code: def rnn_train_fn(
      model: nn.Module,
      x: Union[torch.Tensor, PackedSequence],
      state_init: Optional[
          Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]
      ] = None,
  ):
      model.train()
      criterion = nn.MSELoss()
      logits, _ = model(x, state_init)
      if isinstance(logits, PackedSequence):
          y = torch.zeros_like(logits[0])
          loss = criterion(logits[0], y)
      else:
          y = torch.zeros_like(logits)
          loss = criterion(logits, y)
      loss.backward()
  
file_path: opacus/opacus/tests/dp_layers/dp_rnn_test.py
imports: from typing import Optional, Tuple, Union
  import hypothesis.strategies
  import torch
  import torch.nn
  from hypothesis import given, settings
  from opacus.layers import DPGRU, DPLSTM, DPRNN
  from opacus.utils.packed_sequences import _gen_packed_data
  from torch.nn.utils.rnn import PackedSequence
  from .common import DPModules_test
label_desc: Use image node_id 1 for calling a global function with example usage: rnn_train_fn(model, x, state_init) without return types