code: def test_topk_noncontiguous_gpu(self, device):
      # test different topk paths on cuda
      single_block_t = torch.randn(20, device=device)[::2]
      multi_block_t = torch.randn(20000, device=device)[::2]
      sort_t = torch.randn(200000, device=device)[::2]
      for t in (single_block_t, multi_block_t, sort_t):
          for k in (5, 2000, 10000):
              if k >= t.shape[0]:
                  continue
              top1, idx1 = t.topk(k)
              top2, idx2 = t.contiguous().topk(k)
              self.assertEqual(top1, top2)
              self.assertEqual(idx1, idx2)
  
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 21 for calling the TestSortAndSelect obj's underlying member method code with example usage: obj.test_topk_noncontiguous_gpu(device) without return types