code: def __init__(self):
      super().__init__()
      self.net1 = nn.Linear(5, 10)
      self.relu = nn.ReLU()
      self.net2 = nn.Linear(10, 15)
      self.net3 = nn.Linear(15, 30)
      self.net4 = nn.Linear(30, 5)
  
imports: from copy import deepcopy
  import torch
  import torch.distributed.checkpoint
  import torch.nn
  import torch.nn.functional
  from torch.distributed._tensor import init_device_mesh, Replicate
  from torch.distributed.checkpoint.default_planner import DefaultLoadPlanner, DefaultSavePlanner
  from torch.distributed.fsdp import FullyShardedDataParallel
  from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy, StateDictType
  from torch.testing._internal.common_distributed import skip_if_lt_x_gpu
  from torch.testing._internal.common_utils import instantiate_parametrized_tests, parametrize, run_tests
  from torch.testing._internal.distributed._tensor.common_dtensor import DTensorTestBase, with_comms
  from torch.testing._internal.distributed.checkpoint_utils import with_temp_dir
label_desc: Use image node_id 1 to create a new SimpleModelUneven object from inherited base classes: torch.nn with example: obj = SimpleModelUneven()