/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT 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';
describeWithFlags('dilation2d', ALL_ENVS, () => {
    it('valid padding.', async () => {
        const inputShape = [1, 2, 2, 1];
        const filterShape = [2, 2, 1];
        const x = tf.tensor4d([.1, .2, .3, .4], inputShape);
        const filter = tf.tensor3d([.4, .3, .1, .0], filterShape);
        const result = tf.dilation2d(x, filter, 1 /* strides */, 'valid');
        expect(result.shape).toEqual([1, 1, 1, 1]);
        expectArraysClose(await result.data(), [.5]);
    });
    it('same padding.', async () => {
        const inputShape = [1, 2, 2, 1];
        const filterShape = [2, 2, 1];
        const x = tf.tensor4d([.1, .2, .3, .4], inputShape);
        const filter = tf.tensor3d([.4, .3, .1, .0], filterShape);
        const result = tf.dilation2d(x, filter, 1 /* strides */, 'same');
        expect(result.shape).toEqual([1, 2, 2, 1]);
        expectArraysClose(await result.data(), [.5, .6, .7, .8]);
    });
    it('same padding depth 3.', async () => {
        const inputShape = [1, 2, 2, 3];
        const filterShape = [2, 2, 3];
        const x = tf.tensor4d([.1, .2, .0, .2, .3, .1, .3, .4, .2, .4, .5, .3], inputShape);
        const filter = tf.tensor3d([.4, .5, .3, .3, .4, .2, .1, .2, .0, .0, .1, -.1], filterShape);
        const result = tf.dilation2d(x, filter, 1 /* strides */, 'same');
        expect(result.shape).toEqual([1, 2, 2, 3]);
        expectArraysClose(await result.data(), [.5, .7, .3, .6, .8, .4, .7, .9, .5, .8, 1., .6]);
    });
    it('same padding batch 2.', async () => {
        const inputShape = [2, 2, 2, 1];
        const filterShape = [2, 2, 1];
        const x = tf.tensor4d([.1, .2, .3, .4, .2, .3, .4, .5], inputShape);
        const filter = tf.tensor3d([.4, .3, .1, .0], filterShape);
        const result = tf.dilation2d(x, filter, 1 /* strides */, 'same');
        expect(result.shape).toEqual([2, 2, 2, 1]);
        expectArraysClose(await result.data(), [.5, .6, .7, .8, .6, .7, .8, .9]);
    });
    it('same padding filter 2.', async () => {
        const inputShape = [1, 3, 3, 1];
        const filterShape = [2, 2, 1];
        const x = tf.tensor4d([.1, .2, .3, .4, .5, .6, .7, .8, .9], inputShape);
        const filter = tf.tensor3d([.4, .3, .1, .2], filterShape);
        const result = tf.dilation2d(x, filter, 1 /* strides */, 'same');
        expect(result.shape).toEqual([1, 3, 3, 1]);
        expectArraysClose(await result.data(), [.7, .8, .7, 1, 1.1, 1, 1.1, 1.2, 1.3]);
    });
    it('valid padding non-square-window.', async () => {
        const inputShape = [1, 2, 2, 1];
        const filterShape = [1, 2, 1];
        const x = tf.tensor4d([.1, .2, .3, .4], inputShape);
        const filter = tf.tensor3d([.4, .3], filterShape);
        const result = tf.dilation2d(x, filter, 1 /* strides */, 'valid');
        expect(result.shape).toEqual([1, 2, 1, 1]);
        expectArraysClose(await result.data(), [.5, .7]);
    });
    it('same padding dilations 2.', async () => {
        const inputShape = [1, 3, 3, 1];
        const filterShape = [2, 2, 1];
        const x = tf.tensor4d([.1, .2, .3, .4, .5, .6, .7, .8, .9], inputShape);
        const filter = tf.tensor3d([.4, .3, .1, .2], filterShape);
        const result = tf.dilation2d(x, filter, 1 /* strides */, 'same', 2);
