function sparseReshape_(inputIndices, inputShape, newShape) {
    const $inputIndices = (0, _tensorUtilEnv.convertToTensor)(inputIndices, "inputIndices", "sparseReshape", "int32");
    const $inputShape = (0, _tensorUtilEnv.convertToTensor)(inputShape, "inputShape", "sparseReshape", "int32");
    const $newShape = (0, _tensorUtilEnv.convertToTensor)(newShape, "newShape", "sparseReshape", "int32");
    if ($inputIndices.rank !== 2) throw new Error(`Input indices should be Tensor2D but received shape
        ${$inputIndices.shape}`);
    if ($inputShape.rank !== 1) throw new Error(`Input shape should be Tensor1D but received shape ${$inputShape.shape}`);
    if ($newShape.rank !== 1) throw new Error(`New shape should be Tensor1D but received shape ${$newShape.shape}`);
    const inputs = {
        inputIndices: $inputIndices,
        inputShape: $inputShape,
        newShape: $newShape
    };
    const result = (0, _engine.ENGINE).runKernel((0, _kernelNames.SparseReshape), inputs);
    return {
        outputIndices: result[0],
        outputShape: result[1]
    };
}
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'}