@tensorflow/tfjs-core/dist/ops/max_test.js

/**
 * @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('max', ALL_ENVS, () => {
    it('with one element dominating', async () => {
        const a = tf.tensor1d([3, -1, 0, 100, -7, 2]);
        const r = tf.max(a);
        expectArraysClose(await r.data(), 100);
    });
    it('with all elements being the same', async () => {
        const a = tf.tensor1d([3, 3, 3]);
        const r = tf.max(a);
        expectArraysClose(await r.data(), 3);
    });
    it('with a large dimension', async () => {
        const aData = new Float32Array(1000);
        aData[0] = 1;
        const a = tf.tensor1d(aData);
        const r = tf.max(a);
        expectArraysClose(await r.data(), 1);
    });
    it('return NaNs', async () => {
        expectArraysClose(await tf.max([3, NaN, 2]).data(), NaN);
    });
    it('2D', async () => {
        const a = tf.tensor2d([3, -1, 0, 100, -7, 2], [2, 3]);
        expectArraysClose(await tf.max(a).data(), 100);
    });
    it('2D axis=[0,1]', async () => {
        const a = tf.tensor2d([3, -1, 0, 100, -7, 2], [2, 3]);
        expectArraysClose(await tf.max(a, [0, 1]).data(), 100);
    });
    it('2D, axis=0', async () => {
        const a = tf.tensor2d([3, -1, 0, 100, -7, 2], [2, 3]);
        const r = tf.max(a, [0]);
        expect(r.shape).toEqual([3]);
        expectArraysClose(await r.data(), [100, -1, 2]);
    });
    it('2D, axis=0, keepDims', async () => {
        const a = tf.tensor2d([3, -1, 0, 100, -7, 2], [2, 3]);
        const r = tf.max(a, [0], true /* keepDims */);
        expect(r.shape).toEqual([1, 3]);
        expectArraysClose(await r.data(), [100, -1, 2]);
    });
    it('2D, axis=1 provided as a number', async () => {
        const a = tf.tensor2d([3, 2, 5, 100, -7, 2], [2, 3]);
        const r = tf.max(a, 1);
        expectArraysClose(await r.data(), [5, 100]);
    });
    it('2D, axis = -1 provided as a number', async () => {
        const a = tf.tensor2d([3, 2, 5, 100, -7, 2], [2, 3]);
        const r = tf.max(a, -1);
        expectArraysClose(await r.data(), [5, 100]);
    });
    it('2D, axis=[1]', async () => {
        const a = tf.tensor2d([3, 2, 5, 100, -7, 2], [2, 3]);
        const r = tf.max(a, [1]);
        expectArraysClose(await r.data(), [5, 100]);
    });
    it('6D, axis=[5]', async () => {
        const a = tf.range(0, 64).reshape([2, 2, 2, 2, 2, 2]);
        const r = tf.max(a, [5]);
        const expectedResult = [
            1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31,
            33, 35, 37, 39, 41, 43, 45, 47, 49, 51, 53, 55, 57, 59, 61, 63
        ];
        expectArraysClose(await r.data(), expectedResult);
    });
    it('axis permutation does not change input', async () => {
        const input = tf.tensor2d([3, -1, 0, 100, -7, 2], [2, 3]);
        const inputDataBefore = await input.data();
        tf.max(input, [1, 0]);
        const inputDataAfter = await input.data();
        expectArraysClose(inputDataBefore, inputDataAfter);
    });
    it('throws when passed a non-tensor', () => {
        expect(() => tf.max({}))
            .toThrowError(/Argument 'x' passed to 'max' must be a Tensor/);