        // Because dilations = 2, the effective filter is [3, 3, 1]:
        // filter_eff = [[[.4], [.0], [.3]],
        //               [[.0], [.0], [.0]],
        //               [[.1], [.0], [.2]]]
        expect(result.shape).toEqual([1, 3, 3, 1]);
        expectArraysClose(await result.data(), [.7, .8, .6, 1., 1.1, .9, .8, .9, .9]);
    });
    it('valid padding uneven stride.', async () => {
        const inputShape = [1, 3, 4, 1];
        const filterShape = [2, 2, 1];
        const x = tf.tensor4d([.1, .2, .3, .4, .5, .6, .7, .8, .9, 1., 1.1, 1.2], inputShape);
        const filter = tf.tensor3d([.4, .3, .1, .2], filterShape);
        const result = tf.dilation2d(x, filter, [1, 2] /* strides */, 'valid');
        expect(result.shape).toEqual([1, 2, 2, 1]);
        expectArraysClose(await result.data(), [.8, 1., 1.2, 1.4]);
    });
    it('throws when input rank is not 3 or 4.', async () => {
        const filterShape = [1, 1, 1];
        // tslint:disable-next-line:no-any
        const x = tf.tensor1d([.5]);
        const filter = tf.tensor3d([.4], filterShape);
        expect(() => tf.dilation2d(x, filter, 1, 'valid')).toThrowError();
    });
    it('thorws when filter is not rank 3.', async () => {
        const inputShape = [1, 2, 2, 1];
        const filterShape = [2, 2];
        const x = tf.tensor4d([.1, .2, .3, .4], inputShape);
        // tslint:disable-next-line:no-any
        const filter = tf.tensor2d([.4, .3, .1, .0], filterShape);
        expect(() => tf.dilation2d(x, filter, 1, 'valid')).toThrowError();
    });
    it('throws when data format is not NHWC.', async () => {
        const inputShape = [1, 2, 2, 1];
        const filterShape = [2, 2, 1];
        const x = tf.tensor4d([.1, .2, .3, .4], inputShape);
        const filter = tf.tensor3d([.4, .3, .1, .0], filterShape);
        // tslint:disable-next-line:no-any
        const dataFormat = 'NCHW';
        expect(() => tf.dilation2d(x, filter, 1 /* strides */, 'valid', 1, dataFormat))
            .toThrowError();
    });
    it('dilation gradient valid padding.', async () => {
        const inputShape = [1, 3, 3, 1];
        const filterShape = [1, 1, 1];
        const x = tf.tensor4d([.1, .2, .3, .4, .5, .6, .7, .8, .9], inputShape);
        const filter = tf.tensor3d([.5], filterShape);
        const dy = tf.tensor4d([.2, .3, .4, .2, .1, 1., .2, .3, .4], inputShape);
        const grads = tf.grads((x, filter) => x.dilation2d(filter, 1, 'valid'));
        const [dx, dfilter] = grads([x, filter], dy);
        expect(dx.shape).toEqual(x.shape);
        expectArraysClose(await dx.data(), [.2, .3, .4, .2, .1, 1., .2, .3, .4]);
        expect(dfilter.shape).toEqual(filterShape);
        expectArraysClose(await dfilter.data(), [3.1]);
    });
    it('dilation gradient same padding.', async () => {
        const inputShape = [1, 3, 3, 1];
        const filterShape = [1, 1, 1];
        const x = tf.tensor4d([.1, .2, .3, .4, .5, .6, .7, .8, .9], inputShape);
        const filter = tf.tensor3d([.5], filterShape);
        const dy = tf.tensor4d([.2, .3, .4, .2, .1, 1., .2, .3, .4], inputShape);
        const grads = tf.grads((x, filter) => x.dilation2d(filter, 1, 'same'));
        const [dx, dfilter] = grads([x, filter], dy);
        expect(dx.shape).toEqual(x.shape);
        expectArraysClose(await dx.data(), [.2, .3, .4, .2, .1, 1., .2, .3, .4]);
