]).rank, u = 0; u < a - s; ++u) d = expandDims$1(d, 1);
                                t.push(d)
                            }
                            return r.mergeFunction(t)
                        }
                        for (var l = !1, c = 0, p = e; c < p.length; c++) {
                            var d = p[c];
                            if (null == (s = d.rank)) {
                                var h = shape(d),
                                    f = h[0],
                                    m = h.slice(1).concat([f]),
                                    g = d.reshape([f].concat(arrayProd(h.slice(1))));
                                g = (g = transpose(g, [1, 0])).reshape(m), t.push(g), l = !0
                            } else if (s > 1) {
                                x = range$1(1, s).concat([0]);
                                t.push(transpose(d, x)), l = !0
                            } else t.push(d)
                        }
                        var y = r.mergeFunction(t),
                            v = y.rank;
                        if (l)
                            if (null == v) {
                                var b = shape(y),
                                    m = [f = b[b.length - 1]].concat(b.slice(0, b.length - 1));
                                y = transpose(y.reshape([-1, f]), [1, 0]).reshape(m)
                            } else if (v > 1) {
                            var x = [v - 1].concat(range$1(0, v - 1));
                            y = transpose(y, x)
                        }
                        return y
                    }
                    return r.mergeFunction(e)
                })
            }, t.prototype.computeOutputShape = function(e) {
                var t;
                t = null == (e = e)[0] ? null : e[0].slice(1);
                for (var r = 1; r < e.length; ++r) {
                    var n = null == e[r] ? null : e[r].slice(1);
                    t = this.computeElementwiseOpOutputShape(t, n)
                }
                for (var a = [], o = 0, i = e; o < i.length; o++) null != (n = i[o]) && null !== n[0] && a.push(n[0]);
                return a = unique(a), t = 1 === a.length ? a.concat(t) : [null].concat(t)
            }, t
        }(Layer),
        Add = function(e) {
            function t(t) {
                return e.call(this, t) || this
            }
            return __extends$22(t, e), t.prototype.mergeFunction = function(e) {
                return tidy(function() {
                    for (var t = zeros(e[0].shape), r = 0, n = e; r < n.length; r++) {
                        var a = n[r];
                        t = add(t, a)
                    }
                    return t
                })
            }, t.className = "Add", t
        }(Merge);
    SerializationMap.register(Add);
    var Multiply = function(e) {
        function t(t) {
            return e.call(this, t) || this
        }
        return __extends$22(t, e), t.prototype.mergeFunction = function(e) {
            return tidy(function() {
                for (var t = ones(e[0].shape), r = 0, n = e; r < n.length; r++) {
                    var a = n[r];
                    t = mul(t, a)
                }
                return t
            })
        }, t.className = "Multiply", t
    }(Merge);
    SerializationMap.register(Multiply);
    var Average = function(e) {
        function t(t) {
            return e.call(this, t) || this
        }
        return __extends$22(t, e), t.prototype.mergeFunction = function(e) {
            return tidy(function() {
                for (var t = zeros(e[0].shape), r = 0, n = e; r < n.length; r++) {
                    var a = n[r];
                    t = add(t, a)
                }
                return scalarTimesArray(getScalar(1 / e.length), t)
            })
        }, t.className = "Average", t
    }(Merge);
    SerializationMap.register(Average);
    var Maximum = function(e) {
        function t(t) {
            return e.call(this, t) || this
        }
        return __extends$22(t, e), t.prototype.mergeFunction = function(e) {
            return tidy(function() {
                for (var t = e[0], r = 1; r < e.length; ++r) t = maximum(t, e[r]);
                return t
            })
        }, t.className = "Maximum", t
    }(Merge);
    SerializationMap.register(Maximum);
    var Minimum = function(e) {
        function t(t) {
            return e.call(this, t) || this
        }
        return __extends$22(t, e), t.prototype.mergeFunction = function(e) {
            return tidy(function() {
                for (var t = e[0], r = 1; r < e.length; ++r) t = minimum(t, e[r]);
                return t
            })
        }, t.className = "Minimum", t
    }(Merge);
    SerializationMap.register(Minimum);
    var Concatenate = function(e) {
        function t(t) {
            var r = e.call(this, t) || this;
            return r.DEFAULT_AXIS = -1, null == t && (t = {}), r.axis = null == t.axis ? r.DEFAULT_AXIS : t.axis, r.supportsMasking = !0, r.reshapeRequired = !1, r
        }
        return __extends$22(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, r = 0, n = e = e; r < n.length; r++)
                if (null != (c = n[r])) {
                    t = !1;
                    break
                }
            if (!t) {
                for (var a = [], o = 0; o < e.length; ++o) {
                    var i = e[o].slice();
                    i.splice(this.axis, 1);
                    for (var s = !1, u = 0, l = a; u < l.length; u++) {
                        var c = l[u];
                        if (arraysEqual(c, i)) {
                            s = !0;
                            break
                        }
                    }
                    s || a.push(i)
                }
                if (a.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, r = t[0].slice(), n = this.axis < 0 ? r.length + this.axis : this.axis, a = 0, o = t.slice(1); a < o.length; a++) {
                var i = o[a];
                if (null == r[n] || null == i[n]) {
                    r[n] = null;
                    break
                }
                r[n] += i[n]
            }
            return r
        }, t.prototype.getConfig = function() {
            var t = {
                    axis: this.axis
                },
                r = e.prototype.getConfig.call(this);
            return Object.assign(t, r), t
        }, t.className = "Concatenate", t
    }(Merge);
    SerializationMap.register(Concatenate);
    var __extends$23 = function() {
            var e = 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])
            };
            return function(t, r) {
                function n() {
                    this.constructor = t
                }
                e(t, r), t.prototype = null === r ? Object.create(r) : (n.prototype = r.prototype, new n)
            }
        }(),
        BatchNormalization = function(e) {
            function t(t) {
                var r = e.call(this, t) || this;
                return r.supportsMasking = !0, r.axis = null == t.axis ? -1 : t.axis, r.momentum = null == t.momentum ? .99 : t.momentum, r.epsilon = null == t.epsilon ? .001 : t.epsilon, r.center = null == t.center || t.center, r.scale = null == t.scale || t.scale, r.betaInitializer = getInitializer(t.betaInitializer || "zeros"), r.gammaInitializer = getInitializer(t.gammaInitializer || "ones"), r.movingMeanInitializer = getInitializer(t.movingMeanInitializer || "zeros"), r.movingVarianceInitializer = getInitializer(t.movingVarianceInitializer || "ones"), r.betaConstraint = getConstraint(t.betaConstraint), r.gammaConstraint = getConstraint(t.gammaConstraint), r.betaRegularizer = getRegularizer(t.betaRegularizer), r.gammaRegularizer = getRegularizer(t.gammaRegularizer), r.stepCount = 0, r
            }
            return __extends$23(t, e), t.prototype.build = function(e) {
                e = getExactlyOneShape(e);
                var t = this.axis >= 0 ? this.axis : this.axis + e.length,
                    r = e[t];
                if (null == r) 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: (a = {}, a[t] = r, a)
                })];
                var n = [r];
                this.scale && (this.gamma = this.addWeight("gamma", n, null, this.gammaInitializer, this.gammaRegularizer, !0, this.gammaConstraint)), this.center && (this.beta = this.addWeight("beta", n, null, this.betaInitializer, this.betaRegularizer, !0, this.betaConstraint)), this.movingMean = this.addWeight("moving_mean", n, null, this.movingMeanInitializer, null, !1), this.movingVariance = this.addWeight("moving_variance", n, null, this.movingVarianceInitializer, null, !1), this.built = !0;
                var a
            }, t.prototype.call = function(e, t) {
                var r = this;
                return tidy(function() {
                    var n = null != t.training && t.training,
                        a = getExactlyOneTensor(e),
                        o = shape(a),
                        i = o.length,
                        s = range$1(0, i),
                        u = r.axis >= 0 ? r.axis : r.axis + i;
                    s.splice(u, 1);
                    var l = pyListRepeat(1, i);
                    l[u] = o[u];
                    var c = s.slice();
                    c.sort();
                    var p = !arraysEqual(c, range$1(0, i).slice(0, i - 1));
                    if (!n) return function() {
                        if (p) {
                            var e = r.movingMean.read().reshape(l),
                                t = r.movingVariance.read().reshape(l),
                                n = r.center ? r.beta.read().reshape(l) : null,
                                o = r.scale ? r.gamma.read().reshape(l) : null;
                            return batchNormalization$1(a, e, t, n, o, r.epsilon)
                        }
                        return batchNormalization$1(a, r.movingMean.read(), r.movingVariance.read(), null == r.beta ? null : r.beta.read(), null == r.gamma ? null : r.gamma.read(), r.epsilon)
                    }();
                    var d = normalizeBatchInTraining(a, r.gamma.read(), r.beta.read(), s, r.epsilon),
                        h = d[0],
                        f = d[1],
                        m = d[2],
                        g = arrayProd(s.map(function(e) {
                            return a.shape[e]
                        })),
                        y = m.mul(getScalar(g / (g - (1 + r.epsilon))));
                    return function() {
                        r.stepCount++;
                        var e = movingAverage(r.movingMean.read(), f, r.momentum, r.stepCount);
                        r.movingMean.write(e);
                        var t = movingAverage(r.movingVariance.read(), y, r.momentum, r.stepCount);
                        r.movingVariance.write(t)
                    }(), h
                })
            }, 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)
                    },
                    r = e.prototype.getConfig.call(this);
                return Object.assign(t, r), t
            }, t.className = "BatchNormalization", t
        }(Layer);
    SerializationMap.register(BatchNormalization);
    var __extends$24 = function() {
            var e = 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])
            };
            return function(t, r) {
                function n() {
                    this.constructor = t
                }
                e(t, r), t.prototype = null === r ? Object.create(r) : (n.prototype = r.prototype, new n)
            }
        }(),
        ZeroPadding2D = function(e) {
            function t(t) {
                var r = this;
                if (null == t && (t = {}), r = e.call(this, t) || this, r.dataFormat = null == t.dataFormat ? imageDataFormat() : t.dataFormat, null == t.padding) r.padding = [
                    [1, 1],
                    [1, 1]
                ];
                else if ("number" == typeof t.padding) r.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 n = void 0,
                        a = void 0;
                    if ("number" == typeof t.padding[0]) n = [t.padding[0], t.padding[0]], a = [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 (n = 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.");
                        a = t.padding[1]
                    }
                    r.padding = [n, a]
                }
                return r.inputSpec = [new InputSpec({
                    ndim: 4
                })], r
            }
            return __extends$24(t, e), t.prototype.computeOutputShape = function(e) {
                e = getExactlyOneShape(e);
                var t, r;
                return "channelsFirst" === this.dataFormat ? (t = null != e[2] && e[2] >= 0 ? e[2] + this.padding[0][0] + this.padding[0][1] : null, r = null != e[3] && e[3] >= 0 ? e[3] + this.padding[1][0] + this.padding[1][1] : null, [e[0], e[1], t, r]) : (t = null != e[1] && e[1] >= 0 ? e[1] + this.padding[0][0] + this.padding[0][1] : null, r = null != e[2] && e[2] >= 0 ? e[2] + this.padding[1][0] + this.padding[1][1] : null, [e[0], t, r, e[3]])
            }, t.prototype.call = function(e, t) {
                var r = this;
                return tidy(function() {
                    return spatial2dPadding(getExactlyOneTensor(e), r.padding, r.dataFormat)
                })
            }, t.prototype.getConfig = function() {
                var t = {
                        padding: this.padding,
                        dataFormat: this.dataFormat
                    },
                    r = e.prototype.getConfig.call(this);
                return Object.assign(t, r), t
            }, t.className = "ZeroPadding2D", t
        }(Layer);
    SerializationMap.register(ZeroPadding2D);
    var __extends$25 = function() {
            var e = 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])
            };
            return function(t, r) {
                function n() {
                    this.constructor = t
                }
                e(t, r), t.prototype = null === r ? Object.create(r) : (n.prototype = r.prototype, new n)
            }
        }(),
        Pooling1D = function(e) {
            function t(t) {
                var r = this;
                if (null == t.poolSize && (t.poolSize = 2), r = e.call(this, t) || this, "number" == typeof t.poolSize) r.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));
                    r.poolSize = t.poolSize
                }
                if (null == t.strides) r.strides = r.poolSize;
                else if ("number" == typeof t.strides) r.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));
                    r.strides = t.strides
                }
                return r.padding = null == t.padding ? "valid" : t.padding, checkPaddingMode(r.padding), r.inputSpec = [new InputSpec({
                    ndim: 3
                })], r
            }
            return __extends$25(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 r = this;
                return tidy(function() {
                    r.invokeCallHook(e, t), e = expandDims$1(getExactlyOneTensor(e), 2);
                    var n = r.poolingFunction(getExactlyOneTensor(e), [r.poolSize[0], 1], [r.strides[0], 1], r.padding, "channelsLast");
                    return squeeze(n, [2])
                })
            }, t.prototype.getConfig = function() {
                var t = {
                        poolSize: this.poolSize,
                        padding: this.padding,
                        strides: this.strides
                    },
                    r = e.prototype.getConfig.call(this);
                return Object.assign(t, r), t
            }, t
        }(Layer),
        MaxPooling1D = function(e) {
            function t(t) {
                return e.call(this, t) || this
            }
            return __extends$25(t, e), t.prototype.poolingFunction = function(e, t, r, n, a) {
                return checkDataFormat(a), checkPaddingMode(n), pool2d(e, t, r, n, a, "max")
            }, t.className = "MaxPooling1D", t
        }(Pooling1D);
    SerializationMap.register(MaxPooling1D);
    var AveragePooling1D = function(e) {
        function t(t) {
            return e.call(this, t) || this
        }
        return __extends$25(t, e), t.prototype.poolingFunction = function(e, t, r, n, a) {
            return checkDataFormat(a), checkPaddingMode(n), pool2d(e, t, r, n, a, "avg")
        }, t.className = "AveragePooling1D", t
    }(Pooling1D);
    SerializationMap.register(AveragePooling1D);
    var Pooling2D = function(e) {
            function t(t) {
                var r = this;
                return null == t.poolSize && (t.poolSize = [2, 2]), r = e.call(this, t) || this, r.poolSize = Array.isArray(t.poolSize) ? t.poolSize : [t.poolSize, t.poolSize], r.strides = null == t.strides ? r.poolSize : t.strides, r.padding = null == t.padding ? "valid" : t.padding, r.dataFormat = null == t.dataFormat ? "channelsLast" : t.dataFormat, checkDataFormat(r.dataFormat), checkPaddingMode(r.padding), r.inputSpec = [new InputSpec({
                    ndim: 4
                })], r
            }
            return __extends$25(t, e), t.prototype.computeOutputShape = function(e) {
                e = getExactlyOneShape(e);
                var t = "channelsFirst" === this.dataFormat ? e[2] : e[1],
                    r = "channelsFirst" === this.dataFormat ? e[3] : e[2];
                return t = convOutputLength(t, this.poolSize[0], this.padding, this.strides[0]), r = convOutputLength(r, this.poolSize[1], this.padding, this.strides[1]), "channelsFirst" === this.dataFormat ? [e[0], e[1], t, r] : [e[0], t, r, e[3]]
            }, t.prototype.call = function(e, t) {
                var r = this;
                return tidy(function() {
                    return r.invokeCallHook(e, t), r.poolingFunction(getExactlyOneTensor(e), r.poolSize, r.strides, r.padding, r.dataFormat)
                })
            }, t.prototype.getConfig = function() {
                var t = {
                        poolSize: this.poolSize,
                        padding: this.padding,
                        strides: this.strides,
                        dataFormat: this.dataFormat
                    },
                    r = e.prototype.getConfig.call(this);
                return Object.assign(t, r), t
            }, t
        }(Layer),
        MaxPooling2D = function(e) {
            function t(t) {
                return e.call(this, t) || this
            }
            return __extends$25(t, e), t.prototype.poolingFunction = function(e, t, r, n, a) {
                return checkDataFormat(a), checkPaddingMode(n), pool2d(e, t, r, n, a, "max")
            }, t.className = "MaxPooling2D", t
        }(Pooling2D);
    SerializationMap.register(MaxPooling2D);
    var AveragePooling2D = function(e) {
        function t(t) {
            return e.call(this, t) || this
        }
        return __extends$25(t, e), t.prototype.poolingFunction = function(e, t, r, n, a) {
            return checkDataFormat(a), checkPaddingMode(n), pool2d(e, t, r, n, a, "avg")
        }, t.className = "AveragePooling2D", t
    }(Pooling2D);
    SerializationMap.register(AveragePooling2D);
    var GlobalPooling1D = function(e) {
            function t(t) {
                var r = e.call(this, t) || this;
                return r.inputSpec = [new InputSpec({
                    ndim: 3
                })], r
            }
            return __extends$25(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$25(t, e), t.prototype.call = function(e, t) {
                return tidy(function() {
                    var t = getExactlyOneTensor(e);
                    return mean(t, 1)
                })
            }, t.className = "GlobalAveragePooling1D", t
        }(GlobalPooling1D);
    SerializationMap.register(GlobalAveragePooling1D);
    var GlobalMaxPooling1D = function(e) {
        function t(t) {
            return e.call(this, t) || this
        }
        return __extends$25(t, e), t.prototype.call = function(e, t) {
            return tidy(function() {
                var t = getExactlyOneTensor(e);
                return max(t, 1)
            })
        }, t.className = "GlobalMaxPooling1D", t
    }(GlobalPooling1D);
    SerializationMap.register(GlobalMaxPooling1D);
    var GlobalPooling2D = function(e) {
            function t(t) {
                var r = e.call(this, t) || this;
                return r.dataFormat = null == t.dataFormat ? "channelsLast" : t.dataFormat, checkDataFormat(r.dataFormat), r.inputSpec = [new InputSpec({
                    ndim: 4
                })], r
            }
            return __extends$25(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
                    },
                    r = e.prototype.getConfig.call(this);
                return Object.assign(t, r), t
            }, t
        }(Layer),
        GlobalAveragePooling2D = function(e) {
            function t() {
                return null !== e && e.apply(this, arguments) || this
            }
            return __extends$25(t, e), t.prototype.call = function(e, t) {
                var r = this;
                return tidy(function() {
                    var t = getExactlyOneTensor(e);
                    return "channelsLast" === r.dataFormat ? mean(t, [1, 2]) : mean(t, [2, 3])
                })
            }, t.className = "GlobalAveragePooling2D", t
        }(GlobalPooling2D);
    SerializationMap.register(GlobalAveragePooling2D);
    var GlobalMaxPooling2D = function(e) {
        function t() {
            return null !== e && e.apply(this, arguments) || this
        }
        return __extends$25(t, e), t.prototype.call = function(e, t) {
            var r = this;
            return tidy(function() {
                var t = getExactlyOneTensor(e);
                return "channelsLast" === r.dataFormat ? max(t, [1, 2]) : max(t, [2, 3])
            })
        }, t.className = "GlobalMaxPooling2D", t
    }(GlobalPooling2D);
    SerializationMap.register(GlobalMaxPooling2D);
    var __extends$26 = function() {
            var e = 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])
            };
            return function(t, r) {
                function n() {
                    this.constructor = t
                }
                e(t, r), t.prototype = null === r ? Object.create(r) : (n.prototype = r.prototype, new n)
            }
        }(),
        __decorate$37 = function(e, t, r, n) {
            var a, o = arguments.length,
                i = o < 3 ? t : null === n ? n = Object.getOwnPropertyDescriptor(t, r) : n;
            if ("object" == typeof Reflect && "function" == typeof Reflect.decorate) i = Reflect.decorate(e, t, r, n);
            else
                for (var s = e.length - 1; s >= 0; s--)(a = e[s]) && (i = (o < 3 ? a(i) : o > 3 ? a(t, r, i) : a(t, r)) || i);
            return o > 3 && i && Object.defineProperty(t, r, i), i
        },
        RNN = function(e) {
            function t(t) {
                var r, n = e.call(this, t) || this;
                if (null == t.cell) throw new ValueError("cell property is missing for the constructor of RNN.");
                if (null == (r = 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 n.cell = r, n.returnSequences = null != t.returnSequences && t.returnSequences, n.returnState = null != t.returnState && t.returnState, n.goBackwards = null != t.goBackwards && t.goBackwards, n._stateful = null != t.stateful && t.stateful, n.unroll = null != t.unroll && t.unroll, n.supportsMasking = !0, n.inputSpec = [new InputSpec({
                    ndim: 3
                })], n.stateSpec = null, n.states = null, n.numConstants = null, n
            }
            return __extends$26(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 r, n = t[0];
                if (r = this.returnSequences ? [e[0], e[1], n] : [e[0], n], this.returnState) {
                    for (var a = [], o = 0, i = t; o < i.length; o++) {
                        var s = i[o];
                        a.push([e[0], s])
                    }
                    return [r].concat(a)
                }
                return r
            }, 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,
                    r = e[e.length - 1];
                this.inputSpec[0] = new InputSpec({
                    shape: [t, null, r]
                });
                var n = [e[0]].concat(e.slice(2));
                this.cell.build(n);
                var a;
                if (a = Array.isArray(this.cell.stateSize) ? this.cell.stateSize : [this.cell.stateSize], null != this.stateSpec) {
                    if (!arraysEqual(this.stateSpec.map(function(e) {
                            return e.shape[e.shape.length - 1]
                        }), a)) 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 = a.map(function(e) {
                    return new InputSpec({
                        shape: [null, e]
                    })
                });
                if (this.stateful) throw new NotImplementedError("stateful RNN layer is not implemented yet")
            }, t.prototype.resetStates = function(e) {
                var t = this;
                tidy(function() {
                    if (!t.stateful) throw new AttributeError("Cannot call resetState() on an RNN Layer that is not stateful.");
                    var r = t.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 == t.states) Array.isArray(t.cell.stateSize) ? t.states = t.cell.stateSize.map(function(e) {
                        return zeros([r, e])
                    }) : t.states = [zeros([r, t.cell.stateSize])];
                    else if (null == e) Array.isArray(t.cell.stateSize) ? t.states = t.cell.stateSize.map(function(e) {
                        return zeros([r, e])
                    }) : t.states[0] = zeros([r, t.cell.stateSize]);
                    else {
                        if (Array.isArray(e) || (e = [e]), e.length !== t.states.length) throw new ValueError("Layer " + t.name + " expects " + t.states.length + " state(s), but it received " + e.length + " state value(s). Input received: " + e);
                        for (var n = 0; n < t.states.length; ++n) {
                            var a = e[n],
                                o = Array.isArray(t.cell.stateSize) ? t.cell.stateSize[n] : t.cell.stateSize,
                                i = [r, o];
                            if (!arraysEqual(a.shape, i)) throw new ValueError("State " + n + " is incompatible with layer " + t.name + ": expected shape=" + i + ", received shape=" + a.shape);
                            t.states[n] = a
                        }
                    }
                })
            }, t.prototype.standardizeArgs = function(e, t, r) {
                function n(e) {
                    return null == e || Array.isArray(e) ? e : [e]
                }
                if (Array.isArray(e)) {
                    if (null != t || null != r) throw new ValueError("When inputs is an array, neither initialState or constants should be provided");
                    null != this.numConstants && (r = e.slice(e.length - this.numConstants, e.length), e = e.slice(0, e.length - this.numConstants)), e.length > 1 && (t = e.slice(1, e.length)), e = e[0]
                }
                return t = n(t), r = n(r), {
                    inputs: e,
                    initialState: t,
                    constants: r
                }
            }, t.prototype.apply = function(t, r) {
                var n = null == r ? null : r.initialState,
                    a = null == r ? null : r.constants;
                null == r && (r = {});
                var o = this.standardizeArgs(t, n, a);
                t = o.inputs, n = o.initialState, a = o.constants;
                var i = [],
                    s = [];
                if (null != n) {
                    r.initialState = n, i = i.concat(n), this.stateSpec = [];
                    console.log(l)
                    for (var u = 0, l = n; u < l.length; u++) {
                        var c = l[u];
                        this.stateSpec.push(new InputSpec({
                            shape: c.shape
                        }))
                    }
                    s = s.concat(this.stateSpec)
                }
                if (null != a && (r.constants = a, i = i.concat(a), this.numConstants = a.length), i[0] instanceof SymbolicTensor) {
                    var p = [t].concat(i),
                        d = this.inputSpec.concat(s),
                        h = this.inputSpec;
                    this.inputSpec = d;
                    var f = e.prototype.apply.call(this, p, r);
                    return this.inputSpec = h, f
                }
                return e.prototype.apply.call(this, t, r)
            }, t.prototype.call = function(e, t) {
                var r = this;
                return tidy(function() {
                    var n = null == t ? null : t.mask,
                        a = null == t ? null : t.training,
                        o = null == t ? null : t.initialState;
                    if (e = getExactlyOneTensor(e), null == o) {
                        if (r.stateful) throw new NotImplementedError("stateful RNN layer is not implemented yet.");
                        o = r.getInitialState(e)
                    }
                    console.log(o)
                    if (null != n) throw new NotImplementedError("Masking is not implemented for RNN yet");
                    var i = Array.isArray(r.cell.stateSize) ? r.cell.stateSize.length : 1;
                    if (o.length !== i) throw new ValueError("RNN Layer has " + i + " state(s) but was passed " + o.length + " initial state(s).");
                    e.shape[1];
                    r.unroll && console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");
                    var s = {
                            training: a
                        },
                        u = rnn(function(e, t) {
                            var n = r.cell.call([e].concat(t), s);
                            return [n[0], n.slice(1)]
                        }, e, o, r.goBackwards, null, null, r.unroll),
                        l = u[0],
                        c = u[1],
                        p = u[2];
                    if (r.stateful) throw new NotImplementedError("stateful RNN layer is not implemented yet");
                    var d = r.returnSequences ? c : l;
                    return r.returnState ? [d].concat(p) : d
                })
            }, t.prototype.getInitialState = function(e) {
                var t = this;
                return tidy(function() {
                    var r = zeros(e.shape);
                    return r = sum(r, [1, 2]), r = expandDims$1(r), Array.isArray(t.cell.stateSize) ? t.cell.stateSize.map(function(e) {
                        return e > 1 ? tile$1(r, [1, e]) : r
                    }) : t.cell.stateSize > 1 ? [tile$1(r, [1, t.cell.stateSize])] : [r]
                })
            }, 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 r = this.cell.getConfig();
                t.cell = {
                    className: this.cell.getClassName(),
                    config: r
                };
                var n = e.prototype.getConfig.call(this);
                return Object.assign(t, n), t
            }, t.className = "RNN", t
        }(Layer);
    SerializationMap.register(RNN);
    var RNNCell = function(e) {
            function t() {
                return null !== e && e.apply(this, arguments) || this
            }
            return __extends$26(t, e), t = __decorate$37([doc()], t)
        }(Layer),
        SimpleRNNCell = function(e) {
            function t(t) {
                var r = e.call(this, t) || this;
                return r.DEFAULT_ACTIVATION = "tanh", r.DEFAULT_KERNEL_INITIALIZER = "glorotNormal", r.DEFAULT_RECURRENT_INITIALIZER = "orthogonal", r.DEFAULT_BIAS_INITIALIZER = "zeros", r.units = t.units, r.activation = getActivation(null == t.activation ? r.DEFAULT_ACTIVATION : t.activation), r.useBias = null == t.useBias || t.useBias, r.kernelInitializer = getInitializer(t.kernelInitializer || r.DEFAULT_KERNEL_INITIALIZER), r.recurrentInitializer = getInitializer(t.recurrentInitializer || r.DEFAULT_RECURRENT_INITIALIZER), r.biasInitializer = getInitializer(t.biasInitializer || r.DEFAULT_BIAS_INITIALIZER), r.kernelRegularizer = getRegularizer(t.kernelRegularizer), r.recurrentRegularizer = getRegularizer(t.recurrentRegularizer), r.biasRegularizer = getRegularizer(t.biasRegularizer), r.kernelConstraint = getConstraint(t.kernelConstraint), r.recurrentConstraint = getConstraint(t.recurrentConstraint), r.biasConstraint = getConstraint(t.biasConstraint), r.dropout = min$1([1, max$1([0, null == t.dropout ? 0 : t.dropout])]), r.recurrentDropout = min$1([1, max$1([0, null == t.recurrentDropout ? 0 : t.recurrentDropout])]), r.stateSize = r.units, r
            }
            return __extends$26(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 r = this;
                return tidy(function() {
                    if (2 !== (e = e).length) throw new ValueError("SimpleRNNCell expects 2 input Tensors, got " + e.length + ".");
                    var t = e[1];
                    if (e = e[0], 0 !== r.dropout || 0 !== r.recurrentDropout) throw new NotImplementedError("Dropout is not implemented for SimpleRNNCell yet");
                    var n = dot$1(e, r.kernel.read());
                    null != r.bias && (n = biasAdd(n, r.bias.read()));
                    var a = add(n, dot$1(t, r.recurrentKernel.read()));
                    return null != r.activation && (a = r.activation.apply(a)), [a, a]
                })
            }, 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
                    },
                    r = e.prototype.getConfig.call(this);
                return Object.assign(t, r), t
            }, t.className = "SimpleRNNCell", t
        }(RNNCell);
    SerializationMap.register(SimpleRNNCell);
    var SimpleRNN = function(e) {
        function t(t) {
            return t.cell = new SimpleRNNCell(t), e.call(this, t) || this
        }
        return __extends$26(t, e), t.prototype.call = function(t, r) {
            var n = this;
            return tidy(function() {
                var a = null == r ? null : r.mask,
                    o = null == r ? null : r.training,
                    i = null == r ? null : r.initialState;
                return e.prototype.call.call(n, t, {
                    mask: a,
                    training: o,
                    initialState: i
                })
            })
        }, 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
                },
                r = e.prototype.getConfig.call(this);
            return delete r.cell, Object.assign(t, r), t
        }, t.className = "SimpleRNN", t
    }(RNN);
    SerializationMap.register(SimpleRNN);
    var GRUCell = function(e) {
        function t(t) {
            var r = e.call(this, t) || this;
            return r.DEFAULT_ACTIVATION = "tanh", r.DEFAULT_RECURRENT_ACTIVATION = "hardSigmoid", r.DEFAULT_KERNEL_INITIALIZER = "glorotNormal", r.DEFAULT_RECURRENT_INITIALIZER = "orthogonal", r.DEFAULT_BIAS_INITIALIZER = "zeros", r.units = t.units, r.activation = getActivation(void 0 === t.activation ? r.DEFAULT_ACTIVATION : t.activation), r.recurrentActivation = getActivation(void 0 === t.activation ? r.DEFAULT_RECURRENT_ACTIVATION : t.recurrentActivation), r.useBias = null == t.useBias || t.useBias, r.kernelInitializer = getInitializer(t.kernelInitializer || r.DEFAULT_KERNEL_INITIALIZER), r.recurrentInitializer = getInitializer(t.recurrentInitializer || r.DEFAULT_RECURRENT_INITIALIZER), r.biasInitializer = getInitializer(t.biasInitializer || r.DEFAULT_BIAS_INITIALIZER), r.kernelRegularizer = getRegularizer(t.kernelRegularizer), r.recurrentRegularizer = getRegularizer(t.recurrentRegularizer), r.biasRegularizer = getRegularizer(t.biasRegularizer), r.kernelConstraint = getConstraint(t.kernelConstraint), r.recurrentConstraint = getConstraint(t.recurrentConstraint), r.biasConstraint = getConstraint(t.biasConstraint), r.dropout = min$1([1, max$1([0, null == t.dropout ? 0 : t.dropout])]), r.recurrentDropout = min$1([1, max$1([0, null == t.recurrentDropout ? 0 : t.recurrentDropout])]), r.implementation = t.implementation, r.stateSize = r.units, r
        }
        return __extends$26(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 r = this;
            return tidy(function() {
                if (0 !== r.dropout || 0 !== r.recurrentDropout) throw new NotImplementedError("Dropout is not implemented for GRUCell yet");
                if (2 !== (e = e).length) throw new ValueError("GRUCell expects 2 input Tensors (inputs, h, c), got " + e.length + ".");
                var t = e[1];
                e = e[0];
                var n, a, o;
                if (1 === r.implementation) {
                    var i = sliceAlongLastAxis(r.kernel.read(), 0, r.units),
                        s = sliceAlongLastAxis(r.kernel.read(), r.units, r.units),
                        u = sliceAlongLastAxis(r.kernel.read(), 2 * r.units, r.units),
                        l = sliceAlongLastAxis(r.recurrentKernel.read(), 0, r.units),
                        c = sliceAlongLastAxis(r.recurrentKernel.read(), r.units, r.units),
                        p = sliceAlongLastAxis(r.recurrentKernel.read(), 2 * r.units, r.units),
                        d = e,
                        h = e,
                        f = dot$1(e, i),
                        m = dot$1(d, s),
                        g = dot$1(h, u);
                    if (r.useBias) {
                        var y = sliceAlongFirstAxis(r.bias.read(), 0, r.units),
                            v = sliceAlongFirstAxis(r.bias.read(), r.units, r.units),
                            b = sliceAlongFirstAxis(r.bias.read(), 2 * r.units, r.units);
                        f = biasAdd(f, y), m = biasAdd(m, v), g = biasAdd(g, b)
                    }
                    var x = t,
                        w = t,
                        _ = t;
                    n = r.recurrentActivation.apply(add(f, dot$1(x, l))), a = r.recurrentActivation.apply(add(m, dot$1(w, c))), o = r.activation.apply(add(g, dot$1(mul(a, _), p)))
                } else {
                    var S = dot$1(e, r.kernel.read());
                    r.useBias && (S = biasAdd(S, r.bias.read()));
                    var N = dot$1(t, sliceAlongLastAxis(r.recurrentKernel.read(), 0, 2 * r.units)),
                        f = sliceAlongLastAxis(S, 0, r.units),
                        m = sliceAlongLastAxis(S, r.units, r.units),
                        A = sliceAlongLastAxis(N, 0, r.units),
                        E = sliceAlongLastAxis(N, r.units, r.units);
                    n = r.recurrentActivation.apply(add(f, A)), a = r.recurrentActivation.apply(add(m, E));
                    var g = sliceAlongLastAxis(S, 2 * r.units, r.units),
                        T = dot$1(mul(a, t), sliceAlongLastAxis(r.recurrentKernel.read(), 2 * r.units, r.units));
                    o = r.activation.apply(add(g, T))
                }
                var I = add(mul(n, t), mul(scalarPlusArray(getScalar(1), neg(n)), o));
                return [I, I]
            })
        }, 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,
                    implementation: this.implementation
                },
                r = e.prototype.getConfig.call(this);
            return Object.assign(t, r), t
        }, t.className = "GRUCell", t
    }(RNNCell);
    SerializationMap.register(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$26(t, e), t.prototype.call = function(t, r) {
            var n = this;
            return tidy(function() {
                var a = null == r ? null : r.mask,
                    o = null == r ? null : r.training,
                    i = null == r ? null : r.initialState;
                return e.prototype.call.call(n, t, {
                    mask: a,
                    training: o,
                    initialState: i
                })
            })
        }, 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
        }), 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),
                    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
                },
                r = e.prototype.getConfig.call(this);
            return delete r.cell, Object.assign(t, r), t
        }, t.fromConfig = function(e, t) {
            return 0 === t.implmentation && (t.implementation = 1), new e(t)
        }, t.className = "GRU", t
    }(RNN);
    SerializationMap.register(GRU);
    var LSTMCell = function(e) {
        function t(t) {
            var r = e.call(this, t) || this;
            return r.DEFAULT_ACTIVATION = "tanh", r.DEFAULT_RECURRENT_ACTIVATION = "hardSigmoid", r.DEFAULT_KERNEL_INITIALIZER = "glorotNormal", r.DEFAULT_RECURRENT_INITIALIZER = "orthogonal", r.DEFAULT_BIAS_INITIALIZER = "zeros", r.units = t.units, r.activation = getActivation(void 0 === t.activation ? r.DEFAULT_ACTIVATION : t.activation), r.recurrentActivation = getActivation(void 0 === t.activation ? r.DEFAULT_RECURRENT_ACTIVATION : t.recurrentActivation), r.useBias = null == t.useBias || t.useBias, r.kernelInitializer = getInitializer(t.kernelInitializer || r.DEFAULT_KERNEL_INITIALIZER), r.recurrentInitializer = getInitializer(t.recurrentInitializer || r.DEFAULT_RECURRENT_INITIALIZER), r.biasInitializer = getInitializer(t.biasInitializer || r.DEFAULT_BIAS_INITIALIZER), r.unitForgetBias = t.unitForgetBias, r.kernelRegularizer = getRegularizer(t.kernelRegularizer), r.recurrentRegularizer = getRegularizer(t.recurrentRegularizer), r.biasRegularizer = getRegularizer(t.biasRegularizer), r.kernelConstraint = getConstraint(t.kernelConstraint), r.recurrentConstraint = getConstraint(t.recurrentConstraint), r.biasConstraint = getConstraint(t.biasConstraint), r.dropout = min$1([1, max$1([0, null == t.dropout ? 0 : t.dropout])]), r.recurrentDropout = min$1([1, max$1([0, null == t.recurrentDropout ? 0 : t.recurrentDropout])]), r.implementation = t.implementation, r.stateSize = [r.units, r.units], r
        }
        return __extends$26(t, e), t.prototype.build = function(e) {
            var t = (e = getExactlyOneShape(e))[e.length - 1];
            this.kernel = this.addWeight("kernel", [t, 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);
            var r;
            if (this.useBias) {
                if (this.unitForgetBias) {
                    var n = this.biasInitializer,
                        a = this.units;
                    r = new(o = function(e) {
                        function t() {
                            return null !== e && e.apply(this, arguments) || this
                        }
                        return __extends$26(t, e), t.prototype.apply = function(e, t) {
                            var r = n.apply([a]),
                                o = (new Ones).apply([a]),
                                i = n.apply([2 * a]);
                            return concatAlongFirstAxis(concatAlongFirstAxis(r, o), i)
                        }, t
                    }(Initializer), o.className = "CustomInit", o)
                } else r = this.biasInitializer;
                this.bias = this.addWeight("bias", [4 * this.units], null, r, this.biasRegularizer, !0, this.biasConstraint)
            } else this.bias = null;
            this.built = !0;
            var o
        }, t.prototype.call = function(e, t) {
            var r = this;
            return tidy(function() {
                if (0 !== r.dropout || 0 !== r.recurrentDropout) throw new NotImplementedError("Dropout is not implemented for LSTMCell yet");
                if (3 !== (e = e).length) throw new ValueError("LSTMCell expects 3 input Tensors (inputs, h, c), got " + e.length + ".");
                var t = e[1],
                    n = e[2];
                e = e[0];
                var a, o, i, s;
                if (1 === r.implementation) {
                    var u = sliceAlongLastAxis(r.kernel.read(), 0, r.units),
                        l = sliceAlongLastAxis(r.kernel.read(), r.units, r.units),
                        c = sliceAlongLastAxis(r.kernel.read(), 2 * r.units, r.units),
                        p = sliceAlongLastAxis(r.kernel.read(), 3 * r.units, r.units),
                        d = sliceAlongLastAxis(r.recurrentKernel.read(), 0, r.units),
                        h = sliceAlongLastAxis(r.recurrentKernel.read(), r.units, r.units),
                        f = sliceAlongLastAxis(r.recurrentKernel.read(), 2 * r.units, r.units),
                        m = sliceAlongLastAxis(r.recurrentKernel.read(), 3 * r.units, r.units),
                        g = e,
                        y = e,
                        v = e,
                        b = dot$1(e, u),
                        x = dot$1(g, l),
                        w = dot$1(y, c),
                        _ = dot$1(v, p);
                    if (r.useBias) {
                        var S = sliceAlongFirstAxis(r.bias.read(), 0, r.units),
                            N = sliceAlongFirstAxis(r.bias.read(), r.units, r.units),
                            A = sliceAlongFirstAxis(r.bias.read(), 2 * r.units, r.units),
                            E = sliceAlongFirstAxis(r.bias.read(), 3 * r.units, r.units);
                        b = biasAdd(b, S), x = biasAdd(x, N), w = biasAdd(w, A), _ = biasAdd(_, E)
                    }
                    var T = t,
                        I = t,
                        O = t,
                        P = t;
                    a = r.recurrentActivation.apply(add(b, dot$1(T, d))), o = r.recurrentActivation.apply(add(x, dot$1(I, h))), i = add(mul(o, n), mul(a, r.activation.apply(add(w, dot$1(O, f))))), s = r.recurrentActivation.apply(add(_, dot$1(P, m)))
                } else {
                    var R = dot$1(e, r.kernel.read());
                    R = add(R, dot$1(t, r.recurrentKernel.read())), r.useBias && (R = biasAdd(R, r.bias.read()));
                    var C = sliceAlongLastAxis(R, 0, r.units),
                        k = sliceAlongLastAxis(R, r.units, r.units),
                        D = sliceAlongLastAxis(R, 2 * r.units, r.units),
                        M = sliceAlongLastAxis(R, 3 * r.units, r.units);
                    a = r.recurrentActivation.apply(C), o = r.recurrentActivation.apply(k), i = add(mul(o, n), mul(a, r.activation.apply(D))), s = r.recurrentActivation.apply(M)
                }
                var L = mul(s, r.activation.apply(i));
                return [L, L, i]
            })
        }, 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),
                    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
                },
                r = e.prototype.getConfig.call(this);
            return Object.assign(t, r), t
        }, t.className = "LSTMCell", t
    }(RNNCell);
    SerializationMap.register(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$26(t, e), t.prototype.call = function(t, r) {
            var n = this;
            return tidy(function() {
                var a = null == r ? null : r.mask,
                    o = null == r ? null : r.training,
                    i = null == r ? null : r.initialState;
                return e.prototype.call.call(n, t, {
                    mask: a,
                    training: o,
                    initialState: i
                })
            })
        }, 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, "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),
                    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
                },
                r = e.prototype.getConfig.call(this);
            return delete r.cell, Object.assign(t, r), t
        }, t.fromConfig = function(e, t) {
            return 0 === t.implmentation && (t.implementation = 1), new e(t)
        }, t.className = "LSTM", t
    }(RNN);
    SerializationMap.register(LSTM);
    var StackedRNNCells = function(e) {
        function t(t) {
            var r = e.call(this, t) || this;
            return r.cells = t.cells, r
        }
        return __extends$26(t, e), Object.defineProperty(t.prototype, "stateSize", {
            get: function() {
                for (var e = [], t = 0, r = this.cells.slice().reverse(); t < r.length; t++) {
                    var n = r[t];
                    Array.isArray(n.stateSize) ? e.push.apply(e, n.stateSize) : e.push(n.stateSize)
                }
                return e
            },
            enumerable: !0,
            configurable: !0
        }), t.prototype.call = function(e, t) {
            var r = this;
            return tidy(function() {
                for (var n = (e = e).slice(1), a = [], o = 0, i = r.cells.slice().reverse(); o < i.length; o++) {
                    c = i[o];
                    Array.isArray(c.stateSize) ? a.push(n.splice(0, c.stateSize.length)) : a.push(n.splice(0, 1))
                }
                a.reverse();
                for (var s, u = [], l = 0; l < r.cells.length; ++l) {
                    var c = r.cells[l];
                    n = a[l], s = 0 === l ? [e[0]].concat(n) : [s[0]].concat(n), s = c.call(s, t), u.push(s.slice(1))
                }
                n = [];
                for (var p = 0, d = u.slice().reverse(); p < d.length; p++) {
                    var h = d[p];
                    n.push.apply(n, h)
                }
                return [s[0]].concat(n)
            })
        }, t.prototype.build = function(e) {
            isArrayOfShapes(e) && (e = e[0]), e = e;
            for (var t, r = 0, n = this.cells; r < n.length; r++) {
                var a = n[r];
                a.build(e), t = Array.isArray(a.stateSize) ? a.stateSize[0] : a.stateSize, e = [e[0], t]
            }
            this.built = !0
        }, t.prototype.getConfig = function() {
            for (var t = [], r = 0, n = this.cells; r < n.length; r++) {
                var a = n[r];
                t.push({
                    className: this.getClassName(),
                    config: a.getConfig()
                })
            }
            var o = {
                    cells: t
                },
                i = e.prototype.getConfig.call(this);
            return Object.assign(o, i), o
        }, t.fromConfig = function(e, t, r) {
            void 0 === r && (r = {});
            for (var n = [], a = 0, o = t.cells; a < o.length; a++) {
                var i = o[a];
                n.push(deserialize(i, r))
            }
            return new e({
                cells: n
            })
        }, Object.defineProperty(t.prototype, "trainableWeights", {
            get: function() {
                if (!this.trainable) return [];
                for (var e = [], t = 0, r = this.cells; t < r.length; t++) {
                    var n = r[t];
                    e.push.apply(e, n.trainableWeights)
                }
                return e
            },
            enumerable: !0,
            configurable: !0
        }), Object.defineProperty(t.prototype, "nonTrainableWeights", {
            get: function() {
                for (var e = [], t = 0, r = this.cells; t < r.length; t++) {
                    i = r[t];
                    e.push.apply(e, i.nonTrainableWeights)
                }
                if (!this.trainable) {
                    for (var n = [], a = 0, o = this.cells; a < o.length; a++) {
                        var i = o[a];
                        n.push.apply(n, i.trainableWeights)
                    }
                    return n.concat(e)
                }
                return e
            },
            enumerable: !0,
            configurable: !0
        }), t.prototype.getWeights = function() {
            for (var e = [], t = 0, r = this.cells; t < r.length; t++) {
                var n = r[t];
                e.push.apply(e, n.weights)
            }
            return batchGetValue(e)
        }, t.prototype.setWeights = function(e) {
            for (var t = [], r = 0, n = this.cells; r < n.length; r++)
                for (var a = n[r], o = a.weights.length, i = e.splice(o), s = 0; s < a.weights.length; ++s) t.push([a.weights[s], i[s]]);
            batchSetValue(t)
        }, t.className = "StackedRNNCells", t
    }(RNNCell);
    SerializationMap.register(StackedRNNCells);
    var __extends$27 = function() {
            var e = 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])
            };
            return function(t, r) {
                function n() {
                    this.constructor = t
                }
                e(t, r), t.prototype = null === r ? Object.create(r) : (n.prototype = r.prototype, new n)
            }
        }(),
        Wrapper = function(e) {
            function t(t) {
                var r = e.call(this, t) || this;
                return r.layer = t.layer, r
            }
            return __extends$27(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()
                        }
                    },
                    r = e.prototype.getConfig.call(this);
                return Object.assign(t, r), t
            }, t.fromConfig = function(e, t, r) {
                void 0 === r && (r = {});
                var n = deserialize(t.layer, r);
                delete t.layer;
                var a = {
                    layer: n
                };
                return Object.assign(a, t), new e(a)
            }, t
        }(Layer),
        TimeDistributed = function(e) {
            function t(t) {
                var r = e.call(this, t) || this;
                return r.supportsMasking = !0, r
            }
            return __extends$27(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 r = [t[0]].concat(t.slice(2));
                this.layer.built || (this.layer.build(r), 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)),
                    r = this.layer.computeOutputShape(t),
                    n = e[1];
                return [r[0], n].concat(r.slice(1))
            }, t.prototype.call = function(e, t) {
                var r = this;
                return tidy(function() {
                    return rnn(function(e, n) {
                        return [r.layer.call(e, t), []]
                    }, e = getExactlyOneTensor(e), [], !1, null, null, !1, e.shape[1])[1]
                })
            }, t.className = "TimeDistributed", t
        }(Wrapper);
    SerializationMap.register(TimeDistributed);
    var VALID_BIDIRECTIONAL_MERGE_MODES = ["sum", "mul", "concat", "ave"],
        Bidirectional = function(e) {
            function t(t) {
                var r = e.call(this, t) || this,
                    n = t.layer.getConfig();
                if (r.forwardLayer = deserialize({
                        className: t.layer.getClassName(),
                        config: n
                    }), n.goBackwards = !0 !== n.goBackwards, r.backwardLayer = deserialize({
                        className: t.layer.getClassName(),
                        config: n
                    }), r.forwardLayer.name = "forward_" + r.forwardLayer.name, r.backwardLayer.name = "backward_" + r.backwardLayer.name, checkBidirectionalMergeMode(t.mergeMode), r.mergeMode = t.mergeMode, t.weights) throw new NotImplementedError("weights support is not implemented for Bidirectional layer yet.");
                return r._stateful = t.layer.stateful, r.returnSequences = t.layer.returnSequences, r.returnState = t.layer.returnState, r.supportsMasking = !0, r._trainable = !0, r.inputSpec = t.layer.inputSpec, r
            }
            return __extends$27(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,
                    r = Math.floor(t / 2);
                this.forwardLayer.setWeights(e.slice(0, r)), this.backwardLayer.setWeights(e.slice(r))
            }, t.prototype.computeOutputShape = function(e) {
                var t = this.forwardLayer.computeOutputShape(e);
                Array.isArray(t) && Array.isArray(t[0]) || (t = [t]), t = t;
                var r, n, a;
                return this.returnState ? (a = t.slice(1), r = t[0]) : r = t[0], r = r, "concat" === this.mergeMode ? (r[r.length - 1] *= 2, n = [r]) : n = null == this.mergeMode ? [r, r.slice()] : [r], this.returnState ? null == this.mergeMode ? n.concat(a).concat(a.slice()) : [r].concat(a).concat(a.slice()) : singletonOrArray(n)
            }, t.prototype.apply = function(t, r) {
                var n = null;
                if (null != r && (n = r.initialState), Array.isArray(t) && (n = t.slice(1), t = t[0]), null == n || 0 === n.length) return e.prototype.apply.call(this, t, r);
                throw new NotImplementedError("The support for initial states is not implemented for Bidirectional layers yet.")
            }, t.prototype.call = function(e, t) {
                var r = this;
                return tidy(function() {
                    if (null != t.mask) throw new NotImplementedError("The support for masking is not implemented for Bidirectional layers yet.");
                    if (null != t.initialState) throw new NotImplementedError("The support for initial states is not implemented for Bidirectional layers yet.");
                    var n, a = r.forwardLayer.call(e, t),
                        o = r.backwardLayer.call(e, t);
                    r.returnState && (Array.isArray(a) && (n = a.slice(1).concat(o.slice(1))), a = a[0], o = o[0]), r.returnSequences && (o = reverse(o, 1));
                    var i;
                    return "concat" === r.mergeMode ? i = concatenate([a, o]) : "sum" === r.mergeMode ? i = add(a, o) : "ave" === r.mergeMode ? i = scalarTimesArray(getScalar(.5), add(a, o)) : "mul" === r.mergeMode ? i = mul(a, o) : null == r.mergeMode && (i = [a, o]), r.returnState ? null == r.mergeMode ? i.concat(n) : [i].concat(n) : i
                })
            }, t.prototype.resetStates = function(e) {
                this.forwardLayer.resetStates(), this.backwardLayer.resetStates()
            }, t.prototype.build = function(e) {
                var t = this;
                nameScope$1(this.forwardLayer.name, function() {
                    t.forwardLayer.build(e)
                }), nameScope$1(this.backwardLayer.name, function() {
                    t.backwardLayer.build(e)
                }), this.built = !0
            }, Object.defineProperty(t.prototype, "trainableWeights", {
                get: function() {
                    return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)
                },
                enumerable: !0,
                configurable: !0
            }), Object.defineProperty(t.prototype, "nonTrainableWeights", {
                get: function() {
                    return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)
                },
                enumerable: !0,
                configurable: !0
            }), t.prototype.getConfig = function() {
                var t = {
                        mergeMode: this.mergeMode
                    },
                    r = e.prototype.getConfig.call(this);
                return Object.assign(t, r), t
            }, t.fromConfig = function(e, t) {
                var r = deserialize(t.layer);
                if (delete t.layer, null != t.numConstants) throw new NotImplementedError("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");
                var n = t;
                return n.layer = r, new e(n)
            }, t.className = "Bidirectional", t
        }(Wrapper);
    SerializationMap.register(Bidirectional);
    var __extends$28 = function() {
            var e = 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])
            };
            return function(t, r) {
                function n() {
                    this.constructor = t
                }
                e(t, r), t.prototype = null === r ? Object.create(r) : (n.prototype = r.prototype, new n)
            }
        }(),
        __decorate$38 = function(e, t, r, n) {
            var a, o = arguments.length,
                i = o < 3 ? t : null === n ? n = Object.getOwnPropertyDescriptor(t, r) : n;
            if ("object" == typeof Reflect && "function" == typeof Reflect.decorate) i = Reflect.decorate(e, t, r, n);
            else
                for (var s = e.length - 1; s >= 0; s--)(a = e[s]) && (i = (o < 3 ? a(i) : o > 3 ? a(t, r, i) : a(t, r)) || i);
            return o > 3 && i && Object.defineProperty(t, r, i), i
        },
        __awaiter$16 = function(e, t, r, n) {
            return new(r || (r = Promise))(function(a, o) {
                function i(e) {
                    try {
                        u(n.next(e))
                    } catch (e) {
                        o(e)
                    }
                }

