code: def test_binary_op_with_scalar_self_support(
      self, device, dtype, op, is_fastpath
  ):
      def clone(arg):
          if isinstance(arg, (list, tuple)):
              return [clone(a) for a in arg]
          if torch.is_tensor(arg):
              return arg.clone().detach().requires_grad_()
          else:
              return arg
  
      scalar_self_arg_test_complete = False
      for i, sample in enumerate(
          op.sample_inputs(device, dtype, noncontiguous=not is_fastpath)
      ):
          (rhs_arg,) = sample.args
          kwargs = {} or sample.kwargs
          alpha = kwargs.pop("alpha", None)
          wrapped_op, ref, inplace_op, inplace_ref = self._get_funcs(op)
          if (
              isinstance(rhs_arg, Number)
              and not scalar_self_arg_test_complete
          ):
              scalar_self_arg_test_complete = True
              self._binary_test(
                  dtype,
                  wrapped_op,
                  ref,
                  [rhs_arg, sample.input],
                  is_fastpath,
                  False,
                  alpha=alpha,
                  scalar_self_arg=True,
              )
              if op.supports_autograd and dtype == torch.float32:
                  transformed_sample = sample.transform(
                      get_transform_func(
                          len(sample.input), dtype, device, is_fastpath
                      )
                  )
                  tensors = transformed_sample.input
                  (rhs_arg,) = transformed_sample.args
                  ref_tensors, ref_rhs_arg = clone(tensors), clone(
                      rhs_arg
                  )
                  sum(
                      wrapped_op(
                          [rhs_arg, tensors],
                          is_cuda=False,
                          expect_fastpath=False,
                      )
                  ).mean().backward()
                  sum(
                      [ref.func(ref_rhs_arg, t) for t in ref_tensors]
                  ).mean().backward()
                  self.assertEqual(
                      [t.grad for t in tensors],
                      [t.grad for t in ref_tensors],
                  )
  
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 6 for calling the TestForeach obj's underlying member method code with example usage: obj.test_binary_op_with_scalar_self_support(device, dtype, op, is_fastpath) and returns: unknown, arg, arg