code: def __init__(self):
      super().__init__()
      torch.manual_seed(0)
      self.net1 = nn.Sequential(nn.Linear(8, 16), nn.ReLU())
      self.net2 = nn.Sequential(nn.Linear(16, 32), nn.ReLU())
      self.net3 = nn.Sequential(nn.Linear(32, 64), nn.ReLU())
      self.net4 = nn.Sequential(nn.ReLU(), nn.Linear(64, 8))
  
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 1 to create a new DenseModel object from inherited base classes: torch.nn with example: obj = DenseModel()