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

/**
 * @license
 * Copyright 2018 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, SYNC_BACKEND_ENVS } from '../jasmine_util';
import { encodeStrings, expectArraysClose } from '../test_util';
describeWithFlags('slice ', ALL_ENVS, () => {
    describeWithFlags('ergonomics', ALL_ENVS, () => {
        it('slices 2x2x2 array into 2x1x1 no size', async () => {
            const a = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
            const result = a.slice([0, 1, 1]);
            expect(result.shape).toEqual([2, 1, 1]);
            expectArraysClose(await result.data(), [4, 8]);
        });
        it('slices 2x2x2 array into 1x2x2 with scalar begin no size', async () => {
            const a = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
            const result = a.slice(1);
            expect(result.shape).toEqual([1, 2, 2]);
            expectArraysClose(await result.data(), [5, 6, 7, 8]);
        });
        it('slices 2x2x2 array using 2d size and 2d size', async () => {
            const a = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
            const result = a.slice([0, 1]);
            expect(result.shape).toEqual([2, 1, 2]);
            expectArraysClose(await result.data(), [3, 4, 7, 8]);
        });
        it('slices 2x2x2 array using negative size', async () => {
            const a = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
            const result = a.slice([0, 1], [-1, 1]);
            expect(result.shape).toEqual([2, 1, 2]);
            expectArraysClose(await result.data(), [3, 4, 7, 8]);
        });
        it('slices 2x2x2 array using 1d size', async () => {
            const a = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
            const result = a.slice(0, 1);
            expect(result.shape).toEqual([1, 2, 2]);
            expectArraysClose(await result.data(), [1, 2, 3, 4]);
        });
        it('throws when passed a non-tensor', () => {
            expect(() => tf.slice({}, 0, 0))
                .toThrowError(/Argument 'x' passed to 'slice' must be a Tensor/);
        });
        it('accepts a tensor-like object', async () => {
            const a = [[[1, 2], [3, 4]], [[5, 6], [7, 8]]]; // 2x2x2
            const result = tf.slice(a, [0, 1, 1]);
            expect(result.shape).toEqual([2, 1, 1]);
            expectArraysClose(await result.data(), [4, 8]);
        });
        it('should match source tensor dtype', () => {
            const a = tf.tensor1d([1, 2, 3, 4, 5], 'int32');
            const b = a.asType('float32');
            expect(tf.slice(b, 0).dtype).toEqual('float32');
        });
        it('throws when begin is negative', async () => {
            const a = [[1, 2], [3, 4]]; // 2x2
            expect(() => tf.slice(a, [-1, 1], [
                1, 1
            ])).toThrowError(/slice\(\) does not support negative begin indexing./);
        });
    });
    describeWithFlags('shallow slicing', ALL_ENVS, () => {
        it('shallow slice an input that was cast', async () => {
            const a = tf.tensor([[1, 2], [3, 4]], [2, 2], 'int32');
            const b = a.toFloat();
            const c = b.slice(1, 1);
            expect(c.dtype).toBe('float32');
            expect(c.shape).toEqual([1, 2]);
            expectArraysClose(await c.data(), [3, 4]);
        });
        it('delayed async read of sliced tensor has no mem leak', async () => {
            const a = tf.zeros([10]);
            const b = tf.slice(a, 0, 1);
            const nBefore = tf.memory().numTensors;
            expect(nBefore).toBe(2);
            await b.data();
            const nAfter = tf.memory().numTensors;
            expect(nAfter).toBe(2);
            tf.dispose([a, b]);
            expect(tf.memory().numTensors).toBe(0);
        });
    });
    describeWithFlags('shallow slicing', SYNC_BACKEND_ENVS, () => {
        it('delayed sync read of sliced tensor has no mem leak', () => {
            const a = tf.zeros([10]);
