code: def test_sort_large_slice(self, device):
      # tests direct cub path
      x = torch.randn(4, 1024000, device=device)
      res1val, res1ind = torch.sort(x, stable=True)
      torch.cuda.synchronize()
      # assertIsOrdered is too slow, so just compare to cpu
      res1val_cpu, res1ind_cpu = torch.sort(x.cpu(), stable=True)
      self.assertEqual(res1val, res1val_cpu.cuda())
      self.assertEqual(res1ind, res1ind_cpu.cuda())
      res1val, res1ind = torch.sort(x, descending=True, stable=True)
      torch.cuda.synchronize()
      res1val_cpu, res1ind_cpu = torch.sort(
          x.cpu(), descending=True, stable=True
      )
      self.assertEqual(res1val, res1val_cpu.cuda())
      self.assertEqual(res1ind, res1ind_cpu.cuda())
  
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 4 for calling the TestSortAndSelect obj's underlying member method code with example usage: obj.test_sort_large_slice(device) without return types