/**
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* @license
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* Copyright 2020 Google LLC. All Rights Reserved.
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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* =============================================================================
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*/
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import * as tf from '../index';
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import { ALL_ENVS, describeWithFlags } from '../jasmine_util';
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import { expectArraysClose, expectArraysEqual } from '../test_util';
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describeWithFlags('mean', ALL_ENVS, () => {
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it('basic', async () => {
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const a = tf.tensor2d([
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0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
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16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31
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], [16, 2]);
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const r = tf.mean(a);
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expect(r.dtype).toBe('float32');
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expectArraysClose(await r.data(), 15.5);
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});
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it('propagates NaNs', async () => {
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const a = tf.tensor2d([1, 2, 3, NaN, 0, 1], [3, 2]);
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const r = tf.mean(a);
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expect(r.dtype).toBe('float32');
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expectArraysEqual(await r.data(), NaN);
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});
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it('mean(int32) => float32', async () => {
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const a = tf.tensor1d([1, 5, 7, 3], 'int32');
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const r = tf.mean(a);
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expect(r.dtype).toBe('float32');
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expectArraysClose(await r.data(), 4);
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});
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it('mean(bool) => float32', async () => {
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const a = tf.tensor1d([true, false, false, true, true], 'bool');
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const r = tf.mean(a);
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expect(r.dtype).toBe('float32');
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expectArraysClose(await r.data(), 3 / 5);
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});
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it('2D array with keep dim', async () => {
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const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
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const res = tf.mean(a, null, true /* keepDims */);
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expect(res.shape).toEqual([1, 1]);
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expect(res.dtype).toBe('float32');
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expectArraysClose(await res.data(), [7 / 6]);
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});
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it('axis=0 in 2D array', async () => {
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const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
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const res = tf.mean(a, [0]);
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expect(res.shape).toEqual([2]);
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expect(res.dtype).toBe('float32');
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expectArraysClose(await res.data(), [4 / 3, 1]);
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});
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it('axis=0 in 2D array, keepDims', async () => {
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const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
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const res = tf.mean(a, [0], true /* keepDims */);
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expect(res.shape).toEqual([1, 2]);
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expect(res.dtype).toBe('float32');
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expectArraysClose(await res.data(), [4 / 3, 1]);
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});
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it('axis=1 in 2D array', async () => {
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const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
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const res = tf.mean(a, [1]);
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expect(res.dtype).toBe('float32');
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expect(res.shape).toEqual([3]);
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expectArraysClose(await res.data(), [1.5, 1.5, 0.5]);
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});
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it('axis = -1 in 2D array', async () => {
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const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
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const res = tf.mean(a, [-1]);
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expect(res.dtype).toBe('float32');
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expect(res.shape).toEqual([3]);
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expectArraysClose(await res.data(), [1.5, 1.5, 0.5]);
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});
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it('2D, axis=1 provided as number', async () => {
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const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [2, 3]);
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const res = tf.mean(a, 1);
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expect(res.shape).toEqual([2]);
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expect(res.dtype).toBe('float32');
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expectArraysClose(await res.data(), [2, 1 / 3]);
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});
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it('axis=0,1 in 2D array', async () => {
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const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
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const res = tf.mean(a, [0, 1]);
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expect(res.shape).toEqual([]);
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expect(res.dtype).toBe('float32');
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expectArraysClose(await res.data(), [7 / 6]);
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});
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it('gradients', async () => {
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const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
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const dy = tf.scalar(1.5);
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const da = tf.grad(a => a.mean())(a, dy);
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const dyVal = await dy.array();
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expect(da.shape).toEqual(a.shape);
