code: def train_rllib_policy(config: AlgorithmConfig):
      """Trains a DQN on ALE/MsPacman-v5 for n iterations.
  
      Saves the trained Algorithm to disk and returns the checkpoint path.
  
      Args:
          config: The algo config object for the Algorithm.
  
      Returns:
          str: The saved checkpoint to restore DQN from.
      """
      # Create algorithm from config.
      algo = config.build()
  
      # Train for n iterations, then save, stop, and return the checkpoint path.
      for _ in range(args.train_iters):
          print(algo.train())
      checkpoint_result = algo.save()
      algo.stop()
      return checkpoint_result
  
file_path: ray/python/ray/rllib/examples/inference_and_serving/serve_and_rllib.py
imports: import argparse
  import gymnasium
  import requests
  from starlette.requests import Request
  import ray
  import ray.rllib.algorithms.algorithm
  import ray.rllib.algorithms.algorithm_config
  from ray.rllib.algorithms.dqn import DQNConfig
  from ray.rllib.env.wrappers.atari_wrappers import FrameStack, WarpFrame
  from ray import serve
label_desc: Use image node_id 1 for calling a global function with example usage: train_rllib_policy(config) and returns: checkpoint_result