code: def test_binary_op_scalar_with_overlapping_tensors(
      self, device, dtype, op
  ):
      foreach_op, ref = op.method_variant, op.ref
      tensors = [
          torch.ones(1, 1, device=device, dtype=dtype).expand(2, 1, 3)
      ]
  
      if ref == torch.sub and dtype == torch.bool:
          with self.assertRaisesRegex(
              RuntimeError, re.escape(_BOOL_SUB_ERR_MSG)
          ):
              [ref(t, 1) for t in tensors]
          with self.assertRaisesRegex(
              RuntimeError, re.escape(_BOOL_SUB_ERR_MSG)
          ):
              foreach_op(tensors, 1)
          return
  
      expected = [ref(t, 1) for t in tensors]
      res = foreach_op(tensors, 1)
      self.assertEqual(res, expected)
  
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 10 for calling the TestForeach obj's underlying member method code with example usage: obj.test_binary_op_scalar_with_overlapping_tensors(device, dtype, op) without return types