/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
import * as tf from '../../index';
import { ALL_ENVS, describeWithFlags } from '../../jasmine_util';
import { expectArraysClose } from '../../test_util';
function generateCaseInputs(totalSizeTensor, totalSizeFilter) {
    const inp = new Array(totalSizeTensor);
    const filt = new Array(totalSizeFilter);
    for (let i = 0; i < totalSizeTensor; i++) {
        inp[i] = i * 0.001 - totalSizeTensor * 0.001 / 2;
    }
    for (let i = 0; i < totalSizeFilter; i++) {
        const sign = i % 2 === 0 ? -1 : 1;
        filt[i] = i * 0.001 * sign;
    }
    return { input: inp, filter: filt };
}
describeWithFlags('fused conv2d', ALL_ENVS, () => {
    it('basic', async () => {
        const inputDepth = 2;
        const inShape = [2, 2, 2, inputDepth];
        const outputDepth = 2;
        const fSize = 1;
        const pad = 0;
        const stride = 1;
        const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inShape);
        const w = tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth, outputDepth]);
        const result = tf.fused.conv2d({ x, filter: w, strides: stride, pad });
        expect(result.shape).toEqual([2, 2, 2, 2]);
        const expected = [-5, 2, -11, 5, -17, 8, -23, 11, -29, 14, -35, 17, -41, 20, -47, 23];
        expectArraysClose(await result.data(), expected);
    });
    it('basic with relu', async () => {
        const inputDepth = 2;
        const inShape = [2, 2, 2, inputDepth];
        const outputDepth = 2;
        const fSize = 1;
        const pad = 0;
        const stride = 1;
        const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inShape);
        const w = tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth, outputDepth]);
        const result = tf.fused.conv2d({
            x,
            filter: w,
            strides: stride,
            pad,
            dataFormat: 'NHWC',
            dilations: [1, 1],
            activation: 'relu'
        });
        expect(result.shape).toEqual([2, 2, 2, 2]);
        const expected = [0, 2, 0, 5, 0, 8, 0, 11, 0, 14, 0, 17, 0, 20, 0, 23];
        expectArraysClose(await result.data(), expected);
    });
    it('relu with stride 2 x=[1,8,8,16] f=[3,3,16,1] s=[2,2] d=1 p=same', async () => {
        const inputDepth = 16;
        const xSize = 8;
        const inputShape = [1, xSize, xSize, inputDepth];
        const outputDepth = 1;
        const fSize = 3;
        const pad = 'same';
        const stride = [2, 2];
        // TODO(annxingyuan): Make this test work with large inputs
        // https://github.com/tensorflow/tfjs/issues/3143
        const inputData = [];
        for (let i = 0; i < xSize * xSize * inputDepth; i++) {
            inputData.push(i % 5);
        }
        const wData = [];
        for (let i = 0; i < fSize * fSize * inputDepth * outputDepth; i++) {
            wData.push(i % 5);
        }
        const x = tf.tensor4d(inputData, inputShape);
        const w = tf.tensor4d(wData, [fSize, fSize, inputDepth, outputDepth]);
        const result = tf.fused.conv2d({
            x,
            filter: w,
            strides: stride,
            pad,
            dataFormat: 'NHWC',
            dilations: [1, 1],
            activation: 'relu'
        });
        expect(result.shape).toEqual([1, 4, 4, 1]);
        expectArraysClose(await result.data(), new Float32Array([
            854, 431, 568, 382, 580, 427, 854, 288, 431, 568,
            580, 289, 285, 570, 285, 258
        ]));
    });
    it('relu bias stride 2 x=[1,8,8,16] f=[3,3,16,1] s=[2,2] d=8 p=same', async () => {
        const inputDepth = 16;
        const xSize = 8;
        const inputShape = [1, xSize, xSize, inputDepth];
        const outputDepth = 8;
        const fSize = 3;
        const pad = 'same';
        const stride = [2, 2];
        const inputs = generateCaseInputs(1 * xSize * xSize * inputDepth, fSize * fSize * inputDepth * outputDepth);
        const x = tf.tensor4d(inputs.input, inputShape);
        const w = tf.tensor4d(inputs.filter, [fSize, fSize, inputDepth, outputDepth]);
        const bias = tf.tensor1d([1, 4, 2, 3, 9, 6, 5, 8]);
        const result = tf.fused.conv2d({
            x,
            filter: w,
            strides: stride,
            pad,
            dataFormat: 'NHWC',
            dilations: [1, 1],
            activation: 'relu',
            bias
        });
        expect(result.shape).toEqual([1, 4, 4, 8]);
        expectArraysClose(await result.data(), new Float32Array([
            25.75398063659668,
            0,
            26.857805252075195,
            0,
            33.961631774902344,
            0,
            30.065458297729492,
            0,
            23.118206024169922,
            0,
            24.212820053100586,
            0,
            31.307422637939453,
            0,
            27.402034759521484,
            0,
            20.482431411743164,
            0,
            21.567821502685547,
            0,
            28.653217315673828,
            0,
            24.73861312866211,
            0,
            11.078080177307129,
            0,
            12.130399703979492,
            0,
            19.182720184326172,
            0,
            15.235037803649902,
            0,
            4.6677775382995605,
            0.31717729568481445,
            5.697869777679443,
            0,
            12.727968215942383,
            2.2569849491119385,
            8.758066177368164,
            4.226885795593262,
            2.0319995880126953,
            2.9575586318969727,
            3.052880048751831,
            1.9366796016693115,
            10.073760032653809,
            4.915799617767334,
            6.094639778137207,
            6.89492130279541,
            0,
            5.5979437828063965,
            0.4078875780105591,
            4.586280822753906,
            7.419551849365234,
            7.5746169090271,
            3.43121600151062,
            9.562952041625977,
            0,
            6.404943943023682,
            0,
            5.401776313781738,
            6.5998077392578125,
            8.398608207702637,
            2.602976083755493,
            10.395440101623535,
            0,
            21.440250396728516,
            0,
            20.483882904052734,
            0,
            23.527509689331055,
            0,
            25.571144104003906,
            0,
            24.080629348754883,
            0,
            23.133480072021484,
            0,
            26.186328887939453,
            0,
            28.239177703857422,
            0,
            26.721012115478516,
            0,
            25.783079147338867,
            0,
            28.84514808654785,
            0,
            30.907209396362305,
            0,
            18.914127349853516,
            0,
            17.960111618041992,
            0,
            21.006093978881836,
            0,
            23.052082061767578,
            0,
            17.89089584350586,
            0,
            16.95684814453125,
            0,
            20.022798538208008,
            0,
            22.088754653930664,
            0,
            19.06132698059082,
            0,
            18.133424758911133,
            0,
            21.205520629882812,
            0,
            23.27761459350586,
            0,
            20.23175811767578,
            0,
            19.309999465942383,
            0,
            22.388240814208984,
            0,
            24.46647834777832,
            0,
            13.584352493286133,
            0,
            12.6395845413208,
            0,
            15.694815635681152,
            0,
            17.750045776367188
        ]));
    });
    it('prelu bias stride 2 x=[1,8,8,16] f=[3,3,16,1] s=[2,2] d=8 p=same', async () => {
        const inputDepth = 16;
        const xSize = 8;
        const inputShape = [1, xSize, xSize, inputDepth];
        const outputDepth = 8;
        const fSize = 3;
        const pad = 'same';
        const stride = [2, 2];
        const inputs = generateCaseInputs(1 * xSize * xSize * inputDepth, fSize * fSize * inputDepth * outputDepth);
        const x = tf.tensor4d(inputs.input, inputShape);
        const w = tf.tensor4d(inputs.filter, [fSize, fSize, inputDepth, outputDepth]);
        const bias = tf.tensor1d([1, 4, 2, 3, 9, 6, 5, 8]);
        const preluActivationWeights = tf.tensor1d([1, 2, 3, 4, 5, 6, 7, 8]);
        const result = tf.fused.conv2d({
            x,
            filter: w,
            strides: stride,
            pad,
            dataFormat: 'NHWC',
            dilations: [1, 1],
            activation: 'prelu',
            preluActivationWeights,
            bias
        });
        expect(result.shape).toEqual([1, 4, 4, 8]);
        expectArraysClose(await result.data(), new Float32Array([
            25.75398063659668, -41.61178970336914, 26.857805252075195,
            -87.63885498046875, 33.961631774902344, -114.0812759399414,
            30.065458297729492, -136.93893432617188, 23.118206024169922,
            -36.33102035522461, 24.212820053100586, -77.04048156738281,
            31.307422637939453, -98.12835693359375, 27.402034759521484,
            -115.5947265625, 20.482431411743164, -31.050262451171875,
            21.567821502685547, -66.44209289550781, 28.653217315673828,
            -82.17544555664062, 24.73861312866211, -94.25041198730469,
            11.078080177307129, -12.208478927612305, 12.130399703979492,
            -28.626232147216797, 19.182720184326172, -25.253299713134766,
            15.235037803649902, -18.08960723876953, 4.6677775382995605,
            0.31717729568481445, 5.697869777679443, -2.8516759872436523,
            12.727968215942383, 2.2569849491119385, 8.758066177368164,
            4.226885795593262, 2.0319995880126953, 2.9575586318969727,
            3.052880048751831, 1.9366796016693115, 10.073760032653809,
            4.915799617767334, 6.094639778137207, 6.89492130279541,
            -0.6037763357162476, 5.5979437828063965, 0.4078875780105591,
            4.586280822753906, 7.419551849365234, 7.5746169090271,
            3.43121600151062, 9.562952041625977, -1.4065279960632324,
            6.404943943023682, -1.2100803852081299, 5.401776313781738,
            6.5998077392578125, 8.398608207702637, 2.602976083755493,
            10.395440101623535, -16.418434143066406, 21.440250396728516,
            -46.38618850708008, 20.483882904052734, -42.52848815917969,
            23.527509689331055, -87.84530639648438, 25.571144104003906,
            -19.054208755493164, 24.080629348754883, -54.32115936279297,
            23.133480072021484, -55.79951477050781, 26.186328887939453,
            -106.48924255371094, 28.239177703857422, -21.689987182617188,
            26.721012115478516, -62.25614929199219, 25.783079147338867,
            -69.070556640625, 28.84514808654785, -125.13325500488281,
            30.907209396362305, -13.891133308410645, 18.914127349853516,
            -38.81135940551758, 17.960111618041992, -29.915504455566406,
            21.006093978881836, -70.20361328125, 23.052082061767578,
            -12.857919692993164, 17.89089584350586, -35.771610260009766,
            16.95684814453125, -24.949115753173828, 20.022798538208008,
            -63.39042282104492, 22.088754653930664, -14.02528190612793,
            19.06132698059082, -39.2921257019043, 18.133424758911133,
            -30.847349166870117, 21.205520629882812, -71.69097137451172,
            23.27761459350586, -15.192638397216797, 20.23175811767578,
            -42.8126335144043, 19.309999465942383, -36.74560546875,
            22.388240814208984, -79.99152374267578, 24.46647834777832,
            -8.556736946105957, 13.584352493286133, -22.835901260375977,
            12.6395845413208, -3.336000442504883, 15.694815635681152,
            -33.0570182800293, 17.750045776367188
        ]));
    });
    it('relu6 bias stride 2 x=[1,8,8,16] f=[3,3,16,8] s=[2,2] d=8 p=same', async () => {
        const inputDepth = 16;
        const xSize = 8;
        const inputShape = [1, xSize, xSize, inputDepth];
        const outputDepth = 8;
        const fSize = 3;
        const pad = 'same';
        const stride = [2, 2];
        const inputs = generateCaseInputs(1 * xSize * xSize * inputDepth, fSize * fSize * inputDepth * outputDepth);
        const x = tf.tensor4d(inputs.input, inputShape);
        const w = tf.tensor4d(inputs.filter, [fSize, fSize, inputDepth, outputDepth]);
        const bias = tf.tensor1d([1, 4, 2, 3, 9, 6, 5, 8]);
        const result = tf.fused.conv2d({
            x,
            filter: w,
            strides: stride,
            pad,
            dataFormat: 'NHWC',
            dilations: [1, 1],
            activation: 'relu6',
            bias
        });
        expect(result.shape).toEqual([1, 4, 4, 8]);
        const resultData = await result.data();
        expectArraysClose(resultData, new Float32Array([
            6,
            0,
            6,
            0,
            6,
            0,
            6,
            0,
            6,
            0,
            6,
            0,
            6,
            0,
            6,
            0,
            6,
            0,
            6,
            0,
            6,
            0,
            6,
            0,
            6,
            0,
            6,
            0,
            6,
            0,
            6,
            0,
            4.6677775382995605,
            0.31717729568481445,
            5.697869777679443,
            0,
            6,
            2.2569849491119385,
            6,
            4.226885795593262,
            2.0319995880126953,
            2.9575586318969727,
            3.052880048751831,
            1.9366796016693115,
            6,
            4.915799617767334,
            6,
            6,
            0,
            5.5979437828063965,
            0.4078875780105591,
            4.586280822753906,
            6,
            6,
            3.43121600151062,
            6,
            0,
            6,
            0,
            5.401776313781738,
            6,
            6,
            2.602976083755493,
            6,
            0,
            6,
            0,
            6,
            0,
            6,
            0,
            6,
            0,
            6,
            0,
            6,
            0,
            6,
            0,
            6,
            0,
            6,
            0,
            6,
            0,
            6,
            0,
            6,
            0,
            6,
            0,
            6,
            0,
            6,
            0,
            6,
            0,
            6,
            0,
            6,
            0,
            6,
            0,
            6,
            0,
            6,
            0,
            6,
            0,
            6,
            0,
            6,
            0,
            6,
            0,
            6,
            0,
            6,
            0,
            6,
            0,
            6,
            0,
            6,
            0,
            6,
            0,
            6
        ]));
    });
    it('leakyrelu bias stride 2 x=[1,8,8,16] f=[3,3,16,1] s=[2,2] d=8 p=same', async () => {
        const inputDepth = 16;
        const xSize = 8;
        const inputShape = [1, xSize, xSize, inputDepth];
        const outputDepth = 8;
        const fSize = 3;
        const pad = 'same';
        const stride = [2, 2];
        const inputs = generateCaseInputs(1 * xSize * xSize * inputDepth, fSize * fSize * inputDepth * outputDepth);
        const x = tf.tensor4d(inputs.input, inputShape);
        const w = tf.tensor4d(inputs.filter, [fSize, fSize, inputDepth, outputDepth]);
        const bias = tf.tensor1d([1, 4, 2, 3, 9, 6, 5, 8]);
        const leakyreluAlpha = 0.3;
        const result = tf.fused.conv2d({
            x,
            filter: w,
            strides: stride,
            pad,
            dataFormat: 'NHWC',
            dilations: [1, 1],
            activation: 'leakyrelu',
            leakyreluAlpha,
            bias
        });
        expect(result.shape).toEqual([1, 4, 4, 8]);
        expectArraysClose(await result.data(), new Float32Array([
            25.75398063659668, -6.241768836975098, 26.857805252075195,
            -6.5729146003723145, 33.961631774902344, -5.704063892364502,
            30.065458297729492, -5.135210037231445, 23.118206024169922,
            -5.449653148651123, 24.212820053100586, -5.778036117553711,
            31.307422637939453, -4.906418323516846, 27.402034759521484,
            -4.334802627563477, 20.482431411743164, -4.657539367675781,
            21.567821502685547, -4.983157157897949, 28.653217315673828,
            -4.108772277832031, 24.73861312866211, -3.534390687942505,
            11.078080177307129, -1.8312718868255615, 12.130399703979492,
            -2.1469674110412598, 19.182720184326172, -1.262665033340454,
            15.235037803649902, -0.6783602833747864, 4.6677775382995605,
            0.31717729568481445, 5.697869777679443, -0.21387571096420288,
            12.727968215942383, 2.2569849491119385, 8.758066177368164,