                function s(e) {
                    try {
                        u(n.throw(e))
                    } catch (e) {
                        o(e)
                    }
                }

                function u(e) {
                    e.done ? a(e.value) : new r(function(t) {
                        t(e.value)
                    }).then(i, s)
                }
                u((n = n.apply(e, t || [])).next())
            })
        },
        __generator$16 = function(e, t) {
            function r(e) {
                return function(t) {
                    return n([e, t])
                }
            }

            function n(r) {
                if (a) throw new TypeError("Generator is already executing.");
                for (; u;) try {
                    if (a = 1, o && (i = o[2 & r[0] ? "return" : r[0] ? "throw" : "next"]) && !(i = i.call(o, r[1])).done) return i;
                    switch (o = 0, i && (r = [0, i.value]), r[0]) {
                        case 0:
                        case 1:
                            i = r;
                            break;
                        case 4:
                            return u.label++, {
                                value: r[1],
                                done: !1
                            };
                        case 5:
                            u.label++, o = r[1], r = [0];
                            continue;
                        case 7:
                            r = u.ops.pop(), u.trys.pop();
                            continue;
                        default:
                            if (i = u.trys, !(i = i.length > 0 && i[i.length - 1]) && (6 === r[0] || 2 === r[0])) {
                                u = 0;
                                continue
                            }
                            if (3 === r[0] && (!i || r[1] > i[0] && r[1] < i[3])) {
                                u.label = r[1];
                                break
                            }
                            if (6 === r[0] && u.label < i[1]) {
                                u.label = i[1], i = r;
                                break
                            }
                            if (i && u.label < i[2]) {
                                u.label = i[2], u.ops.push(r);
                                break
                            }
                            i[2] && u.ops.pop(), u.trys.pop();
                            continue
                    }
                    r = t.call(e, u)
                } catch (e) {
                    r = [6, e], o = 0
                } finally {
                    a = i = 0
                }
                if (5 & r[0]) throw r[1];
                return {
                    value: r[0] ? r[1] : void 0,
                    done: !0
                }
            }
            var a, o, i, s, u = {
                label: 0,
                sent: function() {
                    if (1 & i[0]) throw i[1];
                    return i[1]
                },
                trys: [],
                ops: []
            };
            return s = {
                next: r(0),
                throw: r(1),
                return: r(2)
            }, "function" == typeof Symbol && (s[Symbol.iterator] = function() {
                return this
            }), s
        },
        Sequential = function(e) {
            function t(t) {
                var r = e.call(this, {
                    inputs: [],
                    outputs: []
                }) || this;
                if (t = t || {}, r.trainable = !0, r._updatable = !0, r.built = !1, r.name = null != t.name ? t.name : getUid("sequential_"), null != t.layers)
                    for (var n = 0, a = t.layers; n < a.length; n++) {
                        var o = a[n];
                        r.add(o)
                    }
                return r
            }
            return __extends$28(t, e), r = t, t.prototype.add = function(e) {
                var t, n = e instanceof r || e instanceof Model;
                if (n) {
                    if (1 !== (t = 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 !== t.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 a = Input({
                            batchShape: e.batchInputShape,
                            dtype: e.dtype,
                            name: e.name + "_input"
                        });
                        e.apply(a)
                    }
                    if (n) this.outputs = t.outputs, this.inputs = t.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.outputs = [e.inboundNodes[0].outputTensors[0]], this.inputs = getSourceInputs(this.outputs[0])
                    }
                    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 o = e.apply(this.outputs[0]);
                    if (Array.isArray(o)) throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");
                    this.outputs = [o], 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.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, r) {
                if (void 0 === r && (r = {}), !this.built) throw new RuntimeError("The model needs to be compiled before being used.");
                return this.model.evaluate(e, t, r)
            }, 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, r) {
                return void 0 === r && (r = {}), __awaiter$16(this, void 0, void 0, function() {
                    return __generator$16(this, function(n) {
                        if (!this.built) throw new RuntimeError("The model needs to be compiled before being used.");
                        return [2, this.model.fit(e, t, r)]
                    })
                })
            }, t.fromConfig = function(e, t) {
                var n = new e({});
                if (!(n instanceof r)) throw new ValueError("Sequential.fromConfig called on non-Sequential input: " + n);
                if (!(t instanceof Array)) throw new ValueError("Sequential.fromConfig called without an array of configs");
                if (null == t[0].className || "Merge" === t[0].className) throw new ValueError("Legacy serialization format not supported yet.");
                for (var a = 0, o = t; a < o.length; a++) {
                    var i = deserialize(o[a]);
                    n.add(i)
                }
                return n
            }, t.prototype.getConfig = function() {
                for (var e = [], t = 0, r = this.layers; t < r.length; t++) {
                    var n = r[t];
                    e.push({
                        className: n.getClassName(),
                        config: n.getConfig()
                    })
                }
                return e
            }, t.className = "Sequential", __decorate$38([doc()], t.prototype, "add", null), __decorate$38([doc()], t.prototype, "evaluate", null), __decorate$38([doc()], t.prototype, "predict", null), __decorate$38([doc()], t.prototype, "fit", null), t = r = __decorate$38([doc()], t);
            var r
        }(Model);
    SerializationMap.register(Sequential);
    var __decorate$39 = function(e, t, r, n) {
            var a, o = arguments.length,
                i = o < 3 ? t : null === n ? n = Object.getOwnPropertyDescriptor(t, r) : n;
            if ("object" == typeof Reflect && "function" == typeof Reflect.decorate) i = Reflect.decorate(e, t, r, n);
            else
                for (var s = e.length - 1; s >= 0; s--)(a = e[s]) && (i = (o < 3 ? a(i) : o > 3 ? a(t, r, i) : a(t, r)) || i);
            return o > 3 && i && Object.defineProperty(t, r, i), i
        },
        ModelExports = function() {
            function e() {}
            return e.model = function(e) {
                return new Model(e)
            }, e.sequential = function(e) {
                return new Sequential(e)
            }, e.loadModel = function(e) {
                return loadModelInternal(e)
            }, e.input = function(e) {
                return Input(e)
            }, __decorate$39([doc()], e, "model", null), __decorate$39([doc()], e, "sequential", null), __decorate$39([doc()], e, "loadModel", null), __decorate$39([doc()], e, "input", null), e
        }(),
        LayerExports = function() {
            function e() {}
            return e.inputLayer = function(e) {
                return new InputLayer(e)
            }, e.elu = function(e) {
                return new ELU$1(e)
            }, e.leakyReLU = function(e) {
                return new LeakyReLU(e)
            }, e.softmax = function(e) {
                return new Softmax$1(e)
            }, e.thresholdedReLU = function(e) {
                return new ThresholdedReLU(e)
            }, e.conv1d = function(e) {
                return new Conv1D(e)
            }, e.conv2d = function(e) {
                return new Conv2D(e)
            }, e.conv2dTranspose = function(e) {
                return new Conv2DTranspose(e)
            }, e.separableConv2d = function(e) {
                return new SeparableConv2D(e)
            }, e.cropping2D = function(e) {
                return new Cropping2D(e)
            }, e.upSampling2d = function(e) {
                return new UpSampling2D(e)
            }, e.depthwiseConv2d = function(e) {
                return new DepthwiseConv2D(e)
            }, e.activation = function(e) {
                return new Activation$1(e)
            }, e.dense = function(e) {
                return new Dense(e)
            }, e.dropout = function(e) {
                return new Dropout(e)
            }, e.flatten = function(e) {
                return new Flatten(e)
            }, e.repeatVector = function(e) {
                return new RepeatVector(e)
            }, e.reshape = function(e) {
                return new Reshape(e)
            }, e.embedding = function(e) {
                return new Embedding(e)
            }, e.add = function(e) {
                return new Add(e)
            }, e.average = function(e) {
                return new Average(e)
            }, e.concatenate = function(e) {
                return new Concatenate(e)
            }, e.maximum = function(e) {
                return new Maximum(e)
            }, e.minimum = function(e) {
                return new Minimum(e)
            }, e.multiply = function(e) {
                return new Multiply(e)
            }, e.batchNormalization = function(e) {
                return new BatchNormalization(e)
            }, e.zeroPadding2d = function(e) {
                return new ZeroPadding2D(e)
            }, e.averagePooling1d = function(e) {
                return new AveragePooling1D(e)
            }, e.avgPool1d = function(t) {
                return e.averagePooling1d(t)
            }, e.avgPooling1d = function(t) {
                return e.averagePooling1d(t)
            }, e.averagePooling2d = function(e) {
                return new AveragePooling2D(e)
            }, e.avgPool2d = function(t) {
                return e.averagePooling2d(t)
            }, e.avgPooling2d = function(t) {
                return e.averagePooling2d(t)
            }, e.globalAveragePooling1d = function(e) {
                return new GlobalAveragePooling1D(e)
            }, e.globalAveragePooling2d = function(e) {
                return new GlobalAveragePooling2D(e)
            }, e.globalMaxPooling1d = function(e) {
                return new GlobalMaxPooling1D(e)
            }, e.globalMaxPooling2d = function(e) {
                return new GlobalMaxPooling2D(e)
            }, e.maxPooling1d = function(e) {
                return new MaxPooling1D(e)
            }, e.maxPooling2d = function(e) {
                return new MaxPooling2D(e)
            }, e.gru = function(e) {
                return new GRU(e)
            }, e.gruCell = function(e) {
                return new GRUCell(e)
            }, e.lstm = function(e) {
                return new LSTM(e)
            }, e.lstmCell = function(e) {
                return new LSTMCell(e)
            }, e.simpleRNN = function(e) {
                return new SimpleRNN(e)
            }, e.simpleRNNCell = function(e) {
                return new SimpleRNNCell(e)
            }, e.rnn = function(e) {
                return new RNN(e)
            }, e.stackedRNNCells = function(e) {
                return new StackedRNNCells(e)
            }, e.bidirectional = function(e) {
                return new Bidirectional(e)
            }, e.timeDistributed = function(e) {
                return new TimeDistributed(e)
            }, e.Layer = Layer, e.RNN = RNN, e.RNNCell = RNNCell, e.input = ModelExports.input, __decorate$39([doc()], e, "inputLayer", null), __decorate$39([doc()], e, "elu", null), __decorate$39([doc()], e, "leakyReLU", null), __decorate$39([doc()], e, "softmax", null), __decorate$39([doc()], e, "thresholdedReLU", null), __decorate$39([doc()], e, "conv1d", null), __decorate$39([doc()], e, "conv2d", null), __decorate$39([doc()], e, "conv2dTranspose", null), __decorate$39([doc()], e, "separableConv2d", null), __decorate$39([doc()], e, "cropping2D", null), __decorate$39([doc()], e, "upSampling2d", null), __decorate$39([doc()], e, "depthwiseConv2d", null), __decorate$39([doc()], e, "activation", null), __decorate$39([doc()], e, "dense", null), __decorate$39([doc()], e, "dropout", null), __decorate$39([doc()], e, "flatten", null), __decorate$39([doc()], e, "repeatVector", null), __decorate$39([doc()], e, "reshape", null), __decorate$39([doc()], e, "embedding", null), __decorate$39([doc()], e, "add", null), __decorate$39([doc()], e, "average", null), __decorate$39([doc()], e, "concatenate", null), __decorate$39([doc()], e, "maximum", null), __decorate$39([doc()], e, "minimum", null), __decorate$39([doc()], e, "multiply", null), __decorate$39([doc()], e, "batchNormalization", null), __decorate$39([doc()], e, "zeroPadding2d", null), __decorate$39([doc()], e, "averagePooling1d", null), __decorate$39([doc()], e, "averagePooling2d", null), __decorate$39([doc()], e, "globalAveragePooling1d", null), __decorate$39([doc()], e, "globalAveragePooling2d", null), __decorate$39([doc()], e, "globalMaxPooling1d", null), __decorate$39([doc()], e, "globalMaxPooling2d", null), __decorate$39([doc()], e, "maxPooling1d", null), __decorate$39([doc()], e, "maxPooling2d", null), __decorate$39([doc()], e, "gru", null), __decorate$39([doc()], e, "gruCell", null), __decorate$39([doc()], e, "lstm", null), __decorate$39([doc()], e, "lstmCell", null), __decorate$39([doc()], e, "simpleRNN", null), __decorate$39([doc()], e, "simpleRNNCell", null), __decorate$39([doc()], e, "rnn", null), __decorate$39([doc()], e, "stackedRNNCells", null), __decorate$39([doc()], e, "bidirectional", null), __decorate$39([doc()], e, "timeDistributed", null), e
        }(),
        ConstraintExports = function() {
            function e() {}
            return e.maxNorm = function(e) {
                return new MaxNorm(e)
            }, e.unitNorm = function(e) {
                return new UnitNorm(e)
            }, e.nonNeg = function() {
                return new NonNeg
            }, e.minMaxNorm = function(e) {
                return new MinMaxNorm(e)
            }, __decorate$39([doc()], e, "maxNorm", null), __decorate$39([doc()], e, "unitNorm", null), __decorate$39([doc()], e, "nonNeg", null), __decorate$39([doc()], e, "minMaxNorm", null), e
        }(),
        InitializerExports = function() {
            function e() {}
            return e.zeros = function() {
                return new Zeros
            }, e.ones = function() {
                return new Ones
            }, e.constant = function(e) {
                return new Constant(e)
            }, e.randomUniform = function(e) {
                return new RandomUniform(e)
            }, e.randomNormal = function(e) {
                return new RandomNormal(e)
            }, e.truncatedNormal = function(e) {
                return new TruncatedNormal(e)
            }, e.identity = function(e) {
                return new Identity(e)
            }, e.varianceScaling = function(e) {
                return new VarianceScaling(e)
            }, e.glorotUniform = function(e) {
                return new GlorotUniform(e)
            }, e.glorotNormal = function(e) {
                return new GlorotNormal(e)
            }, e.heNormal = function(e) {
                return new HeNormal(e)
            }, e.leCunNormal = function(e) {
                return new LeCunNormal(e)
            }, e.orthogonal = function(e) {
                return new Orthogonal(e)
            }, __decorate$39([doc()], e, "zeros", null), __decorate$39([doc()], e, "ones", null), __decorate$39([doc()], e, "constant", null), __decorate$39([doc()], e, "randomUniform", null), __decorate$39([doc()], e, "randomNormal", null), __decorate$39([doc()], e, "truncatedNormal", null), __decorate$39([doc()], e, "identity", null), __decorate$39([doc()], e, "varianceScaling", null), __decorate$39([doc()], e, "glorotUniform", null), __decorate$39([doc()], e, "glorotNormal", null), __decorate$39([doc()], e, "heNormal", null), __decorate$39([doc()], e, "leCunNormal", null), __decorate$39([doc()], e, "orthogonal", null), e
        }(),
        MetricExports = function() {
            function e() {}
            return e.binaryAccuracy = function(e, t) {
                return binaryAccuracy(e, t)
            }, e.binaryCrossentropy = function(e, t) {
                return binaryCrossentropy$1(e, t)
            }, e.categoricalAccuracy = function(e, t) {
                return categoricalAccuracy(e, t)
            }, e.categoricalCrossentropy = function(e, t) {
                return categoricalCrossentropy(e, t)
            }, e.cosineProximity = function(e, t) {
                return cosineProximity(e, t)
            }, e.prototype.meanAbsoluteError = function(e, t) {
                return meanAbsoluteError(e, t)
            }, e.prototype.meanAbsolutePercentageError = function(e, t) {
                return meanAbsolutePercentageError(e, t)
            }, e.prototype.MAPE = function(e, t) {
                return meanAbsolutePercentageError(e, t)
            }, e.prototype.mape = function(e, t) {
                return meanAbsolutePercentageError(e, t)
            }, e.meanSquaredError = function(e, t) {
                return meanSquaredError(e, t)
            }, e.MSE = function(e, t) {
                return meanSquaredError(e, t)
            }, e.mse = function(e, t) {
                return meanSquaredError(e, t)
            }, __decorate$39([doc()], e.prototype, "meanAbsoluteError", null), __decorate$39([doc()], e.prototype, "meanAbsolutePercentageError", null), __decorate$39([doc()], e, "binaryAccuracy", null), __decorate$39([doc()], e, "binaryCrossentropy", null), __decorate$39([doc()], e, "categoricalAccuracy", null), __decorate$39([doc()], e, "categoricalCrossentropy", null), __decorate$39([doc()], e, "cosineProximity", null), __decorate$39([doc()], e, "meanSquaredError", null), e
        }(),
        RegularizerExports = function() {
            function e() {}
            return e.l1l2 = function(e) {
                return new L1L2(e)
            }, e.l1 = function(e) {
                return l1(e)
            }, e.l2 = function(e) {
                return l2(e)
            }, __decorate$39([doc()], e, "l1l2", null), __decorate$39([doc()], e, "l1", null), __decorate$39([doc()], e, "l2", null), e
        }(),
        model = ModelExports.model,
        sequential = ModelExports.sequential,
        loadModel = ModelExports.loadModel,
        input = ModelExports.input,
        layers = LayerExports,
        constraints = ConstraintExports,
        initializers = InitializerExports,
        metrics = MetricExports,
        regularizers = RegularizerExports,
        punycode = createCommonjsModule(function(e, t) {
            ! function(r) {
                function n(e) {
                    throw RangeError(P[e])
                }

                function a(e, t) {
                    for (var r = e.length, n = []; r--;) n[r] = t(e[r]);
                    return n
                }

                function o(e, t) {
                    var r = e.split("@"),
                        n = "";
                    return r.length > 1 && (n = r[0] + "@", e = r[1]), n + a((e = e.replace(O, ".")).split("."), t).join(".")
                }

                function i(e) {
                    for (var t, r, n = [], a = 0, o = e.length; a < o;)(t = e.charCodeAt(a++)) >= 55296 && t <= 56319 && a < o ? 56320 == (64512 & (r = e.charCodeAt(a++))) ? n.push(((1023 & t) << 10) + (1023 & r) + 65536) : (n.push(t), a--) : n.push(t);
                    return n
                }

                function s(e) {
                    return a(e, function(e) {
                        var t = "";
                        return e > 65535 && (t += k((e -= 65536) >>> 10 & 1023 | 55296), e = 56320 | 1023 & e), t += k(e)
                    }).join("")
                }

                function u(e) {
                    return e - 48 < 10 ? e - 22 : e - 65 < 26 ? e - 65 : e - 97 < 26 ? e - 97 : b
                }

                function l(e, t) {
                    return e + 22 + 75 * (e < 26) - ((0 != t) << 5)
                }

                function c(e, t, r) {
                    var n = 0;
                    for (e = r ? C(e / S) : e >> 1, e += C(e / t); e > R * w >> 1; n += b) e = C(e / R);
                    return C(n + (R + 1) * e / (e + _))
                }

                function p(e) {
                    var t, r, a, o, i, l, p, d, h, f, m = [],
                        g = e.length,
                        y = 0,
                        _ = A,
                        S = N;
                    for ((r = e.lastIndexOf(E)) < 0 && (r = 0), a = 0; a < r; ++a) e.charCodeAt(a) >= 128 && n("not-basic"), m.push(e.charCodeAt(a));
                    for (o = r > 0 ? r + 1 : 0; o < g;) {
                        for (i = y, l = 1, p = b; o >= g && n("invalid-input"), ((d = u(e.charCodeAt(o++))) >= b || d > C((v - y) / l)) && n("overflow"), y += d * l, h = p <= S ? x : p >= S + w ? w : p - S, !(d < h); p += b) l > C(v / (f = b - h)) && n("overflow"), l *= f;
                        S = c(y - i, t = m.length + 1, 0 == i), C(y / t) > v - _ && n("overflow"), _ += C(y / t), y %= t, m.splice(y++, 0, _)
                    }
                    return s(m)
                }