    });
    it('accepts a tensor-like object', async () => {
        const r = tf.max([3, -1, 0, 100, -7, 2]);
        expectArraysClose(await r.data(), 100);
    });
    it('accepts int32 tensor', async () => {
        const a = tf.tensor2d([3, -1, 0, 100, -7, 2], [2, 3], 'int32');
        expect(a.dtype).toEqual('int32');
        expectArraysClose(await tf.max(a).data(), 100);
    });
    it('max gradient: Scalar', async () => {
        const x = tf.scalar(42);
        const dy = tf.scalar(-1);
        const gradients = tf.grad(v => tf.max(v))(x, dy);
        expectArraysClose(await gradients.data(), [-1]);
    });
    it('gradient with clones', async () => {
        const x = tf.scalar(42);
        const dy = tf.scalar(-1);
        const gradients = tf.grad(v => tf.max(v.clone()).clone())(x, dy);
        expectArraysClose(await gradients.data(), [-1]);
    });
    it('max gradient: 1D, ties', async () => {
        const x = tf.tensor1d([1, 3, 7, 7]);
        const dy = tf.scalar(-1);
        const gradients = tf.grad(v => tf.max(v))(x, dy);
        expectArraysClose(await gradients.data(), [0, 0, -1, -1]);
    });
    it('max gradient: 2D, axes=-1, keepDims=false', async () => {
        const x = tf.tensor2d([[0, 20, 10], [-10, -30, -20]]);
        const dy = tf.tensor1d([-1, -1]);
        const axis = -1;
        const gradients = tf.grad(v => tf.max(v, axis))(x, dy);
        expectArraysClose(await gradients.data(), [0, -1, 0, -1, 0, 0]);
        expect(gradients.shape).toEqual([2, 3]);
    });
    it('max gradient: ties, 2D, axes=-1, keepDims=false', async () => {
        const x = tf.tensor2d([[0, 20, 20], [-10, -30, -10]]);
        const dy = tf.tensor1d([-1, -1]);
        const axis = -1;
        const gradients = tf.grad(v => tf.max(v, axis))(x, dy);
        expectArraysClose(await gradients.data(), [0, -1, -1, -1, 0, -1]);
        expect(gradients.shape).toEqual([2, 3]);
    });
    it('max gradient: 2D, axes=0, keepDims=false', async () => {
        const x = tf.tensor2d([[0, 20, 10], [-10, -30, 20]]);
        const dy = tf.tensor1d([-1, -1, -1]);
        const axis = 0;
        const gradients = tf.grad(v => tf.max(v, axis))(x, dy);
        expectArraysClose(await gradients.data(), [-1, -1, 0, 0, 0, -1]);
        expect(gradients.shape).toEqual([2, 3]);
    });
    it('max gradient: 2D, axes=-1, keepDims=true', async () => {
        const x = tf.tensor2d([[0, 20, 10], [-10, -30, -20]]);
        const dy = tf.tensor2d([[-1], [-1]]);
        const axis = -1;
        const keepDims = true;
        const gradients = tf.grad(v => tf.max(v, axis, keepDims))(x, dy);
        expectArraysClose(await gradients.data(), [0, -1, 0, -1, 0, 0]);
        expect(gradients.shape).toEqual([2, 3]);
    });
    it('max gradient: 2D, axes=0, keepDims=true', async () => {
        const x = tf.tensor2d([[0, 20, 10], [-10, -30, 20]]);
        const dy = tf.tensor2d([[-1, -1, -1]]);
        const axis = 0;
        const keepDims = true;
        const gradients = tf.grad(v => tf.max(v, axis, keepDims))(x, dy);
        expectArraysClose(await gradients.data(), [-1, -1, 0, 0, 0, -1]);
        expect(gradients.shape).toEqual([2, 3]);
