# tensorplay.functional.stack

Source: https://www.tensorplay.cn/docs/generated/tensorplay.functional.stack.html

```python
tensorplay.functional.stack(tensors, dim=0, *, out=None) → Tensor
```

Concatenates a sequence of tensors along a new dimension.

All tensors need to be of the same size.

> **See also**
>
> tensorplay.cat() concatenates the given sequence along an existing dimension.

Parameters:

- tensors (sequence of Tensors) – sequence of tensors to concatenate

- dim ([int](https://docs.python.org/3/builtins/functions.html#int), optional) – dimension to insert. Has to be between 0 and the number of dimensions of concatenated tensors (inclusive). Default: 0

Keyword Arguments:

out ([Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor), optional) – the output tensor.

Example:

```
>>> x = tensorplay.randn(2, 3)
>>> x
tensor([[ 0.3367,  0.1288,  0.2345],
        [ 0.2303, -1.1229, -0.1863]])
>>> tensorplay.stack((x, x)) # same as tensorplay.stack((x, x), dim=0)
tensor([[[ 0.3367,  0.1288,  0.2345],
         [ 0.2303, -1.1229, -0.1863]],

        [[ 0.3367,  0.1288,  0.2345],
         [ 0.2303, -1.1229, -0.1863]]])
>>> tensorplay.stack((x, x)).size()
tensorplay.Size([2, 2, 3])
>>> tensorplay.stack((x, x), dim=1)
tensor([[[ 0.3367,  0.1288,  0.2345],
         [ 0.3367,  0.1288,  0.2345]],

        [[ 0.2303, -1.1229, -0.1863],
         [ 0.2303, -1.1229, -0.1863]]])
>>> tensorplay.stack((x, x), dim=2)
tensor([[[ 0.3367,  0.3367],
         [ 0.1288,  0.1288],
         [ 0.2345,  0.2345]],

        [[ 0.2303,  0.2303],
         [-1.1229, -1.1229],
         [-0.1863, -0.1863]]])
>>> tensorplay.stack((x, x), dim=-1)
tensor([[[ 0.3367,  0.3367],
         [ 0.1288,  0.1288],
         [ 0.2345,  0.2345]],

        [[ 0.2303,  0.2303],
         [-1.1229, -1.1229],
         [-0.1863, -0.1863]]])
```