                function d(e) {
                    var t, r, a, o, s, u, p, d, h, f, m, g, y, _, S, T = [];
                    for (g = (e = i(e)).length, t = A, r = 0, s = N, u = 0; u < g; ++u)(m = e[u]) < 128 && T.push(k(m));
                    for (a = o = T.length, o && T.push(E); a < g;) {
                        for (p = v, u = 0; u < g; ++u)(m = e[u]) >= t && m < p && (p = m);
                        for (p - t > C((v - r) / (y = a + 1)) && n("overflow"), r += (p - t) * y, t = p, u = 0; u < g; ++u)
                            if ((m = e[u]) < t && ++r > v && n("overflow"), m == t) {
                                for (d = r, h = b; f = h <= s ? x : h >= s + w ? w : h - s, !(d < f); h += b) S = d - f, _ = b - f, T.push(k(l(f + S % _, 0))), d = C(S / _);
                                T.push(k(l(d, 0))), s = c(r, y, a == o), r = 0, ++a
                            }++r, ++t
                    }
                    return T.join("")
                }
                var h = t && !t.nodeType && t,
                    f = e && !e.nodeType && e,
                    m = "object" == typeof commonjsGlobal && commonjsGlobal;
                m.global !== m && m.window !== m && m.self !== m || (r = m);
                var g, y, v = 2147483647,
                    b = 36,
                    x = 1,
                    w = 26,
                    _ = 38,
                    S = 700,
                    N = 72,
                    A = 128,
                    E = "-",
                    T = /^xn--/,
                    I = /[^\x20-\x7E]/,
                    O = /[\x2E\u3002\uFF0E\uFF61]/g,
                    P = {
                        overflow: "Overflow: input needs wider integers to process",
                        "not-basic": "Illegal input >= 0x80 (not a basic code point)",
                        "invalid-input": "Invalid input"
                    },
                    R = b - x,
                    C = Math.floor,
                    k = String.fromCharCode;
                if (g = {
                        version: "1.3.2",
                        ucs2: {
                            decode: i,
                            encode: s
                        },
                        decode: p,
                        encode: d,
                        toASCII: function(e) {
                            return o(e, function(e) {
                                return I.test(e) ? "xn--" + d(e) : e
                            })
                        },
                        toUnicode: function(e) {
                            return o(e, function(e) {
                                return T.test(e) ? p(e.slice(4).toLowerCase()) : e
                            })
                        }
                    }, h && f)
                    if (e.exports == h) f.exports = g;
                    else
                        for (y in g) g.hasOwnProperty(y) && (h[y] = g[y]);
                else r.punycode = g
            }(commonjsGlobal)
        }),
        util$1 = {
            isString: function(e) {
                return "string" == typeof e
            },
            isObject: function(e) {
                return "object" == typeof e && null !== e
            },
            isNull: function(e) {
                return null === e
            },
            isNullOrUndefined: function(e) {
                return null == e
            }
        },
        decode = function(e, t, r, n) {
            t = t || "&", r = r || "=";
            var a = {};
            if ("string" != typeof e || 0 === e.length) return a;
            var o = /\+/g;
            e = e.split(t);
            var i = 1e3;
            n && "number" == typeof n.maxKeys && (i = n.maxKeys);
            var s = e.length;
            i > 0 && s > i && (s = i);
            for (var u = 0; u < s; ++u) {
                var l, c, p, d, h = e[u].replace(o, "%20"),
                    f = h.indexOf(r);
                f >= 0 ? (l = h.substr(0, f), c = h.substr(f + 1)) : (l = h, c = ""), p = decodeURIComponent(l), d = decodeURIComponent(c), hasOwnProperty(a, p) ? Array.isArray(a[p]) ? a[p].push(d) : a[p] = [a[p], d] : a[p] = d
            }
            return a
        },
        stringifyPrimitive = function(e) {
            switch (typeof e) {
                case "string":
                    return e;
                case "boolean":
                    return e ? "true" : "false";
                case "number":
                    return isFinite(e) ? e : "";
                default:
                    return ""
            }
        },
        encode = function(e, t, r, n) {
            return t = t || "&", r = r || "=", null === e && (e = void 0), "object" == typeof e ? Object.keys(e).map(function(n) {
                var a = encodeURIComponent(stringifyPrimitive(n)) + r;
                return Array.isArray(e[n]) ? e[n].map(function(e) {
                    return a + encodeURIComponent(stringifyPrimitive(e))
                }).join(t) : a + encodeURIComponent(stringifyPrimitive(e[n]))
            }).join(t) : n ? encodeURIComponent(stringifyPrimitive(n)) + r + encodeURIComponent(stringifyPrimitive(e)) : ""
        },
        querystring = createCommonjsModule(function(e, t) {
            t.decode = t.parse = decode, t.encode = t.stringify = encode
        }),
        querystring_1 = querystring.decode,
        querystring_2 = querystring.parse,
        querystring_3 = querystring.encode,
        querystring_4 = querystring.stringify,
        parse = urlParse,
        format = urlFormat,
        protocolPattern = /^([a-z0-9.+-]+:)/i,
        portPattern = /:[0-9]*$/,
        simplePathPattern = /^(\/\/?(?!\/)[^\?\s]*)(\?[^\s]*)?$/,
        delims = ["<", ">", '"', "`", " ", "\r", "\n", "\t"],
        unwise = ["{", "}", "|", "\\", "^", "`"].concat(delims),
        autoEscape = ["'"].concat(unwise),
        nonHostChars = ["%", "/", "?", ";", "#"].concat(autoEscape),
        hostEndingChars = ["/", "?", "#"],
        hostnameMaxLen = 255,
        hostnamePartPattern = /^[+a-z0-9A-Z_-]{0,63}$/,
        hostnamePartStart = /^([+a-z0-9A-Z_-]{0,63})(.*)$/,
        unsafeProtocol = {
            javascript: !0,
            "javascript:": !0
        },
        hostlessProtocol = {
            javascript: !0,
            "javascript:": !0
        },
        slashedProtocol = {
            http: !0,
            https: !0,
            ftp: !0,
            gopher: !0,
            file: !0,
            "http:": !0,
            "https:": !0,
            "ftp:": !0,
            "gopher:": !0,
            "file:": !0
        };
    Url.prototype.parse = function(e, t, r) {
        if (!util$1.isString(e)) throw new TypeError("Parameter 'url' must be a string, not " + typeof e);
        var n = e.indexOf("?"),
            a = -1 !== n && n < e.indexOf("#") ? "?" : "#",
            o = e.split(a),
            i = /\\/g;
        o[0] = o[0].replace(i, "/");
        var s = e = o.join(a);
        if (s = s.trim(), !r && 1 === e.split("#").length) {
            var u = simplePathPattern.exec(s);
            if (u) return this.path = s, this.href = s, this.pathname = u[1], u[2] ? (this.search = u[2], this.query = t ? querystring.parse(this.search.substr(1)) : this.search.substr(1)) : t && (this.search = "", this.query = {}), this
        }
        var l = protocolPattern.exec(s);
        if (l) {
            var c = (l = l[0]).toLowerCase();
            this.protocol = c, s = s.substr(l.length)
        }
        if (r || l || s.match(/^\/\/[^@\/]+@[^@\/]+/)) {
            var p = "//" === s.substr(0, 2);
            !p || l && hostlessProtocol[l] || (s = s.substr(2), this.slashes = !0)
        }
        if (!hostlessProtocol[l] && (p || l && !slashedProtocol[l])) {
            for (var d = -1, h = 0; h < hostEndingChars.length; h++) - 1 !== (g = s.indexOf(hostEndingChars[h])) && (-1 === d || g < d) && (d = g);
            var f, m; - 1 !== (m = -1 === d ? s.lastIndexOf("@") : s.lastIndexOf("@", d)) && (f = s.slice(0, m), s = s.slice(m + 1), this.auth = decodeURIComponent(f)), d = -1;
            for (h = 0; h < nonHostChars.length; h++) {
                var g = s.indexOf(nonHostChars[h]); - 1 !== g && (-1 === d || g < d) && (d = g)
            } - 1 === d && (d = s.length), this.host = s.slice(0, d), s = s.slice(d), this.parseHost(), this.hostname = this.hostname || "";
            var y = "[" === this.hostname[0] && "]" === this.hostname[this.hostname.length - 1];
            if (!y)
                for (var v = this.hostname.split(/\./), h = 0, b = v.length; h < b; h++) {
                    var x = v[h];
                    if (x && !x.match(hostnamePartPattern)) {
                        for (var w = "", _ = 0, S = x.length; _ < S; _++) x.charCodeAt(_) > 127 ? w += "x" : w += x[_];
                        if (!w.match(hostnamePartPattern)) {
                            var N = v.slice(0, h),
                                A = v.slice(h + 1),
                                E = x.match(hostnamePartStart);
                            E && (N.push(E[1]), A.unshift(E[2])), A.length && (s = "/" + A.join(".") + s), this.hostname = N.join(".");
                            break
                        }
                    }
                }
            this.hostname.length > hostnameMaxLen ? this.hostname = "" : this.hostname = this.hostname.toLowerCase(), y || (this.hostname = punycode.toASCII(this.hostname));
            var T = this.port ? ":" + this.port : "",
                I = this.hostname || "";
            this.host = I + T, this.href += this.host, y && (this.hostname = this.hostname.substr(1, this.hostname.length - 2), "/" !== s[0] && (s = "/" + s))
        }
        if (!unsafeProtocol[c])
            for (var h = 0, b = autoEscape.length; h < b; h++) {
                var O = autoEscape[h];
                if (-1 !== s.indexOf(O)) {
                    var P = encodeURIComponent(O);
                    P === O && (P = escape(O)), s = s.split(O).join(P)
                }
            }
        var R = s.indexOf("#"); - 1 !== R && (this.hash = s.substr(R), s = s.slice(0, R));
        var C = s.indexOf("?");
        if (-1 !== C ? (this.search = s.substr(C), this.query = s.substr(C + 1), t && (this.query = querystring.parse(this.query)), s = s.slice(0, C)) : t && (this.search = "", this.query = {}), s && (this.pathname = s), slashedProtocol[c] && this.hostname && !this.pathname && (this.pathname = "/"), this.pathname || this.search) {
            var T = this.pathname || "",
                k = this.search || "";
            this.path = T + k
        }
        return this.href = this.format(), this
    }, Url.prototype.format = function() {
        var e = this.auth || "";
        e && (e = (e = encodeURIComponent(e)).replace(/%3A/i, ":"), e += "@");
        var t = this.protocol || "",
            r = this.pathname || "",
            n = this.hash || "",
            a = !1,
            o = "";
        this.host ? a = e + this.host : this.hostname && (a = e + (-1 === this.hostname.indexOf(":") ? this.hostname : "[" + this.hostname + "]"), this.port && (a += ":" + this.port)), this.query && util$1.isObject(this.query) && Object.keys(this.query).length && (o = querystring.stringify(this.query));
        var i = this.search || o && "?" + o || "";
        return t && ":" !== t.substr(-1) && (t += ":"), this.slashes || (!t || slashedProtocol[t]) && !1 !== a ? (a = "//" + (a || ""), r && "/" !== r.charAt(0) && (r = "/" + r)) : a || (a = ""), n && "#" !== n.charAt(0) && (n = "#" + n), i && "?" !== i.charAt(0) && (i = "?" + i), r = r.replace(/[?#]/g, function(e) {
            return encodeURIComponent(e)
        }), i = i.replace("#", "%23"), t + a + r + i + n
    }, Url.prototype.resolve = function(e) {
        return this.resolveObject(urlParse(e, !1, !0)).format()
    }, Url.prototype.resolveObject = function(e) {
        if (util$1.isString(e)) {
            var t = new Url;
            t.parse(e, !1, !0), e = t
        }
        for (var r = new Url, n = Object.keys(this), a = 0; a < n.length; a++) {
            var o = n[a];
            r[o] = this[o]
        }
        if (r.hash = e.hash, "" === e.href) return r.href = r.format(), r;
        if (e.slashes && !e.protocol) {
            for (var i = Object.keys(e), s = 0; s < i.length; s++) {
                var u = i[s];
                "protocol" !== u && (r[u] = e[u])
            }
            return slashedProtocol[r.protocol] && r.hostname && !r.pathname && (r.path = r.pathname = "/"), r.href = r.format(), r
        }
        if (e.protocol && e.protocol !== r.protocol) {
            if (!slashedProtocol[e.protocol]) {
                for (var l = Object.keys(e), c = 0; c < l.length; c++) {
                    var p = l[c];
                    r[p] = e[p]
                }
                return r.href = r.format(), r
            }
            if (r.protocol = e.protocol, e.host || hostlessProtocol[e.protocol]) r.pathname = e.pathname;
            else {
                for (b = (e.pathname || "").split("/"); b.length && !(e.host = b.shift()););
                e.host || (e.host = ""), e.hostname || (e.hostname = ""), "" !== b[0] && b.unshift(""), b.length < 2 && b.unshift(""), r.pathname = b.join("/")
            }
            if (r.search = e.search, r.query = e.query, r.host = e.host || "", r.auth = e.auth, r.hostname = e.hostname || e.host, r.port = e.port, r.pathname || r.search) {
                var d = r.pathname || "",
                    h = r.search || "";
                r.path = d + h
            }
            return r.slashes = r.slashes || e.slashes, r.href = r.format(), r
        }
        var f = r.pathname && "/" === r.pathname.charAt(0),
            m = e.host || e.pathname && "/" === e.pathname.charAt(0),
            g = m || f || r.host && e.pathname,
            y = g,
            v = r.pathname && r.pathname.split("/") || [],
            b = e.pathname && e.pathname.split("/") || [],
            x = r.protocol && !slashedProtocol[r.protocol];
        if (x && (r.hostname = "", r.port = null, r.host && ("" === v[0] ? v[0] = r.host : v.unshift(r.host)), r.host = "", e.protocol && (e.hostname = null, e.port = null, e.host && ("" === b[0] ? b[0] = e.host : b.unshift(e.host)), e.host = null), g = g && ("" === b[0] || "" === v[0])), m) r.host = e.host || "" === e.host ? e.host : r.host, r.hostname = e.hostname || "" === e.hostname ? e.hostname : r.hostname, r.search = e.search, r.query = e.query, v = b;
        else if (b.length) v || (v = []), v.pop(), v = v.concat(b), r.search = e.search, r.query = e.query;
        else if (!util$1.isNullOrUndefined(e.search)) return x && (r.hostname = r.host = v.shift(), (E = !!(r.host && r.host.indexOf("@") > 0) && r.host.split("@")) && (r.auth = E.shift(), r.host = r.hostname = E.shift())), r.search = e.search, r.query = e.query, util$1.isNull(r.pathname) && util$1.isNull(r.search) || (r.path = (r.pathname ? r.pathname : "") + (r.search ? r.search : "")), r.href = r.format(), r;
        if (!v.length) return r.pathname = null, r.search ? r.path = "/" + r.search : r.path = null, r.href = r.format(), r;
        for (var w = v.slice(-1)[0], _ = (r.host || e.host || v.length > 1) && ("." === w || ".." === w) || "" === w, S = 0, N = v.length; N >= 0; N--) "." === (w = v[N]) ? v.splice(N, 1) : ".." === w ? (v.splice(N, 1), S++) : S && (v.splice(N, 1), S--);
        if (!g && !y)
            for (; S--; S) v.unshift("..");
        !g || "" === v[0] || v[0] && "/" === v[0].charAt(0) || v.unshift(""), _ && "/" !== v.join("/").substr(-1) && v.push("");
        var A = "" === v[0] || v[0] && "/" === v[0].charAt(0);
        if (x) {
            r.hostname = r.host = A ? "" : v.length ? v.shift() : "";
            var E = !!(r.host && r.host.indexOf("@") > 0) && r.host.split("@");
            E && (r.auth = E.shift(), r.host = r.hostname = E.shift())
        }
        return (g = g || r.host && v.length) && !A && v.unshift(""), v.length ? r.pathname = v.join("/") : (r.pathname = null, r.path = null), util$1.isNull(r.pathname) && util$1.isNull(r.search) || (r.path = (r.pathname ? r.pathname : "") + (r.search ? r.search : "")), r.auth = e.auth || r.auth, r.slashes = r.slashes || e.slashes, r.href = r.format(), r
    }, Url.prototype.parseHost = function() {
        var e = this.host,
            t = portPattern.exec(e);
        t && (":" !== (t = t[0]) && (this.port = t.substr(1)), e = e.substr(0, e.length - t.length)), e && (this.hostname = e)
    };
    var aspromise = asPromise,
        base64_1 = createCommonjsModule(function(e, t) {
            var r = t;
            r.length = function(e) {
                var t = e.length;
                if (!t) return 0;
                for (var r = 0; --t % 4 > 1 && "=" === e.charAt(t);) ++r;
                return Math.ceil(3 * e.length) / 4 - r
            };
            for (var n = new Array(64), a = new Array(123), o = 0; o < 64;) a[n[o] = o < 26 ? o + 65 : o < 52 ? o + 71 : o < 62 ? o - 4 : o - 59 | 43] = o++;
            r.encode = function(e, t, r) {
                for (var a, o = null, i = [], s = 0, u = 0; t < r;) {
                    var l = e[t++];
                    switch (u) {
                        case 0:
                            i[s++] = n[l >> 2], a = (3 & l) << 4, u = 1;
                            break;
                        case 1:
                            i[s++] = n[a | l >> 4], a = (15 & l) << 2, u = 2;
                            break;
                        case 2:
                            i[s++] = n[a | l >> 6], i[s++] = n[63 & l], u = 0
                    }
                    s > 8191 && ((o || (o = [])).push(String.fromCharCode.apply(String, i)), s = 0)
                }
                return u && (i[s++] = n[a], i[s++] = 61, 1 === u && (i[s++] = 61)), o ? (s && o.push(String.fromCharCode.apply(String, i.slice(0, s))), o.join("")) : String.fromCharCode.apply(String, i.slice(0, s))
            };
            r.decode = function(e, t, r) {
                for (var n, o = r, i = 0, s = 0; s < e.length;) {
                    var u = e.charCodeAt(s++);
                    if (61 === u && i > 1) break;
                    if (void 0 === (u = a[u])) throw Error("invalid encoding");
                    switch (i) {
                        case 0:
                            n = u, i = 1;
                            break;
                        case 1:
                            t[r++] = n << 2 | (48 & u) >> 4, n = u, i = 2;
                            break;
                        case 2:
                            t[r++] = (15 & n) << 4 | (60 & u) >> 2, n = u, i = 3;
                            break;
                        case 3:
                            t[r++] = (3 & n) << 6 | u, i = 0
                    }
                }
                if (1 === i) throw Error("invalid encoding");
                return r - o
            }, r.test = function(e) {
                return /^(?:[A-Za-z0-9+/]{4})*(?:[A-Za-z0-9+/]{2}==|[A-Za-z0-9+/]{3}=)?$/.test(e)
            }
        }),
        eventemitter = EventEmitter;
    EventEmitter.prototype.on = function(e, t, r) {
        return (this._listeners[e] || (this._listeners[e] = [])).push({
            fn: t,
            ctx: r || this
        }), this
    }, EventEmitter.prototype.off = function(e, t) {
        if (void 0 === e) this._listeners = {};
        else if (void 0 === t) this._listeners[e] = [];
        else
            for (var r = this._listeners[e], n = 0; n < r.length;) r[n].fn === t ? r.splice(n, 1) : ++n;
        return this
    }, EventEmitter.prototype.emit = function(e) {
        var t = this._listeners[e];
        if (t) {
            for (var r = [], n = 1; n < arguments.length;) r.push(arguments[n++]);
            for (n = 0; n < t.length;) t[n].fn.apply(t[n++].ctx, r)
        }
        return this
    };
    var float_1 = factory(factory),
        inquire_1 = inquire,
        utf8_1 = createCommonjsModule(function(e, t) {
            var r = t;
            r.length = function(e) {
                for (var t = 0, r = 0, n = 0; n < e.length; ++n)(r = e.charCodeAt(n)) < 128 ? t += 1 : r < 2048 ? t += 2 : 55296 == (64512 & r) && 56320 == (64512 & e.charCodeAt(n + 1)) ? (++n, t += 4) : t += 3;
                return t
            }, r.read = function(e, t, r) {
                if (r - t < 1) return "";
                for (var n, a = null, o = [], i = 0; t < r;)(n = e[t++]) < 128 ? o[i++] = n : n > 191 && n < 224 ? o[i++] = (31 & n) << 6 | 63 & e[t++] : n > 239 && n < 365 ? (n = ((7 & n) << 18 | (63 & e[t++]) << 12 | (63 & e[t++]) << 6 | 63 & e[t++]) - 65536, o[i++] = 55296 + (n >> 10), o[i++] = 56320 + (1023 & n)) : o[i++] = (15 & n) << 12 | (63 & e[t++]) << 6 | 63 & e[t++], i > 8191 && ((a || (a = [])).push(String.fromCharCode.apply(String, o)), i = 0);
                return a ? (i && a.push(String.fromCharCode.apply(String, o.slice(0, i))), a.join("")) : String.fromCharCode.apply(String, o.slice(0, i))
            }, r.write = function(e, t, r) {
                for (var n, a, o = r, i = 0; i < e.length; ++i)(n = e.charCodeAt(i)) < 128 ? t[r++] = n : n < 2048 ? (t[r++] = n >> 6 | 192, t[r++] = 63 & n | 128) : 55296 == (64512 & n) && 56320 == (64512 & (a = e.charCodeAt(i + 1))) ? (n = 65536 + ((1023 & n) << 10) + (1023 & a), ++i, t[r++] = n >> 18 | 240, t[r++] = n >> 12 & 63 | 128, t[r++] = n >> 6 & 63 | 128, t[r++] = 63 & n | 128) : (t[r++] = n >> 12 | 224, t[r++] = n >> 6 & 63 | 128, t[r++] = 63 & n | 128);
                return r - o
            }
        }),
        pool_1 = pool,
        longbits = LongBits,
        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 r = e >>> 0,
            n = (e - r) / 4294967296 >>> 0;
        return t && (n = ~n >>> 0, r = ~r >>> 0, ++r > 4294967295 && (r = 0, ++n > 4294967295 && (n = 0))), new LongBits(r, n)
    }, 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,
                r = ~this.hi >>> 0;
            return t || (r = r + 1 >>> 0), -(t + 4294967296 * r)
        }
        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,
            r = this.hi >>> 24;
        return 0 === r ? 0 === t ? e < 16384 ? e < 128 ? 1 : 2 : e < 2097152 ? 3 : 4 : t < 16384 ? t < 128 ? 5 : 6 : t < 2097152 ? 7 : 8 : r < 128 ? 9 : 10
    };
    var minimal = createCommonjsModule(function(e, t) {
            function r(e, t, r) {
                for (var n = Object.keys(t), a = 0; a < n.length; ++a) void 0 !== e[n[a]] && r || (e[n[a]] = t[n[a]]);
                return e
            }

