code: def test_aot_export_joint_simple_repro(self):
      class Mod(torch.nn.Module):
          def __init__(self, *args, **kwargs) -> None:
              super().__init__(*args, **kwargs)
              self.linear = torch.nn.Linear(5, 7)
  
          def forward(self, x):
              return self.linear(x)
  
      def mini_backend(gm, sample_inputs):
          from torch._functorch.aot_autograd import (
              aot_export_joint_simple,
          )
  
          fake_mode = torch._dynamo.utils.detect_fake_mode(
              sample_inputs
          )
  
          with patch.object(
              fake_mode, "allow_non_fake_inputs", True
          ), fake_mode:
              return aot_export_joint_simple(
                  gm, sample_inputs, trace_joint=False
              )
  
      sample_inputs = [torch.rand((3, 4, 5))]
      model = Mod()
      m_compiled = torch.compile(model, backend=mini_backend)
  
      out_ref = model(*sample_inputs)
      out_test = m_compiled(*sample_inputs)
      self.assertEqual(out_ref, out_test)
  
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 21 for calling the AotAutogradFallbackTests obj's underlying member method code with example usage: obj.test_aot_export_joint_simple_repro() and returns: self, aot_export_joint_simple