    });
    it('max gradient: 3D, axis=1 keepDims=false', async () => {
        const x = tf.ones([2, 1, 250]);
        const axis = 1;
        const gradients = tf.grad(v => tf.max(v, axis))(x);
        expect(gradients.shape).toEqual(x.shape);
    });
    it('max gradient: 3D, axes=[1, 2], keepDims=false', async () => {
        const x = tf.tensor3d([[[0, 20], [10, 15]], [[-10, -30], [-20, -15]]]);
        const dy = tf.tensor1d([-1, -1]);
        const axis = [1, 2];
        const gradients = tf.grad(v => tf.max(v, axis))(x, dy);
        expectArraysClose(await gradients.data(), [0, -1, 0, 0, -1, 0, 0, 0]);
        expect(gradients.shape).toEqual([2, 2, 2]);
    });
    it('max gradient: ties, 3D, axes=[1, 2], keepDims=false', async () => {
        const x = tf.tensor3d([[[0, 20], [20, 20]], [[-10, -30], [-10, -15]]]);
        const dy = tf.tensor1d([-1, -1]);
        const axis = [1, 2];
        const gradients = tf.grad(v => tf.max(v, axis))(x, dy);
        expectArraysClose(await gradients.data(), [0, -1, -1, -1, -1, 0, -1, 0]);
        expect(gradients.shape).toEqual([2, 2, 2]);
    });
    it('max gradient: 3D, axes=2, keepDims=false', async () => {
        const x = tf.tensor3d([[[0, 20], [10, 15]], [[-10, -30], [-20, -15]]]);
        const dy = tf.tensor2d([[-1, -1], [-1, -1]]);
        const axis = 2;
        const gradients = tf.grad(v => tf.max(v, axis))(x, dy);
        expectArraysClose(await gradients.data(), [0, -1, 0, -1, -1, 0, 0, -1]);
        expect(gradients.shape).toEqual([2, 2, 2]);
    });
    it('max gradient: 3D, axes=2, keepDims=true', async () => {
        const x = tf.tensor3d([[[0, 20], [10, 15]], [[-10, -30], [-20, -15]]]);
        const dy = tf.tensor3d([[[-1], [-1]], [[-1], [-1]]]);
        const axis = 2;
        const keepDims = true;
        const gradients = tf.grad(v => tf.max(v, axis, keepDims))(x, dy);
        expectArraysClose(await gradients.data(), [0, -1, 0, -1, -1, 0, 0, -1]);
        expect(gradients.shape).toEqual([2, 2, 2]);
    });
    it('max gradient: ties, 4D, axes=[1, 2, 3], keepDims=false', async () => {
        const x = tf.tensor4d([
            [[[0, 20], [20, 20]], [[-10, -30], [-10, -30]]],
            [[[0, -20], [-20, -20]], [[10, 30], [10, 30]]]
        ]);
        const dy = tf.tensor1d([-1, -1]);
        const axis = [1, 2, 3];
        const gradients = tf.grad(v => tf.max(v, axis))(x, dy);
        expectArraysClose(await gradients.data(), [0, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, -1]);
        expect(gradients.shape).toEqual([2, 2, 2, 2]);
    });
    it('max gradient: ties, 4D, axes=[2, 3], keepDims=true', async () => {
        const x = tf.tensor4d([
            [[[0, 20], [20, 20]], [[-10, -30], [-10, -30]]],
            [[[0, -20], [-20, -20]], [[10, 30], [10, 30]]]
        ]);
        const dy = tf.tensor4d([[[[-1]], [[-2]]], [[[-3]], [[-4]]]]);
        const axis = [2, 3];
        const keepDims = true;
        const gradients = tf.grad(v => tf.max(v, axis, keepDims))(x, dy);
        expectArraysClose(await gradients.data(), [0, -1, -1, -1, -2, 0, -2, 0, -3, 0, 0, 0, 0, -4, 0, -4]);
        expect(gradients.shape).toEqual([2, 2, 2, 2]);
