/**
 * @license
 * Copyright 2020 Google Inc. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
import * as tf from '../index';
import { ALL_ENVS, describeWithFlags } from '../jasmine_util';
import { expectArraysClose } from '../test_util';
describeWithFlags('gather', ALL_ENVS, (env) => {
    it('1D (gather), scalar indices', async () => {
        const t = tf.tensor1d([1, 2, 3]);
        const t2 = tf.gather(t, tf.scalar(1, 'int32'), 0);
        expect(t2.shape).toEqual([]);
        expectArraysClose(await t2.data(), [2]);
    });
    it('1D (gather), 1D indices', async () => {
        const t = tf.tensor1d([1, 2, 3]);
        const t2 = tf.gather(t, tf.tensor1d([0, 2, 0, 1], 'int32'), 0);
        expect(t2.shape).toEqual([4]);
        expectArraysClose(await t2.data(), [1, 3, 1, 2]);
    });
    it('1D (gather), 2D indices', async () => {
        const t = tf.tensor1d([1, 2, 3]);
        const t2 = tf.gather(t, tf.tensor2d([0, 2, 0, 1], [1, 4], 'int32'), 0);
        expect(t2.shape).toEqual([1, 4]);
        expectArraysClose(await t2.data(), [1, 3, 1, 2]);
    });
    it('2D (gather), scalar indices', async () => {
        const t = tf.tensor2d([1, 11, 2, 22], [2, 2]);
        let t2 = tf.gather(t, tf.scalar(1, 'int32'), 0);
        expect(t2.shape).toEqual([2]);
        expectArraysClose(await t2.data(), [2, 22]);
        t2 = tf.gather(t, tf.scalar(1, 'int32'), 1);
        expect(t2.shape).toEqual([2]);
        expectArraysClose(await t2.data(), [11, 22]);
    });
    it('2D (gather), 1D indices', async () => {
        const t = tf.tensor2d([1, 11, 2, 22], [2, 2]);
        let t2 = tf.gather(t, tf.tensor1d([1, 0, 0, 1], 'int32'), 0);
        expect(t2.shape).toEqual([4, 2]);
        expectArraysClose(await t2.data(), [2, 22, 1, 11, 1, 11, 2, 22]);
        t2 = tf.gather(t, tf.tensor1d([1, 0, 0, 1], 'int32'), 1);
        expect(t2.shape).toEqual([2, 4]);
        expectArraysClose(await t2.data(), [11, 1, 1, 11, 22, 2, 2, 22]);
    });
    it('2D (gather), 2D indices', async () => {
        const t = tf.tensor2d([1, 11, 2, 22], [2, 2]);
        let t2 = tf.gather(t, tf.tensor2d([1, 0, 0, 1], [2, 2], 'int32'), 0);
        expect(t2.shape).toEqual([2, 2, 2]);
        expectArraysClose(await t2.data(), [2, 22, 1, 11, 1, 11, 2, 22]);
        t2 = tf.gather(t, tf.tensor2d([1, 0, 0, 1], [2, 2], 'int32'), 1);
        expect(t2.shape).toEqual([2, 2, 2]);
        expectArraysClose(await t2.data(), [11, 1, 1, 11, 22, 2, 2, 22]);
    });
    it('2D (gather), 2D indices, non-zero batchDims', async () => {
        const t = tf.tensor2d([1, 11, 2, 22], [2, 2]);
        const t2 = tf.gather(t, tf.tensor2d([1, 0, 0, 1], [2, 2], 'int32'), 1, 1);
        expect(t2.shape).toEqual([2, 2]);
        expectArraysClose(await t2.data(), [11, 1, 2, 22]);
    });
    it('2D (gather), 2D indices, negative batchDims', async () => {
        const t = tf.tensor2d([1, 11, 2, 22], [2, 2]);
        const t2 = tf.gather(t, tf.tensor2d([1, 0, 0, 1], [2, 2], 'int32'), 1, -1);
        expect(t2.shape).toEqual([2, 2]);
        expectArraysClose(await t2.data(), [11, 1, 2, 22]);
    });
    it('3D (gather), 1D indices', async () => {
        const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
        const t2 = tf.gather(t, tf.tensor1d([1, 0, 0, 1], 'int32'), 2);
        expect(t2.shape).toEqual([2, 2, 4]);
        expectArraysClose(await t2.data(), [2, 1, 1, 2, 4, 3, 3, 4, 6, 5, 5, 6, 8, 7, 7, 8]);
    });
    it('3D (gather), 2D indices', async () => {
        const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
        const t2 = tf.gather(t, tf.tensor2d([1, 0, 0, 1], [2, 2], 'int32'), 2);
        expect(t2.shape).toEqual([2, 2, 2, 2]);
        expectArraysClose(await t2.data(), [2, 1, 1, 2, 4, 3, 3, 4, 6, 5, 5, 6, 8, 7, 7, 8]);
    });
    it('3D (gather), 2D indices, non-zero batchDims', async () => {
        const t = tf.tensor3d([1, 2, 3, 4], [1, 2, 2]);
        const t2 = tf.gather(t, tf.tensor2d([1, 0, 1], [1, 3], 'int32'), 2, 1);
        expect(t2.shape).toEqual([1, 2, 3]);
        expectArraysClose(await t2.data(), [2, 1, 2, 4, 3, 4]);
    });
    it('throws when batch dims greater than axis', () => {
        const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
        expect(() => tf.gather(t, tf.tensor3d([1, 0, 1], [1, 1, 3], 'int32'), 2, 3))
            .toThrowError(/must be less than or equal to axis/);
    });
    it('throws when batch dims greater than indices rank', () => {
        const t = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2]);
        expect(() => tf.gather(t, tf.tensor2d([1, 0, 1], [1, 3], 'int32'), 2, 3))
            .toThrowError(/Expect batchDims in the range of /);
    });
    it('throws when batch dims do not match', () => {
        const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
        expect(() => tf.gather(t, tf.tensor2d([1, 0, 1], [1, 3], 'int32'), 2, 1))
            .toThrowError(/should be equal to indices.shape/);
    });
    it('bool (gather), 1D indices', async () => {
        const t = tf.tensor1d([true, false, true], 'bool');
        const t2 = tf.gather(t, tf.tensor1d([0, 2, 0, 1], 'int32'), 0);
        expect(t2.shape).toEqual([4]);
        expect(t2.dtype).toBe('bool');
        expect(await t2.data()).toEqual(new Uint8Array([1, 1, 1, 0]));
    });
    it('bool (gather), 2D indices', async () => {
        const t = tf.tensor1d([true, false, true], 'bool');
        const t2 = tf.gather(t, tf.tensor2d([0, 2, 0, 1], [2, 2], 'int32'), 0);
