code: def test_root_module_is_not_FSDP(self):
      class FakeMPModel(torch.nn.Module):
          def __init__(self, device_mesh):
              super().__init__()
              torch.manual_seed(0)
              self.dense = FSDP(
                  DenseModel().cuda(),
                  use_orig_params=True,
                  sharding_strategy=ShardingStrategy.HYBRID_SHARD,
                  device_mesh=device_mesh,
              )
              if dist.get_rank() == 0:
                  self.sparse0 = nn.Sequential(
                      nn.Linear(8, 8), nn.ReLU()
                  )
              else:
                  self.sparse1 = nn.Sequential(
                      nn.Linear(8, 8), nn.ReLU()
                  )
  
          def forward(self, x):
              if dist.get_rank() == 0:
                  sparse = self.sparse0(x)
              else:
                  sparse = self.sparse1(x)
              dist.all_reduce(sparse)
              return self.dense(sparse)
  
      mesh_2d = init_device_mesh(
          self.device_type, (2, self.world_size // 2)
      )
      model = FakeMPModel(device_mesh=mesh_2d).cuda()
      optim = torch.optim.Adam(model.parameters(), lr=1e-2)
  
      batch = torch.rand(5, 8, device=torch.device("cuda"))
      model(batch).sum().backward()
      optim.step()
      osd = optim.state_dict()
  
      with FSDP.state_dict_type(
          model, StateDictType.SHARDED_STATE_DICT
      ):
          osd = FSDP.optim_state_dict(model, optim, osd)
  
      for param, state in osd["state"].items():
          if "dense" in param:
              self.assertIsInstance(state["exp_avg"], DTensor)
              self.assertIsInstance(state["exp_avg_sq"], DTensor)
              self.assertEqual(
                  state["exp_avg"].placements, (Replicate(), Shard(0))
              )
              self.assertEqual(
                  state["exp_avg_sq"].placements,
                  (Replicate(), Shard(0)),
              )
          else:
              self.assertIsInstance(state["exp_avg"], torch.Tensor)
              self.assertIsInstance(state["exp_avg_sq"], torch.Tensor)
  
imports: import io
  from copy import deepcopy
  import torch
  import torch.distributed
  import torch.nn
  from torch.distributed._shard.sharded_tensor import ShardedTensor
  from torch.distributed._tensor import DTensor, Replicate, Shard
  from torch.distributed.device_mesh import _mesh_resources, init_device_mesh
  from torch.distributed.fsdp import FullyShardedDataParallel
  from torch.distributed.fsdp.api import ShardedOptimStateDictConfig, ShardedStateDictConfig, ShardingStrategy, StateDictType
  from torch.testing._internal.common_utils import instantiate_parametrized_tests, parametrize, run_tests
  from torch.testing._internal.distributed._tensor.common_dtensor import DTensorTestBase, skip_if_lt_x_gpu, with_comms
label_desc: Use image node_id 7 for calling the TestHSDPWithDeviceMeshAndDTensor obj's underlying member method code with example usage: obj.test_root_module_is_not_FSDP() and returns: self