            const b = tf.slice(a, 0, 1);
            const nBefore = tf.memory().numTensors;
            expect(nBefore).toBe(2);
            b.dataSync();
            const nAfter = tf.memory().numTensors;
            expect(nAfter).toBe(2);
            tf.dispose([a, b]);
            expect(tf.memory().numTensors).toBe(0);
        });
    });
    describeWithFlags('slice5d', ALL_ENVS, () => {
        it('slices 1x1x1x1x1 into shape 1x1x1x1x1 (effectively a copy)', async () => {
            const a = tf.tensor5d([[[[[5]]]]], [1, 1, 1, 1, 1]);
            const result = tf.slice(a, [0, 0, 0, 0, 0], [1, 1, 1, 1, 1]);
            expect(result.shape).toEqual([1, 1, 1, 1, 1]);
            expectArraysClose(await result.data(), [5]);
        });
        it('slices 2x2x2x2x2 array into 1x2x2x2x2 starting at [1,0,0,0,0]', async () => {
            const a = tf.tensor5d([
                1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
                12, 13, 14, 15, 16, 11, 22, 33, 44, 55, 66,
                77, 88, 111, 222, 333, 444, 555, 666, 777, 888
            ], [2, 2, 2, 2, 2]);
            const result = tf.slice(a, [1, 0, 0, 0, 0], [1, 2, 2, 2, 2]);
            expect(result.shape).toEqual([1, 2, 2, 2, 2]);
            expectArraysClose(await result.data(), [
                11, 22, 33, 44, 55, 66, 77, 88, 111, 222, 333, 444, 555, 666, 777,
                888
            ]);
        });
        it('slices 2x2x2x2x2 array into 2x1x1x1x1 starting at [0,1,1,1,1]', async () => {
            const a = tf.tensor5d([
                1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
                12, 13, 14, 15, 16, 11, 22, 33, 44, 55, 66,
                77, 88, 111, 222, 333, 444, 555, 666, 777, 888
            ], [2, 2, 2, 2, 2]);
            const result = tf.slice(a, [0, 1, 1, 1, 1], [2, 1, 1, 1, 1]);
            expect(result.shape).toEqual([2, 1, 1, 1, 1]);
            expectArraysClose(await result.data(), [16, 888]);
        });
        it('accepts a tensor-like object', async () => {
            const a = [[[[[5]]]]]; // 1x1x1x1x1
            const result = tf.slice(a, [0, 0, 0, 0, 0], [1, 1, 1, 1, 1]);
            expect(result.shape).toEqual([1, 1, 1, 1, 1]);
            expectArraysClose(await result.data(), [5]);
        });
    });
    describeWithFlags('slice6d', ALL_ENVS, () => {
        it('slices 1x1x1x1x1x1 into shape 1x1x1x1x1x1 (effectively a copy)', async () => {
            const a = tf.tensor6d([[[[[[5]]]]]], [1, 1, 1, 1, 1, 1]);
            const result = tf.slice(a, [0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1]);
            expect(result.shape).toEqual([1, 1, 1, 1, 1, 1]);
            expectArraysClose(await result.data(), [5]);
        });
        it('slices 2x2x2x2x2x2 array into 1x2x2x2x2x2 starting at [1,0,0,0,0,0]', async () => {
            const a = tf.tensor6d([
                31, 32, 33, 34, 35, 36, 37, 38, 39, 310, 311,
                312, 313, 314, 315, 316, 311, 322, 333, 344, 355, 366,
                377, 388, 3111, 3222, 3333, 3444, 3555, 3666, 3777, 3888,
                1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
                12, 13, 14, 15, 16, 11, 22, 33, 44, 55, 66,
                77, 88, 111, 222, 333, 444, 555, 666, 777, 888
            ], [2, 2, 2, 2, 2, 2]);
            const result = tf.slice(a, [1, 0, 0, 0, 0, 0], [1, 2, 2, 2, 2, 2]);
            expect(result.shape).toEqual([1, 2, 2, 2, 2, 2]);
            expectArraysClose(await result.data(), [
                1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
                12, 13, 14, 15, 16, 11, 22, 33, 44, 55, 66,
                77, 88, 111, 222, 333, 444, 555, 666, 777, 888
            ]);
        });
        it('slices 2x2x2x2x2x2 array into 2x1x1x1x1x1 starting at [0,1,1,1,1,1]', async () => {
            const a = tf.tensor6d([
                31, 32, 33, 34, 35, 36, 37, 38, 39, 310, 311,
                312, 313, 314, 315, 316, 311, 322, 333, 344, 355, 366,
                377, 388, 3111, 3222, 3333, 3444, 3555, 3666, 3777, 3888,
