# tensorplay.functional.cat

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

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

Concatenates the given sequence of tensors in tensors in the given dimension.
All tensors must either have the same shape (except in the concatenating
dimension) or be a 1-D empty tensor with size (0,).

tensorplay.cat() can be seen as an inverse operation for tensorplay.split()
and tensorplay.chunk().

tensorplay.cat() can be best understood via examples.

> **See also**
>
> tensorplay.stack() concatenates the given sequence along a new dimension.

Parameters:

- tensors (sequence of Tensors) – Non-empty tensors provided must have the same shape, except in the cat dimension.

- dim ([int](https://docs.python.org/3/builtins/functions.html#int), optional) – the dimension over which the tensors are concatenated

Keyword Arguments:

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

Example:

```
>>> x = tensorplay.randn(2, 3)
>>> x
tensor([[ 0.6580, -1.0969, -0.4614],
        [-0.1034, -0.5790,  0.1497]])
>>> tensorplay.cat((x, x, x), 0)
tensor([[ 0.6580, -1.0969, -0.4614],
        [-0.1034, -0.5790,  0.1497],
        [ 0.6580, -1.0969, -0.4614],
        [-0.1034, -0.5790,  0.1497],
        [ 0.6580, -1.0969, -0.4614],
        [-0.1034, -0.5790,  0.1497]])
>>> tensorplay.cat((x, x, x), 1)
tensor([[ 0.6580, -1.0969, -0.4614,  0.6580, -1.0969, -0.4614,  0.6580,
         -1.0969, -0.4614],
        [-0.1034, -0.5790,  0.1497, -0.1034, -0.5790,  0.1497, -0.1034,
         -0.5790,  0.1497]])
```