code: def test_numpy_array_binary_ufunc_promotion(self, device, dtypes):
      import operator
  
      np_type = torch_to_numpy_dtype_dict[dtypes[0]]
      torch_type = dtypes[1]
  
      t = torch.tensor((1,), device=device, dtype=torch_type)
      a = np.array((1,), dtype=np_type)
      a_as_t = torch.from_numpy(a).to(device=device)
  
      for np_first in (True, False):
          for op in (operator.add, torch.add):
              # Acquires results of binary ufunc type promotion.
              try:
                  actual = op(a, t) if np_first else op(t, a)
              except Exception as e:
                  actual = e
  
              try:
                  expected = (
                      op(a_as_t, t) if np_first else op(t, a_as_t)
                  )
              except Exception as e:
                  expected = e
  
              same_result = (
                  type(expected) == type(actual)
              ) and expected == actual
  
              # Note: An "undesired failure," as opposed to an "expected failure"
              # is both expected (we know the test will fail) and
              # undesirable (if PyTorch was working properly the test would
              # not fail). This test is affected by three issues (see below)
              # that will cause undesired failures. It detects when these
              # issues will occur and updates this bool accordingly.
              undesired_failure = False
  
              # A NumPy array as the first argument to the plus operator
              # or as any argument to torch.add is not working as
              # intended.
              # See https://github.com/pytorch/pytorch/issues/36363.
              if np_first and op is operator.add:
                  undesired_failure = True
              if op is torch.add:
                  undesired_failure = True
  
              # Expects the same result if undesired_failure is false
              # and a different result otherwise.
              # Note: These cases prettyprint the failing inputs to make
              # debugging test failures easier.
              if undesired_failure and same_result:
                  msg = (
                      f"Failure: {actual} == {expected}. torch type was {torch_type}. "
                      f"NumPy type was {np_type}. np_first is {np_first} default type is "
                      f"{torch.get_default_dtype()}."
                  )
                  self.fail(msg)
  
              if not undesired_failure and not same_result:
                  msg = (
                      f"Failure: {actual} != {expected}. torch type was {torch_type}. "
                      f"NumPy type was {np_type}. np_first is {np_first} default type is "
                      f"{torch.get_default_dtype()}."
                  )
                  self.fail(msg)
  
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 45 for calling the TestTypePromotion obj's underlying member method code with example usage: obj.test_numpy_array_binary_ufunc_promotion(device, dtypes) without return types