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expectArraysClose(await da.data(), [
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dyVal / a.size, dyVal / a.size, dyVal / a.size, dyVal / a.size,
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dyVal / a.size, dyVal / a.size
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]);
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});
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it('gradient with clones', async () => {
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const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
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const dy = tf.scalar(1.5);
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const da = tf.grad(a => a.clone().mean().clone())(a, dy);
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const dyVal = await dy.array();
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expect(da.shape).toEqual(a.shape);
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expectArraysClose(await da.data(), [
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dyVal / a.size, dyVal / a.size, dyVal / a.size, dyVal / a.size,
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dyVal / a.size, dyVal / a.size
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]);
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});
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it('gradients throws for defined axis', () => {
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const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
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const dy = tf.scalar(1.5);
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expect(() => tf.grad(a => a.mean(1))(a, dy)).toThrowError();
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});
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it('throws when passed a non-tensor', () => {
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expect(() => tf.mean({}))
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.toThrowError(/Argument 'x' passed to 'mean' must be a Tensor/);
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});
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it('accepts a tensor-like object', async () => {
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const r = tf.mean([[1, 2, 3], [0, 0, 1]]);
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expect(r.dtype).toBe('float32');
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expectArraysClose(await r.data(), 7 / 6);
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});
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it('throws error for string tensor', () => {
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expect(() => tf.mean(['a']))
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.toThrowError(/Argument 'x' passed to 'mean' must be numeric tensor/);
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});
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});
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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, expectArraysEqual} from '../test_util';\n\ndescribeWithFlags('mean', ALL_ENVS, () => {\n  it('basic', async () => {\n    const a = tf.tensor2d(\n        [\n          0,  1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14, 15,\n          16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31\n        ],\n        [16, 2]);\n    const r = tf.mean(a);\n\n    expect(r.dtype).toBe('float32');\n    expectArraysClose(await r.data(), 15.5);\n  });\n\n  it('propagates NaNs', async () => {\n    const a = tf.tensor2d([1, 2, 3, NaN, 0, 1], [3, 2]);\n    const r = tf.mean(a);\n\n    expect(r.dtype).toBe('float32');\n    expectArraysEqual(await r.data(), NaN);\n  });\n\n  it('mean(int32) => float32', async () => {\n    const a = tf.tensor1d([1, 5, 7, 3], 'int32');\n    const r = tf.mean(a);\n\n    expect(r.dtype).toBe('float32');\n    expectArraysClose(await r.data(), 4);\n  });\n\n  it('mean(bool) => float32', async () => {\n    const a = tf.tensor1d([true, false, false, true, true], 'bool');\n    const r = tf.mean(a);\n\n    expect(r.dtype).toBe('float32');\n    expectArraysClose(await r.data(), 3 / 5);\n  });\n\n  it('2D array with keep dim', async () => {\n    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);\n    const res = tf.mean(a, null, true /* keepDims */);\n\n    expect(res.shape).toEqual([1, 1]);\n    expect(res.dtype).toBe('float32');\n    expectArraysClose(await res.data(), [7 / 6]);\n  });\n\n  it('axis=0 in 2D array', async () => {\n    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);\n    const res = tf.mean(a, [0]);\n\n    expect(res.shape).toEqual([2]);\n    expect(res.dtype).toBe('float32');\n    expectArraysClose(await res.data(), [4 / 3, 1]);\n  });\n\n  it('axis=0 in 2D array, keepDims', async () => {\n    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);\n    const res = tf.mean(a, [0], true /* keepDims */);\n\n    expect(res.shape).toEqual([1, 2]);\n    expect(res.dtype).toBe('float32');\n    expectArraysClose(await res.data(), [4 / 3, 1]);\n  });\n\n  it('axis=1 in 2D array', async () => {\n    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);\n    const res = tf.mean(a, [1]);\n\n    expect(res.dtype).toBe('float32');\n    expect(res.shape).toEqual([3]);\n    expectArraysClose(await res.data(), [1.5, 1.5, 0.5]);\n  });\n\n  it('axis = -1 in 2D array', async () => {\n    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);\n    const res = tf.mean(a, [-1]);\n\n    expect(res.dtype).toBe('float32');\n    expect(res.shape).toEqual([3]);\n    expectArraysClose(await res.data(), [1.5, 1.5, 0.5]);\n  });\n\n  it('2D, axis=1 provided as number', async () => {\n    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [2, 3]);\n    const res = tf.mean(a, 1);\n\n    expect(res.shape).toEqual([2]);\n    expect(res.dtype).toBe('float32');\n    expectArraysClose(await res.data(), [2, 1 / 3]);\n  });\n\n  it('axis=0,1 in 2D array', async () => {\n    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);\n    const res = tf.mean(a, [0, 1]);\n\n    expect(res.shape).toEqual([]);\n    expect(res.dtype).toBe('float32');\n    expectArraysClose(await res.data(), [7 / 6]);\n  });\n\n  it('gradients', async () => {\n    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);\n    const dy = tf.scalar(1.5);\n\n    const da = tf.grad(a => a.mean())(a, dy);\n    const dyVal = await dy.array();\n    expect(da.shape).toEqual(a.shape);\n    expectArraysClose(await da.data(), [\n      dyVal / a.size, dyVal / a.size, dyVal / a.size, dyVal / a.size,\n      dyVal / a.size, dyVal / a.size\n    ]);\n  });\n\n  it('gradient with clones', async () => {\n    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);\n    const dy = tf.scalar(1.5);\n\n    const da = tf.grad(a => a.clone().mean().clone())(a, dy);\n    const dyVal = await dy.array();\n    expect(da.shape).toEqual(a.shape);\n    expectArraysClose(await da.data(), [\n      dyVal / a.size, dyVal / a.size, dyVal / a.size, dyVal / a.size,\n      dyVal / a.size, dyVal / a.size\n    ]);\n  });\n\n  it('gradients throws for defined axis', () => {\n    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);\n    const dy = tf.scalar(1.5);\n\n    expect(() => tf.grad(a => a.mean(1))(a, dy)).toThrowError();\n  });\n\n  it('throws when passed a non-tensor', () => {\n    expect(() => tf.mean({} as tf.Tensor))\n        .toThrowError(/Argument 'x' passed to 'mean' must be a Tensor/);\n  });\n\n  it('accepts a tensor-like object', async () => {\n    const r = tf.mean([[1, 2, 3], [0, 0, 1]]);\n\n    expect(r.dtype).toBe('float32');\n    expectArraysClose(await r.data(), 7 / 6);\n  });\n\n  it('throws error for string tensor', () => {\n    expect(() => tf.mean(['a']))\n        .toThrowError(/Argument 'x' passed to 'mean' must be numeric tensor/);\n  });\n});\n"]}
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