code: def test_unique(self, device, dtype):
      def ensure_tuple(x):
          if isinstance(x, torch.Tensor):
              return (x,)
          return x
  
      if dtype is torch.bool:
          x = torch.tensor(
              [True, False, False, False, True, False, True, False],
              dtype=torch.bool,
              device=device,
          )
          expected_unique = torch.tensor(
              [False, True], dtype=torch.bool, device=device
          )
          expected_inverse = torch.tensor(
              [1, 0, 0, 0, 1, 0, 1, 0], dtype=torch.long, device=device
          )
          expected_counts = torch.tensor(
              [5, 3], dtype=torch.long, device=device
          )
      else:
          x = torch.tensor(
              [1, 2, 3, 2, 8, 5, 2, 3], dtype=dtype, device=device
          )
          expected_unique = torch.tensor(
              [1, 2, 3, 5, 8], dtype=dtype, device=device
          )
          expected_inverse = torch.tensor(
              [0, 1, 2, 1, 4, 3, 1, 2], device=device
          )
          expected_counts = torch.tensor([1, 3, 2, 1, 1], device=device)
  
      # test sorted unique
      fs = (
          lambda x, **kwargs: torch.unique(x, sorted=True, **kwargs),
          lambda x, **kwargs: x.unique(sorted=True, **kwargs),
      )
      x_sliced = torch.empty(x.size(0) * 2, dtype=dtype, device=device)[
          ::2
      ].copy_(x)
      xs = (x, x_sliced)
      for f, x in product(fs, xs):
          self._test_unique_with_expects(
              device,
              dtype,
              f,
              x,
              expected_unique,
              expected_inverse,
              expected_counts,
              (2, 2, 2),
          )
          self._test_unique_scalar_empty(dtype, device, f)
  
      # test unsorted unique
      fs = (
          lambda x, **kwargs: torch.unique(x, sorted=False, **kwargs),
          lambda x, **kwargs: x.unique(sorted=False, **kwargs),
      )
      for f, x in product(fs, xs):
          self._test_unique_scalar_empty(dtype, device, f)
          for return_inverse, return_counts in product(
              (True, False), repeat=2
          ):
              ret = ensure_tuple(
                  f(
                      x,
                      return_inverse=return_inverse,
                      return_counts=return_counts,
                  )
              )
              self.assertEqual(
                  len(ret), 1 + int(return_inverse) + int(return_counts)
              )
              x_list = x.tolist()
              x_unique_list = ret[0].tolist()
              self.assertEqual(
                  expected_unique.tolist(), sorted(x_unique_list)
              )
              if return_inverse:
                  x_inverse_list = ret[1].tolist()
                  for i, j in enumerate(x_inverse_list):
                      self.assertEqual(x_list[i], x_unique_list[j])
              if return_counts:
                  count_index = 1 + int(return_inverse)
                  x_counts_list = ret[count_index].tolist()
                  for i, j in zip(x_unique_list, x_counts_list):
                      count = 0
                      for k in x_list:
                          if k == i:
                              count += 1
                      self.assertEqual(j, count)
  
imports: import torch
  import numpy
  import random
  from torch import nan
  from itertools import permutations, product
  from torch.testing import make_tensor
  from torch.testing._internal.common_dtype import all_types, all_types_and, floating_types_and, integral_types
  from torch.testing._internal.common_utils import TestCase, run_tests, slowTest, skipIfTorchDynamo
  from torch.testing._internal.common_device_type import instantiate_device_type_tests, dtypes, onlyNativeDeviceTypes, onlyCUDA, dtypesIfCUDA, dtypesIfCPU, onlyCPU, largeTensorTest
label_desc: Use image node_id 30 for calling the TestSortAndSelect obj's underlying member method code with example usage: obj.test_unique(device, dtype) and returns: x, x