code: def test_many_promotions(self, device):
      # Can also include half on CPU in cases where it will be promoted to a
      # supported dtype
      dtypes1 = get_all_math_dtypes("cuda")
      dtypes2 = get_all_math_dtypes(device)
      ops = [torch.add, torch.sub, torch.mul, torch.div, torch.rsub]
      for dt1, dt2 in itertools.product(dtypes1, dtypes2):
          for op, non_contiguous in itertools.product(
              ops, [True, False]
          ):
              common_dtype = torch.promote_types(dt1, dt2)
              if (
                  common_dtype == torch.half
                  and self.device_type == "cpu"
              ):
                  continue
              if op == torch.sub and common_dtype != torch.bool:
                  # Subtraction, the `-` operator, with a bool tensor is not supported.
                  continue
              first = self._get_test_tensor(device, dt1)
              second = self._get_test_tensor(
                  device, dt2, op == torch.div
              )
              # test ops with non-contiguous tensors
              if non_contiguous:
                  first = first.transpose(0, 2)
                  second = second.transpose(2, 1)
                  self.assertNotEqual(
                      first.stride(),
                      second.stride(),
                      msg="some non-contiguous issues could be missed if tensors have same strides",
                  )
  
              self.assertEqual(
                  not first.is_contiguous(), non_contiguous
              )
              self.assertEqual(
                  not second.is_contiguous(), non_contiguous
              )
              result = op(first, second)
              expected = op(
                  first.to(common_dtype), second.to(common_dtype)
              )
              self.assertEqual(
                  result.dtype,
                  expected.dtype,
                  msg=f"{op.__name__} with {dt1}, {dt2}",
              )
              self.assertEqual(
                  result,
                  expected,
                  msg=f"{op.__name__} with {dt1}, {dt2}",
              )
  
imports: from functools import wraps
  import itertools
  import unittest
  import torch
  from torch.testing._internal.common_utils import TestCase, run_tests, load_tests, make_tensor, TEST_NUMPY, set_default_dtype, torch_to_numpy_dtype_dict, numpy_to_torch_dtype_dict, skipIfTorchDynamo
  from torch.testing._internal.common_device_type import instantiate_device_type_tests, onlyNativeDeviceTypes, dtypes, onlyCPU, expectedFailureMeta, skipMeta
  from torch.testing._internal.common_dtype import all_types_and_complex_and, get_all_math_dtypes, floating_types, get_all_dtypes, float_to_corresponding_complex_type_map
  import numpy
  import operator
label_desc: Use image node_id 16 for calling the TestTypePromotion obj's underlying member method code with example usage: obj.test_many_promotions(device) without return types