code: def test_cpu_return_self(self, mode):
      with autograd_fallback_mode(mode):
          # To be clear, none of these situations are OK and will lead
          # to other problems down the line. We're testing them because
          # it is fairly common to actually do these things.
          lib = Library(self.test_ns, "FRAGMENT")
          lib.define("foo(Tensor self) -> Tensor")
          lib.impl("foo", lambda x: x, "CPU")
          op = self.get_op("foo")
  
          x = torch.randn(3, requires_grad=True)
          y = op(x).sum()
          with self._check_ctx(mode):
              y.backward()
              self.assertEqual(x.grad, torch.ones_like(x))
  
          lib.define("bar(Tensor(a!) self) -> Tensor(a!)")
          lib.impl("bar", lambda x: x, "CPU")
          op = self.get_op("bar")
  
          x = torch.randn(3, requires_grad=True)
          y = op(x).sum()
          with self._check_ctx(mode):
              y.backward()
              self.assertEqual(x.grad, torch.ones_like(x))
  
imports: import contextlib
  import warnings
  import numpy
  import torch
  from torch.library import Library
  from torch.testing._internal.common_utils import instantiate_parametrized_tests, parametrize, run_tests, TestCase
label_desc: Use image node_id 8 for calling the TestAutogradFallback obj's underlying member method code with example usage: obj.test_cpu_return_self(mode) without return types