            4.226885795593262, 2.0319995880126953, 2.9575586318969727,
            3.052880048751831, 1.9366796016693115, 10.073760032653809,
            4.915799617767334, 6.094639778137207, 6.89492130279541,
            -0.18113291263580322, 5.5979437828063965, 0.4078875780105591,
            4.586280822753906, 7.419551849365234, 7.5746169090271,
            3.43121600151062, 9.562952041625977, -0.42195841670036316,
            6.404943943023682, -0.12100804597139359, 5.401776313781738,
            6.5998077392578125, 8.398608207702637, 2.602976083755493,
            10.395440101623535, -4.925530433654785, 21.440250396728516,
            -4.6386189460754395, 20.483882904052734, -2.5517091751098633,
            23.527509689331055, -3.764799118041992, 25.571144104003906,
            -5.7162628173828125, 24.080629348754883, -5.432116508483887,
            23.133480072021484, -3.347970962524414, 26.186328887939453,
            -4.5638251304626465, 28.239177703857422, -6.5069966316223145,
            26.721012115478516, -6.225615501403809, 25.783079147338867,
            -4.144233703613281, 28.84514808654785, -5.36285400390625,
            30.907209396362305, -4.167340278625488, 18.914127349853516,
            -3.881135940551758, 17.960111618041992, -1.794930338859558,
            21.006093978881836, -3.0087265968322754, 23.052082061767578,
            -3.8573760986328125, 17.89089584350586, -3.5771610736846924,
            16.95684814453125, -1.4969470500946045, 20.022798538208008,
            -2.7167325019836426, 22.088754653930664, -4.207584857940674,
            19.06132698059082, -3.9292125701904297, 18.133424758911133,
            -1.8508410453796387, 21.205520629882812, -3.0724704265594482,
            23.27761459350586, -4.557791709899902, 20.23175811767578,
            -4.28126335144043, 19.309999465942383, -2.2047364711761475,
            22.388240814208984, -3.428208351135254, 24.46647834777832,
            -2.567021131515503, 13.584352493286133, -2.283590316772461,
            12.6395845413208, -0.20016004145145416, 15.694815635681152,
            -1.41672945022583, 17.750045776367188
        ]));
    });
    it('throws when dimRoundingMode is set and pad is same', () => {
        const inputDepth = 16;
        const xSize = 8;
        const inputShape = [1, xSize, xSize, inputDepth];
        const outputDepth = 8;
        const fSize = 3;
        const pad = 'same';
        const stride = [2, 2];
        const inputs = generateCaseInputs(1 * xSize * xSize * inputDepth, fSize * fSize * inputDepth * outputDepth);
        const x = tf.tensor4d(inputs.input, inputShape);
        const w = tf.tensor4d(inputs.filter, [fSize, fSize, inputDepth, outputDepth]);
        const bias = tf.tensor1d([1, 4, 2, 3, 9, 6, 5, 8]);
        const leakyreluAlpha = 0.3;
        expect(() => tf.fused.conv2d({
            x,
            filter: w,
            strides: stride,
            pad,
            dataFormat: 'NHWC',
            dilations: [1, 1],
            activation: 'leakyrelu',
            leakyreluAlpha,
            bias,
            dimRoundingMode: 'round'
        })).toThrowError();
    });
    it('throws when dimRoundingMode is set and pad is valid', () => {
        const inputDepth = 16;
        const xSize = 8;
        const inputShape = [1, xSize, xSize, inputDepth];
        const outputDepth = 8;
        const fSize = 3;
        const pad = 'valid';
        const stride = [2, 2];
        const inputs = generateCaseInputs(1 * xSize * xSize * inputDepth, fSize * fSize * inputDepth * outputDepth);
        const x = tf.tensor4d(inputs.input, inputShape);
        const w = tf.tensor4d(inputs.filter, [fSize, fSize, inputDepth, outputDepth]);
        const bias = tf.tensor1d([1, 4, 2, 3, 9, 6, 5, 8]);
        const leakyreluAlpha = 0.3;
        expect(() => tf.fused.conv2d({
            x,
            filter: w,
            strides: stride,
            pad,
            dataFormat: 'NHWC',
            dilations: [1, 1],
            activation: 'leakyrelu',
            leakyreluAlpha,
            bias,
            dimRoundingMode: 'round'
        })).toThrowError();
    });
    it('throws when dimRoundingMode is set and pad is a non-integer number', () => {
        const inputDepth = 16;
        const xSize = 8;
        const inputShape = [1, xSize, xSize, inputDepth];
        const outputDepth = 8;
        const fSize = 3;
        const pad = 1.2;
        const stride = [2, 2];
        const inputs = generateCaseInputs(1 * xSize * xSize * inputDepth, fSize * fSize * inputDepth * outputDepth);
        const x = tf.tensor4d(inputs.input, inputShape);
        const w = tf.tensor4d(inputs.filter, [fSize, fSize, inputDepth, outputDepth]);
        const bias = tf.tensor1d([1, 4, 2, 3, 9, 6, 5, 8]);
        const leakyreluAlpha = 0.3;
        expect(() => tf.fused.conv2d({
            x,
            filter: w,
            strides: stride,
            pad,
            dataFormat: 'NHWC',
            dilations: [1, 1],
            activation: 'leakyrelu',
            leakyreluAlpha,
            bias,
            dimRoundingMode: 'round'
        })).toThrowError();
    });
    it('throws when dimRoundingMode is set and pad is explicit by non-integer ' +
        'number', () => {
        const inputDepth = 16;
        const xSize = 8;
        const inputShape = [1, xSize, xSize, inputDepth];
        const outputDepth = 8;
        const fSize = 3;
        const pad = [[0, 0], [0, 2.1], [1, 1], [0, 0]];
        const stride = [2, 2];
        const inputs = generateCaseInputs(1 * xSize * xSize * inputDepth, fSize * fSize * inputDepth * outputDepth);
        const x = tf.tensor4d(inputs.input, inputShape);
        const w = tf.tensor4d(inputs.filter, [fSize, fSize, inputDepth, outputDepth]);
        const bias = tf.tensor1d([1, 4, 2, 3, 9, 6, 5, 8]);
        const leakyreluAlpha = 0.3;
        expect(() => tf.fused.conv2d({
            x,
            filter: w,
            strides: stride,
            pad,
            dataFormat: 'NHWC',
            dilations: [1, 1],
            activation: 'leakyrelu',
            leakyreluAlpha,
            bias,
            dimRoundingMode: 'round'
        })).toThrowError();
    });
    it('basic with bias', async () => {
        const inputDepth = 2;
        const inShape = [2, 2, 2, inputDepth];
        const outputDepth = 2;
        const fSize = 1;
        const pad = 0;
        const stride = 1;
        const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inShape);
        const w = tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth, outputDepth]);
        const result = tf.fused.conv2d({
            x,
            filter: w,
            strides: stride,
            pad,
            dataFormat: 'NHWC',
            dilations: [1, 1],
            bias: tf.tensor1d([5, 6])
        });
        expect(result.shape).toEqual([2, 2, 2, 2]);
        const expected = [0, 8, -6, 11, -12, 14, -18, 17, -24, 20, -30, 23, -36, 26, -42, 29];
        expectArraysClose(await result.data(), expected);
    });
    it('basic with explicit padding', async () => {
        const inputDepth = 1;
        const outputDepth = 1;
        const pad = [[0, 0], [1, 2], [0, 1], [0, 0]];
        const stride = 1;
        const dataFormat = 'NHWC';
        const dilation = 1;
        const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [4, 2, inputDepth]);
        const w = tf.tensor4d([3, 1, 5, 0, 2, 7, 8, 9], [4, 2, inputDepth, outputDepth]);
        const result = tf.fused.conv2d({ x, filter: w, strides: stride, pad, dataFormat, dilations: dilation });
        const resultData = await result.data();
        expect(result.shape).toEqual([4, 2, 1]);
        expectArraysClose(resultData, [133, 66, 200, 102, 108, 58, 56, 58]);
    });
    it('basic with elu', async () => {
        const inputDepth = 2;
        const inShape = [2, 2, 2, inputDepth];
        const outputDepth = 2;
        const fSize = 1;
        const pad = 0;
        const stride = 1;
        const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inShape);
        const w = tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth, outputDepth]);
        const result = tf.fused.conv2d({
            x,
            filter: w,
            strides: stride,
            pad,
            dataFormat: 'NHWC',
            dilations: [1, 1],
            activation: 'elu'
        });
        expect(result.shape).toEqual([2, 2, 2, 2]);
        const expected = [-0.99326, 2, -1, 5, -1, 8, -1, 11, -1, 14, -1, 17, -1, 20, -1, 23];
        expectArraysClose(await result.data(), expected);
    });
    it('basic with prelu', async () => {
        const inputDepth = 2;
        const inShape = [2, 2, 2, inputDepth];
        const outputDepth = 2;
        const fSize = 1;
        const pad = 0;
        const stride = 1;
        const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inShape);
        const alpha = tf.tensor3d([0.25, 0.75], [1, 1, 2]);
        const w = tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth, outputDepth]);
        const result = tf.fused.conv2d({
            x,
            filter: w,
            strides: stride,
            pad,
            dataFormat: 'NHWC',
            dilations: [1, 1],
            activation: 'prelu',
            preluActivationWeights: alpha
        });
        expect(result.shape).toEqual([2, 2, 2, 2]);
        const expected = [
            -1.25, 2, -2.75, 5, -4.25, 8, -5.75, 11, -7.25, 14, -8.75, 17, -10.25, 20,
            -11.75, 23
        ];
        expectArraysClose(await result.data(), expected);
    });
    it('basic with leakyrelu', async () => {
        const inputDepth = 2;
        const inShape = [2, 2, 2, inputDepth];
        const outputDepth = 2;
        const fSize = 1;
        const pad = 0;
        const stride = 1;
        const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inShape);
        const alpha = 0.3;
        const w = tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth, outputDepth]);
        const result = tf.fused.conv2d({
            x,
            filter: w,
            strides: stride,
            pad,
            dataFormat: 'NHWC',
            dilations: [1, 1],
            activation: 'leakyrelu',
            leakyreluAlpha: alpha
        });
        expect(result.shape).toEqual([2, 2, 2, 2]);
        const expected = [
            -1.5, 2, -3.3000001907348633, 5, -5.100000381469727, 8,
            -6.900000095367432, 11, -8.700000762939453, 14, -10.5, 17,
            -12.300000190734863, 20, -14.100000381469727, 23
        ];
        expectArraysClose(await result.data(), expected);
    });
    it('basic with sigmoid', async () => {
        const inputDepth = 2;
        const inShape = [2, 2, 2, inputDepth];
        const outputDepth = 2;
        const fSize = 1;
        const pad = 0;
        const stride = 1;
        const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inShape);
        const alpha = 0.3;
        const w = tf.tensor4d([-0.1, 0.1, -0.2, 0.05], [fSize, fSize, inputDepth, outputDepth]);
        const result = tf.fused.conv2d({
            x,
            filter: w,
            strides: stride,
            pad,
            dataFormat: 'NHWC',
            dilations: [1, 1],
            activation: 'sigmoid',
            leakyreluAlpha: alpha
        });
        expect(result.shape).toEqual([2, 2, 2, 2]);
        const expected = [
            0.3775407, 0.549834, 0.24973989, 0.6224593, 0.15446526, 0.6899744,
            0.09112296, 0.7502601, 0.0521535, 0.80218387, 0.02931219, 0.84553474,
            0.0163025, 0.8807971, 0.0090133, 0.908877
        ];
        expectArraysClose(await result.data(), expected);
    });
    it('basic with broadcasted bias and relu', async () => {
        const inputDepth = 2;
        const inShape = [2, 2, 2, inputDepth];
        const outputDepth = 2;
        const fSize = 1;
        const pad = 0;
        const stride = 1;
        const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inShape);
        const w = tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth, outputDepth]);
        const result = tf.fused.conv2d({
            x,
            filter: w,
            strides: stride,
            pad,
            dataFormat: 'NHWC',
            dilations: [1, 1],
            bias: tf.scalar(5),
            activation: 'relu'
        });
        expect(result.shape).toEqual([2, 2, 2, 2]);
        const expected = [0, 7, 0, 10, 0, 13, 0, 16, 0, 19, 0, 22, 0, 25, 0, 28];
        expectArraysClose(await result.data(), expected);
    });
    it('basic in NCHW', async () => {
        const inputDepth = 2;
        const inShape = [1, inputDepth, 2, 2];
        const outputDepth = 2;
        const fSize = 1;
        const pad = 0;
        const stride = 1;
        const x = tf.tensor4d([1, 3, 5, 7, 2, 4, 6, 8], inShape);
        const w = tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth, outputDepth]);
        const result = tf.fused.conv2d({ x, filter: w, strides: stride, pad, dataFormat: 'NCHW' });
        expect(result.shape).toEqual([1, 2, 2, 2]);
        const expected = [-5, -11, -17, -23, 2, 5, 8, 11];
        expectArraysClose(await result.data(), expected);
    });
    it('basic in NCHW with scalar bias', async () => {
        const inputDepth = 4;
        const inputShape = [1, inputDepth, 2, 2];
        const outputDepth = 4;
        const fSize = 1;
        const pad = 'same';
        const stride = 1;
        const dataFormat = 'NCHW';
        const dilation = 1;
        const x = tf.tensor4d([1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4], inputShape);
        const w = tf.tensor4d([3, 3, 3, 3, 1, 1, 1, 1, 5, 5, 5, 5, 0, 0, 0, 0], [fSize, fSize, inputDepth, outputDepth]);
        const bias = tf.scalar(1);
        const result = tf.fused.conv2d({
            x,
            filter: w,
            strides: stride,
            pad,
            dataFormat,
            dilations: dilation,
            bias
        });
        expect(result.shape).toEqual([1, 4, 2, 2]);
        const expected = [10, 19, 28, 37, 10, 19, 28, 37, 10, 19, 28, 37, 10, 19, 28, 37];
        expectArraysClose(await result.data(), expected);
    });
    it('basic in NCHW with 1-D bias', async () => {
        const inputDepth = 4;
        const inputShape = [1, inputDepth, 2, 2];
        const outputDepth = 4;
        const fSize = 1;
        const pad = 'same';
        const stride = 1;
        const dataFormat = 'NCHW';
        const dilation = 1;
        const x = tf.tensor4d([1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4], inputShape);
        const w = tf.tensor4d([3, 3, 3, 3, 1, 1, 1, 1, 5, 5, 5, 5, 0, 0, 0, 0], [fSize, fSize, inputDepth, outputDepth]);
        const bias = tf.tensor1d([1, 2, 1, 2]);
        const result = tf.fused.conv2d({
            x,
            filter: w,
            strides: stride,
            pad,
            dataFormat,
            dilations: dilation,
            bias
        });
        expect(result.shape).toEqual([1, 4, 2, 2]);
        const expected = [10, 19, 28, 37, 11, 20, 29, 38, 10, 19, 28, 37, 11, 20, 29, 38];
        expectArraysClose(await result.data(), expected);
    });
    it('basic in NCHW with bias and multiple batches', async () => {
        const inputDepth = 4;
        const inputShape = [2, inputDepth, 2, 2];
        const outputDepth = 4;
        const fSize = 1;
        const pad = 'same';
        const stride = 1;
        const dataFormat = 'NCHW';
        const dilation = 1;
        const x = tf.tensor4d([
            1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4,
            1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4
        ], inputShape);
        const w = tf.tensor4d([3, 3, 3, 3, 1, 1, 1, 1, 5, 5, 5, 5, 0, 0, 0, 0], [fSize, fSize, inputDepth, outputDepth]);
        const bias = tf.tensor1d([1, 2, 1, 2]);
        const result = tf.fused.conv2d({
            x,
            filter: w,
            strides: stride,
            pad,
            dataFormat,
            dilations: dilation,
            bias
        });
        expect(result.shape).toEqual([2, 4, 2, 2]);
        const expected = [
            10, 19, 28, 37, 11, 20, 29, 38, 10, 19, 28, 37, 11, 20, 29, 38,
            10, 19, 28, 37, 11, 20, 29, 38, 10, 19, 28, 37, 11, 20, 29, 38
        ];
        expectArraysClose(await result.data(), expected);
    });
    it('basic in NCHW with scalar PReLU actiavation', async () => {
        const inputDepth = 2;
        const inShape = [1, inputDepth, 2, 2];
        const outputDepth = 2;
        const fSize = 1;
        const dataFormat = 'NCHW';
        const dilation = 1;
        const pad = 0;
        const stride = 1;
        const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], inShape);
        const w = tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth, outputDepth]);
        const alpha = tf.scalar(10);
        const result = tf.fused.conv2d({
            x,
            filter: w,
            strides: stride,
            pad,
            dataFormat,
            dilations: dilation,
            activation: 'prelu',
            preluActivationWeights: alpha
        });
