code: def test_context_wrapping_grad_mode_nested_function_decorator(self):
      ctx_wrappers = [(torch.enable_grad, True), (torch.no_grad, False)]
  
      for call in [True, False]:
          for i in range(2):
              torch._dynamo.reset()
  
              ctx_wrapper, mode = ctx_wrappers[i]
              ctx_wrapper_inverse, mode_inverse = ctx_wrappers[
                  (i + 1) % 2
              ]
  
              def fn(x):
                  with ctx_wrapper_inverse():
                      if call:
  
                          @ctx_wrapper()
                          def inner_func(x):
                              return x.sin()
  
                      else:
  
                          @ctx_wrapper
                          def inner_func(x):
                              return x.sin()
  
                      # Calling no_grad or enabled_grad should not mutate global state
                      assert torch.is_grad_enabled() == mode_inverse
  
                  with ctx_wrapper_inverse():
                      return inner_func(x)
  
              x = torch.zeros(10, requires_grad=True)
              opt_fn = torch.compile(
                  fn, backend="eager", fullgraph=True
              )
              self.assertEqual(fn(x), opt_fn(x))
              self.assertEqual(
                  fn(x).requires_grad, opt_fn(x).requires_grad
              )
  
imports: import unittest
  import torch
  import torch._dynamo.test_case
  import torch._dynamo.testing
  import torch.onnx.operators
  from torch._dynamo.testing import EagerAndRecordGraphs, normalize_gm, same
  from torch.nn import functional
  from torch.testing._internal.common_cuda import PLATFORM_SUPPORTS_FLASH_ATTENTION
  from torch.testing._internal.common_utils import TEST_WITH_ROCM
label_desc: Use image node_id 37 for calling the CtxManagerTests obj's underlying member method code with example usage: obj.test_context_wrapping_grad_mode_nested_function_decorator() and returns: inner_func, x, x