/**
 * @license
 * Copyright 2017 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('transpose', ALL_ENVS, () => {
    it('of scalar is no-op', async () => {
        const a = tf.scalar(3);
        expectArraysClose(await tf.transpose(a).data(), [3]);
    });
    it('of 1D is no-op', async () => {
        const a = tf.tensor1d([1, 2, 3]);
        expectArraysClose(await tf.transpose(a).data(), [1, 2, 3]);
    });
    it('of scalar with perm of incorrect rank throws error', () => {
        const a = tf.scalar(3);
        const perm = [0]; // Should be empty array.
        expect(() => tf.transpose(a, perm)).toThrowError();
    });
    it('of 1d with perm out of bounds throws error', () => {
        const a = tf.tensor1d([1, 2, 3]);
        const perm = [1];
        expect(() => tf.transpose(a, perm)).toThrowError();
    });
    it('of 1d with perm incorrect rank throws error', () => {
        const a = tf.tensor1d([1, 2, 3]);
        const perm = [0, 0]; // Should be of length 1.
        expect(() => tf.transpose(a, perm)).toThrowError();
    });
    it('2D (no change)', async () => {
        const t = tf.tensor2d([1, 11, 2, 22, 3, 33, 4, 44], [2, 4]);
        const t2 = tf.transpose(t, [0, 1]);
        expect(t2.shape).toEqual(t.shape);
        expectArraysClose(await t2.array(), await t.array());
    });
    it('2D (transpose)', async () => {
        const t = tf.tensor2d([1, 11, 2, 22, 3, 33, 4, 44], [2, 4]);
        const t2 = tf.transpose(t, [1, 0]);
        expect(t2.shape).toEqual([4, 2]);
        expectArraysClose(await t2.data(), [1, 3, 11, 33, 2, 4, 22, 44]);
    });
    it('2D, shape has ones', async () => {
        const t = tf.tensor2d([1, 2, 3, 4], [1, 4]);
        const t2 = tf.transpose(t, [1, 0]);
        expect(t2.shape).toEqual([4, 1]);
        expectArraysClose(await t2.data(), [1, 2, 3, 4]);
    });
    it('3D [r, c, d] => [d, r, c]', async () => {
        const t = tf.tensor3d([1, 11, 2, 22, 3, 33, 4, 44], [2, 2, 2]);
        const t2 = tf.transpose(t, [2, 0, 1]);
        expect(t2.shape).toEqual([2, 2, 2]);
        expectArraysClose(await t2.data(), [1, 2, 3, 4, 11, 22, 33, 44]);
    });
    it('3D [r, c, d] => [d, c, r]', async () => {
        const t = tf.tensor3d([1, 11, 2, 22, 3, 33, 4, 44], [2, 2, 2]);
        const t2 = tf.transpose(t, [2, 1, 0]);
        expect(t2.shape).toEqual([2, 2, 2]);
        expectArraysClose(await t2.data(), [1, 3, 2, 4, 11, 33, 22, 44]);
    });
    it('3D [r, c, d] => [d, r, c], shape has ones', async () => {
        const perm = [2, 0, 1];
        const t = tf.tensor3d([1, 2, 3, 4], [2, 1, 2]);
        const tt = tf.transpose(t, perm);
        expect(tt.shape).toEqual([2, 2, 1]);
        expectArraysClose(await tt.data(), [1, 3, 2, 4]);
        const t2 = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
        const tt2 = tf.transpose(t2, perm);
        expect(tt2.shape).toEqual([1, 2, 2]);
        expectArraysClose(await tt2.data(), [1, 2, 3, 4]);
