code: def save_checkpoint(self, tmp_checkpoint_dir: str):
      checkpoint_data = {"data": 1}
  
      if self.return_type == "object":
          return checkpoint_data
  
      subdir = os.path.join(tmp_checkpoint_dir, "subdir")
      os.makedirs(subdir, exist_ok=True)
      checkpoint_file = os.path.join(subdir, "checkpoint.pkl")
      with open(checkpoint_file, "w") as f:
          f.write(json.dumps(checkpoint_data))
  
      if self.return_type == "root":
          return tmp_checkpoint_dir
      elif self.return_type == "subdir":
          return subdir
      elif self.return_type == "checkpoint":
          return checkpoint_file
  
imports: import json
  import os
  from typing import Dict, Union
  import pytest
  import ray
  from ray import train, tune
  from ray.train._internal.storage import StorageContext
  from ray.tune.trainable import wrap_function
  from ray.train.tests.util import create_dict_checkpoint
label_desc: Use image node_id 3 for calling the SavingTrainable obj's underlying member method code with example usage: obj.save_checkpoint(tmp_checkpoint_dir) and returns: checkpoint_data, tmp_checkpoint_dir, subdir, checkpoint_file