/TensorFlow 2.4

# tf.stack

Stacks a list of rank-`R` tensors into one rank-`(R+1)` tensor.

Packs the list of tensors in `values` into a tensor with rank one higher than each tensor in `values`, by packing them along the `axis` dimension. Given a list of length `N` of tensors of shape `(A, B, C)`;

if `axis == 0` then the `output` tensor will have the shape `(N, A, B, C)`. if `axis == 1` then the `output` tensor will have the shape `(A, N, B, C)`. Etc.

#### For example:

```x = tf.constant([1, 4])
y = tf.constant([2, 5])
z = tf.constant([3, 6])
tf.stack([x, y, z])
<tf.Tensor: shape=(3, 2), dtype=int32, numpy=
array([[1, 4],
[2, 5],
[3, 6]], dtype=int32)>
tf.stack([x, y, z], axis=1)
<tf.Tensor: shape=(2, 3), dtype=int32, numpy=
array([[1, 2, 3],
[4, 5, 6]], dtype=int32)>
```

This is the opposite of unstack. The numpy equivalent is `np.stack`

```np.array_equal(np.stack([x, y, z]), tf.stack([x, y, z]))
True
```
Args
`values` A list of `Tensor` objects with the same shape and type.
`axis` An `int`. The axis to stack along. Defaults to the first dimension. Negative values wrap around, so the valid range is `[-(R+1), R+1)`.
`name` A name for this operation (optional).
Returns
`output` A stacked `Tensor` with the same type as `values`.
Raises
`ValueError` If `axis` is out of the range [-(R+1), R+1).