        const t3 = tf.tensor3d([1, 2, 3, 4], [1, 2, 2]);
        const tt3 = tf.transpose(t3, perm);
        expect(tt3.shape).toEqual([2, 1, 2]);
        expectArraysClose(await tt3.data(), [1, 3, 2, 4]);
    });
    it('3D [r, c, d] => [r, d, c]', async () => {
        const perm = [0, 2, 1];
        const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
        const tt = tf.transpose(t, perm);
        expect(tt.shape).toEqual([2, 2, 2]);
        expectArraysClose(await tt.data(), [1, 3, 2, 4, 5, 7, 6, 8]);
    });
    it('5D [r, c, d, e, f] => [r, c, d, f, e]', async () => {
        const t = tf.tensor5d(new Array(32).fill(0).map((x, i) => i + 1), [2, 2, 2, 2, 2]);
        const t2 = tf.transpose(t, [0, 1, 2, 4, 3]);
        expect(t2.shape).toEqual([2, 2, 2, 2, 2]);
        expectArraysClose(await t2.data(), [
            1, 3, 2, 4, 5, 7, 6, 8, 9, 11, 10, 12, 13, 15, 14, 16,
            17, 19, 18, 20, 21, 23, 22, 24, 25, 27, 26, 28, 29, 31, 30, 32
        ]);
    });
    it('4D [r, c, d, e] => [c, r, d, e]', async () => {
        const t = tf.tensor4d(new Array(16).fill(0).map((x, i) => i + 1), [2, 2, 2, 2]);
        const t2 = tf.transpose(t, [1, 0, 2, 3]);
        expect(t2.shape).toEqual([2, 2, 2, 2]);
        expectArraysClose(await t2.data(), [1, 2, 3, 4, 9, 10, 11, 12, 5, 6, 7, 8, 13, 14, 15, 16]);
    });
    it('4D [r, c, d, e] => [c, r, e, d]', async () => {
        const t = tf.tensor4d(new Array(16).fill(0).map((x, i) => i + 1), [2, 2, 2, 2]);
        const t2 = tf.transpose(t, [1, 0, 3, 2]);
        expect(t2.shape).toEqual([2, 2, 2, 2]);
        expectArraysClose(await t2.data(), [1, 3, 2, 4, 9, 11, 10, 12, 5, 7, 6, 8, 13, 15, 14, 16]);
    });
    it('4D [r, c, d, e] => [e, r, c, d]', async () => {
        const t = tf.tensor4d(new Array(16).fill(0).map((x, i) => i + 1), [2, 2, 2, 2]);
        const t2 = tf.transpose(t, [3, 0, 1, 2]);
        expect(t2.shape).toEqual([2, 2, 2, 2]);
        expectArraysClose(await t2.data(), [1, 3, 5, 7, 9, 11, 13, 15, 2, 4, 6, 8, 10, 12, 14, 16]);
    });
    it('4D [r, c, d, e] => [d, c, e, r]', async () => {
        const t = tf.tensor4d(new Array(16).fill(0).map((x, i) => i + 1), [2, 2, 2, 2]);
        const t2 = tf.transpose(t, [2, 1, 3, 0]);
        expect(t2.shape).toEqual([2, 2, 2, 2]);
        expectArraysClose(await t2.data(), [1, 9, 2, 10, 5, 13, 6, 14, 3, 11, 4, 12, 7, 15, 8, 16]);
    });
    it('5D [r, c, d, e, f] => [c, r, d, e, f]', async () => {
        const t = tf.tensor5d(new Array(32).fill(0).map((x, i) => i + 1), [2, 2, 2, 2, 2]);
        const t2 = tf.transpose(t, [1, 0, 2, 3, 4]);
        expect(t2.shape).toEqual([2, 2, 2, 2, 2]);
        expectArraysClose(await t2.data(), [
            1, 2, 3, 4, 5, 6, 7, 8, 17, 18, 19, 20, 21, 22, 23, 24,
            9, 10, 11, 12, 13, 14, 15, 16, 25, 26, 27, 28, 29, 30, 31, 32
        ]);
    });
    it('6D [r, c, d, e, f] => [r, c, d, f, e]', async () => {
        const t = tf.tensor6d(new Array(64).fill(0).map((x, i) => i + 1), [2, 2, 2, 2, 2, 2]);
