# tensorplay.nn.utils.rnn.pad_sequence

Source: https://www.tensorplay.cn/docs/generated/tensorplay.nn.utils.rnn.pad_sequence.html

```python
tensorplay.nn.utils.rnn.pad_sequence(sequences, batch_first: bool = False, padding_value: float = 0.0, padding_side: str = 'right') → Tensor
```

Pad a list of variable length Tensors with padding_value.

pad_sequence stacks a list of Tensors along a new dimension, and pads them
to equal length.  sequences can be list of sequences with size L x *,
where L is length of the sequence and * is any number of dimensions
(including 0). If batch_first is False, the output is of size
T x B x *, and B x T x * otherwise, where B is the batch size
(the number of elements in sequences`), T is the length of the
longest sequence.

Example

```
>>> from tensorplay.nn.utils.rnn import pad_sequence
>>> a = tp.ones(25, 300)
>>> b = tp.ones(22, 300)
>>> c = tp.ones(15, 300)
>>> pad_sequence([a, b, c]).size()
tensorplay.Size([25, 3, 300])
```

> **Note**
>
> This function returns a Tensor of size T x B x * or B x T x * where T is the length of the longest sequence. This function assumes trailing dimensions and type of all the Tensors in sequences are same.

Parameters:

- sequences ([list](https://docs.python.org/3/builtins/stdtypes.html#list)[[Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor)]) – list of variable length sequences.

- batch_first ([bool](https://docs.python.org/3/builtins/functions.html#bool), optional) – if True, the output will be in B x T x * format, T x B x * otherwise. Default: False.

- padding_value ([float](https://docs.python.org/3/builtins/functions.html#float), optional) – value for padded elements. Default: 0.

- padding_side ([str](https://docs.python.org/3/builtins/stdtypes.html#str), optional) – the side to pad the sequences on. Default: 'right'.

Returns:

Tensor of size T x B x * if batch_first is False.
Tensor of size B x T x * otherwise