code: def get_cli_args():
      """Create CLI parser and return parsed arguments"""
      parser = argparse.ArgumentParser()
  
      # example-specific args
      # This should be >1, otherwise, remote envs make no sense.
      parser.add_argument("--num-envs-per-worker", type=int, default=4)
  
      # general args
      parser.add_argument(
          "--framework",
          choices=["tf", "tf2", "torch"],
          default="torch",
          help="The DL framework specifier.",
      )
      parser.add_argument(
          "--as-test",
          action="store_true",
          help="Whether this script should be run as a test: --stop-reward must "
          "be achieved within --stop-timesteps AND --stop-iters.",
      )
      parser.add_argument(
          "--stop-iters",
          type=int,
          default=50,
          help="Number of iterations to train.",
      )
      parser.add_argument(
          "--stop-timesteps",
          type=int,
          default=100000,
          help="Number of timesteps to train.",
      )
      parser.add_argument(
          "--stop-reward",
          type=float,
          default=150.0,
          help="Reward at which we stop training.",
      )
      parser.add_argument(
          "--no-tune",
          action="store_true",
          help="Run without Tune using a manual train loop instead. Here,"
          "there is no TensorBoard support.",
      )
      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/remote_envs_with_inference_done_on_main_node.py
imports: import argparse
  import os
  from typing import Union
  import ray
  from ray import air, tune
  from ray.rllib.algorithms.ppo import PPO, PPOConfig
  from ray.rllib.algorithms.algorithm import Algorithm
  from ray.rllib.algorithms.algorithm_config import AlgorithmConfig
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.test_utils import check_learning_achieved
  from ray.rllib.utils.typing import PartialAlgorithmConfigDict
  from ray.tune import PlacementGroupFactory
  from ray.tune.logger import pretty_print
label_desc: Use image node_id 1 for calling a global function with example usage: get_cli_args() and returns: args