code: def test_to_local(self):
      device_mesh = DeviceMesh(
          self.device_type, list(range(self.world_size))
      )
      placements = (Shard(0),)
      dist_tensor_shape = torch.Size([self.world_size * 3, 3])
      local_tensor_with_grad = torch.randn(
          3, 3, device=self.device_type, requires_grad=True
      )
  
      sharded_tensor = DTensor(
          local_tensor_with_grad,
          device_mesh,
          placements,
          shape=dist_tensor_shape,
          dtype=local_tensor_with_grad.dtype,
          requires_grad=True,
          stride=local_tensor_with_grad.stride(),
      )
      self.assertEqual(sharded_tensor.size(), dist_tensor_shape)
      self.assertEqual(
          sharded_tensor.to_local(), local_tensor_with_grad
      )
  
      # test dist tensor works with torch.Tensor during backwards
      # dist tensor created is a leaf node, do some operation on dist tensor
      temp_st = sharded_tensor * 3
  
      # do some operation on local tensor of the dist tensor
      new_tensor_with_grad = torch.randn(
          3, 3, device=self.device_type, requires_grad=True
      )
      res = temp_st.to_local() + new_tensor_with_grad
      # call backward directly on torch.Tensor, and see if it works by
      # propagating through dist tensor
      res.sum().backward()
      self.assertIsNotNone(sharded_tensor.grad)
  
      self.assertEqual(
          sharded_tensor.grad.to_local(), torch.ones(3, 3) * 3
      )
  
      # test the case when grad stride is different from fwd input.
      res = sharded_tensor.to_local()
      model = torch.nn.ReLU()
      res.register_hook(lambda grad: grad.t())
      target = torch.randn(3, 3, device=self.device_type)
      mae_loss = torch.nn.L1Loss()
      output = mae_loss(model(res), target)
      # The manual change to grad stride leads to the failure of the copy op afterwards.
      # so that we need a try-catch here.
      try:
          output.backward()
      except RuntimeError:
          self.assertEqual(
              sharded_tensor.grad.stride(), [1, 3 * self.world_size]
          )
  
imports: import torch
  import torch.nn.functional
  from numpy.testing import assert_array_equal
  from torch.distributed._functional_collectives import AsyncCollectiveTensor
  from torch.distributed._tensor import DeviceMesh, distribute_tensor, DTensor, init_device_mesh
  from torch.distributed._tensor.placement_types import _Partial, Replicate, Shard
  from torch.distributed.tensor.parallel import ColwiseParallel, parallelize_module, RowwiseParallel
  from torch.testing._internal.common_utils import run_tests
  from torch.testing._internal.distributed._tensor.common_dtensor import DTensorTestBase, with_comms
label_desc: Use image node_id 9 for calling the DTensorTest obj's underlying member method code with example usage: obj.test_to_local() without return types