code: def test_dtensor_new_empty_strided(self):
      device_mesh = DeviceMesh(
          self.device_type, torch.arange(self.world_size)
      )
      local_tensor = torch.randn(
          8, 8, requires_grad=True, device=self.device_type
      )
      my_dtensor = distribute_tensor(
          local_tensor, device_mesh, [Shard(0)]
      )
      new_strided_dtensor = my_dtensor.new_empty_strided(
          (8, 8), (8, 1), requires_grad=True
      )
      # test the op produces new dtensor and autograd works
      self.assertEqual(new_strided_dtensor.shape, my_dtensor.shape)
      new_strided_dtensor.sum().backward()
      self.assertIsNotNone(new_strided_dtensor.grad)
      self.assertIsInstance(new_strided_dtensor.grad, DTensor)
  
      # test backward new_empty_strided with sharding works correctly
      my_dtensor.to_local().sum().backward()
      local_tensor.sum().backward()
      self.assertEqual(my_dtensor.grad, new_strided_dtensor.grad)
      self.assertEqual(
          my_dtensor.grad.redistribute(
              placements=[Replicate()]
          ).to_local(),
          local_tensor.grad,
      )
  
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 13 for calling the DTensorTest obj's underlying member method code with example usage: obj.test_dtensor_new_empty_strided() without return types