code: def test_isin_different_dtypes(self, device):
      supported_types = (
          all_types() if device == "cpu" else all_types_and(torch.half)
      )
      for mult in [1, 10]:
          for assume_unique in [False, True]:
              for dtype1, dtype2 in product(
                  supported_types, supported_types
              ):
                  a = torch.tensor(
                      [1, 2, 3], device=device, dtype=dtype1
                  )
                  b = torch.tensor(
                      [3, 4, 5] * mult, device=device, dtype=dtype2
                  )
                  ec = torch.tensor([False, False, True], device=device)
                  c = torch.isin(a, b, assume_unique=assume_unique)
                  self.assertEqual(c, ec)
  
imports: import torch
  import numpy
  import random
  from torch import nan
  from itertools import permutations, product
  from torch.testing import make_tensor
  from torch.testing._internal.common_dtype import all_types, all_types_and, floating_types_and, integral_types
  from torch.testing._internal.common_utils import TestCase, run_tests, slowTest, skipIfTorchDynamo
  from torch.testing._internal.common_device_type import instantiate_device_type_tests, dtypes, onlyNativeDeviceTypes, onlyCUDA, dtypesIfCUDA, dtypesIfCPU, onlyCPU, largeTensorTest
label_desc: Use image node_id 35 for calling the TestSortAndSelect obj's underlying member method code with example usage: obj.test_isin_different_dtypes(device) without return types