code: def forward(self, x):
      return self.net4(self.net3(self.net2(self.net1(x))))
  
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 DenseModel obj's underlying member method code with example usage: obj.forward(x) and returns: self