code: def test_default_value_sub_mesh(self):
      mesh = DeviceMesh(self.device_type, [0, 2])
  
      # test scalar return value
      local_tensor1 = torch.ones(4, 3)
      local_tensor2 = torch.ones(4, 3)
      dtensor1 = DTensor.from_local(local_tensor1, mesh, [Shard(0)])
      dtensor2 = DTensor.from_local(local_tensor2, mesh, [Shard(0)])
      local_res = dtensor1.equal(dtensor2)  # equal returns local result
      self.sub_mesh_assert_equal(
          mesh.mesh,
          True,
          True,
          local_res,
      )
  
      # test 0-d tensor return value
      local_tensor = torch.ones(4, 3)
      dtensor = DTensor.from_local(local_tensor, mesh, [Shard(0)]).sum()
      self.sub_mesh_assert_equal(
          mesh.mesh,
          torch.tensor(12.0),
          torch.tensor(0.0),
          dtensor.to_local(),
      )
  
      # test List[torch.Tensor] return value
      local_tensor = torch.ones(3, 4)
      dtensor = DTensor.from_local(local_tensor, mesh, [Shard(0)])
      dtensor_list = dtensor.split([2, 2], dim=1)
      self.sub_mesh_assert_equal(
          mesh.mesh,
          [torch.ones(3, 2)] * 2,
          [torch.tensor([])] * 2,
          [dt.to_local() for dt in dtensor_list],
      )
  
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 DTensorMeshTest obj's underlying member method code with example usage: obj.test_default_value_sub_mesh() without return types