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# torch.nn.utils.rnn.pack_sequence

torch.nn.utils.rnn.pack_sequence(sequences, enforce_sorted=True)

  Packs a list of variable length Tensors

  Consecutive call of the next functions: `pad_sequence`, `pack_padded_sequence`.

  `sequences` should be a list of Tensors of size `L x *`, where L is the length of a sequence and * is any number of trailing dimensions, including zero.

  For unsorted sequences, use enforce_sorted = False. If `enforce_sorted` is `True`, the sequences should be sorted in the order of decreasing length. `enforce_sorted = True` is only necessary for ONNX export.

  Example

      >>> from torch.nn.utils.rnn import pack_sequence
      >>> a = torch.tensor([1, 2, 3])
      >>> b = torch.tensor([4, 5])
      >>> c = torch.tensor([6])
      >>> pack_sequence([a, b, c])
      PackedSequence(data=tensor([1, 4, 6, 2, 5, 3]), batch_sizes=tensor([3, 2, 1]), sorted_indices=None, unsorted_indices=None)

  Parameters:

    - sequences (list[Tensor]) – A list of sequences of decreasing length.

    - enforce_sorted (bool, optional) – if `True`, checks that the input contains sequences sorted by length in a decreasing order. If `False`, this condition is not checked. Default: `True`.

  Returns:

    a `PackedSequence` object

  Return type:

    PackedSequence

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