        const t2 = tf.transpose(t, [0, 1, 2, 3, 5, 4]);
        expect(t2.shape).toEqual([2, 2, 2, 2, 2, 2]);
        expectArraysClose(await t2.data(), [
            1, 3, 2, 4, 5, 7, 6, 8, 9, 11, 10, 12, 13, 15, 14, 16,
            17, 19, 18, 20, 21, 23, 22, 24, 25, 27, 26, 28, 29, 31, 30, 32,
            33, 35, 34, 36, 37, 39, 38, 40, 41, 43, 42, 44, 45, 47, 46, 48,
            49, 51, 50, 52, 53, 55, 54, 56, 57, 59, 58, 60, 61, 63, 62, 64
        ]);
    });
    it('6D [r, c, d, e, f, g] => [c, r, d, e, f, g]', async () => {
        const t = tf.tensor6d(new Array(64).fill(0).map((x, i) => i + 1), [2, 2, 2, 2, 2, 2]);
        const t2 = tf.transpose(t, [1, 0, 2, 3, 4, 5]);
        expect(t2.shape).toEqual([2, 2, 2, 2, 2, 2]);
        expectArraysClose(await t2.data(), [
            1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
            33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48,
            17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32,
            49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64
        ]);
    });
    it('gradient 3D [r, c, d] => [d, c, r]', async () => {
        const t = tf.tensor3d([1, 11, 2, 22, 3, 33, 4, 44], [2, 2, 2]);
        const perm = [2, 1, 0];
        const dy = tf.tensor3d([111, 211, 121, 221, 112, 212, 122, 222], [2, 2, 2]);
        const dt = tf.grad(t => t.transpose(perm))(t, dy);
        expect(dt.shape).toEqual(t.shape);
        expect(dt.dtype).toEqual('float32');
        expectArraysClose(await dt.data(), [111, 112, 121, 122, 211, 212, 221, 222]);
    });
    it('gradient with clones', async () => {
        const t = tf.tensor3d([1, 11, 2, 22, 3, 33, 4, 44], [2, 2, 2]);
        const perm = [2, 1, 0];
        const dy = tf.tensor3d([111, 211, 121, 221, 112, 212, 122, 222], [2, 2, 2]);
        const dt = tf.grad(t => t.clone().transpose(perm).clone())(t, dy);
        expect(dt.shape).toEqual(t.shape);
        expect(dt.dtype).toEqual('float32');
        expectArraysClose(await dt.data(), [111, 112, 121, 122, 211, 212, 221, 222]);
    });
    it('throws when passed a non-tensor', () => {
        expect(() => tf.transpose({}))
            .toThrowError(/Argument 'x' passed to 'transpose' must be a Tensor/);
    });
    it('accepts a tensor-like object', async () => {
        const t = [[1, 11, 2, 22], [3, 33, 4, 44]];
        const res = tf.transpose(t, [1, 0]);
        expect(res.shape).toEqual([4, 2]);
        expectArraysClose(await res.data(), [1, 3, 11, 33, 2, 4, 22, 44]);
    });
    it('accepts complex64 input', async () => {
        const real = tf.tensor2d([[1, 2], [3, 4]]);
        const imag = tf.tensor2d([[-4, 5], [6, 7]]);
        let res = tf.transpose(tf.complex(real, imag));
        expect(res.shape).toEqual([2, 2]);
        expectArraysClose([[1, 3], [2, 4]], await tf.real(res).data());
        expectArraysClose([[-4, 6], [5, 7]], await tf.imag(res).data());
        // Test taking conjugate tranpose.
