### `tf.reshape(tensor, shape, name=None)` {#reshape}

Reshapes a tensor.

Given `tensor`, this operation returns a tensor that has the same values
as `tensor` with shape `shape`.

If one component of `shape` is the special value -1, the size of that dimension
is computed so that the total size remains constant.  In particular, a `shape`
of `[-1]` flattens into 1-D.  At most one component of `shape` can be -1.

If `shape` is 1-D or higher, then the operation returns a tensor with shape
`shape` filled with the values of `tensor`. In this case, the number of elements
implied by `shape` must be the same as the number of elements in `tensor`.

For example:

```prettyprint
# tensor 't' is [1, 2, 3, 4, 5, 6, 7, 8, 9]
# tensor 't' has shape [9]
reshape(t, [3, 3]) ==> [[1, 2, 3]
                        [4, 5, 6]
                        [7, 8, 9]]

# tensor 't' is [[[1, 1], [2, 2]]
#                [[3, 3], [4, 4]]]
# tensor 't' has shape [2, 2, 2]
reshape(t, [2, 4]) ==> [[1, 1, 2, 2]
                        [3, 3, 4, 4]]

# tensor 't' is [[[1, 1, 1],
#                 [2, 2, 2]],
#                [[3, 3, 3],
#                 [4, 4, 4]],
#                [[5, 5, 5],
#                 [6, 6, 6]]]
# tensor 't' has shape [3, 2, 3]
# pass '[-1]' to flatten 't'
reshape(t, [-1]) ==> [1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6]
# -1 can also be used with higher dimensional shapes
reshape(t, [2, -1]) ==> [[1, 1, 1, 2, 2, 2, 3, 3, 3],
                         [4, 4, 4, 5, 5, 5, 6, 6, 6]]

# tensor 't' is [7]
# shape `[]` reshapes to a scalar
reshape(t, []) ==> 7
```

##### Args:


*  <b>`tensor`</b>: A `Tensor`.
*  <b>`shape`</b>: A `Tensor` of type `int32`. Defines the shape of the output tensor.
*  <b>`name`</b>: A name for the operation (optional).

##### Returns:

  A `Tensor`. Has the same type as `tensor`.

