code: def __init__(self, device):
      super().__init__()
      torch.manual_seed(5)
      self.net1 = torch.nn.Linear(5, 10, device=device)
      self.relu = torch.nn.ReLU()
      self.net2 = torch.nn.Linear(10, 15, device=device)
      self.net3 = torch.nn.Linear(15, 1, device=device)
  
file_path: pytorch/test/distributed/checkpoint/test_tp_checkpoint.py
imports: from copy import deepcopy
  import torch
  import torch.distributed.checkpoint
  from torch.distributed._tensor import init_device_mesh
  from torch.distributed.checkpoint.default_planner import DefaultLoadPlanner, DefaultSavePlanner
  from torch.distributed.tensor.parallel import ColwiseParallel, parallelize_module, RowwiseParallel
  from torch.testing._internal.common_utils import run_tests
  from torch.testing._internal.distributed._tensor.common_dtensor import DTensorTestBase, MLPModule, skip_if_lt_x_gpu, with_comms
  from torch.testing._internal.distributed.checkpoint_utils import with_temp_dir
label_desc: Use image node_id 1 to create a new UnevenShardedModel object from inherited base classes: torch.nn with example: obj = UnevenShardedModel(device)