code: def __call__(self, inputs, is_cuda, expect_fastpath, **kwargs):
      actual = None
      zero_size = kwargs.pop("zero_size", False)
      if (
          is_cuda
          and torch.autograd.kineto_available()
          and torch.profiler.ProfilerActivity.CUDA
          in torch.profiler.supported_activities()
      ):
          with torch.profiler.profile() as p:
              actual = self.func(*inputs, **kwargs)
          keys = tuple([e.key for e in p.key_averages()])
          mta_called = any(
              "multi_tensor_apply_kernel" in k for k in keys
          )
          assert mta_called == (expect_fastpath and (not zero_size))
      else:
          actual = self.func(*inputs, **kwargs)
      # note(mkozuki): inplace foreach functions are void functions.
      return inputs[0] if self.is_inplace else actual
  
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 2 for calling the ForeachFuncWrapper obj's underlying member method code with example usage: obj.__call__(inputs, is_cuda, expect_fastpath) and returns: unknown