    });
    it('throws error for string tensor', () => {
        expect(() => tf.max(['a']))
            .toThrowError(/Argument 'x' passed to 'max' must be numeric tensor/);
    });
});
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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('max', ALL_ENVS, () => {\n  it('with one element dominating', async () => {\n    const a = tf.tensor1d([3, -1, 0, 100, -7, 2]);\n    const r = tf.max(a);\n    expectArraysClose(await r.data(), 100);\n  });\n\n  it('with all elements being the same', async () => {\n    const a = tf.tensor1d([3, 3, 3]);\n    const r = tf.max(a);\n    expectArraysClose(await r.data(), 3);\n  });\n\n  it('with a large dimension', async () => {\n    const aData = new Float32Array(1000);\n    aData[0] = 1;\n    const a = tf.tensor1d(aData);\n    const r = tf.max(a);\n    expectArraysClose(await r.data(), 1);\n  });\n\n  it('return NaNs', async () => {\n    expectArraysClose(await tf.max([3, NaN, 2]).data(), NaN);\n  });\n\n  it('2D', async () => {\n    const a = tf.tensor2d([3, -1, 0, 100, -7, 2], [2, 3]);\n    expectArraysClose(await tf.max(a).data(), 100);\n  });\n\n  it('2D axis=[0,1]', async () => {\n    const a = tf.tensor2d([3, -1, 0, 100, -7, 2], [2, 3]);\n    expectArraysClose(await tf.max(a, [0, 1]).data(), 100);\n  });\n\n  it('2D, axis=0', async () => {\n    const a = tf.tensor2d([3, -1, 0, 100, -7, 2], [2, 3]);\n    const r = tf.max(a, [0]);\n\n    expect(r.shape).toEqual([3]);\n    expectArraysClose(await r.data(), [100, -1, 2]);\n  });\n\n  it('2D, axis=0, keepDims', async () => {\n    const a = tf.tensor2d([3, -1, 0, 100, -7, 2], [2, 3]);\n    const r = tf.max(a, [0], true /* keepDims */);\n\n    expect(r.shape).toEqual([1, 3]);\n    expectArraysClose(await r.data(), [100, -1, 2]);\n  });\n\n  it('2D, axis=1 provided as a number', async () => {\n    const a = tf.tensor2d([3, 2, 5, 100, -7, 2], [2, 3]);\n    const r = tf.max(a, 1);\n    expectArraysClose(await r.data(), [5, 100]);\n  });\n\n  it('2D, axis = -1 provided as a number', async () => {\n    const a = tf.tensor2d([3, 2, 5, 100, -7, 2], [2, 3]);\n    const r = tf.max(a, -1);\n    expectArraysClose(await r.data(), [5, 100]);\n  });\n\n  it('2D, axis=[1]', async () => {\n    const a = tf.tensor2d([3, 2, 5, 100, -7, 2], [2, 3]);\n    const r = tf.max(a, [1]);\n    expectArraysClose(await r.data(), [5, 100]);\n  });\n\n  it('6D, axis=[5]', async () => {\n    const a = tf.range(0, 64).reshape([2, 2, 2, 2, 2, 2]);\n    const r = tf.max(a, [5]);\n    const expectedResult = [\n      1,  3,  5,  7,  9,  11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31,\n      33, 35, 37, 39, 41, 43, 45, 47, 49, 51, 53, 55, 57, 59, 61, 63\n    ];\n    expectArraysClose(await r.data(), expectedResult);\n  });\n\n  it('axis permutation does not change input', async () => {\n    const input = tf.tensor2d([3, -1, 0, 100, -7, 2], [2, 3]);\n    const inputDataBefore = await input.data();\n\n    tf.max(input, [1, 0]);\n\n    const inputDataAfter = await input.data();\n    expectArraysClose(inputDataBefore, inputDataAfter);\n  });\n\n  it('throws when passed a non-tensor', () => {\n    expect(() => tf.max({} as