code: def pad_packed_sequence(
      sequence: PackedSequence,
      batch_first: bool = False,
      padding_value: float = 0.0,
      total_length: Optional[int] = None,
  ) -> Tuple[Tensor, Tensor]:
      r"""Pad a packed batch of variable length sequences.
  
      It is an inverse operation to :func:`pack_padded_sequence`.
  
      The returned Tensor's data will be of size ``T x B x *``, where `T` is the length
      of the longest sequence and `B` is the batch size. If ``batch_first`` is True,
      the data will be transposed into ``B x T x *`` format.
  
      Example:
          >>> from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
          >>> seq = torch.tensor([[1, 2, 0], [3, 0, 0], [4, 5, 6]])
          >>> lens = [2, 1, 3]
          >>> packed = pack_padded_sequence(seq, lens, batch_first=True, enforce_sorted=False)
          >>> packed
          PackedSequence(data=tensor([4, 1, 3, 5, 2, 6]), batch_sizes=tensor([3, 2, 1]),
                         sorted_indices=tensor([2, 0, 1]), unsorted_indices=tensor([1, 2, 0]))
          >>> seq_unpacked, lens_unpacked = pad_packed_sequence(packed, batch_first=True)
          >>> seq_unpacked
          tensor([[1, 2, 0],
                  [3, 0, 0],
                  [4, 5, 6]])
          >>> lens_unpacked
          tensor([2, 1, 3])
  
      .. note::
          :attr:`total_length` is useful to implement the
          ``pack sequence -> recurrent network -> unpack sequence`` pattern in a
          :class:`~torch.nn.Module` wrapped in :class:`~torch.nn.DataParallel`.
          See :ref:`this FAQ section <pack-rnn-unpack-with-data-parallelism>` for
          details.
  
      Args:
          sequence (PackedSequence): batch to pad
          batch_first (bool, optional): if ``True``, the output will be in ``B x T x *``
              format.
          padding_value (float, optional): values for padded elements.
          total_length (int, optional): if not ``None``, the output will be padded to
              have length :attr:`total_length`. This method will throw :class:`ValueError`
              if :attr:`total_length` is less than the max sequence length in
              :attr:`sequence`.
  
      Returns:
          Tuple of Tensor containing the padded sequence, and a Tensor
          containing the list of lengths of each sequence in the batch.
          Batch elements will be re-ordered as they were ordered originally when
          the batch was passed to ``pack_padded_sequence`` or ``pack_sequence``.
  
  
  
  
      """
      max_seq_length = sequence.batch_sizes.size(0)
      if total_length is not None:
          if total_length < max_seq_length:
              raise ValueError(
                  "Expected total_length to be at least the length "
                  "of the longest sequence in input, but got "
                  f"total_length={total_length} and max sequence length being {max_seq_length}"
              )
          max_seq_length = total_length
      padded_output, lengths = _VF._pad_packed_sequence(
          sequence.data,
          sequence.batch_sizes,
          batch_first,
          padding_value,
          max_seq_length,
      )
      unsorted_indices = sequence.unsorted_indices
      if unsorted_indices is not None:
          batch_dim = 0 if batch_first else 1
          return (
              padded_output.index_select(batch_dim, unsorted_indices),
              lengths[unsorted_indices.cpu()],
          )
      return padded_output, lengths
  
imports: import warnings
  from typing import Iterable, List, NamedTuple, Tuple, Union
  import torch
  from torch import Tensor
  from ...None import _VF
  from ..._jit_internal import Optional
label_desc: Use image node_id 6 for calling a global function with example usage: pad_packed_sequence(sequence, batch_first, padding_value, total_length) and returns: padded_output,  lengths, padded_output,  lengths