code: def _transpose_hidden_state(hidden_state):
      """
      Transpose the hidden state so that batch is the first dimension.
  
      RNN modules produce (num_layers x batchsize x dim) hidden state, but DataParallel
      expects batch size to be first. This helper is used to ensure that we're always
      outputting batch-first, in case DataParallel tries to stitch things back together.
      """
      if isinstance(hidden_state, tuple):
          return tuple(map(_transpose_hidden_state, hidden_state))
      elif torch.is_tensor(hidden_state):
          return hidden_state.transpose(0, 1)
      else:
          raise ValueError(
              "Don't know how to transpose {}".format(hidden_state)
          )
  
imports: import math
  import torch
  import torch.nn
  from torch.nn.parameter import Parameter
  from torch.nn.utils.rnn import pad_packed_sequence, pack_padded_sequence
  import torch.nn.functional
  from parlai.utils.torch import neginf
  from parlai.core.torch_generator_agent import TorchGeneratorModel
label_desc: Use image node_id 1 for calling a global function with example usage: _transpose_hidden_state(hidden_state) and returns: tuple, hidden_state