            function n(e) {
                function t(e, n) {
                    if (!(this instanceof t)) return new t(e, n);
                    Object.defineProperty(this, "message", {
                        get: function() {
                            return e
                        }
                    }), Error.captureStackTrace ? Error.captureStackTrace(this, t) : Object.defineProperty(this, "stack", {
                        value: (new Error).stack || ""
                    }), n && r(this, n)
                }
                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 + ": " + this.message
                }, t
            }
            var a = t;
            a.asPromise = aspromise, a.base64 = base64_1, a.EventEmitter = eventemitter, a.float = float_1, a.inquire = inquire_1, a.utf8 = utf8_1, a.pool = pool_1, a.LongBits = longbits, a.emptyArray = Object.freeze ? Object.freeze([]) : [], a.emptyObject = Object.freeze ? Object.freeze({}) : {}, a.isNode = Boolean(commonjsGlobal.process && commonjsGlobal.process.versions && commonjsGlobal.process.versions.node), a.isInteger = Number.isInteger || function(e) {
                return "number" == typeof e && isFinite(e) && Math.floor(e) === e
            }, a.isString = function(e) {
                return "string" == typeof e || e instanceof String
            }, a.isObject = function(e) {
                return e && "object" == typeof e
            }, a.isset = a.isSet = function(e, t) {
                var r = e[t];
                return !(null == r || !e.hasOwnProperty(t)) && ("object" != typeof r || (Array.isArray(r) ? r.length : Object.keys(r).length) > 0)
            }, a.Buffer = function() {
                try {
                    var e = a.inquire("buffer").Buffer;
                    return e.prototype.utf8Write ? e : null
                } catch (e) {
                    return null
                }
            }(), a._Buffer_from = null, a._Buffer_allocUnsafe = null, a.newBuffer = function(e) {
                return "number" == typeof 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.dcodeIO && commonjsGlobal.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 = {}, r = 0; r < e.length; ++r) t[e[r]] = 1;
                return function() {
                    for (var e = Object.keys(this), r = e.length - 1; r > -1; --r)
                        if (1 === t[e[r]] && void 0 !== this[e[r]] && null !== this[e[r]]) return e[r]
                }
            }, a.oneOfSetter = function(e) {
                return function(t) {
                    for (var r = 0; r < e.length; ++r) e[r] !== t && delete this[e[r]]
                }
            }, 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, r) {
                    return new e(t, r)
                }, 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;
    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, r) {
        return this.tail = this.tail.next = new Op(e, t, r), 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, r) {
        t.set(e, r)
    } : function(e, t, r) {
        for (var n = 0; n < e.length; ++n) t[r + n] = e[n]
    };
    Writer.prototype.bytes = function(e) {
        var t = e.length >>> 0;
        if (!t) return this._push(writeByte, 1, 0);
        if (minimal.isString(e)) {
            var r = Writer.alloc(t = base64.length(e));
            base64.decode(e, r, 0), e = r
        }
        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, 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, 0, 0), this.len = 0), this
    }, Writer.prototype.ldelim = function() {
        var e = this.head,
            t = this.tail,
            r = this.len;
        return this.reset().uint32(r), r && (this.tail.next = e.next, this.tail = t, this.len += r), this
    }, Writer.prototype.finish = function() {
        for (var e = this.head.next, t = this.constructor.alloc(this.len), r = 0; e;) e.fn(e.val, t, r), r += 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 = minimal.Buffer;
    BufferWriter$1.alloc = function(e) {
        return (BufferWriter$1.alloc = minimal._Buffer_allocUnsafe)(e)
    };
    var writeBytesBuffer = Buffer && Buffer.prototype instanceof Uint8Array && "set" === Buffer.prototype.set.name ? function(e, t, r) {
        t.set(e, r)
    } : function(e, t, r) {
        if (e.copy) e.copy(t, r, 0, e.length);
        else
            for (var n = 0; n < e.length;) t[r++] = e[n++]
    };
    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.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,
        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")
        };
    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,
            r = this.pos + e;
        if (r > this.len) throw indexOutOfRange(this, e);
        return this.pos += e, Array.isArray(this.buf) ? this.buf.slice(t, r) : t === r ? new this.buf.constructor(0) : this._slice.call(this.buf, t, r)
    }, 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;
    (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;
    (Service.prototype = Object.create(minimal.EventEmitter.prototype)).constructor = Service, Service.prototype.rpcCall = function e(t, r, n, a, o) {
        if (!a) throw TypeError("request must be specified");
        var i = this;
        if (!o) return minimal.asPromise(e, i, t, r, n, a);
        if (i.rpcImpl) try {
            return i.rpcImpl(t, r[i.requestDelimited ? "encodeDelimited" : "encode"](a).finish(), function(e, r) {
                if (e) return i.emit("error", e, t), o(e); {
                    if (null !== r) {
                        if (!(r instanceof n)) try {
                            r = n[i.responseDelimited ? "decodeDelimited" : "decode"](r)
                        } catch (e) {
                            return i.emit("error", e, t), o(e)
                        }
                        return i.emit("data", r, t), o(null, r)
                    }
                    i.end(!0)
                }
            })
        } catch (e) {
            return i.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(function(e, t) {
            t.Service = service
        }),
        roots = {},
        indexMinimal = createCommonjsModule(function(e, t) {
            function r() {
                n.Reader._configure(n.BufferReader), n.util._configure()
            }
            var n = t;
            n.build = "minimal", n.Writer = writer, n.BufferWriter = writer_buffer, n.Reader = reader, n.BufferReader = reader_buffer, n.util = minimal, n.rpc = rpc_1, n.roots = roots, n.configure = r, n.Writer._configure(n.BufferWriter), r()
        }),
        minimal$1 = indexMinimal,
        minimal_1 = minimal$1.roots,
        minimal_2 = minimal$1.Reader,
        minimal_3 = minimal$1.util,
        $Reader = minimal_2,
        $util = minimal_3,
        $root = minimal_1.default || (minimal_1.default = {}),
        tensorflow = $root.tensorflow = function() {
            var e = {};
            return e.Any = function() {
                function e(e) {
                    if (e)
                        for (var t = Object.keys(e), r = 0; r < t.length; ++r) null != e[t[r]] && (this[t[r]] = e[t[r]])
                }
                return e.prototype.typeUrl = "", e.prototype.value = $util.newBuffer([]), e.decode = function(e, t) {
                    e instanceof $Reader || (e = $Reader.create(e));
                    for (var r = void 0 === t ? e.len : e.pos + t, n = new $root.tensorflow.Any; e.pos < r;) {
                        var a = e.uint32();
                        switch (a >>> 3) {
                            case 1:
                                n.typeUrl = e.string();
                                break;
                            case 2:
                                n.value = e.bytes();
                                break;
                            default:
                                e.skipType(7 & a)
                        }
                    }
                    return n
                }, e
            }(), e.DataType = function() {
                var e = {},
                    t = Object.create(e);
                return t[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
            }(), e.TensorShape = function() {
                function e(e) {
                    if (this.dim = [], e)
                        for (var t = Object.keys(e), r = 0; r < t.length; ++r) null != e[t[r]] && (this[t[r]] = e[t[r]])
                }
                return e.prototype.dim = $util.emptyArray, e.prototype.unknownRank = !1, e.decode = function(e, t) {
                    e instanceof $Reader || (e = $Reader.create(e));
                    for (var r = void 0 === t ? e.len : e.pos + t, n = new $root.tensorflow.TensorShape; e.pos < r;) {
                        var a = e.uint32();
                        switch (a >>> 3) {
                            case 2:
                                n.dim && n.dim.length || (n.dim = []), n.dim.push($root.tensorflow.TensorShape.Dim.decode(e, e.uint32()));
                                break;
                            case 3:
                                n.unknownRank = e.bool();
                                break;
                            default:
                                e.skipType(7 & a)
                        }
                    }
                    return n
                }, e.Dim = function() {
                    function e(e) {
                        if (e)
                            for (var t = Object.keys(e), r = 0; r < t.length; ++r) null != e[t[r]] && (this[t[r]] = e[t[r]])
                    }
                    return e.prototype.size = $util.Long ? $util.Long.fromBits(0, 0, !1) : 0, e.prototype.name = "", e.decode = function(e, t) {
                        e instanceof $Reader || (e = $Reader.create(e));
                        for (var r = void 0 === t ? e.len : e.pos + t, n = new $root.tensorflow.TensorShape.Dim; e.pos < r;) {
                            var a = e.uint32();
                            switch (a >>> 3) {
                                case 1:
                                    n.size = e.int64();
                                    break;
                                case 2:
                                    n.name = e.string();
                                    break;
                                default:
                                    e.skipType(7 & a)
                            }
                        }
                        return n
                    }, e
                }(), e
            }(), e.Tensor = function() {
                function e(e) {
                    if (this.floatVal = [], this.doubleVal = [], this.intVal = [], this.stringVal = [], this.scomplexVal = [], this.int64Val = [], this.boolVal = [], this.uint32Val = [], this.uint64Val = [], e)
                        for (var t = Object.keys(e), r = 0; r < t.length; ++r) null != e[t[r]] && (this[t[r]] = e[t[r]])
                }
                return e.prototype.dtype = 0, e.prototype.tensorShape = null, e.prototype.versionNumber = 0, e.prototype.tensorContent = $util.newBuffer([]), e.prototype.floatVal = $util.emptyArray, e.prototype.doubleVal = $util.emptyArray, e.prototype.intVal = $util.emptyArray, e.prototype.stringVal = $util.emptyArray, e.prototype.scomplexVal = $util.emptyArray, e.prototype.int64Val = $util.emptyArray, e.prototype.boolVal = $util.emptyArray, e.prototype.uint32Val = $util.emptyArray, e.prototype.uint64Val = $util.emptyArray, e.decode = function(e, t) {
                    e instanceof $Reader || (e = $Reader.create(e));
                    for (var r = void 0 === t ? e.len : e.pos + t, n = new $root.tensorflow.Tensor; e.pos < r;) {
                        var a = e.uint32();
                        switch (a >>> 3) {
                            case 1:
                                n.dtype = e.int32();
                                break;
                            case 2:
                                n.tensorShape = $root.tensorflow.TensorShape.decode(e, e.uint32());
                                break;
                            case 3:
                                n.versionNumber = e.int32();
                                break;
                            case 4:
                                n.tensorContent = e.bytes();
                                break;
                            case 5:
                                if (n.floatVal && n.floatVal.length || (n.floatVal = []), 2 == (7 & a))
                                    for (o = e.uint32() + e.pos; e.pos < o;) n.floatVal.push(e.float());
                                else n.floatVal.push(e.float());
                                break;
                            case 6:
                                if (n.doubleVal && n.doubleVal.length || (n.doubleVal = []), 2 == (7 & a))
                                    for (o = e.uint32() + e.pos; e.pos < o;) n.doubleVal.push(e.double());
                                else n.doubleVal.push(e.double());
                                break;
                            case 7:
                                if (n.intVal && n.intVal.length || (n.intVal = []), 2 == (7 & a))
                                    for (o = e.uint32() + e.pos; e.pos < o;) n.intVal.push(e.int32());
                                else n.intVal.push(e.int32());
                                break;
                            case 8:
                                n.stringVal && n.stringVal.length || (n.stringVal = []), n.stringVal.push(e.bytes());
                                break;
                            case 9:
                                if (n.scomplexVal && n.scomplexVal.length || (n.scomplexVal = []), 2 == (7 & a))
                                    for (o = e.uint32() + e.pos; e.pos < o;) n.scomplexVal.push(e.float());
                                else n.scomplexVal.push(e.float());
                                break;
                            case 10:
                                if (n.int64Val && n.int64Val.length || (n.int64Val = []), 2 == (7 & a))
                                    for (o = e.uint32() + e.pos; e.pos < o;) n.int64Val.push(e.int64());
                                else n.int64Val.push(e.int64());
                                break;
                            case 11:
                                if (n.boolVal && n.boolVal.length || (n.boolVal = []), 2 == (7 & a))
                                    for (o = e.uint32() + e.pos; e.pos < o;) n.boolVal.push(e.bool());
                                else n.boolVal.push(e.bool());
                                break;
                            case 16:
                                if (n.uint32Val && n.uint32Val.length || (n.uint32Val = []), 2 == (7 & a))
                                    for (o = e.uint32() + e.pos; e.pos < o;) n.uint32Val.push(e.uint32());
                                else n.uint32Val.push(e.uint32());
                                break;
                            case 17:
                                if (n.uint64Val && n.uint64Val.length || (n.uint64Val = []), 2 == (7 & a))
                                    for (var o = e.uint32() + e.pos; e.pos < o;) n.uint64Val.push(e.uint64());
                                else n.uint64Val.push(e.uint64());
                                break;
                            default:
                                e.skipType(7 & a)
                        }
                    }
                    return n
                }, e
            }(), e.AttrValue = function() {
                function e(e) {
                    if (e)
                        for (var t = Object.keys(e), r = 0; r < t.length; ++r) null != e[t[r]] && (this[t[r]] = e[t[r]])
                }
                e.prototype.list = null, e.prototype.s = $util.newBuffer([]), e.prototype.i = $util.Long ? $util.Long.fromBits(0, 0, !1) : 0, e.prototype.f = 0, e.prototype.b = !1, e.prototype.type = 0, e.prototype.shape = null, e.prototype.tensor = null, e.prototype.placeholder = "", e.prototype.func = null;
                var t = void 0;
                return Object.defineProperty(e.prototype, "value", {
                    get: $util.oneOfGetter(t = ["list", "s", "i", "f", "b", "type", "shape", "tensor", "placeholder", "func"]),
                    set: $util.oneOfSetter(t)
                }), e.decode = function(e, t) {
                    e instanceof $Reader || (e = $Reader.create(e));
                    for (var r = void 0 === t ? e.len : e.pos + t, n = new $root.tensorflow.AttrValue; e.pos < r;) {
                        var a = e.uint32();
                        switch (a >>> 3) {
                            case 1:
                                n.list = $root.tensorflow.AttrValue.ListValue.decode(e, e.uint32());
                                break;
                            case 2:
                                n.s = e.bytes();
                                break;
                            case 3:
                                n.i = e.int64();
                                break;
                            case 4:
                                n.f = e.float();
                                break;
                            case 5:
                                n.b = e.bool();
                                break;
                            case 6:
                                n.type = e.int32();
                                break;
                            case 7:
                                n.shape = $root.tensorflow.TensorShape.decode(e, e.uint32());
                                break;
                            case 8:
                                n.tensor = $root.tensorflow.Tensor.decode(e, e.uint32());
                                break;
                            case 9:
                                n.placeholder = e.string();
                                break;
                            case 10:
                                n.func = $root.tensorflow.NameAttrList.decode(e, e.uint32());
                                break;
                            default:
                                e.skipType(7 & a)
                        }
                    }
                    return n
                }, 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), r = 0; r < t.length; ++r) null != e[t[r]] && (this[t[r]] = e[t[r]])
                    }
                    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 r = void 0 === t ? e.len : e.pos + t, n = new $root.tensorflow.AttrValue.ListValue; e.pos < r;) {
                            var a = e.uint32();
                            switch (a >>> 3) {
                                case 2:
                                    n.s && n.s.length || (n.s = []), n.s.push(e.bytes());
                                    break;
                                case 3:
                                    if (n.i && n.i.length || (n.i = []), 2 == (7 & a))
                                        for (o = e.uint32() + e.pos; e.pos < o;) n.i.push(e.int64());
                                    else n.i.push(e.int64());
                                    break;
                                case 4:
                                    if (n.f && n.f.length || (n.f = []), 2 == (7 & a))
                                        for (o = e.uint32() + e.pos; e.pos < o;) n.f.push(e.float());
                                    else n.f.push(e.float());
                                    break;
                                case 5:
                                    if (n.b && n.b.length || (n.b = []), 2 == (7 & a))
                                        for (o = e.uint32() + e.pos; e.pos < o;) n.b.push(e.bool());
                                    else n.b.push(e.bool());
                                    break;
                                case 6:
                                    if (n.type && n.type.length || (n.type = []), 2 == (7 & a))
                                        for (var o = e.uint32() + e.pos; e.pos < o;) n.type.push(e.int32());
                                    else n.type.push(e.int32());
                                    break;
                                case 7:
                                    n.shape && n.shape.length || (n.shape = []), n.shape.push($root.tensorflow.TensorShape.decode(e, e.uint32()));
                                    break;
                                case 8:
                                    n.tensor && n.tensor.length || (n.tensor = []), n.tensor.push($root.tensorflow.Tensor.decode(e, e.uint32()));
                                    break;
                                case 9:
                                    n.func && n.func.length || (n.func = []), n.func.push($root.tensorflow.NameAttrList.decode(e, e.uint32()));
                                    break;
                                default:
                                    e.skipType(7 & a)
                            }
                        }
                        return n
                    }, e
                }(), e
            }(), e.NameAttrList = function() {
                function e(e) {
                    if (this.attr = {}, e)
                        for (var t = Object.keys(e), r = 0; r < t.length; ++r) null != e[t[r]] && (this[t[r]] = e[t[r]])
                }
                return e.prototype.name = "", e.prototype.attr = $util.emptyObject, e.decode = function(e, t) {
                    e instanceof $Reader || (e = $Reader.create(e));
                    for (var r, n = void 0 === t ? e.len : e.pos + t, a = new $root.tensorflow.NameAttrList; e.pos < n;) {
                        var o = e.uint32();
                        switch (o >>> 3) {
                            case 1:
                                a.name = e.string();
                                break;
                            case 2:
                                e.skip().pos++, a.attr === $util.emptyObject && (a.attr = {}), r = e.string(), e.pos++, a.attr[r] = $root.tensorflow.AttrValue.decode(e, e.uint32());
                                break;
                            default:
                                e.skipType(7 & o)
                        }
                    }
                    return a
                }, e
            }(), e.NodeDef = function() {
                function e(e) {
                    if (this.input = [], this.attr = {}, e)
                        for (var t = Object.keys(e), r = 0; r < t.length; ++r) null != e[t[r]] && (this[t[r]] = e[t[r]])
                }
                return e.prototype.name = "", e.prototype.op = "", e.prototype.input = $util.emptyArray, e.prototype.device = "", e.prototype.attr = $util.emptyObject, e.decode = function(e, t) {
                    e instanceof $Reader || (e = $Reader.create(e));
                    for (var r, n = void 0 === t ? e.len : e.pos + t, a = new $root.tensorflow.NodeDef; e.pos < n;) {
                        var o = e.uint32();
                        switch (o >>> 3) {
                            case 1:
                                a.name = e.string();
                                break;
                            case 2:
                                a.op = e.string();
                                break;
                            case 3:
                                a.input && a.input.length || (a.input = []), a.input.push(e.string());
                                break;
                            case 4:
                                a.device = e.string();
                                break;
                            case 5:
                                e.skip().pos++, a.attr === $util.emptyObject && (a.attr = {}), r = e.string(), e.pos++, a.attr[r] = $root.tensorflow.AttrValue.decode(e, e.uint32());
                                break;
                            default:
                                e.skipType(7 & o)
                        }
                    }
                    return a
                }, e
            }(), e.VersionDef = function() {
                function e(e) {
                    if (this.badConsumers = [], e)
                        for (var t = Object.keys(e), r = 0; r < t.length; ++r) null != e[t[r]] && (this[t[r]] = e[t[r]])
                }
                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 r = void 0 === t ? e.len : e.pos + t, n = new $root.tensorflow.VersionDef; e.pos < r;) {
                        var a = e.uint32();
                        switch (a >>> 3) {
                            case 1:
                                n.producer = e.int32();
                                break;
                            case 2:
                                n.minConsumer = e.int32();
                                break;
                            case 3:
                                if (n.badConsumers && n.badConsumers.length || (n.badConsumers = []), 2 == (7 & a))
                                    for (var o = e.uint32() + e.pos; e.pos < o;) n.badConsumers.push(e.int32());
                                else n.badConsumers.push(e.int32());
                                break;
                            default:
                                e.skipType(7 & a)
                        }
                    }
                    return n
                }, e
            }(), e.GraphDef = function() {
                function e(e) {
                    if (this.node = [], e)
                        for (var t = Object.keys(e), r = 0; r < t.length; ++r) null != e[t[r]] && (this[t[r]] = e[t[r]])
                }
                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 r = void 0 === t ? e.len : e.pos + t, n = new $root.tensorflow.GraphDef; e.pos < r;) {
                        var a = e.uint32();
                        switch (a >>> 3) {
                            case 1:
                                n.node && n.node.length || (n.node = []), n.node.push($root.tensorflow.NodeDef.decode(e, e.uint32()));
                                break;
                            case 4:
                                n.versions = $root.tensorflow.VersionDef.decode(e, e.uint32());
                                break;
                            case 2:
                                n.library = $root.tensorflow.FunctionDefLibrary.decode(e, e.uint32());
                                break;
                            default:
                                e.skipType(7 & a)
                        }
                    }
                    return n
                }, e
            }(), e.CollectionDef = function() {
                function e(e) {
                    if (e)
                        for (var t = Object.keys(e), r = 0; r < t.length; ++r) null != e[t[r]] && (this[t[r]] = e[t[r]])
                }
                e.prototype.nodeList = null, e.prototype.bytesList = null, e.prototype.int64List = null, e.prototype.floatList = null, e.prototype.anyList = null;
                var t = void 0;
                return 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 r = void 0 === t ? e.len : e.pos + t, n = new $root.tensorflow.CollectionDef; e.pos < r;) {
                        var a = e.uint32();
                        switch (a >>> 3) {
                            case 1:
                                n.nodeList = $root.tensorflow.CollectionDef.NodeList.decode(e, e.uint32());
                                break;
                            case 2:
                                n.bytesList = $root.tensorflow.CollectionDef.BytesList.decode(e, e.uint32());
                                break;
                            case 3:
                                n.int64List = $root.tensorflow.CollectionDef.Int64List.decode(e, e.uint32());
                                break;
                            case 4:
                                n.floatList = $root.tensorflow.CollectionDef.FloatList.decode(e, e.uint32());
                                break;
                            case 5:
                                n.anyList = $root.tensorflow.CollectionDef.AnyList.decode(e, e.uint32());
                                break;
                            default:
                                e.skipType(7 & a)
                        }
                    }
                    return n
                }, e.NodeList = function() {
                    function e(e) {
                        if (this.value = [], e)
                            for (var t = Object.keys(e), r = 0; r < t.length; ++r) null != e[t[r]] && (this[t[r]] = e[t[r]])
                    }
                    return e.prototype.value = $util.emptyArray, e.decode = function(e, t) {
                        e instanceof $Reader || (e = $Reader.create(e));
                        for (var r = void 0 === t ? e.len : e.pos + t, n = new $root.tensorflow.CollectionDef.NodeList; e.pos < r;) {
                            var a = e.uint32();
                            switch (a >>> 3) {
                                case 1:
                                    n.value && n.value.length || (n.value = []), n.value.push(e.string());
                                    break;
                                default:
                                    e.skipType(7 & a)
                            }
                        }
                        return n
                    }, e
                }(), e.BytesList = function() {
                    function e(e) {
                        if (this.value = [], e)
                            for (var t = Object.keys(e), r = 0; r < t.length; ++r) null != e[t[r]] && (this[t[r]] = e[t[r]])
                    }
                    return e.prototype.value = $util.emptyArray, e.decode = function(e, t) {
                        e instanceof $Reader || (e = $Reader.create(e));
                        for (var r = void 0 === t ? e.len : e.pos + t, n = new $root.tensorflow.CollectionDef.BytesList; e.pos < r;) {
                            var a = e.uint32();
                            switch (a >>> 3) {
                                case 1:
                                    n.value && n.value.length || (n.value = []), n.value.push(e.bytes());
                                    break;
                                default:
                                    e.skipType(7 & a)
                            }
                        }
                        return n
                    }, e
                }(), e.Int64List = function() {
                    function e(e) {
                        if (this.value = [], e)
                            for (var t = Object.keys(e), r = 0; r < t.length; ++r) null != e[t[r]] && (this[t[r]] = e[t[r]])
                    }
                    return e.prototype.value = $util.emptyArray, e.decode = function(e, t) {
                        e instanceof $Reader || (e = $Reader.create(e));
                        for (var r = void 0 === t ? e.len : e.pos + t, n = new $root.tensorflow.CollectionDef.Int64List; e.pos < r;) {
                            var a = e.uint32();
                            switch (a >>> 3) {
                                case 1:
                                    if (n.value && n.value.length || (n.value = []), 2 == (7 & a))
                                        for (var o = e.uint32() + e.pos; e.pos < o;) n.value.push(e.int64());
                                    else n.value.push(e.int64());
                                    break;
                                default:
                                    e.skipType(7 & a)
                            }
                        }
                        return n
                    }, e
                }(), e.FloatList = function() {
                    function e(e) {
                        if (this.value = [], e)
                            for (var t = Object.keys(e), r = 0; r < t.length; ++r) null != e[t[r]] && (this[t[r]] = e[t[r]])
                    }
                    return e.prototype.value = $util.emptyArray, e.decode = function(e, t) {
                        e instanceof $Reader || (e = $Reader.create(e));
                        for (var r = void 0 === t ? e.len : e.pos + t, n = new $root.tensorflow.CollectionDef.FloatList; e.pos < r;) {
                            var a = e.uint32();
                            switch (a >>> 3) {
                                case 1:
                                    if (n.value && n.value.length || (n.value = []), 2 == (7 & a))
                                        for (var o = e.uint32() + e.pos; e.pos < o;) n.value.push(e.float());
                                    else n.value.push(e.float());
                                    break;
                                default:
                                    e.skipType(7 & a)
                            }
                        }
                        return n
                    }, e
                }(), e.AnyList = function() {
                    function e(e) {
                        if (this.value = [], e)
                            for (var t = Object.keys(e), r = 0; r < t.length; ++r) null != e[t[r]] && (this[t[r]] = e[t[r]])
                    }
                    return e.prototype.value = $util.emptyArray, e.decode = function(e, t) {
                        e instanceof $Reader || (e = $Reader.create(e));
                        for (var r = void 0 === t ? e.len : e.pos + t, n = new $root.tensorflow.CollectionDef.AnyList; e.pos < r;) {
                            var a = e.uint32();
                            switch (a >>> 3) {
                                case 1:
                                    n.value && n.value.length || (n.value = []), n.value.push($root.tensorflow.Any.decode(e, e.uint32()));
                                    break;
                                default:
                                    e.skipType(7 & a)
                            }
                        }
                        return n
                    }, e
                }(), e
            }(), e.SaverDef = function() {
                function e(e) {
                    if (e)
                        for (var t = Object.keys(e), r = 0; r < t.length; ++r) null != e[t[r]] && (this[t[r]] = e[t[r]])
                }
                return 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 r = void 0 === t ? e.len : e.pos + t, n = new $root.tensorflow.SaverDef; e.pos < r;) {
                        var a = e.uint32();
                        switch (a >>> 3) {
                            case 1:
                                n.filenameTensorName = e.string();
                                break;
                            case 2:
                                n.saveTensorName = e.string();
                                break;
                            case 3:
                                n.restoreOpName = e.string();
                                break;
                            case 4:
                                n.maxToKeep = e.int32();
                                break;
                            case 5:
                                n.sharded = e.bool();
                                break;
                            case 6:
                                n.keepCheckpointEveryNHours = e.float();
                                break;
                            case 7:
                                n.version = e.int32();
                                break;
                            default:
                                e.skipType(7 & a)
                        }
                    }
                    return n
                }, e.CheckpointFormatVersion = function() {
                    var e = {},
                        t = Object.create(e);
                    return t[e[0] = "LEGACY"] = 0, t[e[1] = "V1"] = 1, t[e[2] = "V2"] = 2, t
                }(), e
            }(), e.TensorInfo = function() {
                function e(e) {
                    if (e)
                        for (var t = Object.keys(e), r = 0; r < t.length; ++r) null != e[t[r]] && (this[t[r]] = e[t[r]])
                }
                e.prototype.name = "", e.prototype.cooSparse = null, e.prototype.dtype = 0, e.prototype.tensorShape = null;
                var t = void 0;
                return Object.defineProperty(e.prototype, "encoding", {
                    get: $util.oneOfGetter(t = ["name", "cooSparse"]),
                    set: $util.oneOfSetter(t)
                }), e.decode = function(e, t) {
                    e instanceof $Reader || (e = $Reader.create(e));
                    for (var r = void 0 === t ? e.len : e.pos + t, n = new $root.tensorflow.TensorInfo; e.pos < r;) {
                        var a = e.uint32();
                        switch (a >>> 3) {
                            case 1:
                                n.name = e.string();
                                break;
                            case 4:
                                n.cooSparse = $root.tensorflow.TensorInfo.CooSparse.decode(e, e.uint32());
                                break;
                            case 2:
                                n.dtype = e.int32();
                                break;
                            case 3:
                                n.tensorShape = $root.tensorflow.TensorShape.decode(e, e.uint32());
                                break;
                            default:
                                e.skipType(7 & a)
                        }
                    }
                    return n
                }, e.CooSparse = function() {
                    function e(e) {
                        if (e)
                            for (var t = Object.keys(e), r = 0; r < t.length; ++r) null != e[t[r]] && (this[t[r]] = e[t[r]])
                    }
                    return e.prototype.valuesTensorName = "", e.prototype.indicesTensorName = "", e.prototype.denseShapeTensorName = "", e.decode = function(e, t) {
                        e instanceof $Reader || (e = $Reader.create(e));
                        for (var r = void 0 === t ? e.len : e.pos + t, n = new $root.tensorflow.TensorInfo.CooSparse; e.pos < r;) {
                            var a = e.uint32();
                            switch (a >>> 3) {
                                case 1:
                                    n.valuesTensorName = e.string();
                                    break;
                                case 2:
                                    n.indicesTensorName = e.string();
                                    break;
                                case 3:
                                    n.denseShapeTensorName = e.string();
                                    break;
                                default:
                                    e.skipType(7 & a)
                            }
                        }
                        return n
                    }, e
                }(), e
            }(), e.SignatureDef = function() {
                function e(e) {
                    if (this.inputs = {}, this.outputs = {}, e)
                        for (var t = Object.keys(e), r = 0; r < t.length; ++r) null != e[t[r]] && (this[t[r]] = e[t[r]])
                }
                return e.prototype.inputs = $util.emptyObject, e.prototype.outputs = $util.emptyObject, e.prototype.methodName = "", e.decode = function(e, t) {
                    e instanceof $Reader || (e = $Reader.create(e));
