code: def main(args):
      """
      Train and save the DQN model, for the mountain car problem
  
      :param args: (ArgumentParser) the input arguments
      """
      env = gym.make("MountainCar-v0")
  
      # using layer norm policy here is important for parameter space noise!
      model = DQN(
          policy="LnMlpPolicy",
          env=env,
          learning_rate=1e-3,
          buffer_size=50000,
          exploration_fraction=0.1,
          exploration_final_eps=0.1,
          param_noise=True,
          policy_kwargs=dict(layers=[64]),
      )
      model.learn(total_timesteps=args.max_timesteps)
  
      print("Saving model to mountaincar_model.zip")
      model.save("mountaincar_model")
  
imports: import argparse
  import gym
  from stable_baselines.deepq import DQN
label_desc: Use image node_id 1 for calling a global function with example usage: main(args) without return types