code: def main():
      logger.info("-----------------Carla_SAC-------------------")
      logger.set_dir("./{}_eval".format(EnvConfig["env_name"]))
  
      # env for eval
      eval_env_params = EnvConfig["test_env_params"]
      eval_env = LocalEnv(EnvConfig["env_name"], eval_env_params)
  
      obs_dim = eval_env.obs_dim
      action_dim = eval_env.action_dim
  
      # Initialize model, algorithm, agent
      model = CarlaModel(obs_dim, action_dim)
      algorithm = SAC(
          model,
          gamma=GAMMA,
          tau=TAU,
          alpha=ALPHA,
          actor_lr=ACTOR_LR,
          critic_lr=CRITIC_LR,
      )
      agent = CarlaAgent(algorithm)
      # Restore trained agent
      agent.restore("./{}".format(args.restore_model))
  
      # Evaluate episode
      for episode in range(args.eval_episodes):
          episode_reward = run_episode(agent, eval_env)
          summary.add_scalar(
              "eval/episode_reward", episode_reward, episode
          )
          logger.info(
              "Evaluation episode reward: {}".format(episode_reward)
          )
  
file_path: PARL/examples/CARLA_SAC/evaluate.py
imports: import argparse
  from env_utils import LocalEnv
  from parl.utils import logger, summary
  from carla_model import CarlaModel
  from carla_agent import CarlaAgent
  from parl.algorithms import SAC
  from env_config import EnvConfig
label_desc: Use image node_id 2 for calling a global function with example usage: main() without return types