        res = tf.transpose(tf.complex(real, imag), [1, 0], true);
        expect(res.shape).toEqual([2, 2]);
        expectArraysClose([[1, 3], [2, 4]], await tf.real(res).data());
        expectArraysClose([[4, -6], [-5, -7]], await tf.imag(res).data());
    });
});
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* @license\n * Copyright 2017 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('transpose', ALL_ENVS, () => {\n  it('of scalar is no-op', async () => {\n    const a = tf.scalar(3);\n    expectArraysClose(await tf.transpose(a).data(), [3]);\n  });\n\n  it('of 1D is no-op', async () => {\n    const a = tf.tensor1d([1, 2, 3]);\n    expectArraysClose(await tf.transpose(a).data(), [1, 2, 3]);\n  });\n\n  it('of scalar with perm of incorrect rank throws error', () => {\n    const a = tf.scalar(3);\n    const perm = [0];  // Should be empty array.\n    expect(() => tf.transpose(a, perm)).toThrowError();\n  });\n\n  it('of 1d with perm out of bounds throws error', () => {\n    const a = tf.tensor1d([1, 2, 3]);\n    const perm = [1];\n    expect(() => tf.transpose(a, perm)).toThrowError();\n  });\n\n  it('of 1d with perm incorrect rank throws error', () => {\n    const a = tf.tensor1d([1, 2, 3]);\n    const perm = [0, 0];  // Should be of length 1.\n    expect(() => tf.transpose(a, perm)).toThrowError();\n  });\n\n  it('2D (no change)', async () => {\n    const t = tf.tensor2d([1, 11, 2, 22, 3, 33, 4, 44], [2, 4]);\n    const t2 = tf.transpose(t, [0, 1]);\n\n    expect(t2.shape).toEqual(t.shape);\n    expectArraysClose(await t2.array(), await t.array());\n  });\n\n  it('2D (transpose)', async () => {\n    const t = tf.tensor2d([1, 11, 2, 22, 3, 33, 4, 44], [2, 4]);\n    const t2 = tf.transpose(t, [1, 0]);\n\n    expect(t2.shape).toEqual([4, 2]);\n    expectArraysClose(await t2.data(), [1, 3, 11, 33, 2, 4, 22, 44]);\n  });\n\n  it('2D, shape has ones', async () => {\n    const t = tf.tensor2d([1, 2, 3, 4], [1, 4]);\n    const t2 = tf.transpose(t, [1, 0]);\n\n    expect(t2.shape).toEqual([4, 1]);\n    expectArraysClose(await t2.data(), [1, 2, 3, 4]);\n  });\n\n  it('3D [r, c, d] => [d, r, c]', async () => {\n    const t = tf.tensor3d([1, 11, 2, 22, 3, 33, 4, 44], [2, 2, 2]);\n    const t2 = tf.transpose(t, [2, 0, 1]);\n\n    expect(t2.shape).toEqual([2, 2, 2]);\n    expectArraysClose(await t2.data(), [1, 2, 3, 4, 11, 22, 33, 44]);\n  });\n\n  it('3D [r, c, d] => [d, c, r]', async () => {\n    const t = tf.tensor3d([1, 11, 2, 22, 3, 33, 4, 44], [2, 2, 2]);\n    const t2 = tf.transpose(t, [2, 1, 0]);\n\n    expect(t2.shape).toEqual([2, 2, 2]);\n    expectArraysClose(await t2.data(), [1, 3, 2, 4, 11, 33, 22, 44]);\n  });\n\n  it('3D [r, c, d] => [d, r, c], shape has ones', async () => {\n    const perm = [2, 0, 1];\n\n    const t = tf.tensor3d([1, 2, 3, 4], [2, 1, 2]);\n    const tt = tf.transpose(t, perm);\n    expect(tt.shape).toEqual([2, 2, 1]);\n    expectArraysClose(await tt.data(), [1, 3, 2, 4]);\n\n    const t2 = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);\n    const tt2 = tf.transpose(t2, perm);\n    expect(tt2.shape).toEqual([1, 2, 2]);\n    