code: def test_binary_op_list_error_cases(self, device, dtype, op):
      foreach_op, foreach_op_, ref, ref_ = (
          op.method_variant,
          op.inplace_variant,
          op.ref,
          op.ref_inplace,
      )
      tensors1 = []
      tensors2 = []
      ops_to_test = [foreach_op, foreach_op_]
  
      # Empty lists
      for fop in ops_to_test:
          with self.assertRaisesRegex(
              RuntimeError,
              "There were no tensor arguments to this function",
          ):
              fop(tensors1, tensors2)
  
      # One empty list
      tensors1.append(torch.tensor([1], device=device, dtype=dtype))
      for fop in ops_to_test:
          with self.assertRaisesRegex(
              RuntimeError,
              "Tensor list must have same number of elements as scalar list.",
          ):
              fop(tensors1, tensors2)
  
      # Lists have different amount of tensors
      tensors2.append(torch.tensor([1], device=device))
      tensors2.append(torch.tensor([1], device=device))
      for fop in ops_to_test:
          with self.assertRaisesRegex(
              RuntimeError,
              "Tensor lists must have the same number of tensors, got 1 and 2",
          ):
              fop(tensors1, tensors2)
          with self.assertRaisesRegex(
              RuntimeError,
              "Tensor lists must have the same number of tensors, got 2 and 1",
          ):
              fop(tensors2, tensors1)
  
      # Corresponding tensors with different sizes that aren't compatible with broadcast
      # If sizes are different then foreach chooses slow path, thus error messages are expected
      # to be the same as torch regular function.
      tensors1 = [
          torch.zeros(10, 10, device=device, dtype=dtype)
          for _ in range(10)
      ]
      tensors2 = [
          torch.ones(11, 11, device=device, dtype=dtype)
          for _ in range(10)
      ]
      try:
          foreach_op(tensors1, tensors2)
      except RuntimeError as e:
          with self.assertRaisesRegex(type(e), re.escape(str(e))):
              [ref(t1, t2) for t1, t2 in zip(tensors1, tensors2)]
      try:
          foreach_op_(tensors1, tensors2)
      except RuntimeError as e:
          with self.assertRaisesRegex(type(e), re.escape(str(e))):
              [ref_(t1, t2) for t1, t2 in zip(tensors1, tensors2)]
  
      # different devices
      if self.device_type == "cuda" and torch.cuda.device_count() > 1:
          tensor1 = torch.zeros(10, 10, device="cuda:0", dtype=dtype)
          tensor2 = torch.ones(10, 10, device="cuda:1", dtype=dtype)
          if dtype == torch.bool and foreach_op == torch._foreach_sub:
              for fop in ops_to_test:
                  with self.assertRaisesRegex(
                      RuntimeError, re.escape(_BOOL_SUB_ERR_MSG)
                  ):
                      fop([tensor1], [tensor2])
              return
          with self.assertRaisesRegex(
              RuntimeError,
              "Expected all tensors to be on the same device",
          ):
              foreach_op([tensor1], [tensor2])
          if (
              dtype in integral_types_and(torch.bool)
              and foreach_op == torch._foreach_div
          ):
              with self.assertRaisesRegex(RuntimeError, "result type"):
                  foreach_op_([tensor1], [tensor2])
          else:
              with self.assertRaisesRegex(
                  RuntimeError,
                  "Expected all tensors to be on the same device",
              ):
                  foreach_op_([tensor1], [tensor2])
  
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 12 for calling the TestForeach obj's underlying member method code with example usage: obj.test_binary_op_list_error_cases(device, dtype, op) without return types