        expect(result.shape).toEqual([1, 2, 2, 2]);
        const expected = [-110, -140, -170, -200, 3.5, 5, 6.5, 8];
        expectArraysClose(await result.data(), expected);
    });
    it('basic in NCHW with 1-D PReLU actiavation', async () => {
        const inputDepth = 2;
        const inShape = [1, inputDepth, 2, 2];
        const outputDepth = 2;
        const fSize = 1;
        const dataFormat = 'NCHW';
        const dilation = 1;
        const pad = 0;
        const stride = 1;
        const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], inShape);
        const w = tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth, outputDepth]);
        const alpha = tf.tensor1d([10, 100]);
        const result = tf.fused.conv2d({
            x,
            filter: w,
            strides: stride,
            pad,
            dataFormat,
            dilations: dilation,
            activation: 'prelu',
            preluActivationWeights: alpha
        });
        expect(result.shape).toEqual([1, 2, 2, 2]);
        const expected = [-110, -140, -170, -200, 3.5, 5, 6.5, 8];
        expectArraysClose(await result.data(), expected);
    });
    it('basic in NCHW with 3-D PReLU actiavation', async () => {
        const inputDepth = 2;
        const inShape = [1, inputDepth, 2, 2];
        const outputDepth = 2;
        const fSize = 1;
        const dataFormat = 'NCHW';
        const dilation = 1;
        const pad = 0;
        const stride = 1;
        const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], inShape);
        const w = tf.tensor4d([-1, -1, -2, -2], [fSize, fSize, inputDepth, outputDepth]);
        const alpha = tf.tensor3d([1, 10], [2, 1, 1]);
        const result = tf.fused.conv2d({
            x,
            filter: w,
            strides: stride,
            pad,
            dataFormat,
            dilations: dilation,
            activation: 'prelu',
            preluActivationWeights: alpha
        });
        expect(result.shape).toEqual([1, 2, 2, 2]);
        const expected = [-11, -14, -17, -20, -110, -140, -170, -200];
        expectArraysClose(await result.data(), expected);
    });
    it('basic in NCHW with full 3-D PReLU actiavation', async () => {
        const inputDepth = 2;
        const inShape = [1, inputDepth, 2, 2];
        const outputDepth = 2;
        const fSize = 1;
        const dataFormat = 'NCHW';
        const dilation = 1;
        const pad = 0;
        const stride = 1;
        const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], inShape);
        const w = tf.tensor4d([-1, -1, -2, -2], [fSize, fSize, inputDepth, outputDepth]);
        const alpha = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
        const result = tf.fused.conv2d({
            x,
            filter: w,
            strides: stride,
            pad,
            dataFormat,
            dilations: dilation,
            activation: 'prelu',
            preluActivationWeights: alpha
        });
        expect(result.shape).toEqual([1, 2, 2, 2]);
        const expected = [-11, -28, -51, -80, -55, -84, -119, -160];
        expectArraysClose(await result.data(), expected);
    });
    it('im2row', async () => {
        const inputDepth = 1;
        const inputShape = [4, 4, inputDepth];
        const outputDepth = 3;
        const fSize = 1;
        const pad = 'same';
        const strides = [2, 2];
        const x = tf.tensor3d([
            10, 30, 50, 70, 20, 40, 60, 80, -10, -30, -50, -70, -20, -40, -60, -80
        ], inputShape);
        const w = tf.tensor4d([1, 0.5, 1], [fSize, fSize, inputDepth, outputDepth]);
        const result = tf.fused.conv2d({ x, filter: w, strides, pad });
        expectArraysClose(await result.data(), [10, 5, 10, 50, 25, 50, -10, -5, -10, -50, -25, -50]);
    });
    it('im2row with relu', async () => {
        const inputDepth = 1;
        const inputShape = [4, 4, inputDepth];
        const outputDepth = 3;
        const fSize = 1;
        const pad = 'same';
        const strides = [2, 2];
        const x = tf.tensor3d([
            10, 30, 50, 70, 20, 40, 60, 80, -10, -30, -50, -70, -20, -40, -60, -80
        ], inputShape);
        const w = tf.tensor4d([1, 0.5, 1], [fSize, fSize, inputDepth, outputDepth]);
        const result = tf.fused.conv2d({
            x,
            filter: w,
            strides,
            pad,
            dataFormat: 'NHWC',
            dilations: [1, 1],
            activation: 'relu'
        });
        expectArraysClose(await result.data(), [10, 5, 10, 50, 25, 50, 0, 0, 0, 0, 0, 0]);
    });
    it('im2row with prelu', async () => {
        const inputDepth = 1;
        const inputShape = [4, 4, inputDepth];
        const outputDepth = 3;
        const fSize = 1;
        const pad = 'same';
        const strides = [2, 2];
        const x = tf.tensor3d([
            10, 30, 50, 70, 20, 40, 60, 80, -10, -30, -50, -70, -20, -40, -60, -80
        ], inputShape);
        const w = tf.tensor4d([1, 0.5, 1], [fSize, fSize, inputDepth, outputDepth]);
        const alpha = tf.tensor3d([0.5], [1, 1, inputDepth]);
        const result = tf.fused.conv2d({
            x,
            filter: w,
            strides,
            pad,
            dataFormat: 'NHWC',
            dilations: [1, 1],
            activation: 'prelu',
            preluActivationWeights: alpha
        });
        expectArraysClose(await result.data(), [10, 5, 10, 50, 25, 50, -5, -2.5, -5, -25, -12.5, -25]);
    });
    it('im2row with leakyrelu', async () => {
        const inputDepth = 1;
        const inputShape = [4, 4, inputDepth];
        const outputDepth = 3;
        const fSize = 1;
        const pad = 'same';
        const strides = [2, 2];
        const x = tf.tensor3d([
            10, 30, 50, 70, 20, 40, 60, 80, -10, -30, -50, -70, -20, -40, -60, -80
        ], inputShape);
        const w = tf.tensor4d([1, 0.5, 1], [fSize, fSize, inputDepth, outputDepth]);
        const alpha = 0.3;
        const result = tf.fused.conv2d({
            x,
            filter: w,
            strides,
            pad,
            dataFormat: 'NHWC',
            dilations: [1, 1],
            activation: 'leakyrelu',
            leakyreluAlpha: alpha
        });
        expectArraysClose(await result.data(), [
            10, 5, 10, 50, 25, 50, -3, -1.5, -3, -15.000000953674316,
            -7.500000476837158, -15.000000953674316
        ]);
    });
    it('pointwise with prelu', async () => {
        const inputDepth = 1;
        const inputShape = [4, 4, inputDepth];
        const outputDepth = 3;
        const fSize = 1;
        const pad = 'same';
        const strides = [1, 1];
        const x = tf.tensor3d([
            10, 30, 50, 70, 20, 40, 60, 80, -10, -30, -50, -70, -20, -40, -60, -80
        ], inputShape);
        const w = tf.tensor4d([1, 0.5, 1], [fSize, fSize, inputDepth, outputDepth]);
        const alpha = tf.tensor3d([0.5], [1, 1, inputDepth]);
        const result = tf.fused.conv2d({
            x,
            filter: w,
            strides,
            pad,
            dataFormat: 'NHWC',
            dilations: [1, 1],
            activation: 'prelu',
            preluActivationWeights: alpha
        });
        expectArraysClose(await result.data(), [
            10, 5, 10, 30, 15, 30, 50, 25, 50, 70, 35, 70,
            20, 10, 20, 40, 20, 40, 60, 30, 60, 80, 40, 80,
            -5, -2.5, -5, -15, -7.5, -15, -25, -12.5, -25, -35, -17.5, -35,
            -10, -5, -10, -20, -10, -20, -30, -15, -30, -40, -20, -40
        ]);
    });
    it('pointwise with leakyrelu', async () => {
        const inputDepth = 1;
        const inputShape = [4, 4, inputDepth];
        const outputDepth = 3;
        const fSize = 1;
        const pad = 'same';
        const strides = [1, 1];
        const x = tf.tensor3d([
            10, 30, 50, 70, 20, 40, 60, 80, -10, -30, -50, -70, -20, -40, -60, -80
        ], inputShape);
        const w = tf.tensor4d([1, 0.5, 1], [fSize, fSize, inputDepth, outputDepth]);
        const alpha = 0.3;
        const result = tf.fused.conv2d({
            x,
            filter: w,
            strides,
            pad,
            dataFormat: 'NHWC',
            dilations: [1, 1],
            activation: 'leakyrelu',
            leakyreluAlpha: alpha
        });
        expectArraysClose(await result.data(), [
            10,
            5,
            10,
            30,
            15,
            30,
            50,
            25,
            50,
            70,
            35,
            70,
            20,
            10,
            20,
            40,
            20,
            40,
            60,
            30,
            60,
            80,
            40,
            80,
            -3,
            -1.5,
            -3,
            -9,
            -4.5,
            -9,
            -15.000000953674316,
            -7.500000476837158,
            -15.000000953674316,
            -21,
            -10.5,
            -21,
            -6,
            -3,
            -6,
            -12,
            -6,
            -12,
            -18,
            -9,
            -18,
            -24,
            -12,
            -24
        ]);
    });
    it('im2row with broadcasted bias and relu', async () => {
        const inputDepth = 1;
        const inputShape = [4, 4, inputDepth];
        const outputDepth = 3;
        const fSize = 1;
        const pad = 'same';
        const strides = [2, 2];
        const x = tf.tensor3d([
            10, 30, 50, 70, 20, 40, 60, 80, -10, -30, -50, -70, -20, -40, -60, -80
        ], inputShape);
        const w = tf.tensor4d([1, 0.5, 1], [fSize, fSize, inputDepth, outputDepth]);
        const result = tf.fused.conv2d({
            x,
            filter: w,
            strides,
            pad,
            dataFormat: 'NHWC',
            dilations: [1, 1],
            bias: tf.scalar(5),
            activation: 'relu'
        });
        expectArraysClose(await result.data(), [15, 10, 15, 55, 30, 55, 0, 0, 0, 0, 0, 0]);
    });
    it('im2row in NCHW', async () => {
        const inputDepth = 2;
        const inputShape = [inputDepth, 2, 2];
        const outputDepth = 2;
        const fSize = 2;
        const pad = 'same';
        const strides = [1, 1];
        const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], inputShape);
        const w = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, -1, -2, -3, -4, -5, -6, -7, -8], [fSize, fSize, inputDepth, outputDepth]);
        const result = tf.fused.conv2d({ x, filter: w, strides, pad, dataFormat: 'NCHW' });
        expectArraysClose(await result.data(), [-32, -8, 100, 28, -40, -12, 122, 40]);
    });
    it('im2row in NCHW with scalar bias', async () => {
        const inputDepth = 4;
        const inputShape = [1, inputDepth, 2, 2];
        const outputDepth = 4;
        const fSize = 2;
        const pad = 'same';
        const stride = 1;
        const dataFormat = 'NCHW';
        const dilation = 1;
        const x = tf.tensor4d([1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4], inputShape);
        const w = tf.tensor4d([
            3, 3, 3, 3, 1, 1, 1, 1, 5, 5, 5, 5, 0, 0, 0, 0, 3, 3, 3, 3, 1, 1,
            1, 1, 5, 5, 5, 5, 0, 0, 0, 0, 3, 3, 3, 3, 1, 1, 1, 1, 5, 5, 5, 5,
            0, 0, 0, 0, 3, 3, 3, 3, 1, 1, 1, 1, 5, 5, 5, 5, 0, 0, 0, 0,
        ], [fSize, fSize, inputDepth, outputDepth]);
        const bias = tf.scalar(1);
        const result = tf.fused.conv2d({
            x,
            filter: w,
            strides: stride,
            pad,
            dataFormat,
            dilations: dilation,
            bias
        });
        expect(result.shape).toEqual([1, 4, 2, 2]);
        const expected = [91, 55, 64, 37, 91, 55, 64, 37, 91, 55, 64, 37, 91, 55, 64, 37];
        expectArraysClose(await result.data(), expected);
    });
    it('im2row in NCHW with 1-D bias', async () => {
        const inputDepth = 4;
        const inputShape = [1, inputDepth, 2, 2];
        const outputDepth = 4;
        const fSize = 2;
        const pad = 'same';
        const stride = 1;
        const dataFormat = 'NCHW';
        const dilation = 1;
        const x = tf.tensor4d([1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4], inputShape);
        const w = tf.tensor4d([
            3, 3, 3, 3, 1, 1, 1, 1, 5, 5, 5, 5, 0, 0, 0, 0, 3, 3, 3, 3, 1, 1,
            1, 1, 5, 5, 5, 5, 0, 0, 0, 0, 3, 3, 3, 3, 1, 1, 1, 1, 5, 5, 5, 5,
            0, 0, 0, 0, 3, 3, 3, 3, 1, 1, 1, 1, 5, 5, 5, 5, 0, 0, 0, 0,
        ], [fSize, fSize, inputDepth, outputDepth]);
        const bias = tf.tensor1d([1, 2, 1, 2]);
        const result = tf.fused.conv2d({
            x,
            filter: w,
            strides: stride,
            pad,
            dataFormat,
            dilations: dilation,
            bias
        });
        expect(result.shape).toEqual([1, 4, 2, 2]);
        const expected = [91, 55, 64, 37, 92, 56, 65, 38, 91, 55, 64, 37, 92, 56, 65, 38];
        expectArraysClose(await result.data(), expected);
    });
    it('im2row in NCHW with scalar PReLU actiavation weights', async () => {
        const inputDepth = 2;
        const inShape = [1, inputDepth, 2, 2];
        const outputDepth = 2;
        const fSize = 2;
        const dataFormat = 'NCHW';
        const dilation = 1;
        const pad = 'same';
        const stride = 1;
        const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], inShape);
        const w = tf.tensor4d([-1, -1, -1, -1, 1, 1, 1, 1, -2, -2, -2, -2, 0.5, 0.5, 0.5, 0.5], [fSize, fSize, inputDepth, outputDepth]);
        const alpha = tf.scalar(10);
        const result = tf.fused.conv2d({
            x,
            filter: w,
            strides: stride,
            pad,
            dataFormat,
            dilations: dilation,
            activation: 'prelu',
            preluActivationWeights: alpha
        });
        expect(result.shape).toEqual([1, 2, 2, 2]);
        const expected = [-120, -320, 2, -120, -120, -320, 2, -120];
        expectArraysClose(await result.data(), expected);
    });
    it('im2row in NCHW with 1-D PReLU actiavation weights', async () => {
        const inputDepth = 2;
        const inShape = [1, inputDepth, 2, 2];
        const outputDepth = 2;
        const fSize = 2;
        const dataFormat = 'NCHW';
        const dilation = 1;
        const pad = 'same';
        const stride = 1;
        const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], inShape);
        const w = tf.tensor4d([-1, -1, -1, -1, 1, 1, 1, 1, -2, -2, -2, -2, 0.5, 0.5, 0.5, 0.5], [fSize, fSize, inputDepth, outputDepth]);
        const alpha = tf.tensor1d([1, 10]);
        const result = tf.fused.conv2d({
            x,
            filter: w,
            strides: stride,
            pad,
            dataFormat,
            dilations: dilation,
            activation: 'prelu',
            preluActivationWeights: alpha
        });
        expect(result.shape).toEqual([1, 2, 2, 2]);
        const expected = [-12, -32, 2, -12, -120, -320, 2, -120];
        expectArraysClose(await result.data(), expected);
    });
    it('im2row in NCHW with 3-D PReLU actiavation weights', async () => {
        const inputDepth = 2;
        const inShape = [1, inputDepth, 2, 2];
        const outputDepth = 2;
        const fSize = 2;
        const dataFormat = 'NCHW';
        const dilation = 1;
        const pad = 'same';
        const stride = 1;
        const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], inShape);
        const w = tf.tensor4d([-1, -1, -1, -1, 1, 1, 1, 1, -2, -2, -2, -2, 0.5, 0.5, 0.5, 0.5], [fSize, fSize, inputDepth, outputDepth]);
        const alpha = tf.tensor3d([1, 10], [2, 1, 1]);
        const result = tf.fused.conv2d({
            x,
            filter: w,
            strides: stride,
            pad,
            dataFormat,
            dilations: dilation,
            activation: 'prelu',
            preluActivationWeights: alpha
        });
        expect(result.shape).toEqual([1, 2, 2, 2]);
        const expected = [-12, -32, 2, -12, -120, -320, 2, -120];
        expectArraysClose(await result.data(), expected);
    });
    it('im2row in NCHW with full 3-D PReLU actiavation weights', async () => {
        const inputDepth = 2;
        const inShape = [1, inputDepth, 2, 2];
        const outputDepth = 2;
        const fSize = 2;
        const dataFormat = 'NCHW';
        const dilation = 1;
        const pad = 'same';
        const stride = 1;
        const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], inShape);
        const w = tf.tensor4d([-1, -1, -1, -1, 1, 1, 1, 1, -2, -2, -2, -2, 0.5, 0.5, 0.5, 0.5], [fSize, fSize, inputDepth, outputDepth]);
        const alpha = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
        const result = tf.fused.conv2d({
            x,
            filter: w,
            strides: stride,
            pad,
            dataFormat,
            dilations: dilation,
            activation: 'prelu',
            preluActivationWeights: alpha
        });
        expect(result.shape).toEqual([1, 2, 2, 2]);
        const expected = [-12, -64, 2, -48, -60, -192, 2, -96];