expectArraysClose(await tt2.data(), [1, 2, 3, 4]);\n\n    const t3 = tf.tensor3d([1, 2, 3, 4], [1, 2, 2]);\n    const tt3 = tf.transpose(t3, perm);\n    expect(tt3.shape).toEqual([2, 1, 2]);\n    expectArraysClose(await tt3.data(), [1, 3, 2, 4]);\n  });\n\n  it('3D [r, c, d] => [r, d, c]', async () => {\n    const perm = [0, 2, 1];\n    const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);\n    const tt = tf.transpose(t, perm);\n    expect(tt.shape).toEqual([2, 2, 2]);\n    expectArraysClose(await tt.data(), [1, 3, 2, 4, 5, 7, 6, 8]);\n  });\n\n  it('5D [r, c, d, e, f] => [r, c, d, f, e]', async () => {\n    const t = tf.tensor5d(\n        new Array(32).fill(0).map((x, i) => i + 1), [2, 2, 2, 2, 2]);\n    const t2 = tf.transpose(t, [0, 1, 2, 4, 3]);\n\n    expect(t2.shape).toEqual([2, 2, 2, 2, 2]);\n    expectArraysClose(await t2.data(), [\n      1,  3,  2,  4,  5,  7,  6,  8,  9,  11, 10, 12, 13, 15, 14, 16,\n      17, 19, 18, 20, 21, 23, 22, 24, 25, 27, 26, 28, 29, 31, 30, 32\n    ]);\n  });\n\n  it('4D [r, c, d, e] => [c, r, d, e]', async () => {\n    const t =\n        tf.tensor4d(new Array(16).fill(0).map((x, i) => i + 1), [2, 2, 2, 2]);\n    const t2 = tf.transpose(t, [1, 0, 2, 3]);\n\n    expect(t2.shape).toEqual([2, 2, 2, 2]);\n    expectArraysClose(\n        await t2.data(),\n        [1, 2, 3, 4, 9, 10, 11, 12, 5, 6, 7, 8, 13, 14, 15, 16]);\n  });\n\n  it('4D [r, c, d, e] => [c, r, e, d]', async () => {\n    const t =\n        tf.tensor4d(new Array(16).fill(0).map((x, i) => i + 1), [2, 2, 2, 2]);\n    const t2 = tf.transpose(t, [1, 0, 3, 2]);\n\n    expect(t2.shape).toEqual([2, 2, 2, 2]);\n    expectArraysClose(\n        await t2.data(),\n        [1, 3, 2, 4, 9, 11, 10, 12, 5, 7, 6, 8, 13, 15, 14, 16]);\n  });\n\n  it('4D [r, c, d, e] => [e, r, c, d]', async () => {\n    const t =\n        tf.tensor4d(new Array(16).fill(0).map((x, i) => i + 1), [2, 2, 2, 2]);\n    const t2 = tf.transpose(t, [3, 0, 1, 2]);\n\n    expect(t2.shape).toEqual([2, 2, 2, 2]);\n    expectArraysClose(\n        await t2.data(),\n        [1, 3, 5, 7, 9, 11, 13, 15, 2, 4, 6, 8, 10, 12, 14, 16]);\n  });\n\n  it('4D [r, c, d, e] => [d, c, e, r]', async () => {\n    const t =\n        tf.tensor4d(new Array(16).fill(0).map((x, i) => i + 1), [2, 2, 2, 2]);\n    const t2 = tf.transpose(t, [2, 1, 3, 0]);\n\n    expect(t2.shape).toEqual([2, 2, 2, 2]);\n    expectArraysClose(\n        await t2.data(),\n        [1, 9, 2, 10, 5, 13, 6, 14, 3, 11, 4, 12, 7, 15, 8, 16]);\n  });\n\n  it('5D [r, c, d, e, f] => [c, r, d, e, f]', async () => {\n    const t = tf.tensor5d(\n        new Array(32).fill(0).map((x, i) => i + 1), [2, 2, 2, 2, 2]);\n    const t2 = tf.transpose(t, [1, 0, 2, 3, 4]);\n\n    expect(t2.shape).toEqual([2, 2, 2, 2, 2]);\n    expectArraysClose(await t2.data(), [\n      1, 2,  3,  4,  5,  6,  7,  8,  17, 18, 19, 20, 21, 22, 23, 24,\n      9, 10, 11, 12, 13, 14, 15, 16, 25, 26, 27, 28, 29, 30, 31, 32\n    ]);\n  });\n\n  it('6D [r, c, d, e, f] => [r, c, d, f, e]', async () => {\n    const t = tf.tensor6d(\n        new Array(64).fill(0).map((x, i) => i + 1), [2, 2, 2, 2, 2, 2]);\n    const t2 = tf.transpose(t, [0, 1, 2, 3, 5, 4]);\n    expect(t2.shape).toEqual([2, 2, 2, 2, 2, 2]);\n    expectArraysClose(await t2.data(), [\n      1,  3,  2,  4,  5,  7,  6,  8,  9,  11, 10, 12, 13, 15, 14, 16,\n      17, 19, 18, 20, 21, 23, 22, 24, 25, 27, 26, 28, 29, 31, 30, 32,\n      33, 35, 34, 36, 37, 39, 38, 40, 41, 43, 42, 44, 45, 47, 46, 48,\n      49, 51, 50, 52, 53, 55, 54, 56, 57, 59, 58, 60, 61, 63, 62, 64\n    ]);\n  });\n\n  it('6D [r, c, d, e, f, g] => [c, r, d, e, f, g]', async () => {\n    const t = tf.tensor6d(\n        new Array(64).fill(0).map((x, i) => i + 1), [2, 2, 2, 2, 2, 2]);\n    const t2 = tf.transpose(t, [1, 0, 2, 3, 4, 5]);\n    expect(t2.shape).toEqual([2, 2, 2, 2, 2, 2]);\n    expectArraysClose(await t2.data(), [\n      1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14, 15, 16,\n      33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48,\n      17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32,\n      49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64\n    ]);\n  });\n\n  it('gradient 3D [r, c, d] => [d, c, r]', async () => {\n    const t = tf.tensor3d([1, 11, 2, 22, 3, 33, 4, 44], [2, 2, 2]);\n    const perm = [2, 1, 0];\n    const dy = tf.tensor3d([111, 211, 121, 221, 112, 212, 122, 222], [2, 2, 2]);\n    const dt = tf.grad(t => t.transpose(perm))(t, dy);\n    expect(dt.shape).toEqual(t.shape);\n    expect(dt.dtype).toEqual('float32');\n    expectArraysClose(\n        await dt.data(), [111, 112, 121, 122, 211, 212, 221, 222]);\n  });\n\n  it('gradient with clones', async () => {\n    const t = tf.tensor3d([1, 11, 2, 22, 3, 33, 4, 44], [2, 2, 2]);\n    const perm = [2, 1, 0];\n    const dy = tf.tensor3d([111, 211, 121, 221, 112, 212, 122, 222], [2, 2, 2]);\n    const dt = tf.grad(t => t.clone().transpose(perm).clone())(t, dy);\n    expect(dt.shape).toEqual(t.shape);\n    expect(dt.dtype).toEqual('float32');\n    expectArraysClose(\n        await dt.data(), [111, 112, 121, 122, 211, 212, 221, 222]);\n  });\n\n  it('throws when passed a non-tensor', () => {\n    expect(() => tf.transpose({} as tf.Tensor))\n        .toThrowError(/Argument 'x' passed to 'transpose' must be a Tensor/);\n  });\n\n  it('accepts a tensor-like object', async () => {\n    const t = [[1, 11, 2, 22], [3, 33, 4, 44]];\n    const res = tf.transpose(t, [1, 0]);\n\n    expect(res.shape).toEqual([4, 2]);\n    expectArraysClose(await res.data(), [1, 3, 11, 33, 2, 4, 22, 44]);\n  });\n\n  it('accepts complex64 input', async () => {\n    const real = tf.tensor2d([[1, 2], [3, 4]]);\n    const imag = tf.tensor2d([[-4, 5], [6, 7]]);\n\n    let res = tf.transpose(tf.complex(real, imag));\n\n    expect(res.shape).toEqual([2, 2]);\n    expectArraysClose([[1, 3], [2, 4]], await tf.real(res).data());\n    expectArraysClose([[-4, 6], [5, 7]], await tf.imag(res).data());\n\n    // Test taking conjugate tranpose.\n    res = tf.transpose(tf.complex(real, imag), [1, 0], true);\n    expect(res.shape).toEqual([2, 2]);\n    expectArraysClose([[1, 3], [2, 4]], await tf.real(res).data());\n    expectArraysClose([[4, -6], [-5, -7]], await tf.imag(res).data());\n  });\n});\n"]}