code: def test_LSTM(self):
      # https://github.com/pytorch/torchdynamo/issues/1147
      class Repro(torch.nn.Module):
          def __init__(self):
              super().__init__()
              self.self_mod_model_lstm_lstm = torch.nn.LSTM(
                  64, 64, num_layers=2, bidirectional=True
              )
  
          def forward(self, permute: torch.Tensor):
              self_mod_model_lstm_lstm = self.self_mod_model_lstm_lstm(
                  permute
              )
              return (self_mod_model_lstm_lstm,)
  
      mod = Repro()
  
      aot_mod = torch._dynamo.optimize("aot_eager")(mod)
  
      args = [((92, 4, 64), (1, 5888, 92), torch.float32, "cpu", False)]
      args = [
          rand_strided(sh, st, dt, dev).requires_grad_(rg)
          for (sh, st, dt, dev, rg) in args
      ]
  
      eager_result = mod(*args)
      aot_result = aot_mod(*args)
      self.assertTrue(
          torch._dynamo.testing.same(eager_result, aot_result)
      )
  
imports: import re
  from textwrap import dedent
  from unittest.mock import patch
  import torch
  import torch._dynamo
  import torch._dynamo.test_case
  import torch.fx.traceback
  import torch.utils._pytree
  from torch._dynamo.testing import CompileCounter, expectedFailureDynamic, rand_strided
  from torch._functorch.aot_autograd import _aot_export_function, create_functional_call
  from torch.profiler import profile
  from torch.testing._internal.common_utils import compare_equal_outs_and_grads
label_desc: Use image node_id 1 for calling the AotAutogradFallbackTests obj's underlying member method code with example usage: obj.test_LSTM() and returns: self_mod_model_lstm_lstm