        expect(t2.shape).toEqual([2, 2]);
        expect(t2.dtype).toBe('bool');
        expect(await t2.data()).toEqual(new Uint8Array([1, 1, 1, 0]));
    });
    it('int32 (gather), 1D indices', async () => {
        const t = tf.tensor1d([1, 2, 5], 'int32');
        const t2 = tf.gather(t, tf.tensor1d([0, 2, 0, 1], 'int32'), 0);
        expect(t2.shape).toEqual([4]);
        expect(t2.dtype).toBe('int32');
        expect(await t2.data()).toEqual(new Int32Array([1, 5, 1, 2]));
    });
    it('int32 (gather), 2D indices', async () => {
        const t = tf.tensor1d([1, 2, 5], 'int32');
        const t2 = tf.gather(t, tf.tensor2d([0, 2, 0, 1], [2, 2], 'int32'), 0);
        expect(t2.shape).toEqual([2, 2]);
        expect(t2.dtype).toBe('int32');
        expect(await t2.data()).toEqual(new Int32Array([1, 5, 1, 2]));
    });
    it('propagates NaNs', async () => {
        const t = tf.tensor1d([1, 2, NaN]);
        const t2 = tf.gather(t, tf.tensor1d([0, 2, 0, 1], 'int32'), 0);
        expect(t2.shape).toEqual([4]);
        expectArraysClose(await t2.data(), [1, NaN, 1, 2]);
    });
    it('chaining, axis=1', () => {
        const x = tf.zeros([2, 4, 6]);
        // [0, 2, 4]
        const indices = tf.range(0, 6, 2, 'int32');
        const axis = 2;
        expect(x.gather(indices, axis).shape).toEqual([2, 4, 3]);
    });
    it('indices not int32 throws error', () => {
        const x = tf.zeros([2, 4, 6]);
        // [0, 2, 4]
        const indices = tf.range(0, 6, 2);
        const axis = 2;
        expect(() => x.gather(indices, axis)).toThrowError();
    });
    it('throws when passed x as a non-tensor', () => {
        expect(() => tf.gather({}, tf.tensor1d([1])))
            .toThrowError(/Argument 'x' passed to 'gather' must be a Tensor/);
    });
    it('throws when passed indices as a non-tensor', () => {
        // tslint:disable-next-line:no-any
        expect(() => tf.gather(tf.tensor1d([1]), {}))
            .toThrowError(/Argument 'indices' passed to 'gather' must be a Tensor/);
    });
    it('throws when index is out of bound', async () => {
        const t = tf.tensor2d([1, 11, 2, 22], [2, 2]);
        expect(() => tf.gather(t, tf.tensor1d([100], 'int32')))
            .toThrowError(/GatherV2: the index value 100 is not in \[0, 1\]/);
        expect(() => tf.gather(t, tf.tensor1d([-1], 'int32')))
            .toThrowError(/GatherV2: the index value -1 is not in \[0, 1\]/);
    });
    it('accepts a tensor-like object', async () => {
        const res = tf.gather([1, 2, 3], [0, 2, 0, 1], 0);
        expect(res.shape).toEqual([4]);
        expectArraysClose(await res.data(), [1, 3, 1, 2]);
    });
    it('gradient 1D (gather), 1D indices', async () => {
        const t = tf.tensor1d([1, 2, 3]);
        const indices = tf.tensor1d([0, 2, 0, 1], 'int32');
        const dy = tf.tensor([3, 4, 5, 6]);
        const gradients = tf.grad(t => tf.gather(t, indices))(t, dy);
        expect(gradients.shape).toEqual(t.shape);
        expectArraysClose(await gradients.data(), [8, 6, 4]);
    });
    it('gradient with clones', () => {
        const t = tf.tensor1d([1, 2, 3]);
        const indices = tf.tensor1d([0, 2, 0, 1], 'int32');
        const gradF = tf.grad(t => tf.gather(t.clone(), indices.clone()).clone());
        const dt = gradF(t);
        expect(dt.shape).toEqual(t.shape);
    });
    it('gradient 1D (gather), 2D indices', async () => {
        const t = tf.tensor1d([1, 2, 3]);
        const indices = tf.tensor2d([0, 2, 0, 1], [2, 2], 'int32');
        const dy = tf.tensor2d([3, 4, 5, 6], [2, 2]);
        const gradients = tf.grad(t => tf.gather(t, indices))(t, dy);
        expect(gradients.shape).toEqual(t.shape);
        expectArraysClose(await gradients.data(), [8, 6, 4]);
    });
    it('gradient 2D (gather) axis=0 shape=[2, 2] 1D indices', async () => {
        const t = tf.tensor2d([1, 11, 2, 22], [2, 2]);
        const indices = tf.tensor1d([1, 0, 0, 1], 'int32');
        const dy = tf.tensor([3, 4, 5, 6, 7, 8, 9, 10], [4, 2]);
        const axis = 0;
        const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);
        expect(gradients.shape).toEqual(t.shape);
        expectArraysClose(await gradients.data(), [12, 14, 12, 14]);
    });
    it('gradient 2D (gather) axis=0 shape=[2, 2] 2D indices', async () => {
        const t = tf.tensor2d([1, 11, 2, 22], [2, 2]);
        const indices = tf.tensor2d([1, 0, 0, 1], [2, 2], 'int32');
        const dy = tf.tensor([3, 4, 5, 6, 7, 8, 9, 10], [2, 2, 2]);
        const axis = 0;
        const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);
        expect(gradients.shape).toEqual(t.shape);
        expectArraysClose(await gradients.data(), [12, 14, 12, 14]);
    });
    it('gradient 2D (gather) axis=0 shape=[4, 1] 1D indices', async () => {
        const t = tf.tensor2d([1, 11, 2, 22], [4, 1]);
        const indices = tf.tensor1d([1, 0, 0, 1], 'int32');
        const dy = tf.tensor([23, 7, 19, 13], [4, 1]);
        const axis = 0;
        const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);
        expect(gradients.shape).toEqual(t.shape);
        expectArraysClose(await gradients.data(), [26, 36, 0, 0]);
    });
    it('gradient 2D (gather) axis=0 shape=[4, 1] 2D indices', async () => {
        const t = tf.tensor2d([1, 11, 2, 22], [4, 1]);
        const indices = tf.tensor2d([1, 0, 0, 1], [2, 2], 'int32');
        const dy = tf.tensor([23, 7, 19, 13], [2, 2, 1]);