        expect(dfilter.shape).toEqual(filterShape);
        expectArraysClose(await dfilter.data(), [3.1]);
    });
    it('dilation gradient same padding depth 2.', async () => {
        const inputShape = [1, 2, 2, 3];
        const filterShape = [1, 1, 3];
        const x = tf.tensor4d([.1, .2, .0, .2, .3, .1, .3, .4, .2, .4, .5, .3], inputShape);
        const filter = tf.tensor3d([.4, .5, .6], filterShape);
        const dy = tf.tensor4d([.2, .3, .4, .2, .1, 1., .2, .3, .4, .8, -.1, .1], inputShape);
        const grads = tf.grads((x, filter) => x.dilation2d(filter, 1, 'same'));
        const [dx, dfilter] = grads([x, filter], dy);
        expect(dx.shape).toEqual(x.shape);
        expectArraysClose(await dx.data(), [.2, .3, .4, .2, .1, 1., .2, .3, .4, .8, -.1, .1]);
        expect(dfilter.shape).toEqual(filterShape);
        expectArraysClose(await dfilter.data(), [1.4, .6, 1.9]);
    });
    it('dilation gradient valid padding filter 2.', async () => {
        const inputShape = [1, 3, 3, 1];
        const filterShape = [2, 2, 1];
        const dyShape = [1, 2, 2, 1];
        const x = tf.tensor4d([.1, .2, .3, .4, .5, .6, .7, .8, .9], inputShape);
        const filter = tf.tensor3d([.4, .3, .1, .2], filterShape);
        const dy = tf.tensor4d([.2, .3, .4, .2], dyShape);
        const grads = tf.grads((x, filter) => x.dilation2d(filter, 1, 'valid'));
        const [dx, dfilter] = grads([x, filter], dy);
        expect(dx.shape).toEqual(x.shape);
        expectArraysClose(await dx.data(), [0, 0, 0, 0, .2, .3, 0, .4, .2]);
        expect(dfilter.shape).toEqual(filterShape);
        expectArraysClose(await dfilter.data(), [0, 0, 0, 1.1]);
    });
    it('dilation gradient same padding filter 2 depth 3.', async () => {
        const inputShape = [1, 3, 3, 3];
        const filterShape = [2, 2, 3];
        const x = tf.tensor4d([
            .1, .2, .3, .4, .5, .6, .7, .8, .9, .3, .2, .3, .4, .5,
            .1, .9, .6, .3, .4, .5, .6, .2, .3, .5, .1, .2, .3
        ], inputShape);
        const filter = tf.tensor3d([.4, .3, .1, .2, .2, .1, .7, .3, .8, .4, .9, .1], filterShape);
        const dy = tf.tensor4d([
            .2, .3, .4, .2, .1, .5, 0, .8, .7, .1, .2, .1, .2, .3,
            .4, .5, .6, .6, .6, .7, .8, .3, .2, .1, .2, .4, .2
        ], inputShape);
        const grads = tf.grads((x, filter) => x.dilation2d(filter, 1, 'same'));
        const [dx, dfilter] = grads([x, filter], dy);
        expect(dx.shape).toEqual(x.shape);
        expectArraysClose(await dx.data(), [
            0, 0, 0, 0, 0, 0, 0, .8, .5, .2, 0, .4, 0, .3,
            0, .9, .7, .7, .7, .7, .9, .3, .4, .5, .2, .7, .8
        ]);
        expect(dfilter.shape).toEqual(filterShape);
        expectArraysClose(await dfilter.data(), [1.6, 2.7, 1.1, .2, 0, .5, .3, 0, 2.2, .2, .9, 0]);
    });
});
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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\ndescribeWithFlags('dilation2d', ALL_ENVS, () => {\n  it('valid padding.', async () => {\n    const inputShape: [number, number, number, number] = [1, 2, 2, 1];\n    const filterShape: [number, number, number] = [2, 2, 1];\n    const x = tf.tensor4d([.1, .2, .3, .4], inputShape);\n    const filter = tf.tensor3d([.4, .3, .1, .0], filterShape);\n\n    const result = tf.dilation2d(x, filter, 1 /* strides */, 