code: def test_rnn(
      self,
      mode: str,
      batch_size: int,
      seq_len: int,
      emb_size: int,
      hidden_size: int,
      num_layers: int,
      bidirectional: bool,
      bias: bool,
      batch_first: bool,
      zero_init: bool,
      packed_input_flag: int,
  ):
      use_cn = False
      if mode == "rnn":
          original_rnn_class = nn.RNN
          dp_rnn_class = DPRNN
      elif mode == "gru":
          original_rnn_class = nn.GRU
          dp_rnn_class = DPGRU
      elif mode == "lstm":
          original_rnn_class = nn.LSTM
          dp_rnn_class = DPLSTM
          use_cn = True
      else:
          raise ValueError("Invalid RNN mode")
  
      rnn = original_rnn_class(
          emb_size,
          hidden_size,
          num_layers=num_layers,
          batch_first=batch_first,
          bidirectional=bidirectional,
          bias=bias,
      )
      dp_rnn = dp_rnn_class(
          emb_size,
          hidden_size,
          num_layers=num_layers,
          batch_first=batch_first,
          bidirectional=bidirectional,
          bias=bias,
      )
  
      dp_rnn.load_state_dict(rnn.state_dict())
  
      # Packed sequences not happy with deterministic
      torch.use_deterministic_algorithms(False)
      if packed_input_flag == 0:
          # no packed sequence input
          x = (
              torch.randn([batch_size, seq_len, emb_size])
              if batch_first
              else torch.randn([seq_len, batch_size, emb_size])
          )
      elif packed_input_flag == 1:
          # packed sequence input in sorted order
          x = _gen_packed_data(
              batch_size, seq_len, emb_size, batch_first, sorted_=True
          )
      elif packed_input_flag == 2:
          # packed sequence input in unsorted order
          x = _gen_packed_data(
              batch_size, seq_len, emb_size, batch_first, sorted_=False
          )
      else:
          raise ValueError("Invalid packed input flag")
      torch.use_deterministic_algorithms(True)
  
      if zero_init:
          self.compare_forward_outputs(
              rnn,
              dp_rnn,
              x,
              output_names=("out", "hn", "cn")
              if use_cn
              else ("out", "hn"),
              atol=1e-5,
              rtol=1e-3,
          )
  
          self.compare_gradients(
              rnn,
              dp_rnn,
              rnn_train_fn,
              x,
              atol=1e-5,
              rtol=1e-3,
          )
  
      else:
          num_directions = 2 if bidirectional else 1
          h0 = torch.randn(
              [num_layers * num_directions, batch_size, hidden_size]
          )
          c0 = torch.randn(
              [num_layers * num_directions, batch_size, hidden_size]
          )
          self.compare_forward_outputs(
              rnn,
              dp_rnn,
              x,
              (h0, c0) if use_cn else h0,
              output_names=("out", "hn", "cn")
              if use_cn
              else ("out", "hn"),
              atol=1e-5,
              rtol=1e-3,
          )
          self.compare_gradients(
              rnn,
              dp_rnn,
              rnn_train_fn,
              x,
              (h0, c0) if use_cn else h0,
              atol=1e-5,
              rtol=1e-3,
          )
  
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 the DPLSTM_test obj's underlying member method code with example usage: obj.test_rnn(mode, batch_size, seq_len, emb_size, hidden_size, num_layers, bidirectional, bias, batch_first, zero_init, packed_input_flag) without return types