                1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
                12, 13, 14, 15, 16, 11, 22, 33, 44, 55, 66,
                77, 88, 111, 222, 333, 444, 555, 666, 777, 888
            ], [2, 2, 2, 2, 2, 2]);
            const result = tf.slice(a, [0, 1, 1, 1, 1, 1], [2, 1, 1, 1, 1, 1]);
            expect(result.shape).toEqual([2, 1, 1, 1, 1, 1]);
            expectArraysClose(await result.data(), [3888, 888]);
        });
        it('accepts a tensor-like object', async () => {
            const a = [[[[[[5]]]]]]; // 1x1x1x1x1x1
            const result = tf.slice(a, [0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1]);
            expect(result.shape).toEqual([1, 1, 1, 1, 1, 1]);
            expectArraysClose(await result.data(), [5]);
        });
    });
    describeWithFlags('accepts string', ALL_ENVS, () => {
        it('slices 2x2x2 array into 2x1x1 no size.', async () => {
            const a = tf.tensor3d(['one', 'two', 'three', 'four', 'five', 'six', 'seven', 'eight'], [2, 2, 2], 'string');
            const result = a.slice([0, 1, 1]);
            expect(result.shape).toEqual([2, 1, 1]);
            expectArraysClose(await result.data(), ['four', 'eight']);
        });
        it('slices 2x2x2 array into 1x2x2 with scalar begin no size.', async () => {
            const a = tf.tensor3d(['one', 'two', 'three', 'four', 'five', 'six', 'seven', 'eight'], [2, 2, 2]);
            const result = a.slice(1);
            expect(result.shape).toEqual([1, 2, 2]);
            expectArraysClose(await result.data(), ['five', 'six', 'seven', 'eight']);
        });
        it('slice encoded string.', async () => {
            const bytes = encodeStrings([
                'one', 'two', 'three', 'four', 'five', 'six', 'seven', 'eight'
            ]);
            const a = tf.tensor3d(bytes, [2, 2, 2], 'string');
            const result = a.slice([0, 1, 1]);
            expect(result.shape).toEqual([2, 1, 1]);
            expectArraysClose(await result.data(), ['four', 'eight']);
        });
    });
});
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* @license\n * Copyright 2018 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, SYNC_BACKEND_ENVS} from '../jasmine_util';\nimport {encodeStrings, expectArraysClose} from '../test_util';\nimport {TensorLike1D} from '../types';\n\ndescribeWithFlags('slice ', ALL_ENVS, () => {\n  describeWithFlags('ergonomics', ALL_ENVS, () => {\n    it('slices 2x2x2 array into 2x1x1 no size', async () => {\n      const a = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);\n      const result = a.slice([0, 1, 1]);\n      expect(result.shape).toEqual([2, 1, 1]);\n      expectArraysClose(await result.data(), [4, 8]);\n    });\n\n    it('slices 2x2x2 array into 1x2x2 with scalar begin no size', async () => {\n      const a = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);\n      const result = a.slice(1);\n      expect(result.shape).toEqual([1, 2, 2]);\n      expectArraysClose(await result.data(), [5, 6, 7, 8]);\n    });\n\n    it('slices 2x2x2 array using 2d size and 2d size', async () => {\n      const a = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);\n      const result = a.slice([0, 1]);\n      expect(result.shape).toEqual([2, 1, 2]);\n      expectArraysClose(await result.data(), [3, 4, 7, 8]);\n    });\n\n    it('slices 2x2x2 array using negative size', async () => {\n      const a = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);\n      const result = a.slice([0, 1], [-1, 1]);\n      expect(result.shape).toEqual([2, 1, 2]);\n      expectArraysClose(await result.data(), [3, 4, 7, 8]);\n    });\n\n    it('slices 2x2x2 array using 1d size', async () => {\n      const a = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);\n      const result = a.slice(0, 1);\n      expect(result.shape).toEqual([1, 2, 2]);\n      expectArraysClose(await result.data(), [1, 2, 3, 4]);\n    });\n\n    it('throws when passed a non-tensor', () => {\n      expect(() => tf.slice({} as tf.Tensor, 