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# tf.sparse.reshape

Reshapes a `SparseTensor` to represent values in a new dense shape.

This operation has the same semantics as `reshape` on the represented dense tensor. The indices of non-empty values in `sp_input` are recomputed based on the new dense shape, and a new `SparseTensor` is returned containing the new indices and new shape. The order of non-empty values in `sp_input` is unchanged.

If one component of `shape` 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 `shape` can be -1. The number of dense elements implied by `shape` must be the same as the number of dense elements originally represented by `sp_input`.

For example, if `sp_input` has shape `[2, 3, 6]` and `indices` / `values`:

``````[0, 0, 0]: a
[0, 0, 1]: b
[0, 1, 0]: c
[1, 0, 0]: d
[1, 2, 3]: e
``````

and `shape` is `[9, -1]`, then the output will be a `SparseTensor` of shape `[9, 4]` and `indices` / `values`:

``````[0, 0]: a
[0, 1]: b
[1, 2]: c
[4, 2]: d
[8, 1]: e
``````

`sp_input` The input `SparseTensor`.
`shape` A 1-D (vector) int64 `Tensor` specifying the new dense shape of the represented `SparseTensor`.
`name` A name prefix for the returned tensors (optional)

A `SparseTensor` with the same non-empty values but with indices calculated by the new dense shape.

`TypeError` If `sp_input` is not a `SparseTensor`.
`ValueError` If argument `shape` requests a `SparseTensor` with a different number of elements than `sp_input`.
`ValueError` If `shape` has more than one inferred (== -1) dimension.

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