code: def main(args):
      """
      Run a trained model for the mountain car problem
  
      :param args: (ArgumentParser) the input arguments
      """
      env = gym.make("MountainCar-v0")
      model = DQN.load("mountaincar_model.zip", env)
  
      while True:
          obs, done = env.reset(), False
          episode_rew = 0
          while not done:
              if not args.no_render:
                  env.render()
              # Epsilon-greedy
              if np.random.random() < 0.02:
                  action = env.action_space.sample()
              else:
                  action, _ = model.predict(obs, deterministic=True)
              obs, rew, done, _ = env.step(action)
              episode_rew += rew
          print("Episode reward", episode_rew)
          # No render is only used for automatic testing
          if args.no_render:
              break
  
imports: import argparse
  import gym
  import numpy
  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