0, 0))\n          .toThrowError(/Argument 'x' passed to 'slice' must be a Tensor/);\n    });\n\n    it('accepts a tensor-like object', async () => {\n      const a = [[[1, 2], [3, 4]], [[5, 6], [7, 8]]];  // 2x2x2\n      const result = tf.slice(a, [0, 1, 1]);\n      expect(result.shape).toEqual([2, 1, 1]);\n      expectArraysClose(await result.data(), [4, 8]);\n    });\n\n    it('should match source tensor dtype', () => {\n      const a = tf.tensor1d([1, 2, 3, 4, 5], 'int32');\n      const b = a.asType('float32');\n\n      expect(tf.slice(b, 0).dtype).toEqual('float32');\n    });\n\n    it('throws when begin is negative', async () => {\n      const a = [[1, 2], [3, 4]];  // 2x2\n      expect(() => tf.slice(a, [-1, 1], [\n        1, 1\n      ])).toThrowError(/slice\\(\\) does not support negative begin indexing./);\n    });\n  });\n\n  describeWithFlags('shallow slicing', ALL_ENVS, () => {\n    it('shallow slice an input that was cast', async () => {\n      const a = tf.tensor([[1, 2], [3, 4]], [2, 2], 'int32');\n      const b = a.toFloat();\n      const c = b.slice(1, 1);\n      expect(c.dtype).toBe('float32');\n      expect(c.shape).toEqual([1, 2]);\n      expectArraysClose(await c.data(), [3, 4]);\n    });\n\n    it('delayed async read of sliced tensor has no mem leak', async () => {\n      const a = tf.zeros([10]);\n      const b = tf.slice(a, 0, 1);\n      const nBefore = tf.memory().numTensors;\n      expect(nBefore).toBe(2);\n      await b.data();\n      const nAfter = tf.memory().numTensors;\n      expect(nAfter).toBe(2);\n      tf.dispose([a, b]);\n      expect(tf.memory().numTensors).toBe(0);\n    });\n  });\n\n  describeWithFlags('shallow slicing', SYNC_BACKEND_ENVS, () => {\n    it('delayed sync read of sliced tensor has no mem leak', () => {\n      const a = tf.zeros([10]);\n      const b = tf.slice(a, 0, 1);\n      const nBefore = tf.memory().numTensors;\n      expect(nBefore).toBe(2);\n      b.dataSync();\n      const nAfter = tf.memory().numTensors;\n      expect(nAfter).toBe(2);\n      tf.dispose([a, b]);\n      expect(tf.memory().numTensors).toBe(0);\n    });\n  });\n\n  describeWithFlags('slice5d', ALL_ENVS, () => {\n    it('slices 1x1x1x1x1 into shape 1x1x1x1x1 (effectively a copy)',\n       async () => {\n         const a = tf.tensor5d([[[[[5]]]]], [1, 1, 1, 1, 1]);\n         const result = tf.slice(a, [0, 0, 0, 0, 0], [1, 1, 1, 1, 1]);\n\n         expect(result.shape).toEqual([1, 1, 1, 1, 1]);\n         expectArraysClose(await result.data(), [5]);\n       });\n\n    it('slices 2x2x2x2x2 array into 1x2x2x2x2 starting at [1,0,0,0,0]',\n       async () => {\n         const a = tf.tensor5d(\n             [\n               1,  2,  3,   4,   5,   6,   7,   8,   9,   10, 11,\n               12, 13, 14,  15,  16,  11,  22,  33,  44,  55, 66,\n               77, 88, 111, 222, 333, 444, 555, 666, 777, 888\n             ],\n             [2, 2, 2, 2, 2]);\n         const result = tf.slice(a, [1, 0, 0, 0, 0], [1, 2, 2, 2, 2]);\n\n         expect(result.shape).toEqual([1, 2, 2, 2, 2]);\n         expectArraysClose(await result.data(), [\n           11, 22, 33, 44, 55, 66, 77, 88, 111, 222, 333, 444, 555, 666, 777,\n           888\n         ]);\n       });\n\n    it('slices 2x2x2x2x2 array into 2x1x1x1x1 starting at [0,1,1,1,1]',\n       async () => {\n         const a = tf.tensor5d(\n             [\n               1,  2,  3,   4,   5,   6,   7,   8,   9,   10, 11,\n               12, 13, 14,  15,  16,  11,  22,  33,  44,  55, 66,\n               77, 88, 111, 222, 333, 444, 555, 666, 777, 888\n             ],\n             [2, 2, 2, 2, 2]);\n         const result = tf.slice(a, [0, 1, 1, 1, 1], [2, 1, 1, 1, 1]);\n\n         expect(result.shape).toEqual([2, 1, 1, 1, 1]);\n         expectArraysClose(await result.data(), [16, 888]);\n       });\n\n    it('accepts a tensor-like object', async () => {\n      const a = [[[[[5]]]]];  // 1x1x1x1x1\n      const result = tf.slice(a, [0, 0, 0, 0, 0], [1, 1, 1, 1, 1]);\n\n      expect(result.shape).toEqual([1, 1, 1, 1, 