code: def _test_unique_scalar_empty(self, dtype, device, f):
      # test scalar
      x = torch.tensor(0, dtype=dtype, device=device)
      unique, inverse, counts = f(
          x, return_inverse=True, return_counts=True
      )
      expected_unique = torch.tensor([0], dtype=dtype, device=device)
      expected_inverse = torch.tensor(0, device=device)
      expected_counts = torch.tensor([1], device=device)
      self.assertEqual(unique, expected_unique)
      self.assertEqual(inverse, expected_inverse)
      self.assertEqual(counts, expected_counts)
  
      # test zero sized tensor
      x = torch.zeros((0, 0, 3), dtype=dtype, device=device)
      unique, inverse, counts = f(
          x, return_inverse=True, return_counts=True
      )
      expected_unique = torch.tensor([], dtype=dtype, device=device)
      expected_inverse = torch.empty(
          (0, 0, 3), dtype=torch.long, device=device
      )
      expected_counts = torch.tensor(
          [], dtype=torch.long, device=device
      )
      self.assertEqual(unique, expected_unique)
      self.assertEqual(inverse, expected_inverse)
      self.assertEqual(counts, expected_counts)
  
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 28 for calling the TestSortAndSelect obj's underlying member method code with example usage: obj._test_unique_scalar_empty(dtype, device, f) without return types