'valid');\n\n    expect(result.shape).toEqual([1, 1, 1, 1]);\n    expectArraysClose(await result.data(), [.5]);\n  });\n\n  it('same padding.', async () => {\n    const inputShape: [number, number, number, number] = [1, 2, 2, 1];\n    const filterShape: [number, number, number] = [2, 2, 1];\n    const x = tf.tensor4d([.1, .2, .3, .4], inputShape);\n    const filter = tf.tensor3d([.4, .3, .1, .0], filterShape);\n\n    const result = tf.dilation2d(x, filter, 1 /* strides */, 'same');\n\n    expect(result.shape).toEqual([1, 2, 2, 1]);\n    expectArraysClose(await result.data(), [.5, .6, .7, .8]);\n  });\n\n  it('same padding depth 3.', async () => {\n    const inputShape: [number, number, number, number] = [1, 2, 2, 3];\n    const filterShape: [number, number, number] = [2, 2, 3];\n    const x = tf.tensor4d(\n        [.1, .2, .0, .2, .3, .1, .3, .4, .2, .4, .5, .3], inputShape);\n    const filter = tf.tensor3d(\n        [.4, .5, .3, .3, .4, .2, .1, .2, .0, .0, .1, -.1], filterShape);\n\n    const result = tf.dilation2d(x, filter, 1 /* strides */, 'same');\n\n    expect(result.shape).toEqual([1, 2, 2, 3]);\n    expectArraysClose(\n        await result.data(), [.5, .7, .3, .6, .8, .4, .7, .9, .5, .8, 1., .6]);\n  });\n\n  it('same padding batch 2.', async () => {\n    const inputShape: [number, number, number, number] = [2, 2, 2, 1];\n    const filterShape: [number, number, number] = [2, 2, 1];\n    const x = tf.tensor4d([.1, .2, .3, .4, .2, .3, .4, .5], inputShape);\n    const filter = tf.tensor3d([.4, .3, .1, .0], filterShape);\n\n    const result = tf.dilation2d(x, filter, 1 /* strides */, 'same');\n\n    expect(result.shape).toEqual([2, 2, 2, 1]);\n    expectArraysClose(await result.data(), [.5, .6, .7, .8, .6, .7, .8, .9]);\n  });\n\n  it('same padding filter 2.', async () => {\n    const inputShape: [number, number, number, number] = [1, 3, 3, 1];\n    const filterShape: [number, number, number] = [2, 2, 1];\n    const x = tf.tensor4d([.1, .2, .3, .4, .5, .6, .7, .8, .9], inputShape);\n    const filter = tf.tensor3d([.4, .3, .1, .2], filterShape);\n\n    const result = tf.dilation2d(x, filter, 1 /* strides */, 'same');\n\n    expect(result.shape).toEqual([1, 3, 3, 1]);\n    expectArraysClose(\n        await result.data(), [.7, .8, .7, 1, 1.1, 1, 1.1, 1.2, 1.3]);\n  });\n\n  it('valid padding non-square-window.', async () => {\n    const inputShape: [number, number, number, number] = [1, 2, 2, 1];\n    const filterShape: [number, number, number] = [1, 2, 1];\n    const x = tf.tensor4d([.1, .2, .3, .4], inputShape);\n    const filter = tf.tensor3d([.4, .3], filterShape);\n\n    const result = tf.dilation2d(x, filter, 1 /* strides */, 'valid');\n\n    expect(result.shape).toEqual([1, 2, 1, 1]);\n    expectArraysClose(await result.data(), [.5, .7]);\n  });\n\n  it('same padding dilations 2.', async () => {\n    const inputShape: [number, number, number, number] = [1, 3, 3, 1];\n    const filterShape: [number, number, number] = [2, 2, 1];\n    const x = tf.tensor4d([.1, .2, .3, .4, .5, .6, .7, .8, .9], inputShape);\n    const filter = tf.tensor3d([.4, .3, .1, .2], filterShape);\n\n    const result = tf.dilation2d(x, filter, 1 /* strides */, 'same', 2);\n\n    // Because dilations = 2, the effective filter is [3, 3, 1]:\n    // filter_eff = [[[.4], [.0], [.3]],\n    //               [[.0], [.0], [.0]],\n    //               [[.1], [.0], [.2]]]\n    expect(result.shape).toEqual([1, 3, 3, 1]);\n    expectArraysClose(\n        await result.data(), [.7, .8, .6, 1., 1.1, .9, .8, .9, .9]);\n  });\n\n  it('valid padding uneven stride.', async () => {\n    const inputShape: [number, number, number, number] = [1, 3, 4, 1];\n    const filterShape: [number, number, number] = [2, 2, 1];\n    const x = tf.tensor4d(\n        [.1, .2, .3, .4, .5, .6, .7, .8, .9, 1., 1.1, 1.2], inputShape);\n    const filter = tf.tensor3d([.4, .3, .1, .2], filterShape);\n\n    const result = tf.dilation2d(x, filter, [1, 2] /* strides */, 'valid');\n\n    expect(result.shape).toEqual([1, 2, 2, 1]);\n    expectArraysClose(await result.data(), [.8, 1., 1.2, 1.4]);\n  });\n\n  it('throws when input rank is not 3 or 4.', async () => {\n    const filterShape: [number, number, number] = [1, 1, 1];\n    // tslint:disable-next-line:no-any\n    const x: any = tf.tensor1d([.5]);\n    const filter = tf.tensor3d([.4], filterShape);\n\n    expect(() => tf.dilation2d(x, filter, 1, 'valid')).toThrowError();\n  });\n\n  it('thorws when filter is not rank 3.', async () => {\n    const inputShape: [number, number, number, number] = [1, 2, 2, 1];\n    const filterShape: [number, number] = [2, 2];\n    const x = tf.tensor4d([.1, .2, .3, .4], inputShape);\n    // tslint:disable-next-line:no-any\n    const filter: any = tf.tensor2d([.4, .3, .1, .0], filterShape);\n\n    expect(() => tf.dilation2d(x, filter, 1, 'valid')).toThrowError();\n  });\n\n  it('throws when data format is not NHWC.', async () => {\n    const inputShape: [number, number, number, number] = [1, 2, 2, 1];\n    const filterShape: [number, number, number] = [2, 2, 1];\n    const x = tf.tensor4d([.1, .2, .3, .4], inputShape);\n    const filter = tf.tensor3d([.4, .3, .1, .0], filterShape);\n    // tslint:disable-next-line:no-any\n    const dataFormat: any = 'NCHW';\n\n    expect(\n        () => tf.dilation2d(x, filter, 1 /* strides */, 'valid', 1, dataFormat))\n        .toThrowError();\n  });\n\n  it('dilation gradient valid padding.', async () => {\n    const inputShape: [number, number, number, number] = [1, 3, 3, 1];\n    const filterShape: [number, number, number] = [1, 1, 1];\n    const x = tf.tensor4d([.1, .2, .3, .4, .5, .6, .7, .8, .9], inputShape);\n    const filter = tf.tensor3d([.5], filterShape);\n    const dy = tf.tensor4d([.2, .3, .4, .2, .1, 1., .2, .3, .4], inputShape);\n\n    const grads = tf.grads(\n        (x: tf.Tensor4D, filter: tf.Tensor3D) =>\n            x.dilation2d(filter, 1, 'valid'));\n\n    const [dx, dfilter] = grads([x, filter], dy);\n\n    expect(dx.shape).toEqual(x.shape);\n    expectArraysClose(await dx.data(), [.2, .3, .4, .2, .1, 1., .2, .3, .4]);\n\n    expect(dfilter.shape).toEqual(filterShape);\n    expectArraysClose(await dfilter.data(), [3.1]);\n  });\n\n  it('dilation gradient same padding.', async () => {\n    const inputShape: [number, number, number, number] = [1, 3, 3, 1];\n    const filterShape: [number, number, number] = [1, 1, 1];\n    const x = tf.tensor4d([.1, .2, .3, .4, .5, .6, .7, .8, .9], inputShape);\n    const filter = tf.tensor3d([.5], filterShape);\n    const dy = tf.tensor4d([.2, .3, .4, .2, .1, 1., .2, .3, .4], inputShape);\n\n    const grads = tf.grads(\n        (x: tf.Tensor4D, filter: tf.Tensor3D) =>\n            x.dilation2d(filter, 1, 'same'));\n\n    