1]);\n      expectArraysClose(await result.data(), [5]);\n    });\n  });\n\n  describeWithFlags('slice6d', ALL_ENVS, () => {\n    it('slices 1x1x1x1x1x1 into shape 1x1x1x1x1x1 (effectively a copy)',\n       async () => {\n         const a = tf.tensor6d([[[[[[5]]]]]], [1, 1, 1, 1, 1, 1]);\n         const result = tf.slice(a, [0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1]);\n\n         expect(result.shape).toEqual([1, 1, 1, 1, 1, 1]);\n         expectArraysClose(await result.data(), [5]);\n       });\n\n    it('slices 2x2x2x2x2x2 array into 1x2x2x2x2x2 starting at [1,0,0,0,0,0]',\n       async () => {\n         const a = tf.tensor6d(\n             [\n               31,  32,  33,   34,   35,   36,   37,   38,   39,   310,  311,\n               312, 313, 314,  315,  316,  311,  322,  333,  344,  355,  366,\n               377, 388, 3111, 3222, 3333, 3444, 3555, 3666, 3777, 3888,\n\n               1,   2,   3,    4,    5,    6,    7,    8,    9,    10,   11,\n               12,  13,  14,   15,   16,   11,   22,   33,   44,   55,   66,\n               77,  88,  111,  222,  333,  444,  555,  666,  777,  888\n             ],\n             [2, 2, 2, 2, 2, 2]);\n         const result = tf.slice(a, [1, 0, 0, 0, 0, 0], [1, 2, 2, 2, 2, 2]);\n\n         expect(result.shape).toEqual([1, 2, 2, 2, 2, 2]);\n         expectArraysClose(await result.data(), [\n           1,  2,  3,   4,   5,   6,   7,   8,   9,   10, 11,\n           12, 13, 14,  15,  16,  11,  22,  33,  44,  55, 66,\n           77, 88, 111, 222, 333, 444, 555, 666, 777, 888\n         ]);\n       });\n\n    it('slices 2x2x2x2x2x2 array into 2x1x1x1x1x1 starting at [0,1,1,1,1,1]',\n       async () => {\n         const a = tf.tensor6d(\n             [\n               31,  32,  33,   34,   35,   36,   37,   38,   39,   310,  311,\n               312, 313, 314,  315,  316,  311,  322,  333,  344,  355,  366,\n               377, 388, 3111, 3222, 3333, 3444, 3555, 3666, 3777, 3888,\n\n               1,   2,   3,    4,    5,    6,    7,    8,    9,    10,   11,\n               12,  13,  14,   15,   16,   11,   22,   33,   44,   55,   66,\n               77,  88,  111,  222,  333,  444,  555,  666,  777,  888\n             ],\n             [2, 2, 2, 2, 2, 2]);\n         const result = tf.slice(a, [0, 1, 1, 1, 1, 1], [2, 1, 1, 1, 1, 1]);\n\n         expect(result.shape).toEqual([2, 1, 1, 1, 1, 1]);\n         expectArraysClose(await result.data(), [3888, 888]);\n       });\n\n    it('accepts a tensor-like object', async () => {\n      const a = [[[[[[5]]]]]];  // 1x1x1x1x1x1\n      const result = tf.slice(a, [0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1]);\n\n      expect(result.shape).toEqual([1, 1, 1, 1, 1, 1]);\n      expectArraysClose(await result.data(), [5]);\n    });\n  });\n\n  describeWithFlags('accepts string', ALL_ENVS, () => {\n    it('slices 2x2x2 array into 2x1x1 no size.', async () => {\n      const a = tf.tensor3d(\n          ['one', 'two', 'three', 'four', 'five', 'six', 'seven', 'eight'],\n          [2, 2, 2], 'string');\n      const result = a.slice([0, 1, 1]);\n      expect(result.shape).toEqual([2, 1, 1]);\n      expectArraysClose(await result.data(), ['four', 'eight']);\n    });\n\n    it('slices 2x2x2 array into 1x2x2 with scalar begin no size.', async () => {\n      const a = tf.tensor3d(\n          ['one', 'two', 'three', 'four', 'five', 'six', 'seven', 'eight'],\n          [2, 2, 2]);\n      const result = a.slice(1);\n      expect(result.shape).toEqual([1, 2, 2]);\n      expectArraysClose(await result.data(), ['five', 'six', 'seven', 'eight']);\n    });\n\n    it('slice encoded string.', async () => {\n      const bytes =\n          encodeStrings([\n            'one', 'two', 'three', 'four', 'five', 'six', 'seven', 'eight'\n          ]) as TensorLike1D;\n      const a = tf.tensor3d(bytes, [2, 2, 2], 'string');\n      const result = a.slice([0, 1, 1]);\n      expect(result.shape).toEqual([2, 1, 1]);\n      expectArraysClose(await result.data(), ['four', 'eight']);\n    });\n  });\n});\n"]}