                    for (var r, n = void 0 === t ? e.len : e.pos + t, a = new $root.tensorflow.SignatureDef; e.pos < n;) {
                        var o = e.uint32();
                        switch (o >>> 3) {
                            case 1:
                                e.skip().pos++, a.inputs === $util.emptyObject && (a.inputs = {}), r = e.string(), e.pos++, a.inputs[r] = $root.tensorflow.TensorInfo.decode(e, e.uint32());
                                break;
                            case 2:
                                e.skip().pos++, a.outputs === $util.emptyObject && (a.outputs = {}), r = e.string(), e.pos++, a.outputs[r] = $root.tensorflow.TensorInfo.decode(e, e.uint32());
                                break;
                            case 3:
                                a.methodName = e.string();
                                break;
                            default:
                                e.skipType(7 & o)
                        }
                    }
                    return a
                }, e
            }(), e.AssetFileDef = function() {
                function e(e) {
                    if (e)
                        for (var t = Object.keys(e), r = 0; r < t.length; ++r) null != e[t[r]] && (this[t[r]] = e[t[r]])
                }
                return e.prototype.tensorInfo = null, e.prototype.filename = "", e.decode = function(e, t) {
                    e instanceof $Reader || (e = $Reader.create(e));
                    for (var r = void 0 === t ? e.len : e.pos + t, n = new $root.tensorflow.AssetFileDef; e.pos < r;) {
                        var a = e.uint32();
                        switch (a >>> 3) {
                            case 1:
                                n.tensorInfo = $root.tensorflow.TensorInfo.decode(e, e.uint32());
                                break;
                            case 2:
                                n.filename = e.string();
                                break;
                            default:
                                e.skipType(7 & a)
                        }
                    }
                    return n
                }, e
            }(), e.OpDef = function() {
                function e(e) {
                    if (this.inputArg = [], this.outputArg = [], this.attr = [], e)
                        for (var t = Object.keys(e), r = 0; r < t.length; ++r) null != e[t[r]] && (this[t[r]] = e[t[r]])
                }
                return e.prototype.name = "", e.prototype.inputArg = $util.emptyArray, e.prototype.outputArg = $util.emptyArray, e.prototype.attr = $util.emptyArray, e.prototype.deprecation = null, e.prototype.summary = "", e.prototype.description = "", e.prototype.isCommutative = !1, e.prototype.isAggregate = !1, e.prototype.isStateful = !1, e.prototype.allowsUninitializedInput = !1, e.decode = function(e, t) {
                    e instanceof $Reader || (e = $Reader.create(e));
                    for (var r = void 0 === t ? e.len : e.pos + t, n = new $root.tensorflow.OpDef; e.pos < r;) {
                        var a = e.uint32();
                        switch (a >>> 3) {
                            case 1:
                                n.name = e.string();
                                break;
                            case 2:
                                n.inputArg && n.inputArg.length || (n.inputArg = []), n.inputArg.push($root.tensorflow.OpDef.ArgDef.decode(e, e.uint32()));
                                break;
                            case 3:
                                n.outputArg && n.outputArg.length || (n.outputArg = []), n.outputArg.push($root.tensorflow.OpDef.ArgDef.decode(e, e.uint32()));
                                break;
                            case 4:
                                n.attr && n.attr.length || (n.attr = []), n.attr.push($root.tensorflow.OpDef.AttrDef.decode(e, e.uint32()));
                                break;
                            case 8:
                                n.deprecation = $root.tensorflow.OpDef.OpDeprecation.decode(e, e.uint32());
                                break;
                            case 5:
                                n.summary = e.string();
                                break;
                            case 6:
                                n.description = e.string();
                                break;
                            case 18:
                                n.isCommutative = e.bool();
                                break;
                            case 16:
                                n.isAggregate = e.bool();
                                break;
                            case 17:
                                n.isStateful = e.bool();
                                break;
                            case 19:
                                n.allowsUninitializedInput = e.bool();
                                break;
                            default:
                                e.skipType(7 & a)
                        }
                    }
                    return n
                }, e.ArgDef = function() {
                    function e(e) {
                        if (e)
                            for (var t = Object.keys(e), r = 0; r < t.length; ++r) null != e[t[r]] && (this[t[r]] = e[t[r]])
                    }
                    return e.prototype.name = "", e.prototype.description = "", e.prototype.type = 0, e.prototype.typeAttr = "", e.prototype.numberAttr = "", e.prototype.typeListAttr = "", e.prototype.isRef = !1, e.decode = function(e, t) {
                        e instanceof $Reader || (e = $Reader.create(e));
                        for (var r = void 0 === t ? e.len : e.pos + t, n = new $root.tensorflow.OpDef.ArgDef; e.pos < r;) {
                            var a = e.uint32();
                            switch (a >>> 3) {
                                case 1:
                                    n.name = e.string();
                                    break;
                                case 2:
                                    n.description = e.string();
                                    break;
                                case 3:
                                    n.type = e.int32();
                                    break;
                                case 4:
                                    n.typeAttr = e.string();
                                    break;
                                case 5:
                                    n.numberAttr = e.string();
                                    break;
                                case 6:
                                    n.typeListAttr = e.string();
                                    break;
                                case 16:
                                    n.isRef = e.bool();
                                    break;
                                default:
                                    e.skipType(7 & a)
                            }
                        }
                        return n
                    }, e
                }(), e.AttrDef = function() {
                    function e(e) {
                        if (e)
                            for (var t = Object.keys(e), r = 0; r < t.length; ++r) null != e[t[r]] && (this[t[r]] = e[t[r]])
                    }
                    return e.prototype.name = "", e.prototype.type = "", e.prototype.defaultValue = null, e.prototype.description = "", e.prototype.hasMinimum = !1, e.prototype.minimum = $util.Long ? $util.Long.fromBits(0, 0, !1) : 0, e.prototype.allowedValues = null, e.decode = function(e, t) {
                        e instanceof $Reader || (e = $Reader.create(e));
                        for (var r = void 0 === t ? e.len : e.pos + t, n = new $root.tensorflow.OpDef.AttrDef; e.pos < r;) {
                            var a = e.uint32();
                            switch (a >>> 3) {
                                case 1:
                                    n.name = e.string();
                                    break;
                                case 2:
                                    n.type = e.string();
                                    break;
                                case 3:
                                    n.defaultValue = $root.tensorflow.AttrValue.decode(e, e.uint32());
                                    break;
                                case 4:
                                    n.description = e.string();
                                    break;
                                case 5:
                                    n.hasMinimum = e.bool();
                                    break;
                                case 6:
                                    n.minimum = e.int64();
                                    break;
                                case 7:
                                    n.allowedValues = $root.tensorflow.AttrValue.decode(e, e.uint32());
                                    break;
                                default:
                                    e.skipType(7 & a)
                            }
                        }
                        return n
                    }, e
                }(), e.OpDeprecation = function() {
                    function e(e) {
                        if (e)
                            for (var t = Object.keys(e), r = 0; r < t.length; ++r) null != e[t[r]] && (this[t[r]] = e[t[r]])
                    }
                    return e.prototype.version = 0, e.prototype.explanation = "", e.decode = function(e, t) {
                        e instanceof $Reader || (e = $Reader.create(e));
                        for (var r = void 0 === t ? e.len : e.pos + t, n = new $root.tensorflow.OpDef.OpDeprecation; e.pos < r;) {
                            var a = e.uint32();
                            switch (a >>> 3) {
                                case 1:
                                    n.version = e.int32();
                                    break;
                                case 2:
                                    n.explanation = e.string();
                                    break;
                                default:
                                    e.skipType(7 & a)
                            }
                        }
                        return n
                    }, e
                }(), e
            }(), e.OpList = function() {
                function e(e) {
                    if (this.op = [], e)
                        for (var t = Object.keys(e), r = 0; r < t.length; ++r) null != e[t[r]] && (this[t[r]] = e[t[r]])
                }
                return e.prototype.op = $util.emptyArray, e.decode = function(e, t) {
                    e instanceof $Reader || (e = $Reader.create(e));
                    for (var r = void 0 === t ? e.len : e.pos + t, n = new $root.tensorflow.OpList; e.pos < r;) {
                        var a = e.uint32();
                        switch (a >>> 3) {
                            case 1:
                                n.op && n.op.length || (n.op = []), n.op.push($root.tensorflow.OpDef.decode(e, e.uint32()));
                                break;
                            default:
                                e.skipType(7 & a)
                        }
                    }
                    return n
                }, e
            }(), e.MetaGraphDef = function() {
                function e(e) {
                    if (this.collectionDef = {}, this.signatureDef = {}, this.assetFileDef = [], e)
                        for (var t = Object.keys(e), r = 0; r < t.length; ++r) null != e[t[r]] && (this[t[r]] = e[t[r]])
                }
                return e.prototype.metaInfoDef = null, e.prototype.graphDef = null, e.prototype.saverDef = null, e.prototype.collectionDef = $util.emptyObject, e.prototype.signatureDef = $util.emptyObject, e.prototype.assetFileDef = $util.emptyArray, e.decode = function(e, t) {
                    e instanceof $Reader || (e = $Reader.create(e));
                    for (var r, n = void 0 === t ? e.len : e.pos + t, a = new $root.tensorflow.MetaGraphDef; e.pos < n;) {
                        var o = e.uint32();
                        switch (o >>> 3) {
                            case 1:
                                a.metaInfoDef = $root.tensorflow.MetaGraphDef.MetaInfoDef.decode(e, e.uint32());
                                break;
                            case 2:
                                a.graphDef = $root.tensorflow.GraphDef.decode(e, e.uint32());
                                break;
                            case 3:
                                a.saverDef = $root.tensorflow.SaverDef.decode(e, e.uint32());
                                break;
                            case 4:
                                e.skip().pos++, a.collectionDef === $util.emptyObject && (a.collectionDef = {}), r = e.string(), e.pos++, a.collectionDef[r] = $root.tensorflow.CollectionDef.decode(e, e.uint32());
                                break;
                            case 5:
                                e.skip().pos++, a.signatureDef === $util.emptyObject && (a.signatureDef = {}), r = e.string(), e.pos++, a.signatureDef[r] = $root.tensorflow.SignatureDef.decode(e, e.uint32());
                                break;
                            case 6:
                                a.assetFileDef && a.assetFileDef.length || (a.assetFileDef = []), a.assetFileDef.push($root.tensorflow.AssetFileDef.decode(e, e.uint32()));
                                break;
                            default:
                                e.skipType(7 & o)
                        }
                    }
                    return a
                }, e.MetaInfoDef = function() {
                    function e(e) {
                        if (this.tags = [], e)
                            for (var t = Object.keys(e), r = 0; r < t.length; ++r) null != e[t[r]] && (this[t[r]] = e[t[r]])
                    }
                    return e.prototype.metaGraphVersion = "", e.prototype.strippedOpList = null, e.prototype.anyInfo = null, e.prototype.tags = $util.emptyArray, e.prototype.tensorflowVersion = "", e.prototype.tensorflowGitVersion = "", e.decode = function(e, t) {
                        e instanceof $Reader || (e = $Reader.create(e));
                        for (var r = void 0 === t ? e.len : e.pos + t, n = new $root.tensorflow.MetaGraphDef.MetaInfoDef; e.pos < r;) {
                            var a = e.uint32();
                            switch (a >>> 3) {
                                case 1:
                                    n.metaGraphVersion = e.string();
                                    break;
                                case 2:
                                    n.strippedOpList = $root.tensorflow.OpList.decode(e, e.uint32());
                                    break;
                                case 3:
                                    n.anyInfo = $root.tensorflow.Any.decode(e, e.uint32());
                                    break;
                                case 4:
                                    n.tags && n.tags.length || (n.tags = []), n.tags.push(e.string());
                                    break;
                                case 5:
                                    n.tensorflowVersion = e.string();
                                    break;
                                case 6:
                                    n.tensorflowGitVersion = e.string();
                                    break;
                                default:
                                    e.skipType(7 & a)
                            }
                        }
                        return n
                    }, e
                }(), e
            }(), e.SavedModel = function() {
                function e(e) {
                    if (this.metaGraphs = [], e)
                        for (var t = Object.keys(e), r = 0; r < t.length; ++r) null != e[t[r]] && (this[t[r]] = e[t[r]])
                }
                return e.prototype.savedModelSchemaVersion = $util.Long ? $util.Long.fromBits(0, 0, !1) : 0, e.prototype.metaGraphs = $util.emptyArray, e.decode = function(e, t) {
                    e instanceof $Reader || (e = $Reader.create(e));
                    for (var r = void 0 === t ? e.len : e.pos + t, n = new $root.tensorflow.SavedModel; e.pos < r;) {
                        var a = e.uint32();
                        switch (a >>> 3) {
                            case 1:
                                n.savedModelSchemaVersion = e.int64();
                                break;
                            case 2:
                                n.metaGraphs && n.metaGraphs.length || (n.metaGraphs = []), n.metaGraphs.push($root.tensorflow.MetaGraphDef.decode(e, e.uint32()));
                                break;
                            default:
                                e.skipType(7 & a)
                        }
                    }
                    return n
                }, e
            }(), e.FunctionDefLibrary = function() {
                function e(e) {
                    if (this.function = [], this.gradient = [], e)
                        for (var t = Object.keys(e), r = 0; r < t.length; ++r) null != e[t[r]] && (this[t[r]] = e[t[r]])
                }
                return e.prototype.function = $util.emptyArray, e.prototype.gradient = $util.emptyArray, e.decode = function(e, t) {
                    e instanceof $Reader || (e = $Reader.create(e));
                    for (var r = void 0 === t ? e.len : e.pos + t, n = new $root.tensorflow.FunctionDefLibrary; e.pos < r;) {
                        var a = e.uint32();
                        switch (a >>> 3) {
                            case 1:
                                n.function && n.function.length || (n.function = []), n.function.push($root.tensorflow.FunctionDef.decode(e, e.uint32()));
                                break;
                            case 2:
                                n.gradient && n.gradient.length || (n.gradient = []), n.gradient.push($root.tensorflow.GradientDef.decode(e, e.uint32()));
                                break;
                            default:
                                e.skipType(7 & a)
                        }
                    }
                    return n
                }, e
            }(), e.FunctionDef = function() {
                function e(e) {
                    if (this.attr = {}, this.nodeDef = [], this.ret = {}, e)
                        for (var t = Object.keys(e), r = 0; r < t.length; ++r) null != e[t[r]] && (this[t[r]] = e[t[r]])
                }
                return e.prototype.signature = null, e.prototype.attr = $util.emptyObject, e.prototype.nodeDef = $util.emptyArray, e.prototype.ret = $util.emptyObject, e.decode = function(e, t) {
                    e instanceof $Reader || (e = $Reader.create(e));
                    for (var r, n = void 0 === t ? e.len : e.pos + t, a = new $root.tensorflow.FunctionDef; e.pos < n;) {
                        var o = e.uint32();
                        switch (o >>> 3) {
                            case 1:
                                a.signature = $root.tensorflow.OpDef.decode(e, e.uint32());
                                break;
                            case 5:
                                e.skip().pos++, a.attr === $util.emptyObject && (a.attr = {}), r = e.string(), e.pos++, a.attr[r] = $root.tensorflow.AttrValue.decode(e, e.uint32());
                                break;
                            case 3:
                                a.nodeDef && a.nodeDef.length || (a.nodeDef = []), a.nodeDef.push($root.tensorflow.NodeDef.decode(e, e.uint32()));
                                break;
                            case 4:
                                e.skip().pos++, a.ret === $util.emptyObject && (a.ret = {}), r = e.string(), e.pos++, a.ret[r] = e.string();
                                break;
                            default:
                                e.skipType(7 & o)
                        }
                    }
                    return a
                }, e
            }(), e.GradientDef = function() {
                function e(e) {
                    if (e)
                        for (var t = Object.keys(e), r = 0; r < t.length; ++r) null != e[t[r]] && (this[t[r]] = e[t[r]])
                }
                return e.prototype.functionName = "", e.prototype.gradientFunc = "", e.decode = function(e, t) {
                    e instanceof $Reader || (e = $Reader.create(e));
                    for (var r = void 0 === t ? e.len : e.pos + t, n = new $root.tensorflow.GradientDef; e.pos < r;) {
                        var a = e.uint32();
                        switch (a >>> 3) {
                            case 1:
                                n.functionName = e.string();
                                break;
                            case 2:
                                n.gradientFunc = e.string();
                                break;
                            default:
                                e.skipType(7 & a)
                        }
                    }
                    return n
                }, e
            }(), e
        }(),
        arithmetic = [{
            tfOpName: "Add",
            dlOpName: "add",
            category: "arithmetic",
            params: [{
                tfInputIndex: 0,
                dlParamName: "a",
                type: "tensor"
            }, {
                tfInputIndex: 1,
                dlParamName: "b",
                type: "tensor"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }, {
            tfOpName: "BiasAdd",
            dlOpName: "add",
            category: "arithmetic",
            params: [{
                tfInputIndex: 0,
                dlParamName: "a",
                type: "tensor"
            }, {
                tfInputIndex: 1,
                dlParamName: "b",
                type: "tensor"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }, {
            tfOpName: "Sub",
            dlOpName: "sub",
            category: "arithmetic",
            params: [{
                tfInputIndex: 0,
                dlParamName: "a",
                type: "tensor"
            }, {
                tfInputIndex: 1,
                dlParamName: "b",
                type: "tensor"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }, {
            tfOpName: "RealDiv",
            dlOpName: "div",
            category: "arithmetic",
            params: [{
                tfInputIndex: 0,
                dlParamName: "a",
                type: "tensor"
            }, {
                tfInputIndex: 1,
                dlParamName: "b",
                type: "tensor"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }, {
            tfOpName: "Div",
            dlOpName: "div",
            category: "arithmetic",
            params: [{
                tfInputIndex: 0,
                dlParamName: "a",
                type: "tensor"
            }, {
                tfInputIndex: 1,
                dlParamName: "b",
                type: "tensor"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }, {
            tfOpName: "Mul",
            dlOpName: "mul",
            category: "arithmetic",
            params: [{
                tfInputIndex: 0,
                dlParamName: "a",
                type: "tensor"
            }, {
                tfInputIndex: 1,
                dlParamName: "b",
                type: "tensor"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }, {
            tfOpName: "Maximum",
            dlOpName: "maximum",
            category: "arithmetic",
            params: [{
                tfInputIndex: 0,
                dlParamName: "a",
                type: "tensor"
            }, {
                tfInputIndex: 1,
                dlParamName: "b",
                type: "tensor"
            }]
        }, {
            tfOpName: "Minimum",
            dlOpName: "minimum",
            category: "arithmetic",
            params: [{
                tfInputIndex: 0,
                dlParamName: "a",
                type: "tensor"
            }, {
                tfInputIndex: 1,
                dlParamName: "b",
                type: "tensor"
            }]
        }, {
            tfOpName: "Pow",
            dlOpName: "pow",
            category: "arithmetic",
            params: [{
                tfInputIndex: 0,
                dlParamName: "a",
                type: "tensor"
            }, {
                tfInputIndex: 1,
                dlParamName: "b",
                type: "tensor"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }, {
            tfOpName: "SquaredDifference",
            dlOpName: "squaredDifference",
            category: "arithmetic",
            params: [{
                tfInputIndex: 0,
                dlParamName: "a",
                type: "tensor"
            }, {
                tfInputIndex: 1,
                dlParamName: "b",
                type: "tensor"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }, {
            tfOpName: "Mod",
            dlOpName: "mod",
            category: "arithmetic",
            params: [{
                tfInputIndex: 0,
                dlParamName: "a",
                type: "tensor"
            }, {
                tfInputIndex: 1,
                dlParamName: "b",
                type: "tensor"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }],
        arithmetic$1 = Object.freeze({
            default: arithmetic
        }),
        basic_math = [{
            tfOpName: "Abs",
            dlOpName: "abs",
            category: "basic_math",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }, {
            tfOpName: "Acos",
            dlOpName: "acos",
            category: "basic_math",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }, {
            tfOpName: "Asin",
            dlOpName: "asin",
            category: "basic_math",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }, {
            tfOpName: "atan",
            dlOpName: "atan",
            category: "basic_math",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }, {
            tfOpName: "Ceil",
            dlOpName: "ceil",
            category: "basic_math",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }, {
            tfOpName: "ClipByValue",
            dlOpName: "clipByValue",
            category: "basic_math",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfParamName: "clip_value_min",
                dlParamName: "clipValueMin",
                type: "number"
            }, {
                tfParamName: "clip_value_max",
                dlParamName: "clipValueMax",
                type: "number"
            }]
        }, {
            tfOpName: "Cos",
            dlOpName: "cos",
            category: "basic_math",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }, {
            tfOpName: "Cosh",
            dlOpName: "cosh",
            category: "basic_math",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }, {
            tfOpName: "Elu",
            dlOpName: "elu",
            category: "basic_math",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }, {
            tfOpName: "Exp",
            dlOpName: "exp",
            category: "basic_math",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }, {
            tfOpName: "Floor",
            dlOpName: "floor",
            category: "basic_math",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }, {
            tfOpName: "Log",
            dlOpName: "log",
            category: "basic_math",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }, {
            tfOpName: "Neg",
            dlOpName: "neg",
            category: "basic_math",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }, {
            tfOpName: "Relu",
            dlOpName: "relu",
            category: "basic_math",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }, {
            tfOpName: "Relu6",
            dlOpName: "clipByValue",
            category: "basic_math",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }, {
                dlParamName: "clipValueMin",
                type: "number",
                defaultValue: 0
            }, {
                dlParamName: "clipValueMax",
                type: "number",
                defaultValue: 6
            }]
        }, {
            tfOpName: "Selu",
            dlOpName: "selu",
            category: "basic_math",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }, {
            tfOpName: "Sigmoid",
            dlOpName: "sigmoid",
            category: "basic_math",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }, {
            tfOpName: "Sin",
            dlOpName: "sin",
            category: "basic_math",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }, {
            tfOpName: "Sinh",
            dlOpName: "sinh",
            category: "basic_math",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }, {
            tfOpName: "Sqrt",
            dlOpName: "sqrt",
            category: "basic_math",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }, {
            tfOpName: "Rsqrt",
            dlOpName: "rsqrt",
            category: "basic_math",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }, {
            tfOpName: "Square",
            dlOpName: "square",
            category: "basic_math",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }, {
            tfOpName: "Tan",
            dlOpName: "tan",
            category: "basic_math",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }, {
            tfOpName: "Tanh",
            dlOpName: "tanh",
            category: "basic_math",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }, {
            tfOpName: "Sign",
            dlOpName: "sign",
            category: "basic_math",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }, {
            tfOpName: "Round",
            dlOpName: "round",
            category: "basic_math",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }, {
            tfOpName: "Expm1",
            dlOpName: "expm1",
            category: "basic_math",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }, {
            tfOpName: "Log1p",
            dlOpName: "log1p",
            category: "basic_math",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }, {
            tfOpName: "Reciprocal",
            dlOpName: "reciprocal",
            category: "basic_math",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }, {
            tfOpName: "Reciprocal",
            dlOpName: "reciprocal",
            category: "basic_math",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }, {
            tfOpName: "Softplus",
            dlOpName: "softplus",
            category: "basic_math",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }, {
            tfOpName: "Asinh",
            dlOpName: "asinh",
            category: "basic_math",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }, {
            tfOpName: "Acosh",
            dlOpName: "acosh",
            category: "basic_math",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }, {
            tfOpName: "Atanh",
            dlOpName: "atanh",
            category: "basic_math",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }, {
            tfOpName: "Erf",
            dlOpName: "erf",
            category: "basic_math",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }],
        basicMath = Object.freeze({
            default: basic_math
        }),
        control = [{
            tfOpName: "LoopCond",
            dlOpName: "loopCond",
            category: "control",
            params: [{
                tfInputIndex: 0,
                dlParamName: "pred",
                type: "tensor"
            }]
        }, {
            tfOpName: "Switch",
            dlOpName: "switch",
            category: "control",
            params: [{
                tfInputIndex: 0,
                dlParamName: "data",
                type: "tensor"
            }, {
                tfInputIndex: 1,
                dlParamName: "pred",
                type: "tensor"
            }]
        }, {
            tfOpName: "Merge",
            dlOpName: "merge",
            category: "control",
            params: [{
                tfInputIndex: 0,
                tfInputParamLength: 0,
                dlParamName: "tensors",
                type: "tensors"
            }]
        }, {
            tfOpName: "Enter",
            dlOpName: "enter",
            category: "control",
            params: [{
                tfInputIndex: 0,
                dlParamName: "tensor",
                type: "tensor"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }, {
                tfParamName: "frame_name",
                dlParamName: "frameName",
                type: "string"
            }, {
                tfParamName: "is_constant",
                dlParamName: "isConstant",
                type: "bool"
            }]
        }, {
            tfOpName: "Exit",
            dlOpName: "exit",
            category: "control",
            params: [{
                tfInputIndex: 0,
                dlParamName: "tensor",
                type: "tensor"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }, {
            tfOpName: "NextIteration",
            dlOpName: "nextIteration",
            category: "control",
            params: [{
                tfInputIndex: 0,
                dlParamName: "tensor",
                type: "tensor"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }],
        control$1 = Object.freeze({
            default: control
        }),
        convolution = [{
            tfOpName: "AvgPool",
            dlOpName: "avgPool",
            category: "convolution",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfParamName: "strides",
                dlParamName: "strides",
                type: "number[]"
            }, {
                tfParamName: "padding",
                dlParamName: "pad",
                type: "string"
            }, {
                tfParamName: "data_format",
                dlParamName: "dataFormat",
                type: "string",
                notSupported: !0
            }, {
                tfParamName: "ksize",
                dlParamName: "kernelSize",
                type: "number[]"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }, {
            tfOpName: "MaxPool",
            dlOpName: "maxPool",
            category: "convolution",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfParamName: "strides",
                dlParamName: "strides",
                type: "number[]"
            }, {
                tfParamName: "padding",
                dlParamName: "pad",
                type: "string"
            }, {
                tfParamName: "data_format",
                dlParamName: "dataFormat",
                type: "string",
                notSupported: !0
            }, {
                tfParamName: "ksize",
                dlParamName: "kernelSize",
                type: "number[]"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }, {
            tfOpName: "Conv1D",
            dlOpName: "conv1d",
            category: "convolution",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfInputIndex: 1,
                dlParamName: "filter",
                type: "tensor"
            }, {
                tfParamName: "stride",
                dlParamName: "stride",
                type: "number"
            }, {
                tfParamName: "padding",
                dlParamName: "pad",
                type: "string"
            }, {
                tfParamName: "data_format",
                dlParamName: "dataFormat",
                type: "string",
                defaultValue: "NWC"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }, {
                tfParamName: "dilation",
                dlParamName: "dilation",
                type: "number",
                defaultValue: 1
            }]
        }, {
            tfOpName: "Conv2D",
            dlOpName: "conv2d",
            category: "convolution",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfInputIndex: 1,
                dlParamName: "filter",
                type: "tensor"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }, {
                tfParamName: "strides",
                dlParamName: "strides",
                type: "number[]"
            }, {
                tfParamName: "padding",
                dlParamName: "pad",
                type: "string"
            }, {
                tfParamName: "useCudnnOnGpu",
                dlParamName: "useCudnnOnGpu",
                type: "bool"
            }, {
                tfParamName: "data_format",
                dlParamName: "dataFormat",
                type: "string",
                defaultValue: "NHWC"
            }, {
                tfParamName: "dilations",
                dlParamName: "dilations",
                type: "number[]"
            }]
        }, {
            tfOpName: "Conv2DBackpropInput",
            dlOpName: "conv2dTranspose",
            category: "convolution",
            params: [{
                tfInputIndex: 2,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfInputIndex: 1,
                dlParamName: "filter",
                type: "tensor"
            }, {
                tfInputIndex: 0,
                dlParamName: "outputShape",
                type: "number[]"
            }, {
                tfParamName: "strides",
                dlParamName: "strides",
                type: "number[]"
            }, {
                tfParamName: "padding",
                dlParamName: "pad",
                type: "string"
            }, {
                tfParamName: "data_format",
                dlParamName: "dataFormat",
                type: "string",
                notSupported: !0
            }]
        }, {
            tfOpName: "DepthwiseConv2d",
            dlOpName: "depthwiseConv2d",
            category: "convolution",
            params: [{
                tfInputIndex: 0,
                dlParamName: "input",
                type: "tensor"
            }, {
                tfInputIndex: 1,
                dlParamName: "filter",
                type: "tensor"
            }, {
                tfParamName: "strides",
                dlParamName: "strides",
                type: "number[]"
            }, {
                tfParamName: "padding",
                dlParamName: "pad",
                type: "string"
            }, {
                tfParamName: "data_format",
                dlParamName: "dataFormat",
                type: "string",
                defaultValue: "NHWC"
            }, {
                tfParamName: "dilations",
                dlParamName: "dilations",
                type: "number[]"
            }]
        }, {
            tfOpName: "DepthwiseConv2dNative",
            dlOpName: "depthwiseConv2d",
            category: "convolution",
            params: [{
                tfInputIndex: 0,
                dlParamName: "input",
                type: "tensor"
            }, {
                tfInputIndex: 1,
                dlParamName: "filter",
                type: "tensor"
            }, {
                tfParamName: "strides",
                dlParamName: "strides",
