code: def _gen_packed_data(
      minibatch_size: int,
      max_seq_length: int,
      input_dim: int,
      batch_first: bool,
      sorted_: Optional[bool] = False,
  ) -> PackedSequence:
      """
      This is used to generate random PackedSequence data, sampled from a normal distribution, for testing DPLSTM.
  
      Args:
          minibatch_size : Total number of sequences to generate
          max_seq_length : The maximum number of timesteps of a sequence
          input_dim : The embedding dimension of a sequence at any timestep
          batch_first : If this is true, data is first generated using a padded sequence of dimension (minibatch_size x max_seq_len x input_dim) , else: (max_seq_length x minibatch_size x input_dim)
          sorted_ : If this is true then the original generated data used to produce the PackedSequence will already be ordered based on sequence lengths, else a random order and the 'sorted_indices'
                      and 'unsorted_indices' fields will be None.
  
      Return Value:
          packed_data : A PackedSequence object with its data sampled from a normal distribution.
      """
  
      if batch_first:
          data = []
          seq_lengths = []
          for _ in range(minibatch_size):
              seq_length = torch.randint(
                  1, max_seq_length + 1, (1,)
              ).item()
              seq_lengths.append(seq_length)
              data.append(torch.randn(seq_length, input_dim))
  
          if sorted_:
              data = sorted(
                  data, key=lambda x: x.shape[0], reverse=True
              )
              seq_lengths = sorted(seq_lengths, reverse=True)
              packed_data = pack_padded_sequence(
                  pad_sequence(data, batch_first=True),
                  seq_lengths,
                  batch_first=True,
                  enforce_sorted=True,
              )
          else:
              packed_data = pack_padded_sequence(
                  pad_sequence(data, batch_first=True),
                  seq_lengths,
                  batch_first=True,
                  enforce_sorted=False,
              )
      else:
          seq_lengths = [
              torch.randint(1, max_seq_length + 1, (1,)).item()
              for _ in range(minibatch_size)
          ]
          if sorted_:
              seq_lengths = sorted(seq_lengths, reverse=True)
          padded_data = torch.zeros(
              (max_seq_length, minibatch_size, input_dim)
          )
          for i in range(minibatch_size):
              padded_data[: seq_lengths[i], i, :] = torch.randn(
                  seq_lengths[i], input_dim
              )
  
          if sorted_:
              packed_data = pack_padded_sequence(
                  padded_data,
                  seq_lengths,
                  batch_first=False,
                  enforce_sorted=True,
              )
          else:
              packed_data = pack_padded_sequence(
                  padded_data,
                  seq_lengths,
                  batch_first=False,
                  enforce_sorted=False,
              )
  
      return packed_data
  
imports: from typing import List, Optional
  import torch
  from torch.nn.utils.rnn import PackedSequence, pack_padded_sequence, pad_sequence
label_desc: Use image node_id 1 for calling a global function with example usage: _gen_packed_data(minibatch_size, max_seq_length, input_dim, batch_first, sorted_) and returns: packed_data