        const axis = 0;
        const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);
        expect(gradients.shape).toEqual(t.shape);
        expectArraysClose(await gradients.data(), [26, 36, 0, 0]);
    });
    it('gradient 2D (gather) axis=1 shape=[4, 2] 1D indices batchDims 1', async () => {
        const t = tf.variable(tf.tensor([[0, 1],
            [1, 2],
            [2, 3],
            [3, 4]]));
        const indices = tf.tensor([0, 1, 0, 1], [4, 1], 'int32');
        const dy = tf.tensor([1, 1, 1, 1], [4, 1]);
        const axis = 1;
        const gradients = tf.grad(t => tf.gather(t, indices, axis, 1))(t, dy);
        expect(gradients.shape).toEqual(t.shape);
        expectArraysClose(await gradients.data(), [1, 0, 0, 1, 1, 0, 0, 1]);
    });
    it('gradient 2D (gather) axis=1 shape=[2, 2] 1D indices', async () => {
        const t = tf.tensor2d([1, 11, 2, 22], [2, 2]);
        const indices = tf.tensor1d([1, 0, 0, 1], 'int32');
        const dy = tf.tensor([3, 4, 5, 6, 7, 8, 9, 10], [2, 4]);
        const axis = 1;
        const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);
        expect(gradients.shape).toEqual(t.shape);
        expectArraysClose(await gradients.data(), [9, 9, 17, 17]);
    });
    it('gradient 2D (gather) axis=1 shape=[2, 2] 2D indices', async () => {
        const t = tf.tensor2d([1, 11, 2, 22], [2, 2]);
        const indices = tf.tensor2d([1, 0, 0, 1], [2, 2], 'int32');
        const dy = tf.tensor([3, 4, 5, 6, 7, 8, 9, 10], [2, 2, 2]);
        const axis = 1;
        const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);
        expect(gradients.shape).toEqual(t.shape);
        expectArraysClose(await gradients.data(), [9, 9, 17, 17]);
    });
    it('gradient 2D (gather) axis=1 shape=[4, 1] 1D indices', async () => {
        const t = tf.tensor2d([1, 11, 2, 22], [4, 1]);
        const indices = tf.tensor1d([0, 0, 0, 0], 'int32');
        const dy = tf.tensor([3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18], [4, 4]);
        const axis = 1;
        const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);
        expect(gradients.shape).toEqual(t.shape);
        expectArraysClose(await gradients.data(), [18, 34, 50, 66]);
    });
    it('gradient 2D (gather) axis=1 shape=[4, 1] 2D indices', async () => {
        const t = tf.tensor2d([1, 11, 2, 22], [4, 1]);
        const indices = tf.tensor2d([0, 0, 0, 0], [2, 2], 'int32');
        const dy = tf.tensor([3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18], [4, 2, 2]);
        const axis = 1;
        const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);
        expect(gradients.shape).toEqual(t.shape);
        expectArraysClose(await gradients.data(), [18, 34, 50, 66]);
    });
    it('gradient 3D (gather) axis=0 shape=[2, 3, 2] 1D indices', async () => {
        const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 3, 2]);
        const indices = tf.tensor1d([1, 0, 0, 1], 'int32');
        const dy = tf.tensor([
            2, -3, 4, 15, 6, 0.7, 1, 18, 0.01, 0, 12, 13,
            4, 15, 12, -7, 18, 19, 2, 21, 6, 23, 24, 25
        ], [4, 3, 2]);
        const axis = 0;
        const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);
        expect(gradients.shape).toEqual(t.shape);
        expectArraysClose(await gradients.data(), [5, 33, 12.01, -7, 30, 32, 4, 18, 10, 38, 30, 25.7]);
    });
    it('gradient 3D (gather) axis=0 shape=[2, 3, 2] 2D indices', async () => {
        const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 3, 2]);
        const indices = tf.tensor2d([1, 0, 0, 1], [2, 2], 'int32');
        const dy = tf.tensor([
            2, -3, 4, 15, 6, 0.7, 1, 18, 0.01, 0, 12, 13,
            4, 15, 12, -7, 18, 19, 2, 21, 6, 23, 24, 25
        ], [2, 2, 3, 2]);
        const axis = 0;
        const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);
        expect(gradients.shape).toEqual(t.shape);
        expectArraysClose(await gradients.data(), [5, 33, 12.01, -7, 30, 32, 4, 18, 10, 38, 30, 25.7]);
    });
    it('gradient 3D (gather) axis=0 shape=[1, 4, 4]', async () => {
        const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], [1, 4, 4]);
        const indices = tf.tensor1d([0, 0], 'int32');
        const dy = tf.tensor([
            2, -3, 4, 15, 6, 0.7, 1, 18, 0.01, 0, 12, 13, 4, 15, 12, -7,
            18, 19, 2, 21, 6, 23, 24, 25, 101, 31, 34, 54, 1, 0, -3, -4
        ], [2, 4, 4]);
        const axis = 0;
        const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);
        expect(gradients.shape).toEqual(t.shape);
        expectArraysClose(await gradients.data(), [20, 16, 6, 36, 12, 23.7, 25, 43, 101.01, 31, 46, 67, 5, 15, 9, -11]);
    });
    it('gradient 3D (gather) axis=0 shape=[1, 4, 4] 1D indices', async () => {
        const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], [1, 4, 4]);
        const indices = tf.tensor1d([0, 0], 'int32');
        const dy = tf.tensor([
            2, -3, 4, 15, 6, 0.7, 1, 18, 0.01, 0, 12, 13, 4, 15, 12, -7,
            18, 19, 2, 21, 6, 23, 24, 25, 101, 31, 34, 54, 1, 0, -3, -4
        ], [2, 4, 4]);
        const axis = 0;
        const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);
        expect(gradients.shape).toEqual(t.shape);
        expectArraysClose(await gradients.data(), [20, 16, 6, 36, 12, 23.7, 25, 43, 101.01, 31, 46, 67, 5, 15, 9, -11]);
    });