                type: "number[]"
            }, {
                tfParamName: "padding",
                dlParamName: "pad",
                type: "string"
            }, {
                tfParamName: "data_format",
                dlParamName: "dataFormat",
                type: "string",
                defaultValue: "NHWC"
            }, {
                tfParamName: "dilations",
                dlParamName: "dilations",
                type: "number[]"
            }]
        }],
        convolution$1 = Object.freeze({
            default: convolution
        }),
        creation = [{
            tfOpName: "Fill",
            dlOpName: "fill",
            category: "creation",
            params: [{
                tfInputIndex: 0,
                dlParamName: "shape",
                type: "number[]"
            }, {
                tfInputIndex: 1,
                dlParamName: "value",
                type: "number"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }, {
            tfOpName: "LinSpace",
            dlOpName: "linspace",
            category: "creation",
            params: [{
                tfInputIndex: 0,
                dlParamName: "start",
                type: "number"
            }, {
                tfInputIndex: 1,
                dlParamName: "stop",
                type: "number"
            }, {
                tfInputIndex: 2,
                dlParamName: "num",
                type: "number"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }, {
            tfOpName: "OneHot",
            dlOpName: "oneHot",
            category: "creation",
            params: [{
                tfInputIndex: 0,
                dlParamName: "indices",
                type: "tensor"
            }, {
                tfInputIndex: 1,
                dlParamName: "depth",
                type: "number"
            }, {
                tfInputIndex: 2,
                dlParamName: "onValue",
                type: "number",
                defaultValue: 1
            }, {
                tfInputIndex: 3,
                dlParamName: "offValue",
                type: "number",
                defaultValue: 0
            }, {
                tfParamName: "axis",
                dlParamName: "axis",
                type: "number",
                notSupported: !0
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }, {
            tfOpName: "Ones",
            dlOpName: "ones",
            category: "creation",
            params: [{
                tfInputIndex: 0,
                dlParamName: "shape",
                type: "number[]"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype"
            }]
        }, {
            tfOpName: "OnesLike",
            dlOpName: "onesLike",
            category: "creation",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfParamName: "dtype",
                dlParamName: "dtype",
                type: "dtype"
            }]
        }, {
            tfOpName: "RandomUniform",
            dlOpName: "randomUniform",
            category: "creation",
            params: [{
                tfInputIndex: 0,
                dlParamName: "shape",
                type: "number[]"
            }, {
                tfParamName: "minval",
                dlParamName: "minval",
                type: "number",
                defaultValue: 0
            }, {
                tfParamName: "maxval",
                dlParamName: "maxval",
                type: "number",
                defaultValue: 1
            }, {
                tfParamName: "dtype",
                dlParamName: "dtype",
                type: "dtype"
            }, {
                tfParamName: "seed",
                dlParamName: "seed",
                type: "number",
                defaultValue: 0
            }, {
                tfParamName: "seed2",
                dlParamName: "seed2",
                type: "number",
                defaultValue: 0,
                notSupported: !0
            }, {
                tfParamName: "T",
                dlParamName: "T",
                type: "number",
                notSupported: !0
            }]
        }, {
            tfOpName: "Range",
            dlOpName: "range",
            category: "creation",
            params: [{
                tfInputIndex: 0,
                dlParamName: "start",
                type: "number"
            }, {
                tfInputIndex: 1,
                dlParamName: "stop",
                type: "number"
            }, {
                tfInputIndex: 2,
                dlParamName: "step",
                type: "number",
                defaultValue: 0
            }, {
                tfParamName: "Tidx",
                dlParamName: "dtype",
                type: "dtype"
            }]
        }, {
            tfOpName: "truncatedNormal",
            dlOpName: "truncatedNormal",
            category: "creation",
            params: [{
                tfInputIndex: 0,
                dlParamName: "shape",
                type: "number[]"
            }, {
                tfParamName: "means",
                dlParamName: "mean",
                type: "number",
                defaultValue: 0
            }, {
                tfParamName: "stddev",
                dlParamName: "stdDev",
                type: "number",
                defaultValue: 1
            }, {
                tfParamName: "seed",
                dlParamName: "seed",
                type: "number"
            }, {
                tfParamName: "seed2",
                dlParamName: "seed2",
                type: "number",
                defaultValue: 0,
                notSupported: !0
            }, {
                tfParamName: "dtype",
                dlParamName: "dtype",
                type: "dtype"
            }, {
                tfParamName: "T",
                dlParamName: "T",
                type: "number",
                notSupported: !0
            }]
        }, {
            tfOpName: "Zeros",
            dlOpName: "zeros",
            category: "creation",
            params: [{
                tfInputIndex: 0,
                dlParamName: "shape",
                type: "number[]"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype"
            }]
        }, {
            tfOpName: "ZerosLike",
            dlOpName: "zerosLike",
            category: "creation",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype"
            }]
        }],
        creation$1 = Object.freeze({
            default: creation
        }),
        graph = [{
            tfOpName: "PlaceholderWithDefault",
            dlOpName: "placeholder",
            category: "graph",
            params: [{
                tfInputIndex: 0,
                dlParamName: "default",
                type: "tensor"
            }]
        }, {
            tfOpName: "Placeholder",
            dlOpName: "placeholder",
            category: "graph"
        }, {
            tfOpName: "Const",
            dlOpName: "const",
            category: "graph"
        }, {
            tfOpName: "Identity",
            dlOpName: "identity",
            category: "graph",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }]
        }, {
            tfOpName: "Snapshot",
            dlOpName: "snapshot",
            category: "graph",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }]
        }, {
            tfOpName: "Shape",
            dlOpName: "shape",
            category: "graph",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }]
        }, {
            tfOpName: "Print",
            dlOpName: "print",
            category: "graph",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfInputIndex: 1,
                tfInputParamLength: 1,
                dlParamName: "data",
                type: "tensors"
            }, {
                tfParamName: "message",
                dlParamName: "message",
                type: "string"
            }, {
                tfParamName: "first_n",
                dlParamName: "firstN",
                type: "number",
                notSupprted: !0
            }, {
                tfParamName: "summarize",
                dlParamName: "summarize",
                type: "number",
                defaultValue: 3
            }]
        }, {
            tfOpName: "NoOp",
            dlOpName: "noop",
            category: "graph",
            params: []
        }, {
            tfOpName: "StopGradient",
            dlOpName: "stopGradient",
            category: "graph",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }]
        }, {
            tfOpName: "FakeQuantWithMinMaxVars",
            dlOpName: "fakeQuantWithMinMaxVars",
            category: "graph",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfParamName: "min",
                dlParamName: "min",
                type: "number"
            }, {
                tfParamName: "max",
                dlParamName: "max",
                type: "number"
            }]
        }],
        graph$1 = Object.freeze({
            default: graph
        }),
        image$1 = [{
            tfOpName: "ResizeBilinear",
            dlOpName: "resizeBilinear",
            category: "image",
            params: [{
                tfInputIndex: 0,
                dlParamName: "images",
                type: "tensor"
            }, {
                tfInputIndex: 1,
                dlParamName: "size",
                type: "number[]"
            }, {
                tfParamName: "align_corners",
                dlParamName: "alignCorners",
                type: "bool"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }, {
            tfOpName: "ResizeNearestNeighbor",
            dlOpName: "resizeNearestNeighbor",
            category: "image",
            params: [{
                tfInputIndex: 0,
                dlParamName: "images",
                type: "tensor"
            }, {
                tfInputIndex: 1,
                dlParamName: "size",
                type: "number[]"
            }, {
                tfParamName: "align_corners",
                dlParamName: "alignCorners",
                type: "bool"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }],
        image$2 = Object.freeze({
            default: image$1
        }),
        logical = [{
            tfOpName: "Equal",
            dlOpName: "equal",
            category: "logical",
            params: [{
                tfInputIndex: 0,
                dlParamName: "a",
                type: "tensor"
            }, {
                tfInputIndex: 1,
                dlParamName: "b",
                type: "tensor"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }, {
            tfOpName: "NotEqual",
            dlOpName: "notEqual",
            category: "logical",
            params: [{
                tfInputIndex: 0,
                dlParamName: "a",
                type: "tensor"
            }, {
                tfInputIndex: 1,
                dlParamName: "b",
                type: "tensor"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }, {
            tfOpName: "Greater",
            dlOpName: "greater",
            category: "logical",
            params: [{
                tfInputIndex: 0,
                dlParamName: "a",
                type: "tensor"
            }, {
                tfInputIndex: 1,
                dlParamName: "b",
                type: "tensor"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }, {
            tfOpName: "GreaterEqual",
            dlOpName: "greaterEqual",
            category: "logical",
            params: [{
                tfInputIndex: 0,
                dlParamName: "a",
                type: "tensor"
            }, {
                tfInputIndex: 1,
                dlParamName: "b",
                type: "tensor"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }, {
            tfOpName: "Less",
            dlOpName: "less",
            category: "logical",
            params: [{
                tfInputIndex: 0,
                dlParamName: "a",
                type: "tensor"
            }, {
                tfInputIndex: 1,
                dlParamName: "b",
                type: "tensor"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }, {
            tfOpName: "LessEqual",
            dlOpName: "lessEqual",
            category: "logical",
            params: [{
                tfInputIndex: 0,
                dlParamName: "a",
                type: "tensor"
            }, {
                tfInputIndex: 1,
                dlParamName: "b",
                type: "tensor"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }, {
            tfOpName: "LogicalAnd",
            dlOpName: "logicalAnd",
            category: "logical",
            params: [{
                tfInputIndex: 0,
                dlParamName: "a",
                type: "tensor"
            }, {
                tfInputIndex: 1,
                dlParamName: "b",
                type: "tensor"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }, {
            tfOpName: "LogicalNot",
            dlOpName: "logicalNot",
            category: "logical",
            params: [{
                tfInputIndex: 0,
                dlParamName: "a",
                type: "tensor"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }, {
            tfOpName: "LogicalOr",
            dlOpName: "logicalOr",
            category: "logical",
            params: [{
                tfInputIndex: 0,
                dlParamName: "a",
                type: "tensor"
            }, {
                tfInputIndex: 1,
                dlParamName: "b",
                type: "tensor"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }, {
            tfOpName: "Select",
            dlOpName: "where",
            category: "logical",
            params: [{
                tfInputIndex: 0,
                dlParamName: "condition",
                type: "tensor"
            }, {
                tfInputIndex: 1,
                dlParamName: "a",
                type: "tensor"
            }, {
                tfInputIndex: 2,
                dlParamName: "b",
                type: "tensor"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }],
        logical$1 = Object.freeze({
            default: logical
        }),
        matrices = [{
            tfOpName: "MatMul",
            dlOpName: "matMul",
            category: "matrices",
            params: [{
                tfInputIndex: 0,
                dlParamName: "a",
                type: "tensor"
            }, {
                tfInputIndex: 1,
                dlParamName: "b",
                type: "tensor"
            }, {
                tfParamName: "transpose_a",
                dlParamName: "transposeA",
                type: "bool",
                defaultValue: !1
            }, {
                tfParamName: "transpose_b",
                dlParamName: "transposeB",
                type: "bool",
                defaultValue: !1
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }, {
            tfOpName: "Transpose",
            dlOpName: "transpose",
            category: "matrices",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfParamName: "perm",
                dlParamName: "perm",
                type: "number[]"
            }, {
                tfParamName: "T",
                dlParamName: "dtype",
                type: "dtype",
                notSupported: !0
            }]
        }],
        matrices$1 = Object.freeze({
            default: matrices
        }),
        normalization = [{
            tfOpName: "FusedBatchNorm",
            dlOpName: "batchNormalization",
            category: "normalization",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfInputIndex: 1,
                dlParamName: "scale",
                type: "tensor"
            }, {
                tfInputIndex: 2,
                dlParamName: "offset",
                type: "tensor"
            }, {
                tfInputIndex: 3,
                dlParamName: "mean",
                type: "tensor"
            }, {
                tfInputIndex: 4,
                dlParamName: "variance",
                type: "tensor"
            }, {
                tfParamName: "epsilon",
                dlParamName: "epsilon",
                type: "number",
                defaultValue: .001
            }, {
                tfParamName: "data_format",
                dlParamName: "dataFormat",
                type: "string",
                notSupported: !0
            }]
        }, {
            tfOpName: "FusedBatchNormV2",
            dlOpName: "batchNormalization",
            category: "normalization",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfInputIndex: 1,
                dlParamName: "scale",
                type: "tensor"
            }, {
                tfInputIndex: 2,
                dlParamName: "offset",
                type: "tensor"
            }, {
                tfInputIndex: 3,
                dlParamName: "mean",
                type: "tensor"
            }, {
                tfInputIndex: 4,
                dlParamName: "variance",
                type: "tensor"
            }, {
                tfParamName: "epsilon",
                dlParamName: "epsilon",
                type: "number",
                defaultValue: .001
            }, {
                tfParamName: "data_format",
                dlParamName: "dataFormat",
                type: "string",
                notSupported: !0
            }]
        }, {
            tfOpName: "LRN",
            dlOpName: "localResponseNormalization",
            category: "normalization",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfParamName: "depth_radius",
                dlParamName: "radius",
                type: "number",
                defaultValue: 5
            }, {
                tfParamName: "bias",
                dlParamName: "bias",
                type: "number",
                defaultValue: 1
            }, {
                tfParamName: "alpha",
                dlParamName: "alpha",
                type: "number",
                defaultValue: 1
            }, {
                tfParamName: "beta",
                dlParamName: "beta",
                type: "number",
                defaultValue: .5
            }]
        }, {
            tfOpName: "Softmax",
            dlOpName: "softmax",
            category: "normalization",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }]
        }],
        normalization$1 = Object.freeze({
            default: normalization
        }),
        reduction = [{
            tfOpName: "Max",
            dlOpName: "max",
            category: "reduction",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfInputIndex: 1,
                dlParamName: "axis",
                type: "number[]"
            }, {
                tfParamName: "keep_dims",
                dlParamName: "keepDims",
                type: "bool"
            }]
        }, {
            tfOpName: "Mean",
            dlOpName: "mean",
            category: "reduction",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfInputIndex: 1,
                dlParamName: "axis",
                type: "number[]"
            }, {
                tfParamName: "keep_dims",
                dlParamName: "keepDims",
                type: "bool"
            }]
        }, {
            tfOpName: "Min",
            dlOpName: "min",
            category: "reduction",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfInputIndex: 1,
                dlParamName: "axis",
                type: "number[]"
            }, {
                tfParamName: "keep_dims",
                dlParamName: "keepDims",
                type: "bool"
            }]
        }, {
            tfOpName: "Sum",
            dlOpName: "sum",
            category: "reduction",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfInputIndex: 1,
                dlParamName: "axis",
                type: "number[]"
            }, {
                tfParamName: "keep_dims",
                dlParamName: "keepDims",
                type: "bool"
            }]
        }, {
            tfOpName: "ArgMax",
            dlOpName: "argMax",
            category: "reduction",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfInputIndex: 1,
                dlParamName: "axis",
                type: "number"
            }]
        }, {
            tfOpName: "ArgMin",
            dlOpName: "argMin",
            category: "reduction",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfInputIndex: 1,
                dlParamName: "axis",
                type: "number"
            }]
        }],
        reduction$1 = Object.freeze({
            default: reduction
        }),
        slice_join = [{
            tfOpName: "ConcatV2",
            dlOpName: "concat",
            category: "slice_join",
            params: [{
                tfInputIndex: 0,
                tfInputParamLength: 1,
                dlParamName: "tensors",
                type: "tensors"
            }, {
                tfInputIndex: -1,
                dlParamName: "axis",
                type: "number"
            }]
        }, {
            tfOpName: "Concat",
            dlOpName: "concat",
            category: "slice_join",
            params: [{
                tfInputIndex: 1,
                tfInputParamLength: 1,
                dlParamName: "tensors",
                type: "tensors"
            }, {
                tfInputIndex: 0,
                dlParamName: "axis",
                type: "number"
            }]
        }, {
            tfOpName: "GatherV2",
            dlOpName: "gather",
            category: "slice_join",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfInputIndex: 1,
                dlParamName: "indices",
                type: "tensor"
            }, {
                tfParamName: "axis",
                dlParamName: "axis",
                type: "number",
                defaultValue: 0
            }]
        }, {
            tfOpName: "Gather",
            dlOpName: "gather",
            category: "slice_join",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfInputIndex: 1,
                dlParamName: "indices",
                type: "tensor"
            }, {
                tfParamName: "axis",
                dlParamName: "axis",
                type: "number",
                defaultValue: 0
            }, {
                tfParamName: "validate_indices",
                dlParamName: "validateIndices",
                type: "bool",
                notSupported: !0
            }]
        }, {
            tfOpName: "Reverse",
            dlOpName: "reverse",
            category: "slice_join",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfInputIndex: 1,
                dlParamName: "axis",
                type: "number"
            }]
        }, {
            tfOpName: "ReverseV2",
            dlOpName: "reverse",
            category: "slice_join",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfInputIndex: 1,
                dlParamName: "axis",
                type: "number"
            }]
        }, {
            tfOpName: "Slice",
            dlOpName: "slice",
            category: "slice_join",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfInputIndex: 1,
                dlParamName: "begin",
                type: "number[]"
            }, {
                tfInputIndex: 2,
                dlParamName: "size",
                type: "number[]"
            }]
        }, {
            tfOpName: "StridedSlice",
            dlOpName: "stridedSlice",
            category: "slice_join",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfInputIndex: 1,
                dlParamName: "begin",
                type: "number[]"
            }, {
                tfInputIndex: 2,
                dlParamName: "end",
                type: "number[]"
            }, {
                tfInputIndex: 3,
                dlParamName: "strides",
                type: "number[]"
            }, {
                tfParamName: "begin_mask",
                dlParamName: "beginMask",
                type: "number",
                defaultValue: 0
            }, {
                tfParamName: "end_mask",
                dlParamName: "endMask",
                type: "number",
                defaultValue: 0
            }]
        }, {
            tfOpName: "Pack",
            dlOpName: "stack",
            category: "slice_join",
            params: [{
                tfInputIndex: 0,
                tfInputParamLength: 0,
                dlParamName: "tensors",
                type: "tensors"
            }, {
                tfParamName: "axis",
                dlParamName: "axis",
                type: "number",
                defaultValue: 0
            }]
        }, {
            tfOpName: "Tile",
            dlOpName: "tile",
            category: "slice_join",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfInputIndex: 1,
                dlParamName: "reps",
                type: "number[]"
            }]
        }, {
            tfOpName: "Split",
            dlOpName: "split",
            category: "slice_join",
            params: [{
                tfInputIndex: 0,
                dlParamName: "axis",
                type: "number",
                defaultValue: 0
            }, {
                tfInputIndex: 1,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfParamName: "num_split",
                dlParamName: "numOrSizeSplits",
                type: "number",
                defaultValue: 1
            }]
        }],
        sliceJoin = Object.freeze({
            default: slice_join
        }),
        transformation = [{
            tfOpName: "Cast",
            dlOpName: "cast",
            category: "transformation",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfParamName: "SrcT",
                dlParamName: "sdtype",
                type: "dtype",
                notSupported: !0
            }, {
                tfParamName: "DstT",
                dlParamName: "dtype",
                type: "dtype"
            }]
        }, {
            tfOpName: "ExpandDims",
            dlOpName: "expandDims",
            category: "transformation",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfInputIndex: 1,
                tfParamNameDeprecated: "dim",
                dlParamName: "axis",
                type: "number"
            }]
        }, {
            tfOpName: "Pad",
            dlOpName: "pad",
            category: "transformation",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfInputIndex: 1,
                dlParamName: "padding",
                type: "number[]"
            }, {
                tfParamName: "constant_value",
                dlParamName: "constantValue",
                type: "number",
                defaultValue: 0
            }]
        }, {
            tfOpName: "PadV2",
            dlOpName: "pad",
            category: "transformation",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfInputIndex: 1,
                dlParamName: "padding",
                type: "number[]"
            }, {
                tfInputIndex: 2,
                dlParamName: "constantValue",
                type: "number",
                defaultValue: 0
            }]
        }, {
            tfOpName: "Reshape",
            dlOpName: "reshape",
            category: "transformation",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfInputIndex: 1,
                dlParamName: "shape",
                type: "number[]"
            }]
        }, {
            tfOpName: "Squeeze",
            dlOpName: "squeeze",
            category: "transformation",
            params: [{
                tfInputIndex: 0,
                dlParamName: "x",
                type: "tensor"
            }, {
                tfParamName: "axis",
                tfParamNameDeprecated: "squeeze_dims",
                dlParamName: "axis",
                type: "number[]"
            }]
        }],
        transformation$1 = Object.freeze({
            default: transformation
        }),
        CONTROL_FLOW_OPS = ["Switch", "Merge", "Enter", "Exit", "NextIteration"],
        OperationMapper = function() {
            function e() {
                var e = [arithmetic$1, basicMath, control$1, convolution$1, creation$1, logical$1, image$2, graph$1, matrices$1, normalization$1, reduction$1, sliceJoin, transformation$1],
                    t = [].concat.apply([], e.map(function(e) {
                        return e.default ? e.default : e
                    }));
                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.transformGraph = function(e) {
                var t = this,
                    r = !1,
                    n = [],
                    a = e.node.reduce(function(e, a) {
                        return e[a.name] = t.mapNode(a), t.isControlFlow(a) && (r = !0), "Placeholder" === a.op && n.push(e[a.name]), e
                    }, {}),
                    o = [],
                    i = [];
                return Object.keys(a).forEach(function(e) {
                    var t = a[e];
                    t.inputNames.forEach(function(e) {
                        var r = getNodeNameAndIndex(e)[0];
                        t.inputs.push(a[r]), a[r].children.push(t)
                    }), 0 === t.inputs.length && o.push(t)
                }), Object.keys(a).forEach(function(e) {
                    var t = a[e];
                    0 === t.children.length && i.push(t)
                }), {
                    nodes: a,
                    inputs: o,
                    outputs: i,
                    placeholders: n,
                    withControlFlow: r
                }
            }, e.prototype.mapNode = function(e) {
                var t = this,
                    r = this.opMappers[e.op];
                if (void 0 === r) throw new Error("Tensorflow Op is not supported: " + e.op);
                var n = {
                    name: e.name,
                    op: r.dlOpName,
                    category: r.category,
                    inputNames: (e.input || []).map(function(e) {
                        return e.startsWith("^") ? e.substr(1) : e
                    }),
                    inputs: [],
                    children: [],
                    params: {}
                };
                return r.params && (n.params = r.params.reduce(function(r, n) {
                    var a = n.tfInputIndex,
                        o = n.tfInputParamLength,
                        i = n.type,
                        s = void 0;
                    if (void 0 === a) switch (n.type) {
                        case "string":
                            void 0 === (s = t.getStringParam(e.attr, n.tfParamName, n.defaultValue)) && n.tfParamNameDeprecated && (s = t.getStringParam(e.attr, n.tfParamNameDeprecated, n.defaultValue));
                            break;
                        case "number":
                            void 0 === (s = t.getNumberParam(e.attr, n.tfParamName, n.defaultValue)) && n.tfParamNameDeprecated && (s = t.getNumberParam(e.attr, n.tfParamNameDeprecated, n.defaultValue));
                            break;
                        case "number[]":
                            void 0 === (s = t.getNumericArrayParam(e.attr, n.tfParamName, n.defaultValue)) && n.tfParamNameDeprecated && (s = t.getNumericArrayParam(e.attr, n.tfParamNameDeprecated, n.defaultValue));
                            break;
                        case "bool":
                            void 0 === (s = t.getBoolParam(e.attr, n.tfParamName, n.defaultValue)) && n.tfParamNameDeprecated && (s = t.getBoolParam(e.attr, n.tfParamNameDeprecated, n.defaultValue));
                            break;
                        case "shape":
                            void 0 === (s = t.getTensorShapeParam(e.attr, n.tfParamName, n.defaultValue)) && n.tfParamNameDeprecated && (s = t.getTensorShapeParam(e.attr, n.tfParamNameDeprecated, n.defaultValue));
                            break;
                        case "dtype":
                            void 0 === (s = t.getDtypeParam(e.attr, n.tfParamName, n.defaultValue)) && n.tfParamNameDeprecated && (s = t.getDtypeParam(e.attr, n.tfParamNameDeprecated, n.defaultValue));
                            break;
                        case "tensor":
                        case "tensors":
                            break;
                        default:
                            throw new Error("Unsupported param type: " + n.type + " for op: " + e.op)
                    }
                    return r[n.dlParamName] = {
                        value: s,
                        inputIndex: a,
                        type: i,
                        inputParamLength: o
                    }, r
                }, {})), n
            }, e.prototype.getStringParam = function(e, t, r, n) {
                void 0 === n && (n = !1);
                var a = e[t];
                if (void 0 !== a) {
                    var o = String.fromCharCode.apply(null, a.s);
                    return n ? o : o.toLowerCase()
                }
                return r
            }, e.prototype.getBoolParam = function(e, t, r) {
                var n = e[t];
                return n ? n.b : r
            }, e.prototype.getNumberParam = function(e, t, r) {
                var n = e[t],
                    a = n ? void 0 !== n.f ? n.f : n.i : r;
                return "number" == typeof a ? a : a.toInt()
            }, e.prototype.getDtypeParam = function(e, t, r) {
                var n = e[t];
                if (n && n.type) switch (n.type) {
                    case tensorflow.DataType.DT_FLOAT:
                        return "float32";
                    case tensorflow.DataType.DT_INT32:
                        return "int32";
                    case tensorflow.DataType.DT_BOOL:
                        return "bool";
                    default:
                        return r
                }
                return r
            }, e.prototype.getTensorShapeParam = function(e, t, r) {
                var n = e[t];
                return n && n.shape ? n.shape.dim.map(function(e) {
                    return e.size
                }) : r
            }, e.prototype.getNumericArrayParam = function(e, t, r) {
                var n = e[t];
                return n ? (n.list.f && n.list.f.length ? n.list.f : n.list.i).map(function(e) {
                    return "number" == typeof e ? e : e.toInt()
                }) : r
            }, e
        }(),
        executeOp = function(e, t, r) {
            switch (e.op) {
                case "add":
                    return [add(getParamValue("a", e, t, r), getParamValue("b", e, t, r))];
                case "mod":
                    return [mod(getParamValue("a", e, t, r), getParamValue("b", e, t, r))];
                case "mul":
                    return [mul(getParamValue("a", e, t, r), getParamValue("b", e, t, r))];
                case "div":
                    return [div(getParamValue("a", e, t, r), getParamValue("b", e, t, r))];
                case "sub":
                    return [sub(getParamValue("a", e, t, r), getParamValue("b", e, t, r))];
                case "minimum":
                    return [minimum(getParamValue("a", e, t, r), getParamValue("b", e, t, r))];
                case "maximum":
                    return [maximum(getParamValue("a", e, t, r), getParamValue("b", e, t, r))];
                case "pow":
                    return [pow(getParamValue("a", e, t, r), getParamValue("b", e, t, r))];
                case "squaredDifference":
                    return [squaredDifference(getParamValue("a", e, t, r), getParamValue("b", e, t, r))];
                default:
                    throw TypeError("Node type " + e.op + " is not implemented")
            }
        },
        executeOp$1 = function(e, t, r) {
            switch (e.op) {
                case "abs":
                    return [abs(getParamValue("x", e, t, r))];
                case "acos":
                    return [acos(getParamValue("x", e, t, r))];
                case "acosh":
                    return [acosh(getParamValue("x", e, t, r))];
                case "asin":
                    return [asin(getParamValue("x", e, t, r))];
                case "asinh":
                    return [asinh(getParamValue("x", e, t, r))];
                case "atan":
                    return [atan(getParamValue("x", e, t, r))];
                case "atanh":
                    return [atanh(getParamValue("x", e, t, r))];
                case "ceil":
                    return [ceil(getParamValue("x", e, t, r))];
                case "cos":
                    return [cos(getParamValue("x", e, t, r))];
                case "cosh":
                    return [cosh(getParamValue("x", e, t, r))];
                case "elu":
                    return [elu(getParamValue("x", e, t, r))];
                case "erf":
                    return [erf(getParamValue("x", e, t, r))];
                case "exp":
                    return [exp(getParamValue("x", e, t, r))];
                case "expm1":
                    return [expm1(getParamValue("x", e, t, r))];
                case "floor":
                    return [floor(getParamValue("x", e, t, r))];
                case "log":
                    return [log(getParamValue("x", e, t, r))];
                case "log1p":
                    return [log1p(getParamValue("x", e, t, r))];
                case "neg":
                    return [neg(getParamValue("x", e, t, r))];
                case "reciprocal":
                    return [reciprocal(getParamValue("x", e, t, r))];
                case "relu":
                    return [relu(getParamValue("x", e, t, r))];
                case "round":
                    return [round(getParamValue("x", e, t, r))];
                case "selu":
                    return [selu(getParamValue("x", e, t, r))];
                case "sigmoid":
                    return [sigmoid(getParamValue("x", e, t, r))];
                case "sin":
                    return [sin(getParamValue("x", e, t, r))];
                case "sign":
                    return [sign(getParamValue("x", e, t, r))];
                case "sinh":
                    return [sinh(getParamValue("x", e, t, r))];
                case "softplus":
                    return [softplus(getParamValue("x", e, t, r))];
                case "sqrt":
                    return [sqrt(getParamValue("x", e, t, r))];
                case "square":
                    return [square(getParamValue("x", e, t, r))];
                case "tanh":
                    return [tanh$1(getParamValue("x", e, t, r))];
                case "tan":
                    return [tan(getParamValue("x", e, t, r))];
                case "clipByValue":
                    return [clipByValue(getParamValue("x", e, t, r), getParamValue("clipValueMin", e, t, r), getParamValue("clipValueMax", e, t, r))];
                case "rsqrt":
                    return [div(scalar(1, "float32"), sqrt(getTensor(e.inputNames[0], t, r)))];
                default:
                    throw TypeError("Node type " + e.op + " is not implemented")
            }
        },
        __awaiter$17 = function(e, t, r, n) {
            return new(r || (r = Promise))(function(a, o) {
                function i(e) {
                    try {
                        u(n.next(e))
                    } catch (e) {
                        o(e)
                    }
                }