        expectArraysClose(await result.data(), expected);
    });
    it('batch in NCHW', async () => {
        const inputDepth = 2;
        const inputShape = [2, inputDepth, 2, 2];
        const outputDepth = 2;
        const fSize = 2;
        const pad = 'same';
        const strides = [1, 1];
        const dataFormat = 'NCHW';
        const dilation = 1;
        const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inputShape);
        const w = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, -1, -2, -3, -4, -5, -6, -7, -8], [fSize, fSize, inputDepth, outputDepth]);
        const result = tf.fused.conv2d({ x, filter: w, strides, pad, dataFormat, dilations: dilation });
        expect(result.shape).toEqual([2, 2, 2, 2]);
        expectArraysClose(await result.data(), [
            -32, -8, 100, 28, -40, -12, 122, 40, -32, -8, 228, 60, -40, -12, 282, 88
        ]);
    });
    it('batch in NCHW with scalar bias', async () => {
        const inputDepth = 2;
        const inputShape = [2, inputDepth, 2, 2];
        const outputDepth = 2;
        const fSize = 2;
        const pad = 'same';
        const strides = [1, 1];
        const dataFormat = 'NCHW';
        const dilation = 1;
        const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inputShape);
        const w = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, -1, -2, -3, -4, -5, -6, -7, -8], [fSize, fSize, inputDepth, outputDepth]);
        const bias = tf.scalar(1);
        const result = tf.fused.conv2d({ x, filter: w, strides, pad, dataFormat, dilations: dilation, bias });
        expect(result.shape).toEqual([2, 2, 2, 2]);
        expectArraysClose(await result.data(), [
            -31, -7, 101, 29, -39, -11, 123, 41, -31, -7, 229, 61, -39, -11, 283, 89
        ]);
    });
    it('batch in NCHW with 1-D bias', async () => {
        const inputDepth = 2;
        const inputShape = [2, inputDepth, 2, 2];
        const outputDepth = 2;
        const fSize = 2;
        const pad = 'same';
        const strides = [1, 1];
        const dataFormat = 'NCHW';
        const dilation = 1;
        const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inputShape);
        const w = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, -1, -2, -3, -4, -5, -6, -7, -8], [fSize, fSize, inputDepth, outputDepth]);
        const bias = tf.tensor1d([1, 2]);
        const result = tf.fused.conv2d({ x, filter: w, strides, pad, dataFormat, dilations: dilation, bias });
        expect(result.shape).toEqual([2, 2, 2, 2]);
        expectArraysClose(await result.data(), [
            -31, -7, 101, 29, -38, -10, 124, 42, -31, -7, 229, 61, -38, -10, 284, 90
        ]);
    });
    it('batch in NCHW with scalar PReLU actiavation weights', async () => {
        const inputDepth = 2;
        const inputShape = [2, inputDepth, 2, 2];
        const outputDepth = 2;
        const fSize = 2;
        const pad = 'same';
        const strides = [1, 1];
        const dataFormat = 'NCHW';
        const dilation = 1;
        const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inputShape);
        const w = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, -1, -2, -3, -4, -5, -6, -7, -8], [fSize, fSize, inputDepth, outputDepth]);
        const alpha = tf.scalar(10);
        const result = tf.fused.conv2d({
            x,
            filter: w,
            strides,
            pad,
            dataFormat,
            dilations: dilation,
            activation: 'prelu',
            preluActivationWeights: alpha
        });
        expect(result.shape).toEqual([2, 2, 2, 2]);
        expectArraysClose(await result.data(), [
            -320, -80, 100, 28, -400, -120, 122, 40, -320, -80, 228, 60, -400, -120,
            282, 88
        ]);
    });
    it('batch in NCHW with 1-D PReLU actiavation weights', async () => {
        const inputDepth = 2;
        const inputShape = [2, inputDepth, 2, 2];
        const outputDepth = 2;
        const fSize = 2;
        const pad = 'same';
        const strides = [1, 1];
        const dataFormat = 'NCHW';
        const dilation = 1;
        const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inputShape);
        const w = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, -1, -2, -3, -4, -5, -6, -7, -8], [fSize, fSize, inputDepth, outputDepth]);
        const alpha = tf.tensor1d([1, 10]);
        const result = tf.fused.conv2d({
            x,
            filter: w,
            strides,
            pad,
            dataFormat,
            dilations: dilation,
            activation: 'prelu',
            preluActivationWeights: alpha
        });
        expect(result.shape).toEqual([2, 2, 2, 2]);
        expectArraysClose(await result.data(), [
            -32, -8, 100, 28, -400, -120, 122, 40, -32, -8, 228, 60, -400, -120, 282,
            88
        ]);
    });
    it('batch in NCHW with 3-D PReLU actiavation weights', async () => {
        const inputDepth = 2;
        const inputShape = [2, inputDepth, 2, 2];
        const outputDepth = 2;
        const fSize = 2;
        const pad = 'same';
        const strides = [1, 1];
        const dataFormat = 'NCHW';
        const dilation = 1;
        const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inputShape);
        const w = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, -1, -2, -3, -4, -5, -6, -7, -8], [fSize, fSize, inputDepth, outputDepth]);
        const alpha = tf.tensor3d([1, 10], [2, 1, 1]);
        const result = tf.fused.conv2d({
            x,
            filter: w,
            strides,
            pad,
            dataFormat,
            dilations: dilation,
            activation: 'prelu',
            preluActivationWeights: alpha
        });
        expect(result.shape).toEqual([2, 2, 2, 2]);
        expectArraysClose(await result.data(), [
            -32, -8, 100, 28, -400, -120, 122, 40, -32, -8, 228, 60, -400, -120, 282,
            88
        ]);
    });
    it('batch in NCHW with full 3-D PReLU actiavation weights', async () => {
        const inputDepth = 2;
        const inputShape = [2, inputDepth, 2, 2];
        const outputDepth = 2;
        const fSize = 2;
        const pad = 'same';
        const strides = [1, 1];
        const dataFormat = 'NCHW';
        const dilation = 1;
        const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inputShape);
        const w = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, -1, -2, -3, -4, -5, -6, -7, -8], [fSize, fSize, inputDepth, outputDepth]);
        const alpha = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
        const result = tf.fused.conv2d({
            x,
            filter: w,
            strides,
            pad,
            dataFormat,
            dilations: dilation,
            activation: 'prelu',
            preluActivationWeights: alpha
        });
        expect(result.shape).toEqual([2, 2, 2, 2]);
        expectArraysClose(await result.data(), [
            -32, -16, 100, 28, -200, -72, 122, 40, -32, -16, 228, 60, -200, -72, 282,
            88
        ]);
    });
    it('backProp input x=[2,3,3,1] f=[2,2,1,1] s=1 p=0', async () => {
        const inputDepth = 1;
        const outputDepth = 1;
        const inputShape = [2, 3, 3, inputDepth];
        const filterSize = 2;
        const strides = 1;
        const pad = 0;
        const filterShape = [filterSize, filterSize, inputDepth, outputDepth];
        const filter = tf.tensor4d([-1, 1, -2, 0.5], filterShape);
        const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9], inputShape);
        const dy = tf.tensor4d([3, 1, 2, 0, 3, 1, 2, 0], [2, 2, 2, 1]);
        const grads = tf.grads((x) => tf.fused.conv2d({ x, filter, strides, pad }));
        const [dx] = grads([x], dy);
        expect(dx.shape).toEqual(x.shape);
        expectArraysClose(await dx.data(), [-3, 2, 1, -8, 1.5, 0.5, -4, 1, 0, -3, 2, 1, -8, 1.5, 0.5, -4, 1, 0]);
    });
    it('gradient x=[2,3,3,1] f=[2,2,1,1] s=1 p=0', async () => {
        const inputDepth = 1;
        const outputDepth = 1;
        const inputShape = [2, 3, 3, inputDepth];
        const filterSize = 2;
        const strides = 1;
        const pad = 0;
        const filterShape = [filterSize, filterSize, inputDepth, outputDepth];
        const filter = tf.tensor4d([-1, 1, -2, 0.5], filterShape);
        const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9], inputShape);
        const dy = tf.tensor4d([3, 1, 2, 0, 3, 1, 2, 0], [2, 2, 2, 1]);
        const grads = tf.grads((x, filter) => tf.fused.conv2d({ x, filter, strides, pad }));
        const [dx, dfilter] = grads([x, filter], dy);
        expect(dx.shape).toEqual(x.shape);
        expectArraysClose(await dx.data(), [-3, 2, 1, -8, 1.5, 0.5, -4, 1, 0, -3, 2, 1, -8, 1.5, 0.5, -4, 1, 0]);
        expect(dfilter.shape).toEqual(filterShape);
        expectArraysClose(await dfilter.data(), [26, 38, 62, 74]);
    });
    it('gradient x=[2,3,3,1] f=[2,2,1,1] s=1 p=0 with bias', async () => {
        const inputDepth = 1;
        const outputDepth = 1;
        const inputShape = [2, 3, 3, inputDepth];
        const filterSize = 2;
        const strides = 1;
        const pad = 0;
        const filterShape = [filterSize, filterSize, inputDepth, outputDepth];
        const filter = tf.tensor4d([-1, 1, -2, 0.5], filterShape);
        const bias = tf.ones([2, 2, 2, 1]);
        const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9], inputShape);
        const dy = tf.tensor4d([3, 1, 2, 0, 3, 1, 2, 0], [2, 2, 2, 1]);
        const fusedGrads = tf.grads((x, w, b) => tf.fused.conv2d({
            x,
            filter: w,
            strides,
            pad,
            dataFormat: 'NHWC',
            dilations: [1, 1],
            bias: b
        }));
        const [dxFused, dfilterFused, dbiasFused] = fusedGrads([x, filter, bias], dy);
        const grads = tf.grads((x, filter, bias) => {
            const conv = tf.conv2d(x, filter, strides, pad);
            const sum = tf.add(conv, bias);
            return sum;
        });
        const [dx, dfilter, dbias] = grads([x, filter, bias], dy);
        expectArraysClose(await dxFused.array(), await dx.array());
        expectArraysClose(await dfilterFused.array(), await dfilter.array());
        expectArraysClose(await dbiasFused.array(), await dbias.array());
    });
    it('gradient x=[2,3,3,1] f=[2,2,1,1] s=1 p=0 with bias and relu', async () => {
        const inputDepth = 1;
        const outputDepth = 1;
        const inputShape = [2, 3, 3, inputDepth];
        const filterSize = 2;
        const strides = 1;
        const pad = 0;
        const filterShape = [filterSize, filterSize, inputDepth, outputDepth];
        const filter = tf.tensor4d([-1, 1, -2, 0.5], filterShape);
        const bias = tf.ones([2, 2, 2, 1]);
        const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9], inputShape);
        const dy = tf.tensor4d([3, 1, 2, 0, 3, 1, 2, 0], [2, 2, 2, 1]);
        const fusedGrads = tf.grads((x, w, b) => tf.fused.conv2d({
            x,
            filter: w,
            strides,
            pad,
            dataFormat: 'NHWC',
            dilations: [1, 1],
            bias: b,
            activation: 'relu'
        }));
        const [dxFused, dfilterFused, dbiasFused] = fusedGrads([x, filter, bias], dy);
        const grads = tf.grads((x, filter, bias) => {
            const conv = tf.conv2d(x, filter, strides, pad);
            const sum = tf.add(conv, bias);
            return tf.relu(sum);
        });
        const [dx, dfilter, dbias] = grads([x, filter, bias], dy);
        expectArraysClose(await dxFused.array(), await dx.array());
        expectArraysClose(await dfilterFused.array(), await dfilter.array());
        expectArraysClose(await dbiasFused.array(), await dbias.array());
    });
    it('gradient x=[2,3,3,1] f=[2,2,1,1] s=1 p=0 with bias and elu', async () => {
        const inputDepth = 1;
        const outputDepth = 1;
        const inputShape = [2, 3, 3, inputDepth];
        const filterSize = 2;
        const strides = 1;
        const pad = 0;
        const filterShape = [filterSize, filterSize, inputDepth, outputDepth];
        const filter = tf.tensor4d([-1, 1, -2, 0.5], filterShape);
        const bias = tf.ones([2, 2, 2, 1]);
        const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9], inputShape);
        const dy = tf.tensor4d([3, 1, 2, 0, 3, 1, 2, 0], [2, 2, 2, 1]);
        const fusedGrads = tf.grads((x, w, b) => tf.fused.conv2d({
            x,
            filter: w,
            strides,
            pad,
            dataFormat: 'NHWC',
            dilations: [1, 1],
            bias: b,
            activation: 'elu'
        }));
        const [dxFused, dfilterFused, dbiasFused] = fusedGrads([x, filter, bias], dy);
        const grads = tf.grads((x, filter, bias) => {
            const conv = tf.conv2d(x, filter, strides, pad);
            const sum = tf.add(conv, bias);
            return tf.elu(sum);
        });
        const [dx, dfilter, dbias] = grads([x, filter, bias], dy);
        expectArraysClose(await dxFused.array(), await dx.array());
        expectArraysClose(await dfilterFused.array(), await dfilter.array());
        expectArraysClose(await dbiasFused.array(), await dbias.array());
    });
    it('throws when input is int32', async () => {
        const inputDepth = 2;
        const inShape = [2, 2, 2, inputDepth];
        const outputDepth = 2;
        const fSize = 1;
        const pad = 0;
        const stride = 1;
        const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inShape, 'int32');
        const w = tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth, outputDepth]);
        expect(() => tf.fused.conv2d({ x, filter: w, strides: stride, pad }))
            .toThrowError(/Argument 'x' passed to 'conv2d' must be float32/);
    });
    it('throws when filter is int32', async () => {
        const inputDepth = 2;
        const inShape = [2, 2, 2, inputDepth];
        const outputDepth = 2;
        const fSize = 1;
        const pad = 0;
        const stride = 1;
        const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inShape);
        const w = tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth, outputDepth], 'int32');
        expect(() => tf.fused.conv2d({ x, filter: w, strides: stride, pad }))
            .toThrowError(/Argument 'filter' passed to 'conv2d' must be float32/);
    });
});
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* @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as tf from '../../index';\nimport {ALL_ENVS, describeWithFlags} from '../../jasmine_util';\nimport {expectArraysClose} from '../../test_util';\n\nfunction generateCaseInputs(totalSizeTensor: number, totalSizeFilter: number) {\n  const inp = new Array(totalSizeTensor);\n  const filt = new Array(totalSizeFilter);\n\n  for (let i = 0; i < totalSizeTensor; i++) {\n    inp[i] = i * 0.001 - totalSizeTensor * 0.001 / 2;\n  }\n  for (let i = 0; i < totalSizeFilter; i++) {\n    const sign = i % 2 === 0 ? -1 : 1;\n    filt[i] = i * 0.001 * sign;\n  }\n\n  return {input: inp, filter: filt};\n}\n\ndescribeWithFlags('fused conv2d', ALL_ENVS, () => {\n  it('basic', async () => {\n    const inputDepth = 2;\n    const inShape: [number, number, number, number] = [2, 2, 2, inputDepth];\n    const outputDepth = 2;\n    const fSize = 1;\n    const pad = 0;\n    const stride = 1;\n\n    const x = tf.tensor4d(\n        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inShape);\n    const w =\n        tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth, outputDepth]);\n\n    const result = tf.fused.conv2d({x, filter: w, strides: stride, pad});\n    expect(result.shape).toEqual([2, 2, 2, 2]);\n    const expected =\n        [-5, 2, -11, 5, -17, 8, -23, 11, -29, 14, -35, 17, -41, 20, -47, 23];\n\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('basic with relu', async () => {\n    const inputDepth = 2;\n    const inShape: [number, number, number, number] = [2, 2, 2, inputDepth];\n    const outputDepth = 2;\n    const fSize = 1;\n    const pad = 0;\n    const stride = 1;\n\n    const x = tf.tensor4d(\n        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inShape);\n    const w =\n        tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth, outputDepth]);\n\n    const result = tf.fused.conv2d({\n      x,\n      filter: w,\n      strides: stride,\n      pad,\n      dataFormat: 'NHWC',\n      dilations: [1, 1],\n      activation: 'relu'\n    });\n    expect(result.shape).toEqual([2, 2, 2, 2]);\n    const expected = [0, 2, 0, 5, 0, 8, 0, 11, 0, 14, 0, 17, 0, 20, 0, 23];\n\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('relu with stride 2 x=[1,8,8,16] f=[3,3,16,1] s=[2,2] d=1 p=same',\n     async () => {\n       const inputDepth = 16;\n       const xSize = 8;\n       const inputShape: [number, number, number, number] =\n           [1, xSize, xSize, inputDepth];\n       const outputDepth = 1;\n       const fSize = 3;\n       const pad = 'same';\n       const stride: [number, number] = [2, 2];\n\n       // TODO(annxingyuan): Make this test work with large inputs\n       // https://github.com/tensorflow/tfjs/issues/3143\n       