code: def test_cat_out_different_dtypes(self, device):
      dtypes = all_types_and_complex_and(torch.half)
      for x_dtype, y_dtype, out_dtype in itertools.product(
          dtypes, dtypes, dtypes
      ):
          out = torch.zeros(6, device=device, dtype=out_dtype)
          x = torch.tensor([1, 2, 3], device=device, dtype=x_dtype)
          y = torch.tensor([4, 5, 6], device=device, dtype=y_dtype)
          expected_out = torch.tensor(
              [1, 2, 3, 4, 5, 6], device=device, dtype=out_dtype
          )
          if (
              (x_dtype.is_floating_point or y_dtype.is_floating_point)
              and not (
                  out_dtype.is_floating_point or out_dtype.is_complex
              )
          ) or (
              (x_dtype.is_complex or y_dtype.is_complex)
              and not out_dtype.is_complex
          ):
              # This combinations do not support type conversion to a different class out type
              with self.assertRaises(RuntimeError):
                  torch.cat([x, y], out=out)
          else:
              torch.cat([x, y], out=out)
              self.assertEqual(out, expected_out, exact_dtype=True)
  
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 47 for calling the TestTypePromotion obj's underlying member method code with example usage: obj.test_cat_out_different_dtypes(device) without return types