code: def test_dtensor_2d_mesh(self):
      mesh_tensor = torch.arange(self.world_size).reshape(2, 4)
      # construct a cuda device mesh
      mesh = DeviceMesh(self.device_type, mesh_tensor)
  
      # construct a dist tensor on 2d device mesh and test if works
      placements = [Shard(0), Shard(1)]
      local_tensor = torch.randn(3, 3)
      dist_tensor = DTensor.from_local(local_tensor, mesh, placements)
      self.assertEqual(
          dist_tensor.size(),
          torch.Size([3 * mesh.size(0), 3 * mesh.size(1)]),
      )
      self.assertEqual(dist_tensor.device.type, self.device_type)
      self.assertEqual(
          dist_tensor.to_local().device.type, self.device_type
      )
  
      # if shard on the same tensor dimension
      # we should correctly construct the global tensor size
      shard_same_dim_spec = [Shard(0), Shard(0)]
      local_tensor = torch.randn(3, 3)
      dist_tensor = DTensor.from_local(
          local_tensor, mesh, shard_same_dim_spec
      )
      self.assertEqual(
          dist_tensor.size(), torch.Size([3 * self.world_size, 3])
      )
  
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 5 for calling the DTensorMeshTest obj's underlying member method code with example usage: obj.test_dtensor_2d_mesh() without return types