code: def test_topk(self, device):
      def topKViaSort(t, k, dim, dir):
          sorted, indices = t.sort(dim, dir)
          return sorted.narrow(dim, 0, k), indices.narrow(dim, 0, k)
  
      def compareTensors(t, res1, ind1, res2, ind2, dim):
          # Values should be exactly equivalent
          self.assertEqual(res1, res2, atol=0, rtol=0)
  
          # Indices might differ based on the implementation, since there is
          # no guarantee of the relative order of selection
          if not ind1.eq(ind2).all():
              # To verify that the indices represent equivalent elements,
              # gather from the input using the topk indices and compare against
              # the sort indices
              vals = t.gather(dim, ind2)
              self.assertEqual(res1, vals, atol=0, rtol=0)
  
      def compare(t, k, dim, dir):
          topKVal, topKInd = t.topk(k, dim, dir, True)
          sortKVal, sortKInd = topKViaSort(t, k, dim, dir)
          compareTensors(t, sortKVal, sortKInd, topKVal, topKInd, dim)
  
      t = torch.rand(
          random.randint(1, SIZE),
          random.randint(1, SIZE),
          random.randint(1, SIZE),
          device=device,
      )
  
      for _kTries in range(3):
          for _dimTries in range(3):
              for transpose in (True, False):
                  for dir in (True, False):
                      testTensor = t
                      if transpose:
                          dim1 = random.randrange(t.ndimension())
                          dim2 = dim1
                          while dim1 == dim2:
                              dim2 = random.randrange(t.ndimension())
  
                          testTensor = t.transpose(dim1, dim2)
  
                      dim = random.randrange(testTensor.ndimension())
                      k = random.randint(1, testTensor.size(dim))
                      compare(testTensor, k, dim, dir)
  
      # This tests the code path where on CUDA, topk is implemented with sort.
      t = torch.randn((2, 100000), device=device)
      compare(t, 2000, 1, True)
      compare(t, 2000, 1, False)
  
      # This tests the code path where on CUDA, topk is implemented with multiblock
      t = torch.randn((2, 10000), device=device)
      compare(t, 2000, 1, True)
      compare(t, 2000, 1, False)
  
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 17 for calling the TestSortAndSelect obj's underlying member method code with example usage: obj.test_topk(device) and returns: sorted,  indices