code: def test_to_local_grad_hint(self):
      device_mesh = DeviceMesh(
          self.device_type, list(range(self.world_size))
      )
      placements = (Shard(0),)
      global_tensor = torch.ones(8, 3, requires_grad=True)
  
      sharded_dtensor = distribute_tensor(
          global_tensor, device_mesh, placements
      )
      local_out = sharded_dtensor.redistribute(
          placements=[Replicate()]
      ).to_local(grad_placements=[_Partial()])
      local_out.sum().backward()
  
      replica_grad = sharded_dtensor.grad.full_tensor()
      self.assertEqual(replica_grad, global_tensor * 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 10 for calling the DTensorTest obj's underlying member method code with example usage: obj.test_to_local_grad_hint() without return types