code: def unpack_packedsequences(X: PackedSequence) -> List[torch.Tensor]:
      r"""
      Produces a list of tensors from X (PackedSequence) such that this list was used to create X with batch_first=True
  
      Args:
          X: A PackedSequence from which the output list of tensors will be produced.
  
      Returns:
          unpacked_data: The list of tensors produced from X.
      """
  
      X_padded = pad_packed_sequence(X)
      X_padded = X_padded[0].permute((1, 0, 2))
  
      if X.sorted_indices is not None:
          X_padded = X_padded[X.sorted_indices]
  
      seq_lens = compute_seq_lengths(X.batch_sizes)
      unpacked_data = [0] * len(seq_lens)
      for idx, length in enumerate(seq_lens):
          unpacked_data[idx] = X_padded[idx][:length, :]
  
      return unpacked_data
  
imports: import io
  from typing import Callable, Dict, Iterable, List, Union
  import numpy
  import torch
  import torch.nn
  from opacus.grad_sample import wrap_model
  from opacus.utils.module_utils import trainable_parameters
  from opacus.utils.packed_sequences import compute_seq_lengths
  from torch.nn.utils.rnn import PackedSequence, pad_packed_sequence
label_desc: Use image node_id 11 for calling a global function with example usage: unpack_packedsequences(X) and returns: unpacked_data