code: def test_rnn(
      self,
      model,
      N: int,
      T: int,
      D: int,
      H: int,
      num_layers: int,
      bias: bool,
      batch_first: bool,
      bidirectional: bool,
      using_packed_sequences: bool,
      packed_sequences_sorted: bool,
  ):
      torch.use_deterministic_algorithms(False)
      rnn = model(
          D,
          H,
          num_layers=num_layers,
          batch_first=batch_first,
          bias=bias,
          bidirectional=bidirectional,
      )
      rnn = DPRNNAdapter(rnn)
  
      if using_packed_sequences:
          x = _gen_packed_data(
              N, T, D, batch_first, packed_sequences_sorted
          )
      else:
          if batch_first:
              x = torch.randn([N, T, D])
          else:
              x = torch.randn([T, N, D])
  
      self.run_test(
          x, rnn, batch_first=batch_first, ew_compatible=False
      )
  
file_path: opacus/opacus/tests/grad_samples/dp_rnn_test.py
imports: 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 .common import GradSampleHooks_test
label_desc: Use image node_id 1 for calling the RNN_test obj's underlying member method code with example usage: obj.test_rnn(model, N, T, D, H, num_layers, bias, batch_first, bidirectional, using_packed_sequences, packed_sequences_sorted) without return types