                function s(e) {
                    try {
                        u(n.throw(e))
                    } catch (e) {
                        o(e)
                    }
                }

                function u(e) {
                    e.done ? a(e.value) : new r(function(t) {
                        t(e.value)
                    }).then(i, s)
                }
                u((n = n.apply(e, t || [])).next())
            })
        },
        __generator$17 = function(e, t) {
            function r(e) {
                return function(t) {
                    return n([e, t])
                }
            }

            function n(r) {
                if (a) throw new TypeError("Generator is already executing.");
                for (; u;) try {
                    if (a = 1, o && (i = o[2 & r[0] ? "return" : r[0] ? "throw" : "next"]) && !(i = i.call(o, r[1])).done) return i;
                    switch (o = 0, i && (r = [0, i.value]), r[0]) {
                        case 0:
                        case 1:
                            i = r;
                            break;
                        case 4:
                            return u.label++, {
                                value: r[1],
                                done: !1
                            };
                        case 5:
                            u.label++, o = r[1], r = [0];
                            continue;
                        case 7:
                            r = u.ops.pop(), u.trys.pop();
                            continue;
                        default:
                            if (i = u.trys, !(i = i.length > 0 && i[i.length - 1]) && (6 === r[0] || 2 === r[0])) {
                                u = 0;
                                continue
                            }
                            if (3 === r[0] && (!i || r[1] > i[0] && r[1] < i[3])) {
                                u.label = r[1];
                                break
                            }
                            if (6 === r[0] && u.label < i[1]) {
                                u.label = i[1], i = r;
                                break
                            }
                            if (i && u.label < i[2]) {
                                u.label = i[2], u.ops.push(r);
                                break
                            }
                            i[2] && u.ops.pop(), u.trys.pop();
                            continue
                    }
                    r = t.call(e, u)
                } catch (e) {
                    r = [6, e], o = 0
                } finally {
                    a = i = 0
                }
                if (5 & r[0]) throw r[1];
                return {
                    value: r[0] ? r[1] : void 0,
                    done: !0
                }
            }
            var a, o, i, s, u = {
                label: 0,
                sent: function() {
                    if (1 & i[0]) throw i[1];
                    return i[1]
                },
                trys: [],
                ops: []
            };
            return s = {
                next: r(0),
                throw: r(1),
                return: r(2)
            }, "function" == typeof Symbol && (s[Symbol.iterator] = function() {
                return this
            }), s
        },
        executeOp$3 = function(e, t, r) {
            switch (e.op) {
                case "conv1d":
                    var n = getParamValue("stride", e, t, r),
                        a = getParamValue("pad", e, t, r),
                        o = getParamValue("dataFormat", e, t, r).toUpperCase(),
                        i = getParamValue("dilation", e, t, r);
                    return [conv1d(getParamValue("x", e, t, r), getParamValue("filter", e, t, r), n, a, o, i)];
                case "conv2d":
                    var n = getParamValue("strides", e, t, r),
                        a = getParamValue("pad", e, t, r),
                        o = getParamValue("dataFormat", e, t, r).toUpperCase(),
                        s = getParamValue("dilations", e, t, r);
                    return [conv2d(getParamValue("x", e, t, r), getParamValue("filter", e, t, r), [n[1], n[2]], a, o, [s[0], s[1]])];
                case "conv2dTranspose":
                    var u = getParamValue("outputShape", e, t, r),
                        n = getParamValue("strides", e, t, r),
                        a = getParamValue("pad", e, t, r);
                    return [conv2dTranspose(getParamValue("x", e, t, r), getParamValue("filter", e, t, r), u, [n[1], n[2]], a)];
                case "depthwiseConv2d":
                    var n = getParamValue("strides", e, t, r),
                        a = getParamValue("pad", e, t, r),
                        s = getParamValue("dilations", e, t, r),
                        o = getParamValue("dataFormat", e, t, r).toUpperCase();
                    return [depthwiseConv2d(getParamValue("input", e, t, r), getParamValue("filter", e, t, r), [n[1], n[2]], a, o, [s[0], s[1]])];
                case "avgPool":
                    var n = getParamValue("strides", e, t, r),
                        a = getParamValue("pad", e, t, r),
                        l = getParamValue("kernelSize", e, t, r);
                    return [avgPool(getParamValue("x", e, t, r), [l[1], l[2]], [n[1], n[2]], a)];
                case "maxPool":
                    var n = getParamValue("strides", e, t, r),
                        a = getParamValue("pad", e, t, r),
                        l = getParamValue("kernelSize", e, t, r);
                    return [maxPool(getParamValue("x", e, t, r), [l[1], l[2]], [n[1], n[2]], a)];
                default:
                    throw TypeError("Node type " + e.op + " is not implemented")
            }
        },
        executeOp$4 = function(e, t, r) {
            switch (e.op) {
                case "fill":
                    var n = getParamValue("shape", e, t, r),
                        a = getParamValue("value", e, t, r);
                    return [fill(n, a)];
                case "linspace":
                    var o = getParamValue("start", e, t, r),
                        i = getParamValue("stop", e, t, r),
                        s = getParamValue("num", e, t, r);
                    return [linspace(o, i, s)];
                case "oneHot":
                    var u = getParamValue("indices", e, t, r),
                        l = getParamValue("depth", e, t, r),
                        c = getParamValue("onValue", e, t, r),
                        p = getParamValue("offValue", e, t, r);
                    return [oneHot(u, l, c, p)];
                case "ones":
                    return [ones(getParamValue("shape", e, t, r), getParamValue("dtype", e, t, r))];
                case "onesLike":
                    return [onesLike(getParamValue("x", e, t, r))];
                case "randomUniform":
                    return [randomUniform(getParamValue("shape", e, t, r), getParamValue("minval", e, t, r), getParamValue("maxval", e, t, r), getParamValue("dtype", e, t, r))];
                case "range":
                    var o = getParamValue("start", e, t, r),
                        d = getParamValue("stop", e, t, r),
                        h = getParamValue("step", e, t, r);
                    return [range(o, d, h, getParamValue("dtype", e, t, r))];
                case "truncatedNormal":
                    var n = getParamValue("shape", e, t, r),
                        f = getParamValue("mean", e, t, r),
                        m = getParamValue("stdDev", e, t, r),
                        g = getParamValue("seed", e, t, r);
                    return [truncatedNormal(n, f, m, getParamValue("dtype", e, t, r), g)];
                case "zeros":
                    return [zeros(getParamValue("shape", e, t, r), getParamValue("dtype", e, t, r))];
                case "zerosLike":
                    return [zerosLike(getParamValue("x", e, t, r))];
                default:
                    throw TypeError("Node type " + e.op + " is not implemented")
            }
        },
        executeOp$5 = function(e, t, r) {
            switch (e.op) {
                case "const":
                    return t[e.name];
                case "placeholder":
                    var n = getParamValue("default", e, t, r);
                    return [getTensor(e.name, t, r) || n];
                case "identity":
                case "stopGradient":
                case "fakeQuantWithMinMaxVars":
                    return [getParamValue("x", e, t, r)];
                case "snapshot":
                    return [getParamValue("x", e, t, r).clone()];
                case "shape":
                    return [tensor1d(getParamValue("x", e, t, r).shape, "int32")];
                case "noop":
                    return [];
                case "print":
                    var a = getParamValue("x", e, t, r),
                        o = getParamValue("data", e, t, r),
                        i = getParamValue("message", e, t, r),
                        s = getParamValue("summarize", e, t, r);
                    console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."), console.log(i);
                    for (var u = 0; u < o.length; u++) console.log(Array.prototype.slice.call(o[0].dataSync()).slice(0, s));
                    return [a];
                default:
                    throw TypeError("Node type " + e.op + " is not implemented")
            }
        },
        executeOp$6 = function(e, t, r) {
            switch (e.op) {
                case "resizeBilinear":
                    var n = getParamValue("images", e, t, r),
                        a = getParamValue("size", e, t, r),
                        o = getParamValue("alignCorners", e, t, r);
                    return [image.resizeBilinear(n, [a[0], a[1]], o)];
                case "resizeNearestNeighbor":
                    var n = getParamValue("images", e, t, r),
                        a = getParamValue("size", e, t, r),
                        o = getParamValue("alignCorners", e, t, r);
                    return [image.resizeNearestNeighbor(n, [a[0], a[1]], o)];
                default:
                    throw TypeError("Node type " + e.op + " is not implemented")
            }
        },
        executeOp$7 = function(e, t, r) {
            switch (e.op) {
                case "equal":
                    return [equal(getParamValue("a", e, t, r), getParamValue("b", e, t, r))];
                case "notEqual":
                    return [notEqual(getParamValue("a", e, t, r), getParamValue("b", e, t, r))];
                case "greater":
                    return [greater(getParamValue("a", e, t, r), getParamValue("b", e, t, r))];
                case "greaterEqual":
                    return [greaterEqual(getParamValue("a", e, t, r), getParamValue("b", e, t, r))];
                case "less":
                    return [less(getParamValue("a", e, t, r), getParamValue("b", e, t, r))];
                case "lessEqual":
                    return [lessEqual(getParamValue("a", e, t, r), getParamValue("b", e, t, r))];
                case "logicalAnd":
                    return [logicalAnd(getParamValue("a", e, t, r), getParamValue("b", e, t, r))];
                case "logicalNot":
                    return [logicalNot(getParamValue("a", e, t, r))];
                case "logicalOr":
                    return [logicalOr(getParamValue("a", e, t, r), getParamValue("b", e, t, r))];
                case "where":
                    return [where(getParamValue("condition", e, t, r), getParamValue("a", e, t, r), getParamValue("b", e, t, r))];
                default:
                    throw TypeError("Node type " + e.op + " is not implemented")
            }
        },
        executeOp$8 = function(e, t, r) {
            switch (e.op) {
                case "matMul":
                    return [matMul(getParamValue("a", e, t, r), getParamValue("b", e, t, r), getParamValue("transposeA", e, t, r), getParamValue("transposeB", e, t, r))];
                case "transpose":
                    return [transpose(getParamValue("x", e, t, r), getParamValue("perm", e, t, r))];
                default:
                    throw TypeError("Node type " + e.op + " is not implemented")
            }
        },
        executeOp$9 = function(e, t, r) {
            switch (e.op) {
                case "batchNormalization":
                    return [batchNormalization(getParamValue("x", e, t, r), getParamValue("mean", e, t, r), getParamValue("variance", e, t, r), getParamValue("epsilon", e, t, r), getParamValue("scale", e, t, r), getParamValue("offset", e, t, r))];
                case "localResponseNormalization":
                    return [localResponseNormalization(getParamValue("x", e, t, r), getParamValue("radius", e, t, r), getParamValue("bias", e, t, r), getParamValue("alpha", e, t, r), getParamValue("beta", e, t, r))];
                case "softmax":
                    return [softmax(getParamValue("x", e, t, r))];
                default:
                    throw TypeError("Node type " + e.op + " is not implemented")
            }
        },
        executeOp$10 = function(e, t, r) {
            switch (e.op) {
                case "max":
                    var n = getParamValue("axis", e, t, r),
                        a = getParamValue("keepDims", e, t, r);
                    return [max(getParamValue("x", e, t, r), n, a)];
                case "mean":
                    var n = getParamValue("axis", e, t, r),
                        a = getParamValue("keepDims", e, t, r);
                    return [mean(getParamValue("x", e, t, r), n, a)];
                case "min":
                    var n = getParamValue("axis", e, t, r),
                        a = getParamValue("keepDims", e, t, r);
                    return [min(getParamValue("x", e, t, r), n, a)];
                case "sum":
                    var n = getParamValue("axis", e, t, r),
                        a = getParamValue("keepDims", e, t, r);
                    return [sum(getParamValue("x", e, t, r), n, a)];
                case "argMax":
                    n = getParamValue("axis", e, t, r);
                    return [argMax(getParamValue("x", e, t, r), n)];
                case "argMin":
                    n = getParamValue("axis", e, t, r);
                    return [argMin(getParamValue("x", e, t, r), n)];
                default:
                    throw TypeError("Node type " + e.op + " is not implemented")
            }
        },
        executeOp$11 = function(e, t, r) {
            switch (e.op) {
                case "concat":
                    var n = getParamValue("axis", e, t, r),
                        a = getParamValue("tensors", e, t, r);
                    return [concat(a, n)];
                case "gather":
                    var n = getParamValue("axis", e, t, r),
                        o = getParamValue("x", e, t, r),
                        i = getParamValue("indices", e, t, r);
                    return [gather(o, i, n)];
                case "reverse":
                    var n = getParamValue("axis", e, t, r),
                        o = getParamValue("x", e, t, r);
                    return [reverse(o, n)];
                case "slice":
                    var s = getParamValue("begin", e, t, r),
                        u = getParamValue("size", e, t, r);
                    return [slice(getParamValue("x", e, t, r), s, u)];
                case "stridedSlice":
                    var s = getParamValue("begin", e, t, r),
                        l = getParamValue("end", e, t, r),
                        c = getParamValue("strides", e, t, r),
                        p = getParamValue("beginMask", e, t, r),
                        d = getParamValue("endMask", e, t, r);
                    return [stridedSlice(getParamValue("x", e, t, r), s, l, c, p, d)];
                case "stack":
                    return tidy(function() {
                        var n = getParamValue("axis", e, t, r),
                            a = getParamValue("tensors", e, t, r),
                            o = a[0].shape,
                            i = a[0].squeeze().shape,
                            s = a.map(function(e) {
                                var t = arraysEqual(e.shape, o);
                                if (!t && !arraysEqual(e.squeeze().shape, i)) throw new Error("the input tensors shape does not match");
                                return t ? e : e.reshape(o)
                            });
                        return [stack(s, n)]
                    });
                case "tile":
                    var h = getParamValue("reps", e, t, r);
                    return [tile(getParamValue("x", e, t, r), h)];
                case "split":
                    var n = getParamValue("axis", e, t, r),
                        f = getParamValue("numOrSizeSplits", e, t, r);
                    return split(getParamValue("x", e, t, r), f, n);
                default:
                    throw TypeError("Node type " + e.op + " is not implemented")
            }
        },
        executeOp$12 = function(e, t, r) {
            switch (e.op) {
                case "cast":
                    return [cast(getParamValue("x", e, t, r), getParamValue("dtype", e, t, r))];
                case "expandDims":
                    n = e.params.axis.value;
                    return [expandDims(getParamValue("x", e, t, r), n)];
                case "squeeze":
                    var n = e.params.axis.value;
                    return [squeeze(getParamValue("x", e, t, r), n)];
                case "reshape":
                    return [reshape(getParamValue("x", e, t, r), getParamValue("shape", e, t, r))];
                case "pad":
                    return [pad(getParamValue("x", e, t, r), split$1(getParamValue("padding", e, t, r), 2), getParamValue("constantValue", e, t, r))];
                default:
                    throw TypeError("Node type " + e.op + " is not implemented")
            }
        },
        ExecutionContext = function() {
            function e(e) {
                this.weightMap = e, 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 r = this.contexts.slice(0, this.contexts.length - t);
                    e.push(this.contextIdforContexts(r))
                }
                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
        }(),
        __assign$1 = Object.assign || function(e) {
            for (var t, r = 1, n = arguments.length; r < n; r++) {
                t = arguments[r];
                for (var a in t) Object.prototype.hasOwnProperty.call(t, a) && (e[a] = t[a])
            }
            return e
        },
        __awaiter$18 = function(e, t, r, n) {
            return new(r || (r = Promise))(function(a, o) {
                function i(e) {
                    try {
                        u(n.next(e))
                    } catch (e) {
                        o(e)
                    }
                }

