code: def test_no_autograd_kernel_inplace(self, mode):
      with autograd_fallback_mode(mode):
          # input modified in-place gets returned as output
          lib = self.get_lib()
          lib.define(
              "foo(Tensor(a!) self, Tensor(b!) y) -> (Tensor(a!), Tensor(b!))"
          )
          op = self.get_op("foo")
  
          def foo_impl(x, y):
              with torch.no_grad():
                  x.sin_()
                  y.cos_()
              return x, y
  
          lib.impl("foo", foo_impl, "CPU")
  
          x = torch.randn(3, requires_grad=True)
          w = x.clone()
          v = x.clone()
          y0 = w[0]
          y1 = v[1]
          z0, z1 = op(y0, y1)
          for tensor in [w, v, z0, z1, y0, y1]:
              with self._check_ctx(mode):
                  tensor.sum().backward(retain_graph=True)
  
          # no outputs: we don't do anything. Maybe we should in the future.
          # This is not a common failure mode.
          lib.define("bar(Tensor(a!) self) -> ()")
          op = self.get_op("bar")
  
          def bar_impl(x):
              with torch.no_grad():
                  x.sin_()
  
          lib.impl("bar", bar_impl, "CPU")
          with warnings.catch_warnings():
              warnings.simplefilter("error")
              x = torch.randn([], requires_grad=True)
              y = x.clone()
              z = op(y)
              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 7 for calling the TestAutogradFallback obj's underlying member method code with example usage: obj.test_no_autograd_kernel_inplace(mode) and returns: x,  y