code: def check_autodiff_sample(op, sample, dtype, is_inplace):
      if (
          op.name == "_foreach_abs"
          and is_inplace
          and dtype == torch.complex128
      ):
          return (
              False,
              "In-place abs is not supported for complex tensors.",
          )
      if op.name == "_foreach_sub" and (
          (
              isinstance(sample.args[0], list)
              and any(isinstance(a, bool) for a in sample.args[0])
          )
          or isinstance(sample.args[0], bool)
      ):
          return False, _BOOL_SUB_ERR_MSG
      if op.name == "_foreach_norm" and (not is_inplace):
          return (
              False,
              "Trying to set a forward gradient that has a different size than that of the original Tensor, "
              "this is not supported. Tensor is of size [] while the given forward gradient is of size [1, 1].",
          )
      rhs_arg_has_complex_number = sample.args and (
          (
              isinstance(sample.args[0], list)
              and any(isinstance(a, complex) for a in sample.args[0])
          )
          or (isinstance(sample.args[0], complex))
      )
      if rhs_arg_has_complex_number and dtype == torch.float64:
          if op.name in (
              "_foreach_clamp_max",
              "_foreach_clamp_min",
              "_foreach_maximum",
              "_foreach_minimum",
          ):
              return False, "clamp is not supported for complex types"
          if not is_inplace:
              return False, ""
          else:
              if op.name == "_foreach_pow":
                  return (
                      False,
                      "Found dtype Double but expected ComplexDouble",
                  )
              if op.name in (
                  "_foreach_add",
                  "_foreach_sub",
                  "_foreach_mul",
                  "_foreach_div",
              ):
                  return (
                      False,
                      "result type ComplexDouble can't be cast to the desired output type Double",
                  )
      return True, ""
  
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 a global function with example usage: check_autodiff_sample(op, sample, dtype, is_inplace) and returns: True,  str, False,  str, False,  _BOOL_SUB_ERR_MSG, False,  str, False,  str, False,  str, False,  str, False,  str