code: def test_contiguous(self):
      device_mesh = DeviceMesh(
          self.device_type, list(range(self.world_size))
      )
      tensor = torch.rand(3, 5, 6, requires_grad=True)
      sharding = [Shard(0)]
      dist_tensor = DTensor.from_local(tensor, device_mesh, sharding)
      self.assertTrue(dist_tensor.is_contiguous())
      # shard on dim 0 should not change stride (30, 6, 1)
      self.assertEqual(dist_tensor.stride(), tensor.stride())
  
      new_dt = dist_tensor.transpose(0, 2)
      self.assertFalse(new_dt.is_contiguous())
      self.assertFalse(new_dt.to_local().is_contiguous())
      # check stride
      self.assertEqual(new_dt.stride(), (1, 6, 30))
  
      new_dt = new_dt.contiguous()
      self.assertTrue(new_dt.is_contiguous())
      self.assertTrue(new_dt.to_local().is_contiguous())
      # check stride
      self.assertEqual(dist_tensor.stride(), tensor.stride())
  
      # check backward
      new_dt.to_local().sum().backward()
      self.assertEqual(tensor.grad, torch.ones(3, 5, 6))
  
imports: import torch
  from torch.distributed._tensor import DeviceMesh, distribute_tensor, DTensor
  from torch.distributed._tensor.placement_types import _Partial, Replicate, Shard
  from torch.testing._internal.common_utils import run_tests
  from torch.testing._internal.distributed._tensor.common_dtensor import DTensorConverter, DTensorTestBase, with_comms
label_desc: Use image node_id 4 for calling the DistTensorOpsTest obj's underlying member method code with example usage: obj.test_contiguous() without return types