code: def test_clamp_type_promotion(self, device, dtypes):
      dtype0, dtype1, dtype2 = dtypes
      S = 4
  
      def make_tensor(size, dtype):
          if dtype == torch.bool:
              return torch.randint(2, size, dtype=dtype, device=device)
          elif dtype == torch.int:
              return torch.randint(10, size, dtype=dtype, device=device)
          else:
              return torch.randn(size, dtype=dtype, device=device)
  
      min_t = make_tensor((S,), dtype1)
      max_t = make_tensor((S,), dtype2)
      mins = (min_t, min_t[0], min_t[0].item())
      maxs = (max_t, max_t[0], max_t[0].item())
      inp = make_tensor((S,), dtype0)
      for min_v, max_v in itertools.product(mins, maxs):
          if type(max_v) != type(min_v):
              continue
          if (
              isinstance(min_v, torch.Tensor)
              and min_v.ndim == 0
              and max_v.ndim == 0
          ):
              continue  # 0d tensors go to scalar overload, and it's tested separately
  
          def expected_type(inp, max, min):
              arg1, arg2 = max, min
              if isinstance(max, torch.Tensor) and max.ndim == 0:
                  # first do a maybe dimensional boundary
                  arg1, arg2 = min, max
              exp_type = torch.result_type(inp, arg1)
              inp_new = torch.empty_like(inp, dtype=exp_type)
              return torch.result_type(inp_new, arg2)
  
          exp_type = expected_type(inp, min_v, max_v)
          if exp_type != torch.bool:
              actual = torch.clamp(inp, min_v, max_v)
              inps = [
                  x.to(exp_type) if isinstance(x, torch.Tensor) else x
                  for x in (inp, min_v, max_v)
              ]
              expected = torch.clamp(inps[0], inps[1], inps[2])
              self.assertEqual(actual, expected)
              if inp.dtype in floating_types() or exp_type == inp.dtype:
                  actual = torch.clamp_(inp, min_v, max_v)
                  self.assertEqual(actual, expected, exact_dtype=False)
      for val in mins:
  
          def expected_type(inp, val):
              return torch.result_type(inp, val)
  
          exp_type = expected_type(inp, val)
          if exp_type != torch.bool:
              actual = torch.clamp_min(inp, val)
              inps = [
                  x.to(exp_type) if isinstance(x, torch.Tensor) else x
                  for x in (inp, val)
              ]
              expected = torch.clamp_min(inps[0], inps[1])
              self.assertEqual(actual.dtype, exp_type)
              self.assertEqual(actual, expected)
              if inp.dtype == exp_type:
                  actual = torch.clamp_min_(inp, val)
                  self.assertEqual(actual, expected)
              actual = torch.clamp_max(inp, val)
              expected = torch.clamp_max(inps[0], inps[1])
              self.assertEqual(actual, expected)
              if inp.dtype in floating_types() or exp_type == inp.dtype:
                  actual = torch.clamp_max_(inp, val)
                  self.assertEqual(actual, expected, 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 50 for calling the TestTypePromotion obj's underlying member method code with example usage: obj.test_clamp_type_promotion(device, dtypes) and returns: torch, torch, torch, torch, torch