tf.Tensor))\n        .toThrowError(/Argument 'x' passed to 'max' must be a Tensor/);\n  });\n\n  it('accepts a tensor-like object', async () => {\n    const r = tf.max([3, -1, 0, 100, -7, 2]);\n    expectArraysClose(await r.data(), 100);\n  });\n\n  it('accepts int32 tensor', async () => {\n    const a = tf.tensor2d([3, -1, 0, 100, -7, 2], [2, 3], 'int32');\n    expect(a.dtype).toEqual('int32');\n    expectArraysClose(await tf.max(a).data(), 100);\n  });\n\n  it('max gradient: Scalar', async () => {\n    const x = tf.scalar(42);\n    const dy = tf.scalar(-1);\n    const gradients = tf.grad(v => tf.max(v))(x, dy);\n    expectArraysClose(await gradients.data(), [-1]);\n  });\n\n  it('gradient with clones', async () => {\n    const x = tf.scalar(42);\n    const dy = tf.scalar(-1);\n    const gradients = tf.grad(v => tf.max(v.clone()).clone())(x, dy);\n    expectArraysClose(await gradients.data(), [-1]);\n  });\n\n  it('max gradient: 1D, ties', async () => {\n    const x = tf.tensor1d([1, 3, 7, 7]);\n    const dy = tf.scalar(-1);\n    const gradients = tf.grad(v => tf.max(v))(x, dy);\n    expectArraysClose(await gradients.data(), [0, 0, -1, -1]);\n  });\n\n  it('max gradient: 2D, axes=-1, keepDims=false', async () => {\n    const x = tf.tensor2d([[0, 20, 10], [-10, -30, -20]]);\n    const dy = tf.tensor1d([-1, -1]);\n    const axis = -1;\n    const gradients = tf.grad(v => tf.max(v, axis))(x, dy);\n    expectArraysClose(await gradients.data(), [0, -1, 0, -1, 0, 0]);\n    expect(gradients.shape).toEqual([2, 3]);\n  });\n\n  it('max gradient: ties, 2D, axes=-1, keepDims=false', async () => {\n    const x = tf.tensor2d([[0, 20, 20], [-10, -30, -10]]);\n    const dy = tf.tensor1d([-1, -1]);\n    const axis = -1;\n    const gradients = tf.grad(v => tf.max(v, axis))(x, dy);\n    expectArraysClose(await gradients.data(), [0, -1, -1, -1, 0, -1]);\n    expect(gradients.shape).toEqual([2, 3]);\n  });\n\n  it('max gradient: 2D, axes=0, keepDims=false', async () => {\n    const x = tf.tensor2d([[0, 20, 10], [-10, -30, 20]]);\n    const dy = tf.tensor1d([-1, -1, -1]);\n    const axis = 0;\n    const gradients = tf.grad(v => tf.max(v, axis))(x, dy);\n    expectArraysClose(await gradients.data(), [-1, -1, 0, 0, 0, -1]);\n    expect(gradients.shape).toEqual([2, 3]);\n  });\n\n  it('max gradient: 2D, axes=-1, keepDims=true', async () => {\n    const x = tf.tensor2d([[0, 20, 10], [-10, -30, -20]]);\n    const dy = tf.tensor2d([[-1], [-1]]);\n    const axis = -1;\n    const keepDims = true;\n    const gradients = tf.grad(v => tf.max(v, axis, keepDims))(x, dy);\n    expectArraysClose(await gradients.data(), [0, -1, 0, -1, 0, 0]);\n    expect(gradients.shape).toEqual([2, 3]);\n  });\n\n  it('max gradient: 2D, axes=0, keepDims=true', async () => {\n    const x = tf.tensor2d([[0, 20, 10], [-10, -30, 20]]);\n    const dy = tf.tensor2d([[-1, -1, -1]]);\n    const axis = 0;\n    const keepDims = true;\n    const gradients = tf.grad(v => tf.max(v, axis, keepDims))(x, dy);\n    expectArraysClose(await gradients.data(), [-1, -1, 0, 0, 0, -1]);\n    expect(gradients.shape).toEqual([2, 3]);\n  });\n\n  it('max gradient: 3D, axis=1 keepDims=false', async () => {\n    const x = tf.ones([2, 1, 250]);\n    const axis = 1;\n    const gradients = tf.grad(v => tf.max(v, axis))(x);\n    expect(gradients.shape).toEqual(x.shape);\n  });\n\n  it('max gradient: 3D, axes=[1, 2], keepDims=false', async () => {\n    const x = tf.tensor3d([[[0, 20], [10, 15]], [[-10, -30], [-20, -15]]]);\n    const dy = tf.tensor1d([-1, -1]);\n    const axis = [1, 2];\n    const gradients = tf.grad(v => tf.max(v, axis))(x, dy);\n    expectArraysClose(await gradients.data(), [0, -1, 0, 0, -1, 0, 0, 0]);\n    expect(gradients.shape).toEqual([2, 2, 2]);\n  });\n\n  it('max gradient: ties, 3D, axes=[1, 2], keepDims=false', async () => {\n    const x = tf.tensor3d([[[0, 20], [20, 20]], [[-10, -30], [-10, -15]]]);\n    const dy = tf.tensor1d([-1, -1]);\n    const axis = [1, 2];\n    const gradients = tf.grad(v => tf.max(v, axis))(x, dy);\n    expectArraysClose(await gradients.data(), [0, -1, -1, -1, -1, 0, -1, 0]);\n    expect(gradients.shape).toEqual([2, 2, 2]);\n  });\n\n  it('max gradient: 3D, axes=2, keepDims=false', async () => {\n    const x = tf.tensor3d([[[0, 20], [10, 15]], [[-10, -30], [-20, -15]]]);\n    const dy = tf.tensor2d([[-1, -1], [-1, -1]]);\n    const axis = 2;\n    const gradients = tf.grad(v => tf.max(v, axis))(x, dy);\n    expectArraysClose(await gradients.data(), [0, -1, 0, -1, -1, 0, 0, -1]);\n    expect(gradients.shape).toEqual([2, 2, 2]);\n  });\n\n  it('max gradient: 3D, axes=2, keepDims=true', async () => {\n    const x = tf.tensor3d([[[0, 20], [10, 15]], [[-10, -30], [-20, -15]]]);\n    const dy = tf.tensor3d([[[-1], [-1]], [[-1], [-1]]]);\n    const axis = 2;\n    const keepDims = true;\n    const gradients = tf.grad(v => tf.max(v, axis, keepDims))(x, dy);\n    expectArraysClose(await gradients.data(), [0, -1, 0, -1, -1, 0, 0, -1]);\n    expect(gradients.shape).toEqual([2, 2, 2]);\n  });\n\n  it('max gradient: ties, 4D, axes=[1, 2, 3], keepDims=false', async () => {\n    const x = tf.tensor4d([\n      [[[0, 20], [20, 20]], [[-10, -30], [-10, -30]]],\n      [[[0, -20], [-20, -20]], [[10, 30], [10, 30]]]\n    ]);\n    const dy = tf.tensor1d([-1, -1]);\n    const axis = [1, 2, 3];\n    const gradients = tf.grad(v => tf.max(v, axis))(x, dy);\n    expectArraysClose(\n        await gradients.data(),\n        [0, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, -1]);\n    expect(gradients.shape).toEqual([2, 2, 2, 2]);\n  });\n\n  it('max gradient: ties, 4D, axes=[2, 3], keepDims=true', async () => {\n    const x = tf.tensor4d([\n      [[[0, 20], [20, 20]], [[-10, -30], [-10, -30]]],\n      [[[0, -20], [-20, -20]], [[10, 30], [10, 30]]]\n    ]);\n    const dy = tf.tensor4d([[[[-1]], [[-2]]], [[[-3]], [[-4]]]]);\n    const axis = [2, 3];\n    const keepDims = true;\n    const gradients = tf.grad(v => tf.max(v, axis, keepDims))(x, dy);\n    expectArraysClose(\n        await gradients.data(),\n        [0, -1, -1, -1, -2, 0, -2, 0, -3, 0, 0, 0, 0, -4, 0, -4]);\n    expect(gradients.shape).toEqual([2, 2, 2, 2]);\n  });\n\n  it('throws error for string tensor', () => {\n    expect(() => tf.max(['a']))\n        .toThrowError(/Argument 'x' passed to 'max' must be numeric tensor/);\n  });\n});\n"]}