code: def test_computation_ignores_out(self, device):
      t = torch.tensor(33000, dtype=torch.float16, device=device)
      out = torch.empty(0, dtype=torch.float64, device=device)
      result = torch.add(t, t, out=out)
      self.assertEqual(result, t + t, exact_dtype=False)
      self.assertNotEqual(result, t.double() + t, exact_dtype=False)
  
      a = torch.tensor(1.5, dtype=torch.float16, device=device)
      b = torch.tensor(0.666, dtype=torch.float16, device=device)
      result = torch.true_divide(a, b, out=out)
      self.assertEqual(result, a / b, exact_dtype=False)
      self.assertNotEqual(result, a.double() / a, exact_dtype=False)
  
      a = torch.tensor(5, dtype=torch.uint8, device=device)
      b = torch.tensor(8, dtype=torch.uint8, device=device)
      result = torch.sub(a, b, out=out)
      self.assertEqual(result, a - b, exact_dtype=False)
      self.assertNotEqual(result, a.double() - b, exact_dtype=False)
  
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 49 for calling the TestTypePromotion obj's underlying member method code with example usage: obj.test_computation_ignores_out(device) without return types