code: def test_autodiff(self, device, dtype, op, inplace):
      if not (op.supports_autograd or op.supports_forward_ad):
          self.skipTest(
              "neither reverse mode nor forward mode supported"
          )
      if (not inplace) and not op.supports_out:
          self.skipTest("out-of-place not implemented")
      if inplace and op.has_no_in_place:
          self.skipTest("in-place not implemented")
  
      # note(crcrpar): without this, some unary functions fail, unlike inplace and/or complex.
      if (
          (not inplace)
          and dtype == torch.float64
          and op.name
          in (
              "_foreach_acos",
              "_foreach_asin",
              "_foreach_log10",
              "_foreach_log1p",
              "_foreach_log2",
              "_foreach_log",
              "_foreach_pow",
              "_foreach_sqrt",
          )
      ):
          value_range = {"low": 0.5, "high": 1.0}
      else:
          value_range = {}
      for sample in op.sample_inputs(
          device,
          dtype,
          requires_grad=True,
          num_input_tensors=[5],
          **value_range,
      ):
          # Skip `_foreach_pow.ScalarAndTensor(Scalar, Tensor[])`
          if op.name == "_foreach_pow" and isinstance(
              sample.input, Number
          ):
              continue
  
          func = None
          if inplace:
              # Call `clone` to avoid inplace modifications likewise
              # `torch.testing._internal.common_utils.TestGradients._get_safe_inplace`
              def inplace_func(*tensorlist):
                  kwargs = (
                      {"alpha": sample.kwargs["alpha"]}
                      if "alpha" in sample.kwargs
                      else {}
                  )
                  op.inplace_variant(
                      tuple(t.clone() for t in tensorlist),
                      *sample.args,
                      **kwargs,
                  )
                  return tensorlist
  
              func = inplace_func
          else:
  
              def outplace_func(*tensorlist):
                  kwargs = (
                      {"alpha": sample.kwargs["alpha"]}
                      if "alpha" in sample.kwargs
                      else {}
                  )
                  return op.method_variant(
                      tensorlist, *sample.args, **kwargs
                  )
  
              func = outplace_func
  
          working_sample, err_msg_pattern = check_autodiff_sample(
              op, sample, dtype, inplace
          )
  
          def call_gradcheck():
              gradcheck(
                  func,
                  sample.input,
                  raise_exception=True,
                  check_forward_ad=op.supports_forward_ad,
                  check_batched_forward_grad=False,
                  check_backward_ad=op.supports_autograd,
                  check_batched_grad=False,
              )
  
          if not working_sample:
              if not err_msg_pattern:
                  # lhs of float64 and rhs of complex.
                  continue
              with self.assertRaisesRegex(
                  RuntimeError, re.escape(err_msg_pattern)
              ):
                  call_gradcheck()
              continue
          call_gradcheck()
  
          # Test per-tensor `grad_fn` behavior.
          if inplace and op.supports_inplace_autograd:
              # per-tensor `grad_fn` check.
              hook_buffer = []
  
              def get_grad_fn_hook(i):
                  def hook(grad_inputs, grad_outputs) -> None:
                      hook_buffer.append(i)
  
                  return hook
  
              _inputs = [
                  t.clone().detach().requires_grad_()
                  for t in sample.input
              ]
              inputs = [t.clone() for t in _inputs]
              kwargs = (
                  {"alpha": sample.kwargs["alpha"]}
                  if "alpha" in sample.kwargs
                  else {}
              )
              op.inplace_variant(inputs, *sample.args, **kwargs)
  
              self.assertEqual(
                  len({t.grad_fn for t in inputs}), len(inputs)
              )
  
              for i, t in enumerate(inputs):
                  t.grad_fn.register_hook(get_grad_fn_hook(i))
  
              torch.autograd.grad(
                  inputs[0],
                  inputs=(_inputs[0],),
                  grad_outputs=(torch.rand_like(inputs[0]),),
                  retain_graph=True,
              )
              self.assertEqual(hook_buffer, [0])
              hook_buffer.clear()
  
              # tensors have different shapes.
              sum_of_cloned_tensors = torch.cat(
                  [t.view(-1) for t in inputs]
              ).sum()
              grad_output = torch.rand_like(sum_of_cloned_tensors)
              torch.autograd.grad(
                  sum_of_cloned_tensors,
                  inputs=tuple(_inputs),
                  grad_outputs=(grad_output,),
                  retain_graph=False,
              )
              self.assertEqual(
                  hook_buffer, list(reversed(range(len(inputs))))
              )
  
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 27 for calling the TestForeach obj's underlying member method code with example usage: obj.test_autodiff(device, dtype, op, inplace) and returns: tensorlist, op, hook