code: def test_backward_context(self):
      def fn(x):
          return x * 3
  
      x = torch.randn(4, device="cuda", requires_grad=True)
      gO = torch.rand_like(x)
      opt_fn = torch.compile(fn)
      out = opt_fn(x)
      out.backward(gO)
  
imports: import math
  import sys
  import unittest
  import torch
  import torch._dynamo.config
  import torch.backends.cuda
  import torch.nn.functional
  from torch import nn
  from torch._dynamo.debug_utils import same_two_models
  from torch._dynamo.testing import rand_strided
  from torch._dynamo.utils import same
  from torch._inductor import config
  from torch._inductor.compile_fx import compile_fx_inner
  from torch.fx.experimental.proxy_tensor import make_fx
  from torch.testing._internal.common_cuda import PLATFORM_SUPPORTS_FLASH_ATTENTION
  from torch.testing._internal.common_utils import DeterministicGuard, freeze_rng_state, IS_FBCODE, skipIfRocm, TEST_WITH_ASAN
label_desc: Use image node_id 4 for calling the CudaReproTests obj's underlying member method code with example usage: obj.test_backward_context() and returns: unknown