code: def get_cli_args():
      """Create CLI parser and return parsed arguments"""
      parser = argparse.ArgumentParser()
  
      # general args
      parser.add_argument(
          "--run",
          type=str,
          default="PPO",
          help="The RLlib-registered algorithm to use.",
      )
      parser.add_argument("--num-cpus", type=int, default=0)
      parser.add_argument(
          "--framework",
          choices=["tf", "tf2", "torch"],
          default="torch",
          help="The DL framework specifier.",
      )
      parser.add_argument(
          "--stop-iters",
          type=int,
          default=10,
          help="Number of iterations to train.",
      )
      parser.add_argument(
          "--stop-timesteps",
          type=int,
          default=10000,
          help="Number of timesteps to train.",
      )
      parser.add_argument(
          "--stop-reward",
          type=float,
          default=80.0,
          help="Reward at which we stop training.",
      )
      parser.add_argument(
          "--local-mode",
          action="store_true",
          help="Init Ray in local mode for easier debugging.",
      )
  
      args = parser.parse_args()
      print(f"Running with following CLI args: {args}")
      return args
  
file_path: ray/python/ray/rllib/examples/multi_agent_different_spaces_for_agents.py
imports: import argparse
  import gymnasium
  import os
  import ray
  from ray import air, tune
  from ray.rllib.env.multi_agent_env import MultiAgentEnv
  from ray.tune.registry import get_trainable_cls
label_desc: Use image node_id 1 for calling a global function with example usage: get_cli_args() and returns: args