const inputData = [];\n       for (let i = 0; i < xSize * xSize * inputDepth; i++) {\n         inputData.push(i % 5);\n       }\n\n       const wData = [];\n       for (let i = 0; i < fSize * fSize * inputDepth * outputDepth; i++) {\n         wData.push(i % 5);\n       }\n\n       const x = tf.tensor4d(inputData, inputShape);\n       const w = tf.tensor4d(wData, [fSize, fSize, inputDepth, outputDepth]);\n\n       const result = tf.fused.conv2d({\n         x,\n         filter: w,\n         strides: stride,\n         pad,\n         dataFormat: 'NHWC',\n         dilations: [1, 1],\n         activation: 'relu'\n       });\n       expect(result.shape).toEqual([1, 4, 4, 1]);\n       expectArraysClose(await result.data(), new Float32Array([\n                           854, 431, 568, 382, 580, 427, 854, 288, 431, 568,\n                           580, 289, 285, 570, 285, 258\n                         ]));\n     });\n\n  it('relu bias stride 2 x=[1,8,8,16] f=[3,3,16,1] s=[2,2] d=8 p=same',\n     async () => {\n       const inputDepth = 16;\n       const xSize = 8;\n       const inputShape: [number, number, number, number] =\n           [1, xSize, xSize, inputDepth];\n       const outputDepth = 8;\n       const fSize = 3;\n       const pad = 'same';\n       const stride: [number, number] = [2, 2];\n\n       const inputs = generateCaseInputs(\n           1 * xSize * xSize * inputDepth,\n           fSize * fSize * inputDepth * outputDepth);\n       const x = tf.tensor4d(inputs.input, inputShape);\n       const w =\n           tf.tensor4d(inputs.filter, [fSize, fSize, inputDepth, outputDepth]);\n       const bias = tf.tensor1d([1, 4, 2, 3, 9, 6, 5, 8]);\n       const result = tf.fused.conv2d({\n         x,\n         filter: w,\n         strides: stride,\n         pad,\n         dataFormat: 'NHWC',\n         dilations: [1, 1],\n         activation: 'relu',\n         bias\n       });\n       expect(result.shape).toEqual([1, 4, 4, 8]);\n       expectArraysClose(await result.data(), new Float32Array([\n                           25.75398063659668,\n                           0,\n                           26.857805252075195,\n                           0,\n                           33.961631774902344,\n                           0,\n                           30.065458297729492,\n                           0,\n                           23.118206024169922,\n                           0,\n                           24.212820053100586,\n                           0,\n                           31.307422637939453,\n                           0,\n                           27.402034759521484,\n                           0,\n                           20.482431411743164,\n                           0,\n                           21.567821502685547,\n                           0,\n                           28.653217315673828,\n                           0,\n                           24.73861312866211,\n                           0,\n                           11.078080177307129,\n                           0,\n                           12.130399703979492,\n                           0,\n                           19.182720184326172,\n                           0,\n                           15.235037803649902,\n                           0,\n                           4.6677775382995605,\n                           0.31717729568481445,\n                           5.697869777679443,\n                           0,\n                           12.727968215942383,\n                           2.2569849491119385,\n                           8.758066177368164,\n                           4.226885795593262,\n                           2.0319995880126953,\n                           2.9575586318969727,\n                           3.052880048751831,\n                           1.9366796016693115,\n                           10.073760032653809,\n                           4.915799617767334,\n                           6.094639778137207,\n                           6.89492130279541,\n                           0,\n                           5.5979437828063965,\n                           0.4078875780105591,\n                           4.586280822753906,\n                           7.419551849365234,\n                           7.5746169090271,\n                           3.43121600151062,\n                           9.562952041625977,\n                           0,\n                           6.404943943023682,\n                           0,\n                           5.401776313781738,\n                           6.5998077392578125,\n                           8.398608207702637,\n                           2.602976083755493,\n                           10.395440101623535,\n                           0,\n                           21.440250396728516,\n                           0,\n                           20.483882904052734,\n                           0,\n                           23.527509689331055,\n                           0,\n                           25.571144104003906,\n                           0,\n                           24.080629348754883,\n                           0,\n                           23.133480072021484,\n                           0,\n                           26.186328887939453,\n                           0,\n                           28.239177703857422,\n                           0,\n                           26.721012115478516,\n                           0,\n                           25.783079147338867,\n                           0,\n                           28.84514808654785,\n                           0,\n                           30.907209396362305,\n                           0,\n                           18.914127349853516,\n                           0,\n                           17.960111618041992,\n                           0,\n                           21.006093978881836,\n                           0,\n                           23.052082061767578,\n                           0,\n                           17.89089584350586,\n                           0,\n                           16.95684814453125,\n                           0,\n                           20.022798538208008,\n                           0,\n                           22.088754653930664,\n                           0,\n                           19.06132698059082,\n                           0,\n                           18.133424758911133,\n                           0,\n                           21.205520629882812,\n                           0,\n                           23.27761459350586,\n                           0,\n                           20.23175811767578,\n                           0,\n                           19.309999465942383,\n                           0,\n                           22.388240814208984,\n                           0,\n                           24.46647834777832,\n                           0,\n                           13.584352493286133,\n                           0,\n                           12.6395845413208,\n                           0,\n                           15.694815635681152,\n                           0,\n                           17.750045776367188\n                         ]));\n     });\n\n  it('prelu bias stride 2 x=[1,8,8,16] f=[3,3,16,1] s=[2,2] d=8 p=same',\n     async () => {\n       const inputDepth = 16;\n       const xSize = 8;\n       const inputShape: [number, number, number, number] =\n           [1, xSize, xSize, inputDepth];\n       const outputDepth = 8;\n       const fSize = 3;\n       const pad = 'same';\n       const stride: [number, number] = [2, 2];\n\n       const inputs = generateCaseInputs(\n           1 * xSize * xSize * inputDepth,\n           fSize * fSize * inputDepth * outputDepth);\n       const x = tf.tensor4d(inputs.input, inputShape);\n       const w =\n           tf.tensor4d(inputs.filter, [fSize, fSize, inputDepth, outputDepth]);\n       const bias = tf.tensor1d([1, 4, 2, 3, 9, 6, 5, 8]);\n       const preluActivationWeights = tf.tensor1d([1, 2, 3, 4, 5, 6, 7, 8]);\n\n       const result = tf.fused.conv2d({\n         x,\n         filter: w,\n         strides: stride,\n         pad,\n         dataFormat: 'NHWC',\n         dilations: [1, 1],\n         activation: 'prelu',\n         preluActivationWeights,\n         bias\n       });\n       expect(result.shape).toEqual([1, 4, 4, 8]);\n       expectArraysClose(\n           await result.data(), new Float32Array([\n             25.75398063659668,   -41.61178970336914,  26.857805252075195,\n             -87.63885498046875,  33.961631774902344,  -114.0812759399414,\n             30.065458297729492,  -136.93893432617188, 23.118206024169922,\n             -36.33102035522461,  24.212820053100586,  -77.04048156738281,\n             31.307422637939453,  -98.12835693359375,  27.402034759521484,\n             -115.5947265625,     20.482431411743164,  -31.050262451171875,\n             21.567821502685547,  -66.44209289550781,  28.653217315673828,\n             -82.17544555664062,  24.73861312866211,   -94.25041198730469,\n             11.078080177307129,  -12.208478927612305, 12.130399703979492,\n             -28.626232147216797, 19.182720184326172,  -25.253299713134766,\n             15.235037803649902,  -18.08960723876953,  4.6677775382995605,\n             0.31717729568481445, 5.697869777679443,   -2.8516759872436523,\n             12.727968215942383,  2.2569849491119385,  8.758066177368164,\n             4.226885795593262,   2.0319995880126953,  2.9575586318969727,\n             3.052880048751831,   1.9366796016693115,  10.073760032653809,\n             4.915799617767334,   6.094639778137207,   6.89492130279541,\n             -0.6037763357162476, 5.5979437828063965,  0.4078875780105591,\n             4.586280822753906,   7.419551849365234,   7.5746169090271,\n             3.43121600151062,    9.562952041625977,   -1.4065279960632324,\n             6.404943943023682,   -1.2100803852081299, 5.401776313781738,\n             6.5998077392578125,  8.398608207702637,   2.602976083755493,\n             10.395440101623535,  -16.418434143066406, 21.440250396728516,\n             -46.38618850708008,  20.483882904052734,  -42.52848815917969,\n             23.527509689331055,  -87.84530639648438,  25.571144104003906,\n             -19.054208755493164, 24.080629348754883,  -54.32115936279297,\n             23.133480072021484,  -55.79951477050781,  26.186328887939453,\n             -106.48924255371094, 28.239177703857422,  -21.689987182617188,\n             26.721012115478516,  -62.25614929199219,  25.783079147338867,\n             -69.070556640625,    28.84514808654785,   -125.13325500488281,\n             30.907209396362305,  -13.891133308410645, 18.914127349853516,\n             -38.81135940551758,  17.960111618041992,  -29.915504455566406,\n             21.006093978881836,  -70.20361328125,     23.052082061767578,\n             -12.857919692993164, 17.89089584350586,   -35.771610260009766,\n             16.95684814453125,   -24.949115753173828, 20.022798538208008,\n             -63.39042282104492,  22.088754653930664,  -14.02528190612793,\n             19.06132698059082,   -39.2921257019043,   18.133424758911133,\n             -30.847349166870117, 21.205520629882812,  -71.69097137451172,\n             23.27761459350586,   -15.192638397216797, 20.23175811767578,\n             -42.8126335144043,   19.309999465942383,  -36.74560546875,\n             22.388240814208984,  -79.99152374267578,  24.46647834777832,\n             -8.556736946105957,  13.584352493286133,  -22.835901260375977,\n             12.6395845413208,    -3.336000442504883,  15.694815635681152,\n             -33.0570182800293,   17.750045776367188\n           ]));\n     });\n\n  it('relu6 bias stride 2 x=[1,8,8,16] f=[3,3,16,8] s=[2,2] d=8 p=same',\n     async () => {\n       const inputDepth = 16;\n       const xSize = 8;\n       const inputShape: [number, number, number, number] =\n           [1, xSize, xSize, inputDepth];\n       const outputDepth = 8;\n       const fSize = 3;\n       const pad = 'same';\n       const stride: [number, number] = [2, 2];\n\n       const inputs = generateCaseInputs(\n           1 * xSize * xSize * inputDepth,\n           fSize * fSize * inputDepth * outputDepth);\n       const x = tf.tensor4d(inputs.input, inputShape);\n       const w =\n           tf.tensor4d(inputs.filter, [fSize, fSize, inputDepth, outputDepth]);\n       const bias = tf.tensor1d([1, 4, 2, 3, 9, 6, 5, 8]);\n\n       const result = tf.fused.conv2d({\n         x,\n         filter: w,\n         strides: stride,\n         pad,\n         dataFormat: 'NHWC',\n         dilations: [1, 1],\n         activation: 'relu6',\n         bias\n       });\n       expect(result.shape).toEqual([1, 4, 4, 8]);\n       const resultData = await result.data();\n       expectArraysClose(resultData, new Float32Array([\n                           6,\n                           0,\n                           6,\n                           0,\n                           6,\n                           0,\n                           6,\n                           0,\n                           6,\n                           0,\n                           6,\n                           0,\n                           6,\n                           0,\n                           6,\n                           0,\n                           6,\n                           0,\n                           6,\n                           0,\n                           6,\n                           0,\n                           6,\n                           0,\n                           6,\n                           0,\n                           6,\n                           0,\n                           6,\n                           0,\n                           6,\n                           0,\n                           4.6677775382995605,\n                           0.31717729568481445,\n                           5.697869777679443,\n                           0,\n                           6,\n                           2.2569849491119385,\n                           6,\n                           4.226885795593262,\n                           2.0319995880126953,\n                           2.9575586318969727,\n                           3.052880048751831,\n                           1.9366796016693115,\n                           6,\n                           4.915799617767334,\n                           6,\n                           6,\n                           0,\n                           5.5979437828063965,\n                           0.4078875780105591,\n                           4.586280822753906,\n                           6,\n                           6,\n                           3.43121600151062,\n                           6,\n                           0,\n                           6,\n                           0,\n                           5.401776313781738,\n                           6,\n                           6,\n                           2.602976083755493,\n                           6,\n                           0,\n                           6,\n                           0,\n                           6,\n                           0,\n                           6,\n                           0,\n                           6,\n                           0,\n                           6,\n                           0,\n                           6,\n                           0,\n                           6,\n                           0,\n                           6,\n                           0,\n                           6,\n                           0,\n                           6,\n                           0,\n                           6,\n                           0,\n                           6,\n                           0,\n                           6,\n                           0,\n                           6,\n                           0,\n                           6,\n                           0,\n                           6,\n                           0,\n                           6,\n                           0,\n                           6,\n                           0,\n                           6,\n                           0,\n                           6,\n                           0,\n                           6,\n                           0,\n                           6,\n                           0,\n                           6,\n                           0,\n                           6,\n                           0,\n                           6,\n                           0,\n                           6,\n                           0,\n                           6,\n                           0,\n                           6,\n                           0,\n                           6,\n                           0,\n                           6,\n                           0,\n                           6,\n                           0,\n                           6\n                         ]));\n     });\n\n  it('leakyrelu bias stride 2 x=[1,8,8,16] f=[3,3,16,1] s=[2,2] d=8 p=same',\n     async () => {\n       const inputDepth = 16;\n       const xSize = 8;\n       const inputShape: [number, number, number, number] =\n           [1, xSize, xSize, inputDepth];\n       const outputDepth = 8;\n       const fSize = 3;\n       const pad = 'same';\n       const stride: [number, number] = [2, 2];\n\n       const inputs = generateCaseInputs(\n           1 * xSize * xSize * inputDepth,\n           fSize * fSize * inputDepth * outputDepth);\n       const x = tf.tensor4d(inputs.input, inputShape);\n       const w =\n           tf.tensor4d(inputs.filter, [fSize, fSize, inputDepth, outputDepth]);\n       const bias = tf.tensor1d([1, 4, 2, 3, 9, 6, 5, 8]);\n       const leakyreluAlpha = 0.3;\n\n       const result = tf.fused.conv2d({\n         x,\n         filter: w,\n         strides: stride,\n         pad,\n         dataFormat: 'NHWC',\n         dilations: [1, 1],\n         activation: 'leakyrelu',\n         leakyreluAlpha,\n         bias\n       });\n       expect(result.shape).toEqual([1, 4, 4, 8]);\n       expectArraysClose(\n           await result.data(), new Float32Array([\n             25.75398063659668,    -6.241768836975098,   26.857805252075195,\n             -6.5729146003723145,  33.961631774902344,   -5.704063892364502,\n             30.065458297729492,   -5.135210037231445,   23.118206024169922,\n             -5.449653148651123,   24.212820053100586,   -5.778036117553711,\n             31.307422637939453,   -4.906418323516846,   27.402034759521484,\n             -4.334802627563477,   20.482431411743164,   -4.657539367675781,\n             21.567821502685547,   -4.983157157897949,   28.653217315673828,\n             -4.108772277832031,   24.73861312866211,    -3.534390687942505,\n             11.078080177307129,   -1.8312718868255615,  12.130399703979492,\n             -2.1469674110412598,  19.182720184326172,   -1.262665033340454,\n             15.235037803649902,   -0.6783602833747864,  4.6677775382995605,\n             0.31717729568481445,  5.697869777679443,    -0.21387571096420288,\n             12.727968215942383,   2.2569849491119385,   8.758066177368164,\n             4.226885795593262,    2.0319995880126953,   2.9575586318969727,\n             3.052880048751831,    1.9366796016693115,   10.073760032653809,\n             4.915799617767334,    6.094639778137207,    6.89492130279541,\n             -0.18113291263580322, 5.5979437828063965,   0.4078875780105591,\n             4.586280822753906,    7.419551849365234,    7.5746169090271,\n             3.43121600151062,     9.562952041625977,    -0.42195841670036316,\n             6.404943943023682,    -0.12100804597139359, 5.401776313781738,\n             6.5998077392578125,   8.398608207702637,    2.602976083755493,\n             10.395440101623535,   -4.925530433654785,   21.440250396728516,\n             -4.6386189460754395,  20.483882904052734,   -2.5517091751098633,\n             23.527509689331055,   -3.764799118041992,   25.571144104003906,\n             -5.7162628173828125,  24.080629348754883,   -5.432116508483887,\n             23.133480072021484,   -3.347970962524414,   26.186328887939453,\n             -4.5638251304626465,  28.239177703857422,   -6.5069966316223145,\n             26.721012115478516,   -6.225615501403809,   25.783079147338867,\n             -4.144233703613281,   28.84514808654785,    -5.36285400390625,\n             30.907209396362305,   -4.167340278625488,   18.914127349853516,\n             -3.881135940551758,   17.960111618041992,   -1.794930338859558,\n             21.006093978881836,   -3.0087265968322754,  23.052082061767578,\n             -3.8573760986328125,  17.89089584350586,    -3.5771610736846924,\n             16.95684814453125,    -1.4969470500946045,  20.022798538208008,\n             -2.7167325019836426,  22.088754653930664,   -4.207584857940674,\n             19.06132698059082,    -3.9292125701904297,  18.133424758911133,\n             -1.8508410453796387,  21.205520629882812,   -3.0724704265594482,\n             23.27761459350586,    -4.557791709899902,   20.23175811767578,\n             -4.28126335144043,    19.309999465942383,   -2.2047364711761475,\n             22.388240814208984,   -3.428208351135254,   24.46647834777832,\n             -2.567021131515503,   13.584352493286133,   -2.283590316772461,\n             12.6395845413208,     -0.20016004145145416, 15.694815635681152,\n             -1.41672945022583,    17.750045776367188\n           ]));\n     });\n\n  it('throws when dimRoundingMode is set and pad is same', () => {\n    const inputDepth = 16;\n    const xSize = 8;\n    const inputShape: [number, number, number, number] =\n        [1, xSize, xSize, inputDepth];\n    const outputDepth = 8;\n    const fSize = 3;\n    const pad = 'same';\n    const stride: [number, number] = [2, 2];\n\n    const inputs = generateCaseInputs(\n        1 * xSize * xSize * inputDepth,\n        fSize * fSize * inputDepth * outputDepth);\n    const x = tf.tensor4d(inputs.input, inputShape);\n    const w =\n        tf.tensor4d(inputs.filter, [fSize, fSize, inputDepth, outputDepth]);\n    const bias = tf.tensor1d([1, 4, 2, 3, 9, 6, 5, 8]);\n    const leakyreluAlpha = 0.3;\n\n    expect(() => tf.fused.conv2d({\n      x,\n      filter: w,\n      strides: stride,\n      pad,\n      dataFormat: 'NHWC',\n      dilations: [1, 1],\n      activation: 'leakyrelu',\n      leakyreluAlpha,\n      bias,\n      dimRoundingMode: 'round'\n    })).toThrowError();\n  });\n\n  it('throws when dimRoundingMode is set and pad is valid', () => {\n    const inputDepth = 16;\n    const xSize = 8;\n    const inputShape: [number, number, number, number] =\n        [1, xSize, xSize, inputDepth];\n    const outputDepth = 8;\n    const fSize = 3;\n    const pad = 'valid';\n    const stride: [number, number] = [2, 2];\n\n    const inputs = generateCaseInputs(\n        1 * xSize * xSize * inputDepth,\n        fSize * fSize * inputDepth * outputDepth);\n    const x = tf.tensor4d(inputs.input, inputShape);\n    const w =\n        tf.tensor4d(inputs.filter, [fSize, fSize, inputDepth, outputDepth]);\n    const bias = tf.tensor1d([1, 4, 2, 3, 9, 6, 5, 8]);\n    const leakyreluAlpha = 0.3;\n\n    expect(() => tf.fused.conv2d({\n      x,\n      filter: w,\n      strides: stride,\n      pad,\n      dataFormat: 'NHWC',\n      dilations: [1, 1],\n      activation: 'leakyrelu',\n      leakyreluAlpha,\n      bias,\n      dimRoundingMode: 'round'\n    })).toThrowError();\n  });\n\n  it('throws when dimRoundingMode is set and pad is a non-integer number',\n     () => {\n       const inputDepth = 16;\n       const xSize = 8;\n       const inputShape: [number, number, number, number] =\n           [1, xSize, xSize, inputDepth];\n       const outputDepth = 8;\n       const fSize = 3;\n       const pad = 1.2;\n       const stride: [number, number] = [2, 2];\n\n       const inputs = generateCaseInputs(\n           1 * xSize * xSize * inputDepth,\n           fSize * fSize * inputDepth * outputDepth);\n       const x = tf.tensor4d(inputs.input, inputShape);\n       const w =\n           tf.tensor4d(inputs.filter, [fSize, fSize, inputDepth, outputDepth]);\n       const bias = tf.tensor1d([1, 4, 2, 3, 9, 6, 5, 8]);\n       const leakyreluAlpha = 0.3;\n\n       expect(() => tf.fused.conv2d({\n         x,\n         filter: w,\n         strides: stride,\n         pad,\n         dataFormat: 'NHWC',\n         dilations: [1, 1],\n         activation: 'leakyrelu',\n         leakyreluAlpha,\n         bias,\n         dimRoundingMode: 'round'\n       })).toThrowError();\n     });\n\n  it('throws when dimRoundingMode is set and pad is explicit by non-integer ' +\n         'number',\n     () => {\n       const inputDepth = 16;\n       const xSize = 8;\n       const inputShape: [number, number, number, number] =\n           [1, xSize, xSize, inputDepth];\n       const outputDepth = 8;\n       const fSize = 3;\n       const pad = [[0, 0], [0, 2.1], [1, 1], [0, 0]] as\n           tf.backend_util.ExplicitPadding;\n       const stride: [number, number] = [2, 2];\n\n       const inputs = generateCaseInputs(\n           1 * xSize * xSize * inputDepth,\n           fSize * fSize * inputDepth * outputDepth);\n       const x = tf.tensor4d(inputs.input, inputShape);\n       const w =\n           tf.tensor4d(inputs.filter, [fSize, fSize, inputDepth, outputDepth]);\n       const bias = tf.tensor1d([1, 4, 2, 3, 9, 6, 5, 8]);\n       const leakyreluAlpha = 0.3;\n\n       expect(() => tf.fused.conv2d({\n         x,\n         filter: w,\n         strides: stride,\n         pad,\n         dataFormat: 'NHWC',\n         dilations: [1, 1],\n         activation: 'leakyrelu',\n         leakyreluAlpha,\n         bias,\n         dimRoundingMode: 'round'\n       })).toThrowError();\n     });\n\n  it('basic with bias', async () => {\n    const inputDepth = 2;\n    const inShape: [number, number, number, number] = [2, 2, 2, inputDepth];\n    const outputDepth = 2;\n    const fSize = 1;\n    const pad = 0;\n    const stride = 1;\n\n    const x = tf.tensor4d(\n        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inShape);\n    const w =\n        tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth, outputDepth]);\n\n    const result = tf.fused.conv2d({\n      x,\n      filter: w,\n      strides: stride,\n      pad,\n      dataFormat: 'NHWC',\n      dilations: [1, 1],\n      bias: tf.tensor1d([5, 6])\n    });\n    expect(result.shape).toEqual([2, 2, 2, 2]);\n    const expected =\n        [0, 8, -6, 11, -12, 14, -18, 17, -24, 20, -30, 23, -36, 26, -42, 29];\n\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('basic with explicit padding', async () => {\n    const inputDepth = 1;\n    const outputDepth = 1;\n    const pad =\n        [[0, 0], [1, 2], [0, 1], [0, 0]] as tf.backend_util.ExplicitPadding;\n    const stride = 1;\n    const dataFormat = 'NHWC';\n    const dilation = 1;\n\n    const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [4, 2, inputDepth]);\n    const w =\n        tf.tensor4d([3, 1, 5, 0, 2, 7, 8, 9], [4, 2, inputDepth, outputDepth]);\n\n    const result = tf.fused.conv2d(\n        {x, filter: w, strides: stride, pad, dataFormat, dilations: dilation});\n\n    const resultData = await result.data();\n    expect(result.shape).toEqual([4, 2, 1]);\n    expectArraysClose(resultData, [133, 66, 200, 102, 108, 58, 56, 58]);\n  });\n\n  it('basic with elu', async () => {\n    const inputDepth = 2;\n    const inShape: [number, number, number, number] = [2, 2, 2, inputDepth];\n    const outputDepth = 2;\n    const fSize = 1;\n    const pad = 0;\n    const stride = 1;\n\n    const x = tf.tensor4d(\n        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inShape);\n    const w =\n        tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth, outputDepth]);\n\n    const result = tf.fused.conv2d({\n      x,\n      filter: w,\n      strides: stride,\n      pad,\n      dataFormat: 'NHWC',\n      dilations: [1, 1],\n      activation: 'elu'\n    });\n    expect(result.shape).toEqual([2, 2, 2, 2]);\n    const expected =\n        [-0.99326, 2, -1, 5, -1, 8, -1, 11, -1, 14, -1, 17, -1, 20, -1, 23];\n\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('basic with prelu', async () => {\n    const inputDepth = 2;\n    const inShape: [number, number, number, number] = [2, 2, 2, inputDepth];\n    const outputDepth = 2;\n    const fSize = 1;\n    const pad = 0;\n    const stride = 1;\n\n    const x = tf.tensor4d(\n        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inShape);\n    const alpha = tf.tensor3d([0.25, 0.75], [1, 1, 2]);\n    const w =\n        tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth, outputDepth]);\n\n    const result = tf.fused.conv2d({\n      x,\n      filter: w,\n      strides: stride,\n      pad,\n      dataFormat: 'NHWC',\n      dilations: [1, 1],\n      activation: 'prelu',\n      preluActivationWeights: alpha\n    });\n    expect(result.shape).toEqual([2, 2, 2, 2]);\n    const expected = [\n      -1.25, 2, -2.75, 5, -4.25, 8, -5.75, 11, -7.25, 14, -8.75, 17, -10.25, 20,\n      -11.75, 23\n    ];\n\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('basic with leakyrelu', async () => {\n    const inputDepth = 2;\n    const inShape: [number, number, number, number] = [2, 2, 2, inputDepth];\n    const outputDepth = 2;\n    const fSize = 1;\n    const pad = 0;\n    const stride = 1;\n\n    const x = tf.tensor4d(\n        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inShape);\n    const alpha = 0.3;\n    const w =\n        tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth, outputDepth]);\n\n    const result = tf.fused.conv2d({\n      x,\n      filter: w,\n      strides: stride,\n      pad,\n      dataFormat: 'NHWC',\n      dilations: [1, 1],\n      activation: 'leakyrelu',\n      leakyreluAlpha: alpha\n    });\n    expect(result.shape).toEqual([2, 2, 2, 2]);\n    const expected = [\n      -1.5, 2, -3.3000001907348633, 5, -5.100000381469727, 8,\n      -6.900000095367432, 11, -8.700000762939453, 14, -10.5, 17,\n      -12.300000190734863, 20, -14.100000381469727, 23\n    ];\n\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('basic with sigmoid', async () => {\n    const inputDepth = 2;\n    const inShape: [number, number, number, number] = [2, 2, 2, inputDepth];\n    const outputDepth = 2;\n    const fSize = 1;\n    const pad = 0;\n    const stride = 1;\n\n    const x = tf.tensor4d(\n        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inShape);\n    const alpha = 0.3;\n    const w = tf.tensor4d(\n        [-0.1, 0.1, -0.2, 0.05], [fSize, fSize, inputDepth, outputDepth]);\n\n    const result = tf.fused.conv2d({\n      x,\n      filter: w,\n      strides: stride,\n      pad,\n      dataFormat: 'NHWC',\n      dilations: [1, 1],\n      activation: 'sigmoid',\n      leakyreluAlpha: alpha\n    });\n    expect(result.shape).toEqual([2, 2, 2, 2]);\n    const expected = [\n      0.3775407, 0.549834, 0.24973989, 0.6224593, 0.15446526, 0.6899744,\n      0.09112296, 0.7502601, 0.0521535, 0.80218387, 0.02931219, 0.84553474,\n      0.0163025, 0.8807971, 0.0090133, 0.908877\n    ];\n\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('basic with broadcasted bias and relu', async () => {\n    const inputDepth = 2;\n    const inShape: [number, number, number, number] = [2, 2, 2, inputDepth];\n    const outputDepth = 2;\n    const fSize = 1;\n    const pad = 0;\n    const stride = 1;\n\n    const x = tf.tensor4d(\n        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inShape);\n    const w =\n        tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth, outputDepth]);\n\n    const result = tf.fused.conv2d({\n      x,\n      filter: w,\n      strides: stride,\n      pad,\n      dataFormat: 'NHWC',\n      dilations: [1, 1],\n      bias: tf.scalar(5),\n      activation: 'relu'\n    });\n    expect(result.shape).toEqual([2, 2, 2, 2]);\n    const expected = [0, 7, 0, 10, 0, 13, 0, 16, 0, 19, 0, 22, 0, 25, 0, 28];\n\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('basic in NCHW', async () => {\n    const inputDepth = 2;\n    const inShape: [number, number, number, number] = [1, inputDepth, 2, 2];\n    const outputDepth = 2;\n    const fSize = 1;\n    const pad = 0;\n    const stride = 1;\n\n    const x = tf.tensor4d([1, 3, 5, 7, 2, 4, 6, 8], inShape);\n    const w =\n        tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth, outputDepth]);\n\n    const result = tf.fused.conv2d(\n        {x, filter: w, strides: stride, pad, dataFormat: 'NCHW'});\n    expect(result.shape).toEqual([1, 2, 2, 2]);\n    const expected = [-5, -11, -17, -23, 2, 5, 8, 11];\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('basic in NCHW with scalar bias', async () => {\n    const inputDepth = 4;\n    const inputShape: [number, number, number, number] = [1, inputDepth, 2, 2];\n    const outputDepth = 4;\n    const fSize = 1;\n    const pad = 'same';\n    const stride = 1;\n    const dataFormat = 'NCHW';\n    const dilation = 1;\n\n    const x = tf.tensor4d(\n        [1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4], inputShape);\n    const w = tf.tensor4d(\n        [3, 3, 3, 3, 1, 1, 1, 1, 5, 5, 5, 5, 0, 0, 0, 0],\n        [fSize, fSize, inputDepth, outputDepth]);\n    const bias = tf.scalar(1);\n\n    const result = tf.fused.conv2d({\n      x,\n      filter: w,\n      strides: stride,\n      pad,\n      dataFormat,\n      dilations: dilation,\n      bias\n    });\n\n    expect(result.shape).toEqual([1, 4, 2, 2]);\n    const expected =\n        [10, 19, 28, 37, 10, 19, 28, 37, 10, 19, 28, 37, 10, 19, 28, 37];\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('basic in NCHW with 1-D bias', async () => {\n    const inputDepth = 4;\n    const inputShape: [number, number, number, number] = [1, inputDepth, 2, 2];\n    const outputDepth = 4;\n    const fSize = 1;\n    const pad = 'same';\n    const stride = 1;\n    const dataFormat = 'NCHW';\n    const dilation = 1;\n\n    const x = tf.tensor4d(\n        [1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4], inputShape);\n    const w = tf.tensor4d(\n        [3, 