code: def test_aot_grad_mode_mutation(self):
      for compiler in ["aot_eager", "inductor"]:
  
          def f(x):
              y = x * x
              torch.set_grad_enabled(False)
              return y.clone(), y
  
          f_compiled = torch.compile(
              f, backend=compiler, fullgraph=True
          )
  
          torch.set_grad_enabled(True)
          x = torch.ones(3, requires_grad=True) * 3
          y_ref = f(x)
          self.assertEqual(torch.is_grad_enabled(), False)
          torch.set_grad_enabled(True)
          y = f_compiled(x)
          self.assertEqual(torch.is_grad_enabled(), False)
          torch.set_grad_enabled(True)
          self.assertEqual(y_ref, y)
  
          self.assertIsNone(y_ref[0].grad_fn)
          self.assertIsNone(y[0].grad_fn)
  
          self.assertIsNotNone(y_ref[1].grad_fn)
          self.assertIsNotNone(y[1].grad_fn)
  
          # Check that the grad computed for the inputs, given the input, is the same
          # The tangent to `y[0]`, which has grad_required=False, is irrelevant
          self.assertEqual(
              sum(y_ref[1].grad_fn(torch.tensor([-1.0, 2.0, 0.0]))),
              sum(
                  x
                  for x in y[1].grad_fn.apply(
                      None, torch.tensor([-1.0, 2.0, 0.0])
                  )
                  if x is not None
              ),
          )
  
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 23 for calling the AotAutogradFallbackTests obj's underlying member method code with example usage: obj.test_aot_grad_mode_mutation() and returns: y,  y