    it('gradient 3D (gather) axis=1 shape=[2, 3, 2] 2D indices', async () => {
        const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 3, 2]);
        const indices = tf.tensor2d([1, 2, 2, 1], [2, 2], 'int32');
        const dy = tf.tensor([2, -3, 4, 15, 6, 0.7, 1, 18, 0.01, 0, 12, 13, 4, 15, 12, -7], [2, 2, 2, 2]);
        const axis = 1;
        const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);
        expect(gradients.shape).toEqual(t.shape);
        expectArraysClose(await gradients.data(), [0, 0, 3, 15, 10, 15.7, 0, 0, 12.01, -7, 16, 28]);
    });
    it('gradient 3D (gather) axis=1 shape=[1, 4, 4] 1D indices', async () => {
        const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], [1, 4, 4]);
        const indices = tf.tensor1d([1, 2, 2, 1], 'int32');
        const dy = tf.tensor([2, -3, 4, 15, 6, 0.7, 1, 18, 0.01, 0, 12, 13, 4, 15, 12, -7], [1, 4, 4]);
        const axis = 1;
        const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);
        expect(gradients.shape).toEqual(t.shape);
        expectArraysClose(await gradients.data(), [0, 0, 0, 0, 6, 12, 16, 8, 6.01, .7, 13, 31, 0, 0, 0, 0]);
    });
    it('gradient 3D (gather) axis=1 shape=[1, 4, 4] 2D indices', async () => {
        const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], [1, 4, 4]);
        const indices = tf.tensor2d([1, 2, 2, 1], [2, 2], 'int32');
        const dy = tf.tensor([2, -3, 4, 15, 6, 0.7, 1, 18, 0.01, 0, 12, 13, 4, 15, 12, -7], [1, 2, 2, 4]);
        const axis = 1;
        const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);
        expect(gradients.shape).toEqual(t.shape);
        expectArraysClose(await gradients.data(), [0, 0, 0, 0, 6, 12, 16, 8, 6.01, .7, 13, 31, 0, 0, 0, 0]);
    });
    it('gradient 3D (gather) axis=2 shape=[2, 3, 2] 1D indices', async () => {
        const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 3, 2]);
        const indices = tf.tensor1d([1, 0, 1, 0], 'int32');
        const dy = tf.tensor([
            2, -3, 4, 15, 6, 0.7, 1, 18, 0.01, 0, 12, 13,
            4, 15, 12, -7, 18, 19, 2, 21, 6, 23, 24, 25
        ], [2, 3, 4]);
        const axis = 2;
        const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);
        expect(gradients.shape).toEqual(t.shape);
        expectArraysClose(await gradients.data(), [12, 6, 18.7, 7, 13, 12.01, 8, 16, 40, 20, 48, 30]);
    });
    it('gradient 3D (gather) axis=2 shape=[2, 3, 2] 2D indices', async () => {
        const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 3, 2]);
        const indices = tf.tensor2d([1, 0, 1, 0], [2, 2], 'int32');
        const dy = tf.tensor([
            2, -3, 4, 15, 6, 0.7, 1, 18, 0.01, 0, 12, 13,
            4, 15, 12, -7, 18, 19, 2, 21, 6, 23, 24, 25
        ], [2, 3, 2, 2]);
        const axis = 2;
        const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);
        expect(gradients.shape).toEqual(t.shape);
        expectArraysClose(await gradients.data(), [12, 6, 18.7, 7, 13, 12.01, 8, 16, 40, 20, 48, 30]);
    });
    it('gradient 3D (gather) axis=2 shape=[4, 1, 4] 1D indices', async () => {
        const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], [4, 1, 4]);
        const indices = tf.tensor1d([1, 3, 1], 'int32');
        const dy = tf.tensor([2, -3, 4, 15, 6, 0.7, 1, 18, 0.01, 0, 4, 15], [4, 1, 3]);
        const axis = 2;
        const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);
        expect(gradients.shape).toEqual(t.shape);
        expectArraysClose(await gradients.data(), [0, 6, 0, -3, 0, 15.7, 0, 6, 0, 1.01, 0, 18, 0, 15, 0, 4]);
    });
    it('gradient 3D (gather) axis=2 shape=[4, 1, 4] 2D indices', async () => {
        const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], [4, 1, 4]);
        const indices = tf.tensor2d([1, 3, 1], [1, 3], 'int32');
        const dy = tf.tensor([2, -3, 4, 15, 6, 0.7, 1, 18, 0.01, 0, 4, 15], [4, 1, 1, 3]);
        const axis = 2;
        const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);
        expect(gradients.shape).toEqual(t.shape);
        expectArraysClose(await gradients.data(), [0, 6, 0, -3, 0, 15.7, 0, 6, 0, 1.01, 0, 18, 0, 15, 0, 4]);
    });
    it('ensure no memory leak', async () => {
        const numTensorsBefore = tf.memory().numTensors;
        const numDataIdBefore = tf.engine().backend.numDataIds();
        const t = tf.tensor1d([1, 2, 3]);
        const t1 = tf.scalar(1, 'int32');
        const t2 = tf.gather(t, t1, 0);
        expect(t2.shape).toEqual([]);
        expectArraysClose(await t2.data(), [2]);
        t.dispose();
        t1.dispose();
        t2.dispose();
        const numTensorsAfter = tf.memory().numTensors;
        const numDataIdAfter = tf.engine().backend.numDataIds();
        expect(numTensorsAfter).toBe(numTensorsBefore);
        expect(numDataIdAfter).toBe(numDataIdBefore);
    });
    it('fills with zero when index is out of bound', async () => {
        if (env.backendName === 'webgl' || env.backendName === 'webgpu') {
            const t = tf.tensor2d([1, 11, 2, 22], [2, 2]);
            const tInt = tf.tensor2d([1, 11, 2, 22], [2, 2], 'int32');
            const index = tf.tensor1d([0, 1, 100, -1, 2, -4], 'int32');
            const res = tf.gather(t, index);
            const resInt = tf.gather(tInt, index);
            const expected = [1, 11, 2, 22, 0, 0, 0, 0, 0, 0, 0, 0];
            expectArraysClose(await res.data(), expected);
            expectArraysClose(await resInt.data(), expected);
