code: def _create_model(self, device_mesh=None):
      if device_mesh:
          model = FSDP(
              DenseModel().cuda(),
              device_mesh=device_mesh,
              sharding_strategy=ShardingStrategy.HYBRID_SHARD,
          )
      else:
          mesh_2d = init_device_mesh(
              self.device_type, (2, self.world_size // 2)
          )
          intra_node_pg = mesh_2d.get_group(mesh_dim=1)
          inter_node_pg = mesh_2d.get_group(mesh_dim=0)
          model = FSDP(
              DenseModel().cuda(),
              process_group=(intra_node_pg, inter_node_pg),
              sharding_strategy=ShardingStrategy.HYBRID_SHARD,
          )
  
      optim = torch.optim.Adam(model.parameters(), lr=0.1)
      model(model.get_input()).sum().backward()
      optim.step()
  
      return model, optim
  
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 2 for calling the TestHSDPWithDeviceMeshAndDTensor obj's underlying member method code with example usage: obj._create_model(device_mesh) and returns: model,  optim