code: def unpack_sequence(packed_sequences: PackedSequence) -> List[Tensor]:
      r"""Unpack PackedSequence into a list of variable length Tensors.
  
      ``packed_sequences`` should be a PackedSequence object.
  
  
      Example:
          >>> from torch.nn.utils.rnn import pack_sequence, unpack_sequence
          >>> a = torch.tensor([1, 2, 3])
          >>> b = torch.tensor([4, 5])
          >>> c = torch.tensor([6])
          >>> sequences = [a, b, c]
          >>> print(sequences)
          [tensor([1, 2, 3]), tensor([4, 5]), tensor([6])]
          >>> packed_sequences = pack_sequence(sequences)
          >>> print(packed_sequences)
          PackedSequence(data=tensor([1, 4, 6, 2, 5, 3]), batch_sizes=tensor([3, 2, 1]), sorted_indices=None, unsorted_indices=None)
          >>> unpacked_sequences = unpack_sequence(packed_sequences)
          >>> print(unpacked_sequences)
          [tensor([1, 2, 3]), tensor([4, 5]), tensor([6])]
  
  
      Args:
          packed_sequences (PackedSequence): A PackedSequence object.
  
      Returns:
          a list of :class:`Tensor` objects
      """
      padded_sequences, lengths = pad_packed_sequence(
          packed_sequences, batch_first=True
      )
      unpacked_sequences = unpad_sequence(
          padded_sequences, lengths, batch_first=True
      )
      return unpacked_sequences
  
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 10 for calling a global function with example usage: unpack_sequence(packed_sequences) and returns: unpacked_sequences