        }
    });
});
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* @license\n * Copyright 2020 Google Inc. 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('gather', ALL_ENVS, (env) => {\n  it('1D (gather), scalar indices', async () => {\n    const t = tf.tensor1d([1, 2, 3]);\n\n    const t2 = tf.gather(t, tf.scalar(1, 'int32'), 0);\n\n    expect(t2.shape).toEqual([]);\n    expectArraysClose(await t2.data(), [2]);\n  });\n\n  it('1D (gather), 1D indices', async () => {\n    const t = tf.tensor1d([1, 2, 3]);\n\n    const t2 = tf.gather(t, tf.tensor1d([0, 2, 0, 1], 'int32'), 0);\n\n    expect(t2.shape).toEqual([4]);\n    expectArraysClose(await t2.data(), [1, 3, 1, 2]);\n  });\n\n  it('1D (gather), 2D indices', async () => {\n    const t = tf.tensor1d([1, 2, 3]);\n\n    const t2 = tf.gather(t, tf.tensor2d([0, 2, 0, 1], [1, 4], 'int32'), 0);\n\n    expect(t2.shape).toEqual([1, 4]);\n    expectArraysClose(await t2.data(), [1, 3, 1, 2]);\n  });\n\n  it('2D (gather), scalar indices', async () => {\n    const t = tf.tensor2d([1, 11, 2, 22], [2, 2]);\n    let t2 = tf.gather(t, tf.scalar(1, 'int32'), 0);\n    expect(t2.shape).toEqual([2]);\n    expectArraysClose(await t2.data(), [2, 22]);\n\n    t2 = tf.gather(t, tf.scalar(1, 'int32'), 1);\n    expect(t2.shape).toEqual([2]);\n    expectArraysClose(await t2.data(), [11, 22]);\n  });\n\n  it('2D (gather), 1D indices', async () => {\n    const t = tf.tensor2d([1, 11, 2, 22], [2, 2]);\n    let t2 = tf.gather(t, tf.tensor1d([1, 0, 0, 1], 'int32'), 0);\n    expect(t2.shape).toEqual([4, 2]);\n    expectArraysClose(await t2.data(), [2, 22, 1, 11, 1, 11, 2, 22]);\n\n    t2 = tf.gather(t, tf.tensor1d([1, 0, 0, 1], 'int32'), 1);\n    expect(t2.shape).toEqual([2, 4]);\n    expectArraysClose(await t2.data(), [11, 1, 1, 11, 22, 2, 2, 22]);\n  });\n\n  it('2D (gather), 2D indices', async () => {\n    const t = tf.tensor2d([1, 11, 2, 22], [2, 2]);\n    let t2 = tf.gather(t, tf.tensor2d([1, 0, 0, 1], [2, 2], 'int32'), 0);\n    expect(t2.shape).toEqual([2, 2, 2]);\n    expectArraysClose(await t2.data(), [2, 22, 1, 11, 1, 11, 2, 22]);\n\n    t2 = tf.gather(t, tf.tensor2d([1, 0, 0, 1], [2, 2], 'int32'), 1);\n    expect(t2.shape).toEqual([2, 2, 2]);\n    expectArraysClose(await t2.data(), [11, 1, 1, 11, 22, 2, 2, 22]);\n  });\n\n  it('2D (gather), 2D indices, non-zero batchDims', async () => {\n    const t = tf.tensor2d([1, 11, 2, 22], [2, 2]);\n    const t2 = tf.gather(t, tf.tensor2d([1, 0, 0, 1], [2, 2], 'int32'), 1, 1);\n    expect(t2.shape).toEqual([2, 2]);\n    expectArraysClose(await t2.data(), [11, 1, 2, 22]);\n  });\n\n  it('2D (gather), 2D indices, negative batchDims', async () => {\n    const t = tf.tensor2d([1, 11, 2, 22], [2, 2]);\n    const t2 = tf.gather(t, tf.tensor2d([1, 0, 0, 1], [2, 2], 'int32'), 1, -1);\n    expect(t2.shape).toEqual([2, 2]);\n    expectArraysClose(await t2.data(), [11, 1, 2, 22]);\n  });\n\n  it('3D (gather), 1D indices', async () => {\n    const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);\n\n    const t2 = tf.gather(t, tf.tensor1d([1, 0, 0, 1], 'int32'), 2);\n\n    expect(t2.shape).toEqual([2, 2, 4]);\n    expectArraysClose(\n        await t2.data(), [2, 1, 1, 2, 4, 3, 3, 4, 6, 5, 5, 6, 8, 7, 7, 8]);\n  });\n\n  it('3D (gather), 2D indices', async () => {\n    const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);\n\n    const t2 = tf.gather(t, tf.tensor2d([1, 0, 0, 1], [2, 2], 'int32'), 2);\n\n    expect(t2.shape).toEqual([2, 2, 2, 2]);\n    expectArraysClose(\n        await t2.data(), [2, 1, 1, 2, 4, 3, 3, 4, 6, 5, 5, 6, 8, 7, 7, 8]);\n  });\n\n  it('3D (gather), 2D indices, non-zero batchDims', async () => {\n    const t = tf.tensor3d([1, 2, 3, 4], [1, 2, 2]);\n\n    const t2 = tf.gather(t, tf.tensor2d([1, 0, 1], [1, 3], 'int32'), 2, 1);\n\n    expect(t2.shape).toEqual([1, 2, 3]);\n    expectArraysClose(await t2.data(), [2, 1, 2, 4, 3, 4]);\n  });\n\n  it('throws when batch dims greater than axis', () => {\n    const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);\n\n    expect(() => tf.gather(t, tf.tensor3d([1, 0, 1], [1, 1, 3], 'int32'), 2, 3))\n        .toThrowError(/must be less than or equal to axis/);\n  });\n\n  it('throws when batch dims greater than indices rank', () => {\n    const t = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2]);\n\n    expect(() => tf.gather(t, tf.tensor2d([1, 0, 1], [1, 3], 'int32'), 2, 3))\n        .toThrowError(/Expect batchDims in the range of /);\n  });\n\n  it('throws when batch dims do not match', () => {\n    const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);\n\n    expect(() => tf.gather(t, tf.tensor2d([1, 0, 1], [1, 3], 'int32'), 2, 1))\n        .toThrowError(/should be equal to indices.shape/);\n  });\n\n  it('bool (gather), 1D indices', async () => {\n    const t = tf.tensor1d([true, false, true], 'bool');\n\n    const t2 = tf.gather(t, tf.tensor1d([0, 2, 0, 1], 'int32'), 0);\n\n    expect(t2.shape).toEqual([4]);\n    expect(t2.dtype).toBe('bool');\n    expect(await t2.data()).toEqual(new Uint8Array([1, 1, 1, 0]));\n  });\n\n  it('bool (gather), 2D indices', async () => {\n    const t = tf.tensor1d([true, false, true], 'bool');\n\n    const t2 = tf.gather(t, tf.tensor2d([0, 2, 0, 1], [2, 2], 'int32'), 0);\n\n    expect(t2.shape).toEqual([2, 2]);\n    expect(t2.dtype).toBe('bool');\n    expect(await t2.data()).toEqual(new Uint8Array([1, 1, 1, 0]));\n  });\n\n  it('int32 (gather), 1D indices', async () => {\n    const t = tf.tensor1d([1, 2, 5], 'int32');\n\n    const t2 = tf.gather(t, tf.tensor1d([0, 2, 0, 1], 'int32'), 0);\n\n    expect(t2.shape).toEqual([4]);\n    expect(t2.dtype).toBe('int32');\n    expect(await t2.data()).toEqual(new Int32Array([1, 5, 1, 2]));\n  });\n\n  it('int32 (gather), 2D indices', async () => {\n    const t = tf.tensor1d([1, 2, 5], 'int32');\n\n    const t2 = tf.gather(t, tf.tensor2d([0, 2, 0, 1], [2, 2], 'int32'), 0);\n\n    expect(t2.shape).toEqual([2, 2]);\n    expect(t2.dtype).toBe('int32');\n    expect(await t2.data()).toEqual(new Int32Array([1, 5, 1, 2]));\n  });\n\n  it('propagates NaNs', async () => {\n    const t = tf.tensor1d([1, 2, NaN]);\n\n    const t2 = tf.gather(t, tf.tensor1d([0, 2, 0, 1], 'int32'), 0);\n\n    expect(t2.shape).toEqual([4]);\n    expectArraysClose(await t2.data(), [1, NaN, 1, 2]);\n  });\n\n  it('chaining, axis=1', () => {\n    const x = tf.zeros([2, 4, 6]);\n    // [0, 2, 4]\n    const indices = tf.range(0, 6, 2, 'int32');\n    const axis = 2;\n    expect(x.gather(indices, axis).shape).toEqual([2, 4, 3]);\n  });\n\n  it('indices not int32 throws error', () => {\n    const x = tf.zeros([2, 4, 6]);\n    // [0, 2, 4]\n    const indices = tf.range(0, 6, 2);\n    const axis = 2;\n    expect(() => x.gather(indices, axis)).toThrowError();\n  });\n\n  it('throws when passed x as a non-tensor', () => {\n    expect(() => tf.gather({} as tf.Tensor, tf.tensor1d([1])))\n        .toThrowError(/Argument 'x' passed to 'gather' must be a Tensor/);\n  });\n\n  it('throws when passed indices as a non-tensor', () => {\n    // tslint:disable-next-line:no-any\n    expect(() => tf.gather(tf.tensor1d([1]), {} as any))\n        .toThrowError(/Argument 'indices' passed to 'gather' must be a Tensor/);\n  });\n\n  it('throws when index is out of bound', async () => {\n    const t = tf.tensor2d([1, 11, 2, 22], [2, 2]);\n    expect(() => tf.gather(t, tf.tensor1d([100], 'int32')))\n        .toThrowError(/GatherV2: the index value 100 is not in \\[0, 1\\]/);\n    expect(() => tf.gather(t, tf.tensor1d([-1], 'int32')))\n        .toThrowError(/GatherV2: the index value -1 is not in \\[0, 1\\]/);\n  });\n\n  it('accepts a tensor-like object', async () => {\n    const res = tf.gather([1, 2, 3], [0, 2, 0, 1], 0);\n    expect(res.shape).toEqual([4]);\n    expectArraysClose(await res.data(), [1, 3, 1, 2]);\n  });\n\n  it('gradient 1D (gather), 1D indices', async () => {\n    const t = tf.tensor1d([1, 2, 3]);\n    const indices = tf.tensor1d([0, 2, 0, 1], 'int32');\n    const dy = tf.tensor([3, 4, 5, 6]);\n\n    const gradients = tf.grad(t => tf.gather(t, indices))(t, dy);\n\n    expect(gradients.shape).toEqual(t.shape);\n    expectArraysClose(await gradients.data(), [8, 6, 4]);\n  });\n\n  it('gradient with clones', () => {\n    const t = tf.tensor1d([1, 2, 3]);\n    const indices = tf.tensor1d([0, 2, 0, 1], 'int32');\n    const gradF = tf.grad(t => tf.gather(t.clone(), indices.clone()).clone());\n    const dt = gradF(t);\n    expect(dt.shape).toEqual(t.shape);\n  });\n\n  it('gradient 1D (gather), 2D indices', async () => {\n    const t = tf.tensor1d([1, 2, 3]);\n    const indices = tf.tensor2d([0, 2, 0, 1], [2, 2], 'int32');\n    const dy = tf.tensor2d([3, 4, 5, 6], [2, 2]);\n\n    const gradients = tf.grad(t => tf.gather(t, indices))(t, dy);\n\n    expect(gradients.shape).toEqual(t.shape);\n    expectArraysClose(await gradients.data(), [8, 6, 4]);\n  });\n\n  it('gradient 2D (gather) axis=0 shape=[2, 2] 1D indices', async () => {\n    const t = tf.tensor2d([1, 11, 2, 22], [2, 2]);\n    const indices = tf.tensor1d([1, 0, 0, 1], 'int32');\n    const dy = tf.tensor([3, 4, 5, 6, 7, 8, 9, 10], [4, 2]);\n    const axis = 0;\n\n    const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);\n\n    expect(gradients.shape).toEqual(t.shape);\n    expectArraysClose(await gradients.data(), [12, 14, 12, 14]);\n  });\n\n  it('gradient 2D (gather) axis=0 shape=[2, 2] 2D indices', async () => {\n    const t = tf.tensor2d([1, 11, 2, 22], [2, 2]);\n    const indices = tf.tensor2d([1, 0, 0, 1], [2, 2], 'int32');\n    const dy = tf.tensor([3, 4, 5, 6, 7, 8, 9, 10], [2, 2, 2]);\n    const axis = 0;\n\n    const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);\n\n    expect(gradients.shape).toEqual(t.shape);\n    expectArraysClose(await gradients.data(), [12, 14, 12, 14]);\n  });\n\n  it('gradient 2D (gather) axis=0 shape=[4, 1] 1D indices', async () => {\n    const t = tf.tensor2d([1, 11, 2, 22], [4, 1]);\n    const indices = tf.tensor1d([1, 0, 0, 1], 'int32');\n    const dy = tf.tensor([23, 7, 19, 13], [4, 1]);\n    const axis = 0;\n\n    const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);\n\n    expect(gradients.shape).toEqual(t.shape);\n    expectArraysClose(await gradients.data(), [26, 36, 0, 0]);\n  });\n\n  it('gradient 2D (gather) axis=0 shape=[4, 1] 2D indices', async () => {\n    const t = tf.tensor2d([1, 11, 2, 22], [4, 1]);\n    const indices = tf.tensor2d([1, 0, 0, 1], [2, 2], 'int32');\n    const dy = tf.tensor([23, 7, 19, 13], [2, 2, 1]);\n    const axis = 0;\n\n    const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);\n\n    expect(gradients.shape).toEqual(t.shape);\n    expectArraysClose(await gradients.data(), [26, 36, 0, 0]);\n  });\n\n  it('gradient 2D (gather) axis=1 shape=[4, 2] 1D indices batchDims 1',\n     async () => {\n       const t = tf.variable(tf.tensor([[0, 1],\n                                        [1, 2],\n                                        [2, 3],\n                                        [3, 4]]));\n       const indices = tf.tensor([0, 1, 0, 1], [4, 1], 'int32');\n       const dy = tf.tensor([1, 1, 1, 1], [4, 1]);\n       const axis = 1;\n\n       const gradients = tf.grad(t => tf.gather(t, indices, axis, 1))(t, dy);\n\n       expect(gradients.shape).toEqual(t.shape);\n       expectArraysClose(await gradients.data(), [1, 0, 0, 1, 1, 0, 0, 1]);\n     });\n\n  it('gradient 2D (gather) axis=1 shape=[2, 2] 1D indices', async () => {\n    const t = tf.tensor2d([1, 11, 2, 22], [2, 2]);\n    const indices = tf.tensor1d([1, 0, 0, 1], 'int32');\n    const dy = tf.tensor([3, 4, 5, 6, 7, 8, 9, 10], [2, 4]);\n    const axis = 1;\n\n    const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);\n\n    expect(gradients.shape).toEqual(t.shape);\n    expectArraysClose(await gradients.data(), [9, 9, 17, 17]);\n  });\n\n  it('gradient 2D (gather) axis=1 shape=[2, 2] 2D indices', async () => {\n    const t = tf.tensor2d([1, 11, 2, 22], [2, 2]);\n    const indices = tf.tensor2d([1, 0, 0, 1], [2, 2], 'int32');\n    const dy = tf.tensor([3, 4, 5, 6, 7, 8, 9, 10], [2, 2, 2]);\n    const axis = 1;\n\n    const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);\n\n    expect(gradients.shape).toEqual(t.shape);\n    expectArraysClose(await gradients.data(), [9, 9, 17, 17]);\n  });\n\n  it('gradient 2D (gather) axis=1 shape=[4, 1] 1D indices', async () => {\n    const t = tf.tensor2d([1, 11, 2, 22], [4, 1]);\n    const indices = tf.tensor1d([0, 0, 0, 0], 'int32');\n    const dy = tf.tensor(\n        [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18], [4, 4]);\n    const axis = 1;\n\n    const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);\n\n    expect(gradients.shape).toEqual(t.shape);\n    expectArraysClose(await gradients.data(), [18, 34, 50, 66]);\n  });\n\n  it('gradient 2D (gather) axis=1 shape=[4, 1] 2D indices', async () => {\n    const t = tf.tensor2d([1, 11, 2, 22], [4, 1]);\n    const indices = tf.tensor2d([0, 0, 0, 0], [2, 2], 'int32');\n    const dy = tf.tensor(\n        [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18], [4, 2, 2]);\n    const axis = 1;\n\n    const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);\n\n    expect(gradients.shape).toEqual(t.shape);\n    expectArraysClose(await gradients.data(), [18, 34, 50, 66]);\n  });\n\n  it('gradient 3D (gather) axis=0 shape=[2, 3, 2] 1D indices', async () => {\n    const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 3, 2]);\n    const indices = tf.tensor1d([1, 0, 0, 1], 'int32');\n    const dy = tf.tensor(\n        [\n          2, -3, 4,  15, 6,  0.7, 1, 18, 0.01, 0,  12, 13,\n          4, 15, 12, -7, 18, 19,  2, 21, 6,    23, 24, 25\n        ],\n        [4, 3, 2]);\n    const axis = 0;\n\n    const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);\n\n    expect(gradients.shape).toEqual(t.shape);\n    expectArraysClose(\n        await gradients.data(),\n        [5, 33, 12.01, -7, 30, 32, 4, 18, 10, 38, 30, 25.7]);\n  });\n\n  it('gradient 3D (gather) axis=0 shape=[2, 3, 2] 2D indices', async () => {\n    const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 3, 2]);\n    const indices = tf.tensor2d([1, 0, 0, 1], [2, 2], 'int32');\n    const dy = tf.tensor(\n        [\n          2, -3, 4,  15, 6,  0.7, 1, 18, 0.01, 0,  12, 13,\n          4, 15, 12, -7, 18, 19,  2, 21, 6,    23, 24, 25\n        ],\n        [2, 2, 3, 2]);\n    const axis = 0;\n\n    const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);\n\n    expect(gradients.shape).toEqual(t.shape);\n    expectArraysClose(\n        await gradients.data(),\n        [5, 33, 12.01, -7, 30, 32, 4, 18, 10, 38, 30, 25.7]);\n  });\n\n  it('gradient 3D (gather) axis=0 shape=[1, 4, 4]', async () => {\n    const t = tf.tensor3d(\n        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], [1, 4, 4]);\n    const indices = tf.tensor1d([0, 0], 'int32');\n    const dy = tf.tensor(\n        [\n          2,  -3, 4, 15, 6, 0.7, 1,  18, 0.01, 0,  12, 13, 4, 15, 12, -7,\n          18, 19, 2, 21, 6, 23,  24, 25, 101,  31, 34, 54, 1, 0,  -3, -4\n        ],\n        [2, 4, 4]);\n    const axis = 0;\n\n    const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);\n\n    expect(gradients.shape).toEqual(t.shape);\n    expectArraysClose(\n        await gradients.data(),\n        [20, 16, 6, 36, 12, 23.7, 25, 43, 101.01, 31, 46, 67, 5, 15, 9, -11]);\n  });\n\n  it('gradient 3D (gather) axis=0 shape=[1, 4, 4] 1D indices', async () => {\n    const t = tf.tensor3d(\n        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], [1, 4, 4]);\n    const indices = tf.tensor1d([0, 0], 'int32');\n    const dy = tf.tensor(\n        [\n          2,  -3, 4, 15, 6, 0.7, 1,  18, 0.01, 0,  12, 13, 4, 15, 12, -7,\n          18, 19, 2, 21, 6, 23,  24, 25, 101,  31, 34, 54, 1, 0,  -3, -4\n        ],\n        [2, 4, 4]);\n    const axis = 0;\n\n    const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);\n\n    expect(gradients.shape).toEqual(t.shape);\n    expectArraysClose(\n        await gradients.data(),\n        [20, 16, 6, 36, 12, 23.7, 25, 43, 101.01, 31, 46, 67, 5, 15, 9, -11]);\n  });\n\n  it('gradient 3D (gather) axis=1 shape=[2, 3, 2] 2D indices', async () => {\n    const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 3, 2]);\n    const indices = tf.tensor2d([1, 2, 2, 1], [2, 2], 'int32');\n    const dy = tf.tensor(\n        [2, -3, 4, 15, 6, 0.7, 1, 18, 0.01, 0, 12, 13, 4, 15, 12, -7],\n        [2, 2, 2, 2]);\n    const axis = 1;\n\n    const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);\n\n    expect(gradients.shape).toEqual(t.shape);\n    expectArraysClose(\n        await gradients.data(),\n        [0, 0, 3, 15, 10, 15.7, 0, 0, 12.01, -7, 16, 28]);\n  });\n\n  it('gradient 3D (gather) axis=1 shape=[1, 4, 4] 1D indices', async () => {\n    const t = tf.tensor3d(\n        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], [1, 4, 4]);\n    const indices = tf.tensor1d([1, 2, 2, 1], 'int32');\n    const dy = tf.tensor(\n        [2, -3, 4, 15, 6, 0.7, 1, 18, 0.01, 0, 12, 13, 4, 15, 12, -7],\n        [1, 4, 4]);\n    const axis = 1;\n\n    const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);\n\n    expect(gradients.shape).toEqual(t.shape);\n    expectArraysClose(\n        await gradients.data(),\n        [0, 0, 0, 0, 6, 12, 16, 8, 6.01, .7, 13, 31, 0, 0, 0, 0]);\n  });\n\n  it('gradient 3D (gather) axis=1 shape=[1, 4, 4] 2D indices', async () => {\n    const t = tf.tensor3d(\n        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], [1, 4, 4]);\n    const indices = tf.tensor2d([1, 2, 2, 1], [2, 2], 'int32');\n    const dy = tf.tensor(\n        [2, -3, 4, 15, 6, 0.7, 1, 18, 0.01, 0, 12, 13, 4, 15, 12, -7],\n        [1, 2, 2, 4]);\n    const axis = 1;\n\n    const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);\n\n    expect(gradients.shape).toEqual(t.shape);\n    expectArraysClose(\n        await gradients.data(),\n        [0, 0, 0, 0, 6, 12, 16, 8, 6.01, .7, 13, 31, 0, 0, 0, 0]);\n  });\n\n  it('gradient 3D (gather) axis=2 shape=[2, 3, 2] 1D indices', async () => {\n    const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 3, 2]);\n    const indices = tf.tensor1d([1, 0, 1, 0], 'int32');\n    const dy = tf.tensor(\n        [\n          2, -3, 4,  15, 6,  0.7, 1, 18, 0.01, 0,  12, 13,\n          4, 15, 12, -7, 18, 19,  2, 21, 6,    23, 24, 25\n        ],\n        [2, 3, 4]);\n    const axis = 2;\n\n    const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);\n\n    expect(gradients.shape).toEqual(t.shape);\n    expectArraysClose(\n        await gradients.data(),\n        [12, 6, 18.7, 7, 13, 12.01, 8, 16, 40, 20, 48, 30]);\n  });\n\n  it('gradient 3D (gather) axis=2 shape=[2, 3, 2] 2D indices', async () => {\n    const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 3, 2]);\n    const indices = tf.tensor2d([1, 0, 1, 0], [2, 2], 'int32');\n    const dy = tf.tensor(\n        [\n          2, -3, 4,  15, 6,  0.7, 1, 18, 0.01, 0,  12, 13,\n          4, 15, 12, -7, 18, 19,  2, 21, 6,    23, 24, 25\n        ],\n        [2, 3, 2, 2]);\n    const axis = 2;\n\n    const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);\n\n    expect(gradients.shape).toEqual(t.shape);\n    expectArraysClose(\n        await gradients.data(),\n        [12, 6, 18.7, 7, 13, 12.01, 8, 16, 40, 20, 48, 30]);\n  });\n\n  it('gradient 3D (gather) axis=2 shape=[4, 1, 4] 1D indices', async () => {\n    const t = tf.tensor3d(\n        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], [4, 1, 4]);\n    const indices = tf.tensor1d([1, 3, 1], 'int32');\n    const dy =\n        tf.tensor([2, -3, 4, 15, 6, 0.7, 1, 18, 0.01, 0, 4, 15], [4, 1, 3]);\n    const axis = 2;\n\n    const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);\n\n    expect(gradients.shape).toEqual(t.shape);\n    expectArraysClose(\n        await gradients.data(),\n        [0, 6, 0, -3, 0, 15.7, 0, 6, 0, 1.01, 0, 18, 0, 15, 0, 4]);\n  });\n\n  it('gradient 3D (gather) axis=2 shape=[4, 1, 4] 2D indices', async () => {\n    const t = tf.tensor3d(\n        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], [4, 1, 4]);\n    const indices = tf.tensor2d([1, 3, 1], [1, 3], 'int32');\n    const dy =\n        tf.tensor([2, -3, 4, 15, 6, 0.7, 1, 18, 0.01, 0, 4, 15], [4, 1, 1, 3]);\n    const axis = 2;\n\n    const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);\n\n    expect(gradients.shape).toEqual(t.shape);\n    expectArraysClose(\n        await gradients.data(),\n        [0, 6, 0, -3, 0, 15.7, 0, 6, 0, 1.01, 0, 18, 0, 15, 0, 4]);\n  });\n\n  it('ensure no memory leak', async () => {\n    const numTensorsBefore = tf.memory().numTensors;\n    const numDataIdBefore = tf.engine().backend.numDataIds();\n    const t = tf.tensor1d([1, 2, 3]);\n    const t1 = tf.scalar(1, 'int32');\n    const t2 = tf.gather(t, t1, 0);\n\n    expect(t2.shape).toEqual([]);\n    expectArraysClose(await t2.data(), [2]);\n\n    t.dispose();\n    t1.dispose();\n    t2.dispose();\n\n    const numTensorsAfter = tf.memory().numTensors;\n    const numDataIdAfter = tf.engine().backend.numDataIds();\n    expect(numTensorsAfter).toBe(numTensorsBefore);\n    expect(numDataIdAfter).toBe(numDataIdBefore);\n  });\n\n  it('fills with zero when index is out of bound', async () => {\n    if (env.backendName === 'webgl' || env.backendName === 'webgpu') {\n      const t = tf.tensor2d([1, 11, 2, 22], [2, 2]);\n      const tInt = tf.tensor2d([1, 11, 2, 22], [2, 2], 'int32');\n\n      const index = tf.tensor1d([0, 1, 100, -1, 2, -4], 'int32');\n      const res = tf.gather(t, index);\n      const resInt = tf.gather(tInt, index);\n\n      const expected = [1, 11, 2, 22, 0, 0, 0, 0, 0, 0, 0, 0];\n      expectArraysClose(await res.data(), expected);\n      expectArraysClose(await resInt.data(), expected);\n    }\n  });\n});\n"]}