code: def run_episode(agent, env):
      episode_reward = 0.0
      obs = env.reset()
      done = False
      steps = 0
      while not done and steps < env._max_episode_steps:
          steps += 1
          action = agent.predict(obs)
          obs, reward, done, _ = env.step(action)
          episode_reward += reward
      return 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 1 for calling a global function with example usage: run_episode(agent, env) and returns: episode_reward