# tensorplay.functional.squeeze

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

```python
tensorplay.functional.squeeze(input: Tensor, dim: int | List[int] | None) → Tensor
```

Returns a tensor with all specified dimensions of input of size 1 removed.

For example, if input is of shape:
$(A \times 1 \times B \times C \times 1 \times D)$ then the input.squeeze()
will be of shape: $(A \times B \times C \times D)$.

When dim is given, a squeeze operation is done only in the given
dimension(s). If input is of shape: $(A \times 1 \times B)$,
squeeze(input, 0) leaves the tensor unchanged, but squeeze(input, 1)
will squeeze the tensor to the shape $(A \times B)$.

> **Note**
>
> The returned tensor shares the storage with the input tensor, so changing the contents of one will change the contents of the other.

> **Warning**
>
> If the tensor has a batch dimension of size 1, then squeeze(input) will also remove the batch dimension, which can lead to unexpected errors. Consider specifying only the dims you wish to be squeezed.

Parameters:

- input ([Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor)) – the input tensor.

- dim ([int](https://docs.python.org/3/builtins/functions.html#int) or [tuple](https://docs.python.org/3/builtins/stdtypes.html#tuple) of ints, optional) – if given, the input will be squeezedonly in the specified dimensions. Changed in version 2.0: dim now accepts tuples of dimensions.

Example:

```
>>> x = tensorplay.zeros(2, 1, 2, 1, 2)
>>> x.size()
tensorplay.Size([2, 1, 2, 1, 2])
>>> y = tensorplay.squeeze(x)
>>> y.size()
tensorplay.Size([2, 2, 2])
>>> y = tensorplay.squeeze(x, 0)
>>> y.size()
tensorplay.Size([2, 1, 2, 1, 2])
>>> y = tensorplay.squeeze(x, 1)
>>> y.size()
tensorplay.Size([2, 2, 1, 2])
>>> y = tensorplay.squeeze(x, (1, 2, 3))
tensorplay.Size([2, 2, 2])
```