3, 3, 3, 1, 1, 1, 1, 5, 5, 5, 5, 0, 0, 0, 0],\n        [fSize, fSize, inputDepth, outputDepth]);\n    const bias = tf.tensor1d([1, 2, 1, 2]);\n\n    const result = tf.fused.conv2d({\n      x,\n      filter: w,\n      strides: stride,\n      pad,\n      dataFormat,\n      dilations: dilation,\n      bias\n    });\n\n    expect(result.shape).toEqual([1, 4, 2, 2]);\n    const expected =\n        [10, 19, 28, 37, 11, 20, 29, 38, 10, 19, 28, 37, 11, 20, 29, 38];\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('basic in NCHW with bias and multiple batches', async () => {\n    const inputDepth = 4;\n    const inputShape: [number, number, number, number] = [2, inputDepth, 2, 2];\n    const outputDepth = 4;\n    const fSize = 1;\n    const pad = 'same';\n    const stride = 1;\n    const dataFormat = 'NCHW';\n    const dilation = 1;\n\n    const x = tf.tensor4d(\n        [\n          1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4,\n          1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4\n        ],\n        inputShape);\n    const w = tf.tensor4d(\n        [3, 3, 3, 3, 1, 1, 1, 1, 5, 5, 5, 5, 0, 0, 0, 0],\n        [fSize, fSize, inputDepth, outputDepth]);\n    const bias = tf.tensor1d([1, 2, 1, 2]);\n\n    const result = tf.fused.conv2d({\n      x,\n      filter: w,\n      strides: stride,\n      pad,\n      dataFormat,\n      dilations: dilation,\n      bias\n    });\n\n    expect(result.shape).toEqual([2, 4, 2, 2]);\n    const expected = [\n      10, 19, 28, 37, 11, 20, 29, 38, 10, 19, 28, 37, 11, 20, 29, 38,\n      10, 19, 28, 37, 11, 20, 29, 38, 10, 19, 28, 37, 11, 20, 29, 38\n    ];\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('basic in NCHW with scalar PReLU actiavation', async () => {\n    const inputDepth = 2;\n    const inShape: [number, number, number, number] = [1, inputDepth, 2, 2];\n    const outputDepth = 2;\n    const fSize = 1;\n    const dataFormat = 'NCHW';\n    const dilation = 1;\n    const pad = 0;\n    const stride = 1;\n\n    const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], inShape);\n    const w =\n        tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth, outputDepth]);\n    const alpha = tf.scalar(10);\n\n    const result = tf.fused.conv2d({\n      x,\n      filter: w,\n      strides: stride,\n      pad,\n      dataFormat,\n      dilations: dilation,\n      activation: 'prelu',\n      preluActivationWeights: alpha\n    });\n\n    expect(result.shape).toEqual([1, 2, 2, 2]);\n    const expected = [-110, -140, -170, -200, 3.5, 5, 6.5, 8];\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('basic in NCHW with 1-D PReLU actiavation', async () => {\n    const inputDepth = 2;\n    const inShape: [number, number, number, number] = [1, inputDepth, 2, 2];\n    const outputDepth = 2;\n    const fSize = 1;\n    const dataFormat = 'NCHW';\n    const dilation = 1;\n    const pad = 0;\n    const stride = 1;\n\n    const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], inShape);\n    const w =\n        tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth, outputDepth]);\n    const alpha = tf.tensor1d([10, 100]);\n\n    const result = tf.fused.conv2d({\n      x,\n      filter: w,\n      strides: stride,\n      pad,\n      dataFormat,\n      dilations: dilation,\n      activation: 'prelu',\n      preluActivationWeights: alpha\n    });\n\n    expect(result.shape).toEqual([1, 2, 2, 2]);\n    const expected = [-110, -140, -170, -200, 3.5, 5, 6.5, 8];\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('basic in NCHW with 3-D PReLU actiavation', async () => {\n    const inputDepth = 2;\n    const inShape: [number, number, number, number] = [1, inputDepth, 2, 2];\n    const outputDepth = 2;\n    const fSize = 1;\n    const dataFormat = 'NCHW';\n    const dilation = 1;\n    const pad = 0;\n    const stride = 1;\n\n    const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], inShape);\n    const w =\n        tf.tensor4d([-1, -1, -2, -2], [fSize, fSize, inputDepth, outputDepth]);\n    const alpha = tf.tensor3d([1, 10], [2, 1, 1]);\n\n    const result = tf.fused.conv2d({\n      x,\n      filter: w,\n      strides: stride,\n      pad,\n      dataFormat,\n      dilations: dilation,\n      activation: 'prelu',\n      preluActivationWeights: alpha\n    });\n\n    expect(result.shape).toEqual([1, 2, 2, 2]);\n    const expected = [-11, -14, -17, -20, -110, -140, -170, -200];\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('basic in NCHW with full 3-D PReLU actiavation', async () => {\n    const inputDepth = 2;\n    const inShape: [number, number, number, number] = [1, inputDepth, 2, 2];\n    const outputDepth = 2;\n    const fSize = 1;\n    const dataFormat = 'NCHW';\n    const dilation = 1;\n    const pad = 0;\n    const stride = 1;\n\n    const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], inShape);\n    const w =\n        tf.tensor4d([-1, -1, -2, -2], [fSize, fSize, inputDepth, outputDepth]);\n    const alpha = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);\n\n    const result = tf.fused.conv2d({\n      x,\n      filter: w,\n      strides: stride,\n      pad,\n      dataFormat,\n      dilations: dilation,\n      activation: 'prelu',\n      preluActivationWeights: alpha\n    });\n\n    expect(result.shape).toEqual([1, 2, 2, 2]);\n    const expected = [-11, -28, -51, -80, -55, -84, -119, -160];\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('im2row', async () => {\n    const inputDepth = 1;\n    const inputShape: [number, number, number] = [4, 4, inputDepth];\n    const outputDepth = 3;\n    const fSize = 1;\n    const pad = 'same';\n    const strides: [number, number] = [2, 2];\n\n    const x = tf.tensor3d(\n        [\n          10, 30, 50, 70, 20, 40, 60, 80, -10, -30, -50, -70, -20, -40, -60, -80\n        ],\n        inputShape);\n    const w = tf.tensor4d([1, 0.5, 1], [fSize, fSize, inputDepth, outputDepth]);\n\n    const result = tf.fused.conv2d({x, filter: w, strides, pad});\n\n    expectArraysClose(\n        await result.data(),\n        [10, 5, 10, 50, 25, 50, -10, -5, -10, -50, -25, -50]);\n  });\n\n  it('im2row with relu', async () => {\n    const inputDepth = 1;\n    const inputShape: [number, number, number] = [4, 4, inputDepth];\n    const outputDepth = 3;\n    const fSize = 1;\n    const pad = 'same';\n    const strides: [number, number] = [2, 2];\n\n    const x = tf.tensor3d(\n        [\n          10, 30, 50, 70, 20, 40, 60, 80, -10, -30, -50, -70, -20, -40, -60, -80\n        ],\n        inputShape);\n    const w = tf.tensor4d([1, 0.5, 1], [fSize, fSize, inputDepth, outputDepth]);\n\n    const result = tf.fused.conv2d({\n      x,\n      filter: w,\n      strides,\n      pad,\n      dataFormat: 'NHWC',\n      dilations: [1, 1],\n      activation: 'relu'\n    });\n\n    expectArraysClose(\n        await result.data(), [10, 5, 10, 50, 25, 50, 0, 0, 0, 0, 0, 0]);\n  });\n\n  it('im2row with prelu', async () => {\n    const inputDepth = 1;\n    const inputShape: [number, number, number] = [4, 4, inputDepth];\n    const outputDepth = 3;\n    const fSize = 1;\n    const pad = 'same';\n    const strides: [number, number] = [2, 2];\n\n    const x = tf.tensor3d(\n        [\n          10, 30, 50, 70, 20, 40, 60, 80, -10, -30, -50, -70, -20, -40, -60, -80\n        ],\n        inputShape);\n    const w = tf.tensor4d([1, 0.5, 1], [fSize, fSize, inputDepth, outputDepth]);\n    const alpha = tf.tensor3d([0.5], [1, 1, inputDepth]);\n\n    const result = tf.fused.conv2d({\n      x,\n      filter: w,\n      strides,\n      pad,\n      dataFormat: 'NHWC',\n      dilations: [1, 1],\n      activation: 'prelu',\n      preluActivationWeights: alpha\n    });\n\n    expectArraysClose(\n        await result.data(),\n        [10, 5, 10, 50, 25, 50, -5, -2.5, -5, -25, -12.5, -25]);\n  });\n\n  it('im2row with leakyrelu', async () => {\n    const inputDepth = 1;\n    const inputShape: [number, number, number] = [4, 4, inputDepth];\n    const outputDepth = 3;\n    const fSize = 1;\n    const pad = 'same';\n    const strides: [number, number] = [2, 2];\n\n    const x = tf.tensor3d(\n        [\n          10, 30, 50, 70, 20, 40, 60, 80, -10, -30, -50, -70, -20, -40, -60, -80\n        ],\n        inputShape);\n    const w = tf.tensor4d([1, 0.5, 1], [fSize, fSize, inputDepth, outputDepth]);\n    const alpha = 0.3;\n\n    const result = tf.fused.conv2d({\n      x,\n      filter: w,\n      strides,\n      pad,\n      dataFormat: 'NHWC',\n      dilations: [1, 1],\n      activation: 'leakyrelu',\n      leakyreluAlpha: alpha\n    });\n\n    expectArraysClose(await result.data(), [\n      10, 5, 10, 50, 25, 50, -3, -1.5, -3, -15.000000953674316,\n      -7.500000476837158, -15.000000953674316\n    ]);\n  });\n\n  it('pointwise with prelu', async () => {\n    const inputDepth = 1;\n    const inputShape: [number, number, number] = [4, 4, inputDepth];\n    const outputDepth = 3;\n    const fSize = 1;\n    const pad = 'same';\n    const strides: [number, number] = [1, 1];\n\n    const x = tf.tensor3d(\n        [\n          10, 30, 50, 70, 20, 40, 60, 80, -10, -30, -50, -70, -20, -40, -60, -80\n        ],\n        inputShape);\n    const w = tf.tensor4d([1, 0.5, 1], [fSize, fSize, inputDepth, outputDepth]);\n    const alpha = tf.tensor3d([0.5], [1, 1, inputDepth]);\n\n    const result = tf.fused.conv2d({\n      x,\n      filter: w,\n      strides,\n      pad,\n      dataFormat: 'NHWC',\n      dilations: [1, 1],\n      activation: 'prelu',\n      preluActivationWeights: alpha\n    });\n\n    expectArraysClose(await result.data(), [\n      10,  5,    10,  30,  15,   30,  50,  25,    50,  70,  35,    70,\n      20,  10,   20,  40,  20,   40,  60,  30,    60,  80,  40,    80,\n      -5,  -2.5, -5,  -15, -7.5, -15, -25, -12.5, -25, -35, -17.5, -35,\n      -10, -5,   -10, -20, -10,  -20, -30, -15,   -30, -40, -20,   -40\n    ]);\n  });\n\n  it('pointwise with leakyrelu', async () => {\n    const inputDepth = 1;\n    const inputShape: [number, number, number] = [4, 4, inputDepth];\n    const outputDepth = 3;\n    const fSize = 1;\n    const pad = 'same';\n    const strides: [number, number] = [1, 1];\n\n    const x = tf.tensor3d(\n        [\n          10, 30, 50, 70, 20, 40, 60, 80, -10, -30, -50, -70, -20, -40, -60, -80\n        ],\n        inputShape);\n    const w = tf.tensor4d([1, 0.5, 1], [fSize, fSize, inputDepth, outputDepth]);\n    const alpha = 0.3;\n\n    const result = tf.fused.conv2d({\n      x,\n      filter: w,\n      strides,\n      pad,\n      dataFormat: 'NHWC',\n      dilations: [1, 1],\n      activation: 'leakyrelu',\n      leakyreluAlpha: alpha\n    });\n\n    expectArraysClose(await result.data(), [\n      10,\n      5,\n      10,\n      30,\n      15,\n      30,\n      50,\n      25,\n      50,\n      70,\n      35,\n      70,\n      20,\n      10,\n      20,\n      40,\n      20,\n      40,\n      60,\n      30,\n      60,\n      80,\n      40,\n      80,\n      -3,\n      -1.5,\n      -3,\n      -9,\n      -4.5,\n      -9,\n      -15.000000953674316,\n      -7.500000476837158,\n      -15.000000953674316,\n      -21,\n      -10.5,\n      -21,\n      -6,\n      -3,\n      -6,\n      -12,\n      -6,\n      -12,\n      -18,\n      -9,\n      -18,\n      -24,\n      -12,\n      -24\n    ]);\n  });\n\n  it('im2row with broadcasted bias and relu', async () => {\n    const inputDepth = 1;\n    const inputShape: [number, number, number] = [4, 4, inputDepth];\n    const outputDepth = 3;\n    const fSize = 1;\n    const pad = 'same';\n    const strides: [number, number] = [2, 2];\n\n    const x = tf.tensor3d(\n        [\n          10, 30, 50, 70, 20, 40, 60, 80, -10, -30, -50, -70, -20, -40, -60, -80\n        ],\n        inputShape);\n    const w = tf.tensor4d([1, 0.5, 1], [fSize, fSize, inputDepth, outputDepth]);\n\n    const result = tf.fused.conv2d({\n      x,\n      filter: w,\n      strides,\n      pad,\n      dataFormat: 'NHWC',\n      dilations: [1, 1],\n      bias: tf.scalar(5),\n      activation: 'relu'\n    });\n\n    expectArraysClose(\n        await result.data(), [15, 10, 15, 55, 30, 55, 0, 0, 0, 0, 0, 0]);\n  });\n\n  it('im2row in NCHW', async () => {\n    const inputDepth = 2;\n    const inputShape: [number, number, number] = [inputDepth, 2, 2];\n    const outputDepth = 2;\n    const fSize = 2;\n    const pad = 'same';\n    const strides: [number, number] = [1, 1];\n\n    const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], inputShape);\n    const w = tf.tensor4d(\n        [1, 2, 3, 4, 5, 6, 7, 8, -1, -2, -3, -4, -5, -6, -7, -8],\n        [fSize, fSize, inputDepth, outputDepth]);\n\n    const result =\n        tf.fused.conv2d({x, filter: w, strides, pad, dataFormat: 'NCHW'});\n\n    expectArraysClose(\n        await result.data(), [-32, -8, 100, 28, -40, -12, 122, 40]);\n  });\n\n  it('im2row in NCHW with scalar bias', async () => {\n    const inputDepth = 4;\n    const inputShape: [number, number, number, number] = [1, inputDepth, 2, 2];\n    const outputDepth = 4;\n    const fSize = 2;\n    const pad = 'same';\n    const stride = 1;\n    const dataFormat = 'NCHW';\n    const dilation = 1;\n\n    const x = tf.tensor4d(\n        [1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4], inputShape);\n    const w = tf.tensor4d(\n        [\n          3, 3, 3, 3, 1, 1, 1, 1, 5, 5, 5, 5, 0, 0, 0, 0, 3, 3, 3, 3, 1, 1,\n          1, 1, 5, 5, 5, 5, 0, 0, 0, 0, 3, 3, 3, 3, 1, 1, 1, 1, 5, 5, 5, 5,\n          0, 0, 0, 0, 3, 3, 3, 3, 1, 1, 1, 1, 5, 5, 5, 5, 0, 0, 0, 0,\n        ],\n        [fSize, fSize, inputDepth, outputDepth]);\n    const bias = tf.scalar(1);\n\n    const result = tf.fused.conv2d({\n      x,\n      filter: w,\n      strides: stride,\n      pad,\n      dataFormat,\n      dilations: dilation,\n      bias\n    });\n\n    expect(result.shape).toEqual([1, 4, 2, 2]);\n    const expected =\n        [91, 55, 64, 37, 91, 55, 64, 37, 91, 55, 64, 37, 91, 55, 64, 37];\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('im2row in NCHW with 1-D bias', async () => {\n    const inputDepth = 4;\n    const inputShape: [number, number, number, number] = [1, inputDepth, 2, 2];\n    const outputDepth = 4;\n    const fSize = 2;\n    const pad = 'same';\n    const stride = 1;\n    const dataFormat = 'NCHW';\n    const dilation = 1;\n\n    const x = tf.tensor4d(\n        [1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4], inputShape);\n    const w = tf.tensor4d(\n        [\n          3, 3, 3, 3, 1, 1, 1, 1, 5, 5, 5, 5, 0, 0, 0, 0, 3, 3, 3, 3, 1, 1,\n          1, 1, 5, 5, 5, 5, 0, 0, 0, 0, 3, 3, 3, 3, 1, 1, 1, 1, 5, 5, 5, 5,\n          0, 0, 0, 0, 3, 3, 3, 3, 1, 1, 1, 1, 5, 5, 5, 5, 0, 0, 0, 0,\n        ],\n        [fSize, fSize, inputDepth, outputDepth]);\n    const bias = tf.tensor1d([1, 2, 1, 2]);\n\n    const result = tf.fused.conv2d({\n      x,\n      filter: w,\n      strides: stride,\n      pad,\n      dataFormat,\n      dilations: dilation,\n      bias\n    });\n\n    expect(result.shape).toEqual([1, 4, 2, 2]);\n    const expected =\n        [91, 55, 64, 37, 92, 56, 65, 38, 91, 55, 64, 37, 92, 56, 65, 38];\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('im2row in NCHW with scalar PReLU actiavation weights', async () => {\n    const inputDepth = 2;\n    const inShape: [number, number, number, number] = [1, inputDepth, 2, 2];\n    const outputDepth = 2;\n    const fSize = 2;\n    const dataFormat = 'NCHW';\n    const dilation = 1;\n    const pad = 'same';\n    const stride = 1;\n\n    const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], inShape);\n    const w = tf.tensor4d(\n        [-1, -1, -1, -1, 1, 1, 1, 1, -2, -2, -2, -2, 0.5, 0.5, 0.5, 0.5],\n        [fSize, fSize, inputDepth, outputDepth]);\n    const alpha = tf.scalar(10);\n\n    const result = tf.fused.conv2d({\n      x,\n      filter: w,\n      strides: stride,\n      pad,\n      dataFormat,\n      dilations: dilation,\n      activation: 'prelu',\n      preluActivationWeights: alpha\n    });\n\n    expect(result.shape).toEqual([1, 2, 2, 2]);\n    const expected = [-120, -320, 2, -120, -120, -320, 2, -120];\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('im2row in NCHW with 1-D PReLU actiavation weights', async () => {\n    const inputDepth = 2;\n    const inShape: [number, number, number, number] = [1, inputDepth, 2, 2];\n    const outputDepth = 2;\n    const fSize = 2;\n    const dataFormat = 'NCHW';\n    const dilation = 1;\n    const pad = 'same';\n    const stride = 1;\n\n    const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], inShape);\n    const w = tf.tensor4d(\n        [-1, -1, -1, -1, 1, 1, 1, 1, -2, -2, -2, -2, 0.5, 0.5, 0.5, 0.5],\n        [fSize, fSize, inputDepth, outputDepth]);\n    const alpha = tf.tensor1d([1, 10]);\n\n    const result = tf.fused.conv2d({\n      x,\n      filter: w,\n      strides: stride,\n      pad,\n      dataFormat,\n      dilations: dilation,\n      activation: 'prelu',\n      preluActivationWeights: alpha\n    });\n\n    expect(result.shape).toEqual([1, 2, 2, 2]);\n    const expected = [-12, -32, 2, -12, -120, -320, 2, -120];\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('im2row in NCHW with 3-D PReLU actiavation weights', async () => {\n    const inputDepth = 2;\n    const inShape: [number, number, number, number] = [1, inputDepth, 2, 2];\n    const outputDepth = 2;\n    const fSize = 2;\n    const dataFormat = 'NCHW';\n    const dilation = 1;\n    const pad = 'same';\n    const stride = 1;\n\n    const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], inShape);\n    const w = tf.tensor4d(\n        [-1, -1, -1, -1, 1, 1, 1, 1, -2, -2, -2, -2, 0.5, 0.5, 0.5, 0.5],\n        [fSize, fSize, inputDepth, outputDepth]);\n    const alpha = tf.tensor3d([1, 10], [2, 1, 1]);\n\n    const result = tf.fused.conv2d({\n      x,\n      filter: w,\n      strides: stride,\n      pad,\n      dataFormat,\n      dilations: dilation,\n      activation: 'prelu',\n      preluActivationWeights: alpha\n    });\n\n    expect(result.shape).toEqual([1, 2, 2, 2]);\n    const expected = [-12, -32, 2, -12, -120, -320, 2, -120];\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('im2row in NCHW with full 3-D PReLU actiavation weights', async () => {\n    const inputDepth = 2;\n    const inShape: [number, number, number, number] = [1, inputDepth, 2, 2];\n    const outputDepth = 2;\n    const fSize = 2;\n    const dataFormat = 'NCHW';\n    const dilation = 1;\n    const pad = 'same';\n    const stride = 1;\n\n    const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], inShape);\n    const w = tf.tensor4d(\n        [-1, -1, -1, -1, 1, 1, 1, 1, -2, -2, -2, -2, 0.5, 0.5, 0.5, 0.5],\n        [fSize, fSize, inputDepth, outputDepth]);\n    const alpha = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);\n\n    const result = tf.fused.conv2d({\n      x,\n      filter: w,\n      strides: stride,\n      pad,\n      dataFormat,\n      dilations: dilation,\n      activation: 'prelu',\n      preluActivationWeights: alpha\n    });\n\n    expect(result.shape).toEqual([1, 2, 2, 2]);\n    const expected = [-12, -64, 2, -48, -60, -192, 2, -96];\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('batch in NCHW', async () => {\n    const inputDepth = 2;\n    const inputShape: [number, number, number, number] = [2, inputDepth, 2, 2];\n    const outputDepth = 2;\n    const fSize = 2;\n    const pad = 'same';\n    const strides: [number, number] = [1, 1];\n    const dataFormat = 'NCHW';\n    const dilation = 1;\n\n    const x = tf.tensor4d(\n        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inputShape);\n    const w = tf.tensor4d(\n        [1, 2, 3, 4, 5, 6, 7, 8, -1, -2, -3, -4, -5, -6, -7, -8],\n        [fSize, fSize, inputDepth, outputDepth]);\n\n    const result = tf.fused.conv2d(\n        {x, filter: w, strides, pad, dataFormat, dilations: dilation});\n\n    expect(result.shape).toEqual([2, 2, 2, 2]);\n    expectArraysClose(await result.data(), [\n      -32, -8, 100, 28, -40, -12, 122, 40, -32, -8, 228, 60, -40, -12, 282, 88\n    ]);\n  });\n\n  it('batch in NCHW with scalar bias', async () => {\n    const inputDepth = 2;\n    const inputShape: [number, number, number, number] = [2, inputDepth, 2, 2];\n    const outputDepth = 2;\n    const fSize = 2;\n    const pad = 'same';\n    const strides: [number, number] = [1, 1];\n    const dataFormat = 'NCHW';\n    const dilation = 1;\n\n    const x = tf.tensor4d(\n        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inputShape);\n    const w = tf.tensor4d(\n        [1, 2, 3, 4, 5, 6, 7, 8, -1, -2, -3, -4, -5, -6, -7, -8],\n        [fSize, fSize, inputDepth, outputDepth]);\n    const bias = tf.scalar(1);\n\n    const result = tf.fused.conv2d(\n        {x, filter: w, strides, pad, dataFormat, dilations: dilation, bias});\n\n    expect(result.shape).toEqual([2, 2, 2, 2]);\n    expectArraysClose(await result.data(), [\n      -31, -7, 101, 29, -39, -11, 123, 41, -31, -7, 229, 61, -39, -11, 283, 89\n    ]);\n  });\n\n  it('batch in NCHW with 1-D bias', async () => {\n    const inputDepth = 2;\n    const inputShape: [number, number, number, number] = [2, inputDepth, 2, 2];\n    const outputDepth = 2;\n    const fSize = 2;\n    const pad = 'same';\n    const strides: [number, number] = [1, 1];\n    const dataFormat = 'NCHW';\n    const dilation = 1;\n\n    const x = tf.tensor4d(\n        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inputShape);\n    const w = tf.tensor4d(\n        [1, 2, 3, 4, 5, 6, 7, 8, -1, -2, -3, -4, -5, -6, -7, -8],\n        [fSize, fSize, inputDepth, outputDepth]);\n    const bias = tf.tensor1d([1, 2]);\n\n    const result = tf.fused.conv2d(\n        {x, filter: w, strides, pad, dataFormat, dilations: dilation, bias});\n\n    expect(result.shape).toEqual([2, 2, 2, 2]);\n    expectArraysClose(await result.data(), [\n      -31, -7, 101, 29, -38, -10, 124, 42, -31, -7, 229, 61, -38, -10, 284, 90\n    ]);\n  });\n\n  it('batch in NCHW with scalar PReLU actiavation weights', async () => {\n    const inputDepth = 2;\n    const inputShape: [number, number, number, number] = [2, inputDepth, 2, 2];\n    const outputDepth = 2;\n    const fSize = 2;\n    const pad = 'same';\n    const strides: [number, number] = [1, 1];\n    const dataFormat = 'NCHW';\n    const dilation = 1;\n\n    const x = tf.tensor4d(\n        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inputShape);\n    const w = tf.tensor4d(\n        [1, 2, 3, 4, 5, 6, 7, 8, -1, -2, -3, -4, -5, -6, -7, -8],\n        [fSize, fSize, inputDepth, outputDepth]);\n    const alpha = tf.scalar(10);\n\n    const result = tf.fused.conv2d({\n      x,\n      filter: w,\n      strides,\n      pad,\n      dataFormat,\n      dilations: dilation,\n      activation: 'prelu',\n      preluActivationWeights: alpha\n    });\n\n    expect(result.shape).toEqual([2, 2, 2, 2]);\n    expectArraysClose(await result.data(), [\n      -320, -80, 100, 28, -400, -120, 122, 40, -320, -80, 228, 60, -400, -120,\n      282, 88\n    ]);\n  });\n\n  it('batch in NCHW with 1-D PReLU actiavation weights', async () => {\n    const inputDepth = 2;\n    const inputShape: [number, number, number, number] = [2, inputDepth, 2, 2];\n    const outputDepth = 2;\n    const fSize = 2;\n    const pad = 'same';\n    const strides: [number, number] = [1, 1];\n    const dataFormat = 'NCHW';\n    const dilation = 1;\n\n    const x = tf.tensor4d(\n        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inputShape);\n    const w = tf.tensor4d(\n        [1, 2, 3, 4, 5, 6, 7, 8, -1, -2, -3, -4, -5, -6, -7, -8],\n        [fSize, fSize, inputDepth, outputDepth]);\n    const alpha = tf.tensor1d([1, 10]);\n\n    const result = tf.fused.conv2d({\n      x,\n      filter: w,\n      strides,\n      pad,\n      dataFormat,\n      dilations: dilation,\n      activation: 'prelu',\n      preluActivationWeights: alpha\n    });\n\n    expect(result.shape).toEqual([2, 2, 2, 2]);\n    expectArraysClose(await result.data(), [\n      -32, -8, 100, 28, -400, -120, 122, 40, -32, -8, 228, 60, -400, -120, 282,\n      88\n    ]);\n  });\n\n  it('batch in NCHW with 3-D PReLU actiavation weights', async () => {\n    const inputDepth = 2;\n    const inputShape: [number, number, number, number] = [2, inputDepth, 2, 2];\n    const outputDepth = 2;\n    const fSize = 2;\n    const pad = 'same';\n    const strides: [number, number] = [1, 1];\n    const dataFormat = 'NCHW';\n    const dilation = 1;\n\n    const x = tf.tensor4d(\n        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inputShape);\n    const w = tf.tensor4d(\n        [1, 2, 3, 4, 5, 6, 7, 8, -1, -2, -3, -4, -5, -6, -7, -8],\n        [fSize, fSize, inputDepth, outputDepth]);\n    const alpha = tf.tensor3d([1, 10], [2, 1, 1]);\n\n    const result = tf.fused.conv2d({\n      x,\n      filter: w,\n      strides,\n      pad,\n      dataFormat,\n      dilations: dilation,\n      activation: 'prelu',\n      preluActivationWeights: alpha\n    });\n\n    expect(result.shape).toEqual([2, 2, 2, 2]);\n    expectArraysClose(await result.data(), [\n      -32, -8, 100, 28, -400, -120, 122, 40, -32, -8, 228, 60, -400, -120, 282,\n      88\n    ]);\n  });\n\n  it('batch in NCHW with full 3-D PReLU actiavation weights', async () => {\n    const inputDepth = 2;\n    const inputShape: [number, number, number, number] = [2, inputDepth, 2, 2];\n    const outputDepth = 2;\n    const fSize = 2;\n    const pad = 'same';\n    const strides: [number, number] = [1, 1];\n    const dataFormat = 'NCHW';\n    const dilation = 1;\n\n    const x = tf.tensor4d(\n        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inputShape);\n    const w = tf.tensor4d(\n        [1, 2, 3, 4, 5, 6, 7, 8, -1, -2, -3, -4, -5, -6, -7, -8],\n        [fSize, fSize, inputDepth, outputDepth]);\n    const alpha = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);\n\n    const result = tf.fused.conv2d({\n      x,\n      filter: w,\n      strides,\n      pad,\n      dataFormat,\n      dilations: dilation,\n      activation: 'prelu',\n      preluActivationWeights: alpha\n    });\n\n    expect(result.shape).toEqual([2, 2, 2, 2]);\n    expectArraysClose(await result.data(), [\n      -32, -16, 100, 28, -200, -72, 122, 40, -32, -16, 228, 60, -200, -72, 282,\n      88\n    ]);\n  });\n\n  it('backProp input x=[2,3,3,1] f=[2,2,1,1] s=1 p=0', async () => {\n    const inputDepth = 1;\n    const outputDepth = 1;\n    const inputShape: [number, number, number, number] = [2, 3, 3, inputDepth];\n    const filterSize = 2;\n    const strides = 1;\n    const pad = 0;\n\n    const filterShape: [number, number, number, number] =\n        [filterSize, filterSize, inputDepth, outputDepth];\n    const filter = tf.tensor4d([-1, 1, -2, 0.5], filterShape);\n\n    const x = tf.tensor4d(\n        [1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9], inputShape);\n    const dy = tf.tensor4d([3, 1, 2, 0, 3, 1, 2, 0], [2, 2, 2, 1]);\n\n    const grads = tf.grads(\n        (x: tf.Tensor4D) => tf.fused.conv2d({x, filter, strides, pad}));\n    const [dx] = grads([x], dy);\n\n    expect(dx.shape).toEqual(x.shape);\n    expectArraysClose(\n        await dx.data(),\n        [-3, 2, 1, -8, 1.5, 0.5, -4, 1, 0, -3, 2, 1, -8, 1.5, 0.5, -4, 1, 0]);\n  });\n\n  it('gradient x=[2,3,3,1] f=[2,2,1,1] s=1 p=0', async () => {\n    const inputDepth = 1;\n    const outputDepth = 1;\n    const inputShape: [number, number, number, number] = [2, 3, 3, inputDepth];\n    const filterSize = 2;\n    const strides = 1;\n    const pad = 0;\n\n    const filterShape: [number, number, number, number] =\n        [filterSize, filterSize, inputDepth, outputDepth];\n    const filter = tf.tensor4d([-1, 1, -2, 0.5], filterShape);\n\n    const x = tf.tensor4d(\n        [1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9], inputShape);\n    const dy = tf.tensor4d([3, 1, 2, 0, 3, 1, 2, 0], [2, 2, 2, 1]);\n\n    const grads = tf.grads(\n        (x: tf.Tensor4D, filter: tf.Tensor4D) =>\n            tf.fused.conv2d({x, filter, strides, pad}));\n    const [dx, dfilter] = grads([x, filter], dy);\n\n    expect(dx.shape).toEqual(x.shape);\n    expectArraysClose(\n        await dx.data(),\n        [-3, 2, 1, -8, 1.5, 0.5, -4, 1, 0, -3, 2, 1, -8, 1.5, 0.5, -4, 1, 0]);\n\n    expect(dfilter.shape).toEqual(filterShape);\n    expectArraysClose(await dfilter.data(), [26, 38, 62, 74]);\n  });\n\n  it('gradient x=[2,3,3,1] f=[2,2,1,1] s=1 p=0 with bias', async () => {\n    const inputDepth = 1;\n    const outputDepth = 1;\n    const inputShape: [number, number, number, number] = [2, 3, 3, inputDepth];\n    const filterSize = 2;\n    const strides = 1;\n    const pad = 0;\n\n    const filterShape: [number, number, number, number] =\n        [filterSize, filterSize, inputDepth, outputDepth];\n    const filter = tf.tensor4d([-1, 1, -2, 0.5], filterShape);\n    const bias = tf.ones([2, 2, 2, 1]);\n\n    const x = tf.tensor4d(\n        [1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9], inputShape);\n    const dy = tf.tensor4d([3, 1, 2, 0, 3, 1, 2, 0], [2, 2, 2, 1]);\n\n    const fusedGrads =\n        tf.grads((x: tf.Tensor4D, w: tf.Tensor4D, b) => tf.fused.conv2d({\n          x,\n          filter: w,\n          strides,\n          pad,\n          dataFormat: 'NHWC',\n          dilations: [1, 1],\n          bias: b\n        }));\n    const [dxFused, dfilterFused, dbiasFused] =\n        fusedGrads([x, filter, bias], dy);\n\n    const grads = tf.grads((x: tf.Tensor4D, filter: tf.Tensor4D, bias) => {\n      const conv = tf.conv2d(x, filter, strides, pad);\n      const sum = tf.add(conv, bias);\n      return sum;\n    });\n    const [dx, dfilter, dbias] = grads([x, filter, bias], dy);\n\n    expectArraysClose(await dxFused.array(), await dx.array());\n    expectArraysClose(await dfilterFused.array(), await dfilter.array());\n    expectArraysClose(await dbiasFused.array(), await dbias.array());\n  });\n\n  it('gradient x=[2,3,3,1] f=[2,2,1,1] s=1 p=0 with bias and relu',\n     async () => {\n       const inputDepth = 1;\n       const outputDepth = 1;\n       const inputShape: [number, number, number, number] =\n           [2, 3, 3, inputDepth];\n       const filterSize = 2;\n       const strides = 1;\n       const pad = 0;\n\n       const filterShape: [number, number, number, number] =\n           [filterSize, filterSize, inputDepth, outputDepth];\n       const filter = tf.tensor4d([-1, 1, -2, 0.5], filterShape);\n       const bias = tf.ones([2, 2, 2, 1]);\n\n       const x = tf.tensor4d(\n           [1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9], inputShape);\n       const dy = tf.tensor4d([3, 1, 2, 0, 3, 1, 2, 0], [2, 2, 2, 1]);\n\n       const fusedGrads =\n           tf.grads((x: tf.Tensor4D, w: tf.Tensor4D, b) => tf.fused.conv2d({\n             x,\n             filter: w,\n             strides,\n             pad,\n             dataFormat: 'NHWC',\n             dilations: [1, 1],\n             bias: b,\n             activation: 'relu'\n           }));\n       const [dxFused, dfilterFused, dbiasFused] =\n           fusedGrads([x, filter, bias], dy);\n\n       const grads = tf.grads((x: tf.Tensor4D, filter: tf.Tensor4D, bias) => {\n         const conv = tf.conv2d(x, filter, strides, pad);\n         const sum = tf.add(conv, bias);\n         return tf.relu(sum);\n       });\n       const [dx, dfilter, dbias] = grads([x, filter, bias], dy);\n\n       expectArraysClose(await dxFused.array(), await dx.array());\n       expectArraysClose(await dfilterFused.array(), await dfilter.array());\n       expectArraysClose(await dbiasFused.array(), await dbias.array());\n     });\n\n  it('gradient x=[2,3,3,1] f=[2,2,1,1] s=1 p=0 with bias and elu', async () => {\n    const inputDepth = 1;\n    const outputDepth = 1;\n    const inputShape: [number, number, number, number] = [2, 3, 3, inputDepth];\n    const filterSize = 2;\n    const strides = 1;\n    const pad = 0;\n\n    const filterShape: [number, number, number, number] =\n        [filterSize, filterSize, inputDepth, outputDepth];\n    const filter = tf.tensor4d([-1, 1, -2, 0.5], filterShape);\n    const bias = tf.ones([2, 2, 2, 1]);\n\n    const x = tf.tensor4d(\n        [1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9], inputShape);\n    const dy = tf.tensor4d([3, 1, 2, 0, 3, 1, 2, 0], [2, 2, 2, 1]);\n\n    const fusedGrads =\n        tf.grads((x: tf.Tensor4D, w: tf.Tensor4D, b) => tf.fused.conv2d({\n          x,\n          filter: w,\n          strides,\n          pad,\n          dataFormat: 'NHWC',\n          dilations: [1, 1],\n          bias: b,\n          activation: 'elu'\n        }));\n    const [dxFused, dfilterFused, dbiasFused] =\n        fusedGrads([x, filter, bias], dy);\n\n    const grads = tf.grads((x: tf.Tensor4D, filter: tf.Tensor4D, bias) => {\n      const conv = tf.conv2d(x, filter, strides, pad);\n      const sum = tf.add(conv, bias);\n      return tf.elu(sum);\n    });\n    const [dx, dfilter, dbias] = grads([x, filter, bias], dy);\n\n    expectArraysClose(await dxFused.array(), await dx.array());\n    expectArraysClose(await dfilterFused.array(), await dfilter.array());\n    expectArraysClose(await dbiasFused.array(), await dbias.array());\n  });\n\n  it('throws when input is int32', async () => {\n    const inputDepth = 2;\n    const inShape: [number, number, number, number] = [2, 2, 2, inputDepth];\n    const outputDepth = 2;\n    const fSize = 1;\n    const pad = 0;\n    const stride = 1;\n\n    const x = tf.tensor4d(\n        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inShape,\n        'int32');\n    const w =\n        tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth, outputDepth]);\n\n    expect(() => tf.fused.conv2d({x, filter: w, strides: stride, pad}))\n        .toThrowError(/Argument 'x' passed to 'conv2d' must be float32/);\n  });\n\n  it('throws when filter is int32', async () => {\n    const inputDepth = 2;\n    const inShape: [number, number, number, number] = [2, 2, 2, inputDepth];\n    const outputDepth = 2;\n    const fSize = 1;\n    const pad = 0;\n    const stride = 1;\n\n    const x = tf.tensor4d(\n        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inShape);\n    const w = tf.tensor4d(\n        [-1, 1, -2, 0.5], [fSize, fSize, inputDepth, outputDepth], 'int32');\n\n    expect(() => tf.fused.conv2d({x, filter: w, strides: stride, pad}))\n        .toThrowError(/Argument 'filter' passed to 'conv2d' must be float32/);\n  });\n});\n"]}