code: def test_call_fn_with_non_const_inputs_aot_unsafe_control_flow(self):
      class ModuleSpecialFwd(torch.nn.Module):
          def _some_bad_fwd(self, param, y):
              if y[0][0] < 3:
                  return y + param
              return param * y
  
          def forward(self, x, y):
              a = x * y
              a = self._some_bad_fwd(a, a)
              b = x + y
              return a * b
  
      # Init mod
      mod = ModuleSpecialFwd()
      x = torch.nn.Parameter(torch.randn([2, 2]))
      y = torch.randn([2, 2])
  
      # Run it for real
      real = mod(x, y)
  
      # Run it through optimize, with our capturing fn
  
      gms = []
      counter = CompileCounter()
  
      def capturing_fn(gm, inputs):
          nonlocal gms
          gms.append(gm)
          return counter(gm, inputs)
  
      optimized_mod = torch._dynamo.optimize(capturing_fn)(mod)
  
      # Assert equal
      self.assertTrue(
          torch._dynamo.testing.same(real, optimized_mod(x, y))
      )
  
      # Uncomment to reproduce commented out graphs below.
      # for gm in gms:
      #     print("GM CODE", gm.code)
  
      self.assertEqual(counter.frame_count, 4)
      self.assertEqual(counter.op_count, 7)
      # Graph 1
      # def forward(self, x : torch.nn.parameter.Parameter, y : torch.Tensor):
      #     mul = x * y;  x = y = None
      #     return (mul,)
      # BREAK
      # Graph 2
      # def forward(self, y : torch.Tensor):
      #     getitem = y[0];  y = None
      #     getitem_1 = getitem[0];  getitem = None
      #     lt = getitem_1 < 3;  getitem_1 = None
      #     return (lt,)
      # BREAK
      # Graph 3
      # def forward(self, param : torch.Tensor, y : torch.Tensor):
      #     add = y + param;  y = param = None
      #     return (add,)
      # BREAK
      # Graph 4
      # def forward(self, _stack0 : torch.Tensor, x : torch.nn.parameter.Parameter, y : torch.Tensor):
      #     add = x + y;  x = y = None
      #     mul = _stack0 * add;  _stack0 = add = None
      #     return (mul,)
  
      # Run fn with AOT
      torch._dynamo.reset()
  
      aot_fn = torch._dynamo.optimize("aot_eager")(optimized_mod)
      aot_fn(x, y)
  
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 8 for calling the AotAutogradFallbackTests obj's underlying member method code with example usage: obj.test_call_fn_with_non_const_inputs_aot_unsafe_control_flow() and returns: counter, unknown, unknown, unknown