code: def get_packed_sequence_info(packed):
      """Get `batch_sizes` and `sorted_indices` of `PackedSequence`.
  
      Args:
          packed (object): Packed sequences. If it contains multiple
              `PackedSequence`s, then only one of them are sampled assuming that
              all of them have same `batch_sizes` and `sorted_indices`.
  
      Returns:
          Tensor: `PackedSequence.batch_sizes`.
          Tensor: `PackedSequence.sorted_indices`.
      """
      if isinstance(packed, torch.nn.utils.rnn.PackedSequence):
          return packed.batch_sizes, packed.sorted_indices
      if isinstance(packed, tuple):
          for y in packed:
              ret = get_packed_sequence_info(y)
              if ret is not None:
                  return ret
      return None
  
file_path: pfrl/pfrl/utils/recurrent.py
imports: import itertools
  import numpy
  import torch
  from torch import nn
label_desc: Use image node_id 13 for calling a global function with example usage: get_packed_sequence_info(packed) and returns: None, packed,  packed, ret