code: def test_dtensor_async_output(self):
      # Tests that if the output of some dtensor operations  isn't used in any compute,
      # the output should be an AsyncCollectiveTensor (representing the fact that
      # we haven't synced the collective yet).
      from torch.distributed._functional_collectives_impl import (
          _tensor_needs_wait,
      )
  
      mesh = DeviceMesh(self.device_type, torch.arange(self.world_size))
  
      def fn(dt):
          dt_out_redistribute = dt.redistribute(mesh, [Replicate()])
          # Make sure we haven't synced yet
          # TODO: figure out why this is returning None
          # self.assertTrue(_tensor_needs_wait(dt_out_redistribute))
          dt_out_redistribute_view = dt_out_redistribute.view(
              dt_out_redistribute.shape
          )
          local_tensor = dt_out_redistribute_view.to_local()
          return local_tensor
  
      x = torch.ones((4, 2), device=self.device_type)
      dt = distribute_tensor(x, mesh, [Shard(0)])
      out = fn(dt)
      # Make sure we haven't synced yet
      self.assertEqual(type(out), AsyncCollectiveTensor)
      self.assertTrue(_tensor_needs_wait(out.elem))
      out_view = out.view(-1)
  
      # Assert that output is a `AsyncCollectiveTensor`
      self.assertEqual(type(out_view), AsyncCollectiveTensor)
      self.assertTrue(_tensor_needs_wait(out_view.elem))
  
      # Use the daa, requiring a sync
      ref = torch.ones((4, 2), device=self.device_type) + 1
      ref = ref.view(-1)
      out_data = out_view + 1
      self.assertEqual(type(out_data), torch.Tensor)
      self.assertEqual(out_data, ref)
  
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 14 for calling the DTensorTest obj's underlying member method code with example usage: obj.test_dtensor_async_output() and returns: local_tensor