                function s(e) {
                    try {
                        u(n.throw(e))
                    } catch (e) {
                        o(e)
                    }
                }

                function u(e) {
                    e.done ? a(e.value) : new r(function(t) {
                        t(e.value)
                    }).then(i, s)
                }
                u((n = n.apply(e, t || [])).next())
            })
        },
        __generator$18 = function(e, t) {
            function r(e) {
                return function(t) {
                    return n([e, t])
                }
            }

            function n(r) {
                if (a) throw new TypeError("Generator is already executing.");
                for (; u;) try {
                    if (a = 1, o && (i = o[2 & r[0] ? "return" : r[0] ? "throw" : "next"]) && !(i = i.call(o, r[1])).done) return i;
                    switch (o = 0, i && (r = [0, i.value]), r[0]) {
                        case 0:
                        case 1:
                            i = r;
                            break;
                        case 4:
                            return u.label++, {
                                value: r[1],
                                done: !1
                            };
                        case 5:
                            u.label++, o = r[1], r = [0];
                            continue;
                        case 7:
                            r = u.ops.pop(), u.trys.pop();
                            continue;
                        default:
                            if (i = u.trys, !(i = i.length > 0 && i[i.length - 1]) && (6 === r[0] || 2 === r[0])) {
                                u = 0;
                                continue
                            }
                            if (3 === r[0] && (!i || r[1] > i[0] && r[1] < i[3])) {
                                u.label = r[1];
                                break
                            }
                            if (6 === r[0] && u.label < i[1]) {
                                u.label = i[1], i = r;
                                break
                            }
                            if (i && u.label < i[2]) {
                                u.label = i[2], u.ops.push(r);
                                break
                            }
                            i[2] && u.ops.pop(), u.trys.pop();
                            continue
                    }
                    r = t.call(e, u)
                } catch (e) {
                    r = [6, e], o = 0
                } finally {
                    a = i = 0
                }
                if (5 & r[0]) throw r[1];
                return {
                    value: r[0] ? r[1] : void 0,
                    done: !0
                }
            }
            var a, o, i, s, u = {
                label: 0,
                sent: function() {
                    if (1 & i[0]) throw i[1];
                    return i[1]
                },
                trys: [],
                ops: []
            };
            return s = {
                next: r(0),
                throw: r(1),
                return: r(2)
            }, "function" == typeof Symbol && (s[Symbol.iterator] = function() {
                return this
            }), s
        },
        GraphExecutor = function() {
            function e(e) {
                this.graph = e, this.compiledOrder = [], this._weightMap = {}, this.placeholders = e.placeholders.map(function(e) {
                    return e.name
                }), this.outputs = e.outputs.map(function(e) {
                    return e.name
                }), 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, "inputNodes", {
                get: function() {
                    return this.placeholders
                },
                enumerable: !0,
                configurable: !0
            }), Object.defineProperty(e.prototype, "outputNodes", {
                get: function() {
                    return this.outputs
                },
                enumerable: !0,
                configurable: !0
            }), Object.defineProperty(e.prototype, "isControlFlowModel", {
                get: function() {
                    return this.graph.withControlFlow
                },
                enumerable: !0,
                configurable: !0
            }), e.prototype.compile = function() {
                if (!this.graph.withControlFlow)
                    for (var e = this.graph.inputs.slice(), t = {}; e.length > 0;) {
                        var r = e.pop();
                        t[r.name] = !0, this.compiledOrder.push(r), r.children.forEach(function(r) {
                            !t[r.name] && r.inputNames.every(function(e) {
                                var r = getNodeNameAndIndex(e)[0];
                                return t[r]
                            }) && e.push(r)
                        })
                    }
            }, e.prototype.execute = function(e, t) {
                var r = this;
                return this.checkInput(e), tidy(function() {
                    var n = new ExecutionContext(r._weightMap),
                        a = r.compiledOrder.reduce(function(e, t) {
                            return e[t.name] = executeOp$13(t, e, n), e
                        }, __assign$1({}, r.weightMap, e));
                    return r.findOutputs(a, n, t)
                })
            }, e.prototype.executeAsync = function(e, t) {
                return __awaiter$18(this, void 0, void 0, function() {
                    var r, n, a, o, i, s, u = this;
                    return __generator$18(this, function(l) {
                        switch (l.label) {
                            case 0:
                                return r = new ExecutionContext(this._weightMap), [4, this.executeWithControlFlow(e, r)];
                            case 1:
                                return n = l.sent(), a = this.findOutputs(n, r, t), o = Object.keys(a).map(function(e) {
                                    return a[e].id
                                }), i = Object.keys(e).map(function(t) {
                                    return e[t].map(function(e) {
                                        return e.id
                                    })
                                }), s = [].concat.apply([], i), Object.keys(n).forEach(function(e) {
                                    n[e].forEach(function(e) {
                                        e && -1 === o.indexOf(e.id) && -1 === s.indexOf(e.id) && -1 === u.weightIds.indexOf(e.id) && e.dispose()
                                    })
                                }), [2, a]
                        }
                    })
                })
            }, e.prototype.executeWithControlFlow = function(e, t) {
                return __awaiter$18(this, void 0, void 0, function() {
                    var r, n, a, o, i, s, u, l;
                    return __generator$18(this, function(c) {
                        switch (c.label) {
                            case 0:
                                r = this.graph.inputs.map(function(e) {
                                    return {
                                        node: e,
                                        contexts: t.currentContext
                                    }
                                }), n = __assign$1({}, this.weightMap, e), a = {}, c.label = 1;
                            case 1:
                                return r.length > 0 ? (o = r.pop(), t.currentContext = o.contexts, i = executeOp$13(o.node, n, t), s = getNodeNameAndIndex(o.node.name, t)[0], u = n, l = s, [4, i]) : [3, 3];
                            case 2:
                                return u[l] = c.sent(), o.node.children.forEach(function(e) {
                                    var o = getNodeNameAndIndex(e.name, t)[0];
                                    a[o] || ("merge" === e.op ? e.inputNames.some(function(e) {
                                        return !!getTensor(e, n, t)
                                    }) && (a[o] = !0, r.push({
                                        contexts: t.currentContext,
                                        node: e
                                    })) : e.inputNames.every(function(e) {
                                        return !!getTensor(e, n, t)
                                    }) && (a[o] = !0, r.push({
                                        contexts: t.currentContext,
                                        node: e
                                    })))
                                }), [3, 1];
                            case 3:
                                return [2, n]
                        }
                    })
                })
            }, e.prototype.findOutputs = function(e, t, r) {
                return !r || r instanceof Array || (r = [r]), (r || this.graph.outputs.map(function(e) {
                    return e.name
                })).reduce(function(r, n) {
                    return r[n] = getTensor(n, e, t), r
                }, {})
            }, 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.checkInput = function(e) {
                var t = this,
                    r = Object.keys(e),
                    n = [],
                    a = [];
                if (this.placeholders.forEach(function(e) {
                        -1 === r.indexOf(e) && n.push(e)
                    }), r.forEach(function(e) {
                        -1 === t.placeholders.indexOf(e) && a.push(e)
                    }), n.length > 0) throw new Error("The dict provided in model.execute(dict) has the keys [" + r + "], but is missing the required keys: [" + n + "].");
                if (a.length > 0) throw new Error("The dict provided in model.execute(dict) has unused keys: [" + a + "]. Please provide only the following keys: [" + this.placeholders + "].")
            }, e
        }(),
        __awaiter$19 = function(e, t, r, n) {
            return new(r || (r = Promise))(function(a, o) {
                function i(e) {
                    try {
                        u(n.next(e))
                    } catch (e) {
                        o(e)
                    }
                }

                function s(e) {
                    try {
                        u(n.throw(e))
                    } catch (e) {
                        o(e)
                    }
                }

                function u(e) {
                    e.done ? a(e.value) : new r(function(t) {
                        t(e.value)
                    }).then(i, s)
                }
                u((n = n.apply(e, t || [])).next())
            })
        },
        __generator$19 = function(e, t) {
            function r(e) {
                return function(t) {
                    return n([e, t])
                }
            }

            function n(r) {
                if (a) throw new TypeError("Generator is already executing.");
                for (; u;) try {
                    if (a = 1, o && (i = o[2 & r[0] ? "return" : r[0] ? "throw" : "next"]) && !(i = i.call(o, r[1])).done) return i;
                    switch (o = 0, i && (r = [0, i.value]), r[0]) {
                        case 0:
                        case 1:
                            i = r;
                            break;
                        case 4:
                            return u.label++, {
                                value: r[1],
                                done: !1
                            };
                        case 5:
                            u.label++, o = r[1], r = [0];
                            continue;
                        case 7:
                            r = u.ops.pop(), u.trys.pop();
                            continue;
                        default:
                            if (i = u.trys, !(i = i.length > 0 && i[i.length - 1]) && (6 === r[0] || 2 === r[0])) {
                                u = 0;
                                continue
                            }
                            if (3 === r[0] && (!i || r[1] > i[0] && r[1] < i[3])) {
                                u.label = r[1];
                                break
                            }
                            if (6 === r[0] && u.label < i[1]) {
                                u.label = i[1], i = r;
                                break
                            }
                            if (i && u.label < i[2]) {
                                u.label = i[2], u.ops.push(r);
                                break
                            }
                            i[2] && u.ops.pop(), u.trys.pop();
                            continue
                    }
                    r = t.call(e, u)
                } catch (e) {
                    r = [6, e], o = 0
                } finally {
                    a = i = 0
                }
                if (5 & r[0]) throw r[1];
                return {
                    value: r[0] ? r[1] : void 0,
                    done: !0
                }
            }
            var a, o, i, s, u = {
                label: 0,
                sent: function() {
                    if (1 & i[0]) throw i[1];
                    return i[1]
                },
                trys: [],
                ops: []
            };
            return s = {
                next: r(0),
                throw: r(1),
                return: r(2)
            }, "function" == typeof Symbol && (s[Symbol.iterator] = function() {
                return this
            }), s
        },
        FrozenModel = function() {
            function e(e, t, r) {
                this.modelUrl = e, this.weightManifestUrl = t, this.requestOption = r, this.version = "n/a", this.pathPrefix = this.getPathPrefix()
            }
            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
            }), e.prototype.getPathPrefix = function() {
                var e = parse(this.weightManifestUrl),
                    t = e.pathname.split("/");
                return t.splice(-1), e.pathname = t.join("/"), format(e) + "/"
            }, e.prototype.loadRemoteProtoFile = function() {
                return __awaiter$19(this, void 0, void 0, function() {
                    var e, t, r, n, a;
                    return __generator$19(this, function(o) {
                        switch (o.label) {
                            case 0:
                                return o.trys.push([0, 3, , 4]), [4, fetch(this.modelUrl, this.requestOption)];
                            case 1:
                                return e = o.sent(), r = (t = tensorflow.GraphDef).decode, n = Uint8Array.bind, [4, e.arrayBuffer()];
                            case 2:
                                return [2, r.apply(t, [new(n.apply(Uint8Array, [void 0, o.sent()]))])];
                            case 3:
                                throw a = o.sent(), new Error(this.modelUrl + " not found. " + a);
                            case 4:
                                return [2]
                        }
                    })
                })
            }, e.prototype.loadWeightManifest = function() {
                return __awaiter$19(this, void 0, void 0, function() {
                    var e, t, r;
                    return __generator$19(this, function(n) {
                        switch (n.label) {
                            case 0:
                                return n.trys.push([0, 3, , 4]), [4, fetch(this.weightManifestUrl, this.requestOption)];
                            case 1:
                                return e = n.sent(), t = this, [4, e.clone().json()];
                            case 2:
                                return t.weightManifest = n.sent(), [3, 4];
                            case 3:
                                throw r = n.sent(), new Error(this.weightManifestUrl + " not found. " + r);
                            case 4:
                                return [2]
                        }
                    })
                })
            }, e.prototype.load = function() {
                return __awaiter$19(this, void 0, void 0, function() {
                    var e, t, r, n;
                    return __generator$19(this, function(a) {
                        switch (a.label) {
                            case 0:
                                return e = this.loadRemoteProtoFile(), t = this.loadWeightManifest(), [4, Promise.all([e, t])];
                            case 1:
                                return r = a.sent()[0], this.version = r.versions.producer + "." + r.versions.minConsumer, [4, loadWeights(this.weightManifest, this.pathPrefix, void 0, this.requestOption)];
                            case 2:
                                return n = a.sent(), this.executor = new GraphExecutor(OperationMapper.Instance.transformGraph(r)), this.executor.weightMap = this.convertTensorMapToTensorsMap(n), [2, !0]
                        }
                    })
                })
            }, e.prototype.predict = function(e, t) {
                return this.execute(e, 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, r, n) {
                    return e[r] = t[n], e
                }, {})
            }, e.prototype.execute = function(e, t) {
                if (t = t || this.outputNodes, (e instanceof Tensor || Array.isArray(e)) && (e = this.constructTensorMap(e)), this.executor.isControlFlowModel) throw new Error("The model contains control flow ops, please use executeAsync method");
                var r = this.executor.execute(this.convertTensorMapToTensorsMap(e), t),
                    n = Object.keys(r);
                return Array.isArray(t) && t.length > 1 ? t.map(function(e) {
                    return r[e]
                }) : r[n[0]]
            }, e.prototype.executeAsync = function(e, t) {
                return __awaiter$19(this, void 0, void 0, function() {
                    var r, n;
                    return __generator$19(this, function(a) {
                        switch (a.label) {
                            case 0:
                                if (!this.executor.isControlFlowModel) throw new Error("The model does not contain control flow 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 r = a.sent(), n = Object.keys(r), [2, Array.isArray(t) && t.length > 1 ? t.map(function(e) {
                                    return r[e]
                                }) : r[n[0]]]
                        }
                    })
                })
            }, e.prototype.convertTensorMapToTensorsMap = function(e) {
                return Object.keys(e).reduce(function(t, r) {
                    return t[r] = [e[r]], t
                }, {})
            }, e.prototype.dispose = function() {
                this.executor.dispose()
            }, e
        }(),
        version$2 = "0.4.1",
        version$3 = "0.11.6",
        version$4 = {
            "tfjs-core": version,
            "tfjs-layers": version$1,
            "tfjs-converter": version$2,
            tfjs: version$3
        };
    exports.version = version$4, exports.setBackend = setBackend, exports.getBackend = getBackend, exports.disposeVariables = disposeVariables, exports.memory = memory, exports.version_core = version, exports.nextFrame = nextFrame, exports.environment = environment, exports.io = io, exports.serialization = serialization, exports.test_util = test_util, exports.util = util, exports.webgl = webgl, exports.AdadeltaOptimizer = AdadeltaOptimizer, exports.AdagradOptimizer = AdagradOptimizer, exports.AdamOptimizer = AdamOptimizer, exports.AdamaxOptimizer = AdamaxOptimizer, exports.MomentumOptimizer = MomentumOptimizer, exports.Optimizer = Optimizer, exports.RMSPropOptimizer = RMSPropOptimizer, exports.SGDOptimizer = SGDOptimizer, exports.Tensor = Tensor, exports.TensorBuffer = TensorBuffer, exports.variable = variable, exports.Variable = Variable, exports.ENV = ENV, exports.Environment = Environment, exports.doc = doc, exports.batchNormalization = batchNormalization, exports.batchNormalization2d = batchNormalization2d, exports.batchNormalization3d = batchNormalization3d, exports.batchNormalization4d = batchNormalization4d, exports.concat = concat, exports.concat1d = concat1d, exports.concat2d = concat2d, exports.concat3d = concat3d, exports.concat4d = concat4d, exports.conv1d = conv1d, exports.conv2d = conv2d, exports.conv2dTranspose = conv2dTranspose, exports.depthwiseConv2d = depthwiseConv2d, exports.separableConv2d = separableConv2d, exports.matMul = matMul, exports.matrixTimesVector = matrixTimesVector, exports.outerProduct = outerProduct, exports.vectorTimesMatrix = vectorTimesMatrix, exports.dot = dot, exports.avgPool = avgPool, exports.maxPool = maxPool, exports.transpose = transpose, exports.reverse = reverse, exports.reverse1d = reverse1d, exports.reverse2d = reverse2d, exports.reverse3d = reverse3d, exports.reverse4d = reverse4d, exports.slice = slice, exports.slice1d = slice1d, exports.slice2d = slice2d, exports.slice3d = slice3d, exports.slice4d = slice4d, exports.stridedSlice = stridedSlice, exports.argMax = argMax, exports.argMin = argMin, exports.logSumExp = logSumExp, exports.max = max, exports.mean = mean, exports.min = min, exports.moments = moments, exports.sum = sum, exports.unsortedSegmentSum = unsortedSegmentSum, exports.equal = equal, exports.equalStrict = equalStrict, exports.greater = greater, exports.greaterStrict = greaterStrict, exports.greaterEqual = greaterEqual, exports.greaterEqualStrict = greaterEqualStrict, exports.less = less, exports.lessStrict = lessStrict, exports.lessEqual = lessEqual, exports.lessEqualStrict = lessEqualStrict, exports.notEqual = notEqual, exports.notEqualStrict = notEqualStrict, exports.logicalNot = logicalNot, exports.logicalAnd = logicalAnd, exports.logicalOr = logicalOr, exports.logicalXor = logicalXor, exports.where = where, exports.abs = abs, exports.acos = acos, exports.acosh = acosh, exports.asin = asin, exports.asinh = asinh, exports.atan = atan, exports.atanh = atanh, exports.ceil = ceil, exports.clipByValue = clipByValue, exports.cos = cos, exports.cosh = cosh, exports.elu = elu, exports.exp = exp, exports.expm1 = expm1, exports.floor = floor, exports.sign = sign, exports.leakyRelu = leakyRelu, exports.log = log, exports.log1p = log1p, exports.logSigmoid = logSigmoid, exports.neg = neg, exports.prelu = prelu, exports.relu = relu, exports.reciprocal = reciprocal, exports.round = round, exports.selu = selu, exports.sigmoid = sigmoid, exports.sin = sin, exports.sinh = sinh, exports.softplus = softplus, exports.sqrt = sqrt, exports.rsqrt = rsqrt, exports.square = square, exports.step = step, exports.tan = tan, exports.tanh = tanh$1, exports.erf = erf, exports.add = add, exports.addStrict = addStrict, exports.atan2 = atan2, exports.div = div, exports.floorDiv = floorDiv, exports.divStrict = divStrict, exports.maximum = maximum, exports.maximumStrict = maximumStrict, exports.minimum = minimum, exports.minimumStrict = minimumStrict, exports.mod = mod, exports.modStrict = modStrict, exports.mul = mul, exports.mulStrict = mulStrict, exports.pow = pow, exports.powStrict = powStrict, exports.sub = sub, exports.subStrict = subStrict, exports.squaredDifference = squaredDifference, exports.squaredDifferenceStrict = squaredDifferenceStrict, exports.norm = norm, exports.cast = cast, exports.clone = clone, exports.fromPixels = fromPixels, exports.toPixels = toPixels, exports.ones = ones, exports.onesLike = onesLike, exports.zeros = zeros, exports.zerosLike = zerosLike, exports.eye = eye, exports.rand = rand, exports.randomNormal = randomNormal, exports.truncatedNormal = truncatedNormal, exports.randomUniform = randomUniform, exports.multinomial = multinomial, exports.reshape = reshape, exports.squeeze = squeeze, exports.tile = tile, exports.gather = gather, exports.oneHot = oneHot, exports.linspace = linspace, exports.range = range, exports.buffer = buffer, exports.fill = fill, exports.tensor = tensor, exports.scalar = scalar, exports.tensor1d = tensor1d, exports.tensor2d = tensor2d, exports.tensor3d = tensor3d, exports.tensor4d = tensor4d, exports.tensor5d = tensor5d, exports.print = print, exports.expandDims = expandDims, exports.stack = stack, exports.unstack = unstack, exports.split = split, exports.cumsum = cumsum, exports.pad = pad, exports.pad1d = pad1d, exports.pad2d = pad2d, exports.pad3d = pad3d, exports.pad4d = pad4d, exports.movingAverage = movingAverage, exports.basicLSTMCell = basicLSTMCell, exports.multiRNNCell = multiRNNCell, exports.softmax = softmax, exports.localResponseNormalization = localResponseNormalization, exports.linalg = linalg, exports.losses = losses, exports.image = image, exports.operation = operation, exports.train = train, exports.tidy = tidy, exports.keep = keep, exports.dispose = dispose, exports.time = time, exports.grad = grad, exports.valueAndGrad = valueAndGrad, exports.grads = grads, exports.valueAndGrads = valueAndGrads, exports.variableGrads = variableGrads, exports.customGrad = customGrad, exports.model = model, exports.sequential = sequential, exports.loadModel = loadModel, exports.input = input, exports.layers = layers, exports.constraints = constraints, exports.initializers = initializers, exports.metrics = metrics, exports.regularizers = regularizers, exports.Callback = Callback, exports.CallbackList = CallbackList, exports.CustomCallback = CustomCallback, exports.Model = Model, exports.RNN = RNN, exports.Sequential = Sequential, exports.SymbolicTensor = SymbolicTensor, exports.version_layers = version$1, exports.FrozenModel = FrozenModel, exports.loadFrozenModel = loadFrozenModel, exports.version_converter = version$2, Object.defineProperty(exports, "__esModule", {
        value: !0
    })
});