code: def save_with_pipeline(self, pipeline_dir: str) -> None:
      with torch.device("meta"):
          model = PipelineModel()
  
      pipeline_modules = [
          model.layer1,
          model.layer2,
          model.layer3,
          model.layer4,
      ]
  
      # Materialize the model
      submodule = pipeline_modules[self.rank]
      submodule.to_empty(device=torch.device("cuda"))
      # submodule.reset_parameters()
      optim = torch.optim.Adam(submodule.parameters(), lr=1e-3)
  
      # Ignore the training as we don't have a real pipeline parallelism.
  
      # Save state_dict
      model_state_dict, optim_state_dict = get_state_dict(
          model, optimizers=optim
      )
      saved_state_dict = {
          "model": model_state_dict,
          "optim": optim_state_dict,
      }
      dcp.save_state_dict(
          state_dict=saved_state_dict,
          storage_writer=dcp.FileSystemWriter(pipeline_dir),
      )
  
imports: import os
  import sys
  import torch
  import torch.distributed
  import torch.distributed.checkpoint
  import torch.nn
  from torch.distributed.checkpoint.state_dict import get_state_dict, set_state_dict
  from torch.distributed.fsdp import FullyShardedDataParallel
  from torch.testing._internal.common_distributed import skip_if_lt_x_gpu
  from torch.testing._internal.common_fsdp import FSDPTest
  from torch.testing._internal.common_utils import run_tests, TEST_WITH_DEV_DBG_ASAN
  from torch.testing._internal.distributed.checkpoint_utils import with_temp_dir
label_desc: Use image node_id 2 for calling the TestPipeline obj's underlying member method code with example usage: obj.save_with_pipeline(pipeline_dir) without return types