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# torch.nn.utils.rnn.pack_padded_sequence

torch.nn.utils.rnn.pack_padded_sequence(input, lengths, batch_first=False, enforce_sorted=True)

  Packs a Tensor containing padded sequences of variable length.

  `input` can be of size `T x B x *` where T is the length of the longest sequence (equal to `lengths[0]`), `B` is the batch size, and `*` is any number of dimensions (including 0). If `batch_first` is `True`, `B x T x *` `input` is expected.

  For unsorted sequences, use enforce_sorted = False. If `enforce_sorted` is `True`, the sequences should be sorted by length in a decreasing order, i.e. `input[:,0]` should be the longest sequence, and `input[:,B-1]` the shortest one. enforce_sorted = True is only necessary for ONNX export.

  Note

  This function accepts any input that has at least two dimensions. You can apply it to pack the labels, and use the output of the RNN with them to compute the loss directly. A Tensor can be retrieved from a `PackedSequence` object by accessing its `.data` attribute.

  Parameters:

    - input (Tensor) – padded batch of variable length sequences.

    - lengths (Tensor or list(int)) – list of sequence lengths of each batch element (must be on the CPU if provided as a tensor).

    - batch_first (bool, optional) – if `True`, the input is expected in `B x T x *` format.

    - enforce_sorted (bool, optional) – if `True`, the input is expected to contain sequences sorted by length in a decreasing order. If `False`, the input will get sorted unconditionally. Default: `True`.

  Returns:

    a `PackedSequence` object

  Return type:

    PackedSequence

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