code: def __init__(self):
      super().__init__()
      self.net1 = nn.Sequential(nn.Linear(8, 16), nn.ReLU())
      self.net2 = nn.Sequential(nn.Linear(16, 32), nn.ReLU())
      self.net3 = nn.Linear(32, 64)
      self.net4 = nn.Sequential(nn.ReLU(), nn.Linear(64, 8))
  
imports: import shutil
  import tempfile
  from functools import wraps
  from typing import Any, Callable, Dict, Optional, Tuple
  import torch
  import torch.distributed
  import torch.distributed.checkpoint
  import torch.nn
  from torch.distributed.checkpoint._fsspec_filesystem import FsspecReader, FsspecWriter
  from torch.distributed.checkpoint.optimizer import load_sharded_optimizer_state_dict
  from torch.distributed.fsdp import FullyShardedDataParallel
  from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType
  from torch.testing._internal.common_distributed import requires_nccl, skip_if_lt_x_gpu
  from torch.testing._internal.common_utils import run_tests, TestCase
  from torch.testing._internal.distributed._shard.sharded_tensor import ShardedTensorTestBase, with_comms
label_desc: Use image node_id 1 to create a new MyTestModule object from inherited base classes: torch.nn with example: obj = MyTestModule()