code: def test_lifetime_of_grad_fn_when_result_is_saved(
      self, device, dtype, op
  ):
      def get_ref(func, sample):
          class Foo:
              pass
  
          out = func(
              (sample.input, *sample.args),
              is_cuda=False,
              expect_fastpath=False,
              **sample.kwargs,
          )
          foo = Foo()
          meta_dict = out[0].grad_fn.metadata
          meta_dict[0] = foo
          ref = weakref.ref(foo)
          return out, ref
  
      def _test(func, sample):
          out, ref = get_ref(func, sample)
          self.assertIsNotNone(ref())
          del out
          self.assertIsNone(ref())
  
      func = self._get_funcs(op)[0]
      for sample in op.sample_inputs(
          device, dtype, requires_grad=True, num_input_tensors=[1]
      ):
          for key in ("is_fastpath", "disable_fastpath"):
              if key in sample.kwargs:
                  del sample.kwargs[key]
          # note: `_foreach_pow.Scalar` and `_foreach_pow.ScalarList` don't depend on `result`
          # see: https://github.com/pytorch/pytorch/blob/5403c777/tools/autograd/derivatives.yaml#L3048-L3049
          if op.name == "_foreach_pow":
              if (
                  isinstance(sample.args[0], list)
                  and isinstance(sample.args[0][0], Number)
              ) or (
                  isinstance(sample.args[0], Number)
                  and not isinstance(sample.args[0], float)
              ):
                  continue
              if isinstance(sample.args[0], float):
                  new_args = (sample.input,)
                  sample.input = sample.args[0]
                  sample.args = new_args
          _test(func, sample)
  
imports: from contextlib import nullcontext
  from numbers import Number
  import random
  import re
  import torch
  import unittest
  import itertools
  import weakref
  from torch.testing import make_tensor
  from torch.testing._comparison import default_tolerances
  from torch.testing._internal.common_cuda import TEST_MULTIGPU
  from torch.testing._internal.common_utils import TestCase, run_tests, TEST_WITH_ROCM, skipIfTorchDynamo, parametrize, gradcheck
  from torch.testing._internal.common_device_type import instantiate_device_type_tests, dtypes, onlyCUDA, ops, OpDTypes
  from torch.testing._internal.common_methods_invocations import foreach_unary_op_db, foreach_binary_op_db, foreach_pointwise_op_db, foreach_reduce_op_db, foreach_other_op_db
  from torch.testing._internal.common_dtype import all_types_and_complex_and, floating_types_and, floating_types, integral_types_and
label_desc: Use image node_id 22 for calling the TestForeach obj's underlying member method code with example usage: obj.test_lifetime_of_grad_fn_when_result_is_saved(device, dtype, op) and returns: out,  ref