const [dx, dfilter] = grads([x, filter], dy);\n\n    expect(dx.shape).toEqual(x.shape);\n    expectArraysClose(await dx.data(), [.2, .3, .4, .2, .1, 1., .2, .3, .4]);\n\n    expect(dfilter.shape).toEqual(filterShape);\n    expectArraysClose(await dfilter.data(), [3.1]);\n  });\n\n  it('dilation gradient same padding depth 2.', async () => {\n    const inputShape: [number, number, number, number] = [1, 2, 2, 3];\n    const filterShape: [number, number, number] = [1, 1, 3];\n    const x = tf.tensor4d(\n        [.1, .2, .0, .2, .3, .1, .3, .4, .2, .4, .5, .3], inputShape);\n    const filter = tf.tensor3d([.4, .5, .6], filterShape);\n    const dy = tf.tensor4d(\n        [.2, .3, .4, .2, .1, 1., .2, .3, .4, .8, -.1, .1], inputShape);\n\n    const grads = tf.grads(\n        (x: tf.Tensor4D, filter: tf.Tensor3D) =>\n            x.dilation2d(filter, 1, 'same'));\n\n    const [dx, dfilter] = grads([x, filter], dy);\n\n    expect(dx.shape).toEqual(x.shape);\n    expectArraysClose(\n        await dx.data(), [.2, .3, .4, .2, .1, 1., .2, .3, .4, .8, -.1, .1]);\n\n    expect(dfilter.shape).toEqual(filterShape);\n    expectArraysClose(await dfilter.data(), [1.4, .6, 1.9]);\n  });\n\n  it('dilation gradient valid padding filter 2.', async () => {\n    const inputShape: [number, number, number, number] = [1, 3, 3, 1];\n    const filterShape: [number, number, number] = [2, 2, 1];\n    const dyShape: [number, number, number, number] = [1, 2, 2, 1];\n    const x = tf.tensor4d([.1, .2, .3, .4, .5, .6, .7, .8, .9], inputShape);\n    const filter = tf.tensor3d([.4, .3, .1, .2], filterShape);\n    const dy = tf.tensor4d([.2, .3, .4, .2], dyShape);\n\n    const grads = tf.grads(\n        (x: tf.Tensor4D, filter: tf.Tensor3D) =>\n            x.dilation2d(filter, 1, 'valid'));\n\n    const [dx, dfilter] = grads([x, filter], dy);\n\n    expect(dx.shape).toEqual(x.shape);\n    expectArraysClose(await dx.data(), [0, 0, 0, 0, .2, .3, 0, .4, .2]);\n\n    expect(dfilter.shape).toEqual(filterShape);\n    expectArraysClose(await dfilter.data(), [0, 0, 0, 1.1]);\n  });\n\n  it('dilation gradient same padding filter 2 depth 3.', async () => {\n    const inputShape: [number, number, number, number] = [1, 3, 3, 3];\n    const filterShape: [number, number, number] = [2, 2, 3];\n    const x = tf.tensor4d(\n        [\n          .1, .2, .3, .4, .5, .6, .7, .8, .9, .3, .2, .3, .4, .5,\n          .1, .9, .6, .3, .4, .5, .6, .2, .3, .5, .1, .2, .3\n        ],\n        inputShape);\n    const filter = tf.tensor3d(\n        [.4, .3, .1, .2, .2, .1, .7, .3, .8, .4, .9, .1], filterShape);\n    const dy = tf.tensor4d(\n        [\n          .2, .3, .4, .2, .1, .5, 0,  .8, .7, .1, .2, .1, .2, .3,\n          .4, .5, .6, .6, .6, .7, .8, .3, .2, .1, .2, .4, .2\n        ],\n        inputShape);\n\n    const grads = tf.grads(\n        (x: tf.Tensor4D, filter: tf.Tensor3D) =>\n            x.dilation2d(filter, 1, 'same'));\n\n    const [dx, dfilter] = grads([x, filter], dy);\n\n    expect(dx.shape).toEqual(x.shape);\n    expectArraysClose(await dx.data(), [\n      0, 0,  0,  0,  0,  0,  0,  .8, .5, .2, 0,  .4, 0, .3,\n      0, .9, .7, .7, .7, .7, .9, .3, .4, .5, .2, .7, .8\n    ]);\n\n    expect(dfilter.shape).toEqual(filterShape);\n    expectArraysClose(\n        await dfilter.data(),\n        [1.6, 2.7, 1.1, .2, 0, .5, .3, 0, 2.2, .2, .9, 0]);\n  });\n});\n"]}