code: def test_kthvalue(self, device, dtype):
      SIZE = 50
      x = torch.rand(SIZE, SIZE, SIZE, dtype=dtype, device=device)
      x0 = x.clone()
  
      k = random.randint(1, SIZE)
      res1val, res1ind = torch.kthvalue(x, k, keepdim=False)
      res2val, res2ind = torch.sort(x)
  
      self.assertEqual(
          res1val[:, :], res2val[:, :, k - 1], atol=0, rtol=0
      )
      self.assertEqual(
          res1ind[:, :], res2ind[:, :, k - 1], atol=0, rtol=0
      )
      # test use of result tensors
      k = random.randint(1, SIZE)
      res1val = torch.tensor([], dtype=dtype, device=device)
      res1ind = torch.tensor([], dtype=torch.long, device=device)
      torch.kthvalue(x, k, keepdim=False, out=(res1val, res1ind))
      res2val, res2ind = torch.sort(x)
      self.assertEqual(
          res1val[:, :], res2val[:, :, k - 1], atol=0, rtol=0
      )
      self.assertEqual(
          res1ind[:, :], res2ind[:, :, k - 1], atol=0, rtol=0
      )
  
      # test non-default dim
      k = random.randint(1, SIZE)
      res1val, res1ind = torch.kthvalue(x, k, 0, keepdim=False)
      res2val, res2ind = torch.sort(x, 0)
      self.assertEqual(res1val, res2val[k - 1], atol=0, rtol=0)
      self.assertEqual(res1ind, res2ind[k - 1], atol=0, rtol=0)
  
      # non-contiguous
      y = x.narrow(1, 0, 1)
      y0 = y.contiguous()
      k = random.randint(1, SIZE)
      res1val, res1ind = torch.kthvalue(y, k)
      res2val, res2ind = torch.kthvalue(y0, k)
      self.assertEqual(res1val, res2val, atol=0, rtol=0)
      self.assertEqual(res1ind, res2ind, atol=0, rtol=0)
  
      # non-contiguous [Reference: https://github.com/pytorch/pytorch/issues/45721]
      non_contig_t = torch.tensor(
          [0, -1, 1, -2, 2], dtype=dtype, device=device
      )[::2]
      expected_val, expected_ind = non_contig_t.contiguous().kthvalue(2)
      non_contig_cpu_t = non_contig_t.cpu()
      expected_val_cpu, expected_ind_cpu = non_contig_cpu_t.kthvalue(2)
  
      out_val, out_ind = non_contig_t.kthvalue(2)
      self.assertEqual(expected_val, out_val, atol=0, rtol=0)
      self.assertEqual(expected_ind, out_ind, atol=0, rtol=0)
      self.assertEqual(expected_val_cpu, out_val, atol=0, rtol=0)
      self.assertEqual(expected_ind_cpu, out_ind, atol=0, rtol=0)
  
      # check that the input wasn't modified
      self.assertEqual(x, x0, atol=0, rtol=0)
  
      # simple test case (with repetitions)
      y = torch.tensor((3.0, 5, 4, 1, 1, 5), dtype=dtype, device=device)
      self.assertEqual(torch.kthvalue(y, 3)[0], 3, atol=0, rtol=0)
      self.assertEqual(torch.kthvalue(y, 2)[0], 1, atol=0, rtol=0)
  
      # simple test case (with NaN)
      SIZE = 50
      x = torch.rand(SIZE, SIZE, SIZE, dtype=dtype, device=device)
      x[torch.arange(SIZE), :, torch.randint(50, (50,))] = nan
      ks = [random.randint(1, SIZE), 1, SIZE, SIZE - 1]
      res2val, res2ind = torch.sort(x)
      for k in ks:
          res1val, res1ind = torch.kthvalue(x, k, keepdim=False)
          self.assertEqual(
              res1val[:, :], res2val[:, :, k - 1], atol=0, rtol=0
          )
          self.assertEqual(
              res1ind[:, :], res2ind[:, :, k - 1], atol=0, rtol=0
          )
  
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 32 for calling the TestSortAndSelect obj's underlying member method code with example usage: obj.test_kthvalue(device, dtype) without return types