code: def test_pointwise_op_with_tensor_of_scalarlist_overload(
      self, device, dtype, op, is_fastpath
  ):
      for sample in op.sample_inputs(
          device, dtype, noncontiguous=not is_fastpath
      ):
          assert isinstance(sample.args, tuple)
          assert len(sample.args) == 2
          inputs = [sample.input, *sample.args]
          kwargs = sample.kwargs.copy()
          disable_fastpath = sample.disable_fastpath and is_fastpath
          wrapped_op, ref, inplace_op, inplace_ref = self._get_funcs(op)
          scalars = kwargs.pop("scalars", None)
  
          if is_fastpath and scalars:
              sample = sample.transform(
                  lambda t: t.clone().detach()
                  if torch.is_tensor(t)
                  else t
              )
              inputs = [sample.input, *sample.args]
              tensor_values = torch.tensor(scalars)
              # 1D Tensor of scalars
              for is_inplace, op_, ref_ in (
                  (False, wrapped_op, ref),
                  (True, inplace_op, inplace_ref),
              ):
                  self._pointwise_test(
                      op_,
                      ref_,
                      inputs,
                      is_fastpath and not disable_fastpath,
                      is_inplace,
                      scalars=tensor_values,
                      **kwargs,
                  )
                  self._pointwise_test(
                      op_,
                      ref_,
                      inputs,
                      is_fastpath and not disable_fastpath,
                      is_inplace,
                      scalars=tensor_values[0],
                      custom_values_err="Expected packed scalar Tensor to be of dimension 1. Got 0 instead.",
                      **kwargs,
                  )
                  if self.is_cuda:
                      self._pointwise_test(
                          op_,
                          ref_,
                          inputs,
                          is_fastpath and not disable_fastpath,
                          is_inplace,
                          scalars=tensor_values.cuda(),
                          custom_values_err="Expected scalars to be on CPU, got cuda:0 instead.",
                          **kwargs,
                      )
                  self._pointwise_test(
                      op_,
                      ref_,
                      inputs,
                      is_fastpath and not disable_fastpath,
                      is_inplace,
                      scalars=tensor_values[:2],
                      custom_values_err=f"Expected length of scalars to match input of length {len(scalars)} but got 2 instead.",
                      **kwargs,
                  )
                  self._pointwise_test(
                      op_,
                      ref_,
                      inputs,
                      is_fastpath and not disable_fastpath,
                      is_inplace,
                      scalars=torch.tensor([[0, 1], [2, 3]])[:, 1],
                      custom_values_err="Expected scalars to be contiguous.",
                      **kwargs,
                  )
  
          # Tests of implicit broadcasting
          N = len(sample.input)
          inputs = [
              [
                  make_tensor(
                      (N, N),
                      device=device,
                      dtype=dtype,
                      noncontiguous=not is_fastpath,
                  )
                  for _ in range(N)
              ],
              [
                  make_tensor(
                      (N - i, 1),
                      device=device,
                      dtype=dtype,
                      noncontiguous=not is_fastpath,
                  )
                  for i in range(N)
              ],
              [
                  make_tensor(
                      (1, N - i),
                      device=device,
                      dtype=dtype,
                      noncontiguous=not is_fastpath,
                  )
                  for i in range(N)
              ],
          ]
          self._pointwise_test(
              wrapped_op,
              ref,
              inputs,
              is_fastpath and disable_fastpath,
              is_inplace=False,
              scalars=scalars,
              **kwargs,
          )
          self._pointwise_test(
              inplace_op,
              inplace_ref,
              inputs,
              is_fastpath and disable_fastpath,
              is_inplace=True,
              scalars=scalars,
              **kwargs,
          )
  
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 7 for calling the TestForeach obj's underlying member method code with example usage: obj.test_pointwise_op_with_tensor_of_scalarlist_overload(device, dtype, op, is_fastpath) without return types