/**

* @license

* Copyright 2021 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 {ENGINE} from '../../engine';

import {SparseReshape, SparseReshapeInputs} from '../../kernel_names';

import {Tensor, Tensor1D, Tensor2D} from '../../tensor';

import {NamedTensorMap} from '../../tensor_types';

import {convertToTensor} from '../../tensor_util_env';

import {TensorLike} from '../../types';

import {op} from '../operation';

/**

* This operation has the same semantics as reshape on the represented dense

* tensor. The `inputIndices` are recomputed based on the requested `newShape`.

* If one component of `newShape` is the special value -1, the size of that

* dimension is computed so that the total dense size remains constant. At most

* one component of `newShape` can be -1. The number of dense elements implied

* by `newShape` must be the same as the number of dense elements originally

* implied by `inputShape`. Reshaping does not affect the order of values in the

* SparseTensor. If the input tensor has rank R_in and N non-empty values, and

* `newShape` has length R_out, then `inputIndices` has shape [N, R_in],

* `inputShape` has length R_in, `outputIndices` has shape [N, R_out], and

* `outputShape` has length R_out.

*

* ```js

* const result = tf.sparse.sparseReshape(

* [[0, 0, 0], [0, 0, 1], [0, 1, 0], [1, 0, 0], [1, 2, 3]],

* [2, 3, 6], [9, -1]);

* console.log(result);

* result['outputIndices'].print(); //[[0, 0], [0, 1], [1, 2], [4, 2], [8, 1]]

* result['outputShape'].print(); // [9, 4]

* ```

* @param inputIndices: 2-D. N x R_in matrix with the indices of non-empty

* values in a SparseTensor.

* @param inputShape: 1-D. R_in Tensor1D with the input SparseTensor's dense

* shape.

* @param newShape: 1-D. R_out Tensor1D with the requested new dense shape.

* @return A map with the following properties:

* - outputIndices: 2-D. N x R_out matrix with the updated indices of

* non-empty values in the output SparseTensor.

* - outputShape: 1-D. R_out vector with the full dense shape of the output

* SparseTensor. This is the same as newShape but with any -1 dimensions

* filled in.

* @doc {heading: 'Operations', subheading: 'Sparse'}

*/

function sparseReshape_(

}

export const sparseReshape = /* @__PURE__ */ op({sparseReshape_});