code: def run_evaluate_episodes(agent, eval_env, eval_episodes):
      eval_episode_rewards = []
      while len(eval_episode_rewards) < eval_episodes:
          obs = eval_env.reset()
          done = False
          while not done:
              action = agent.predict(obs)
              obs, reward, done, info = eval_env.step(action)
          if "episode" in info.keys():
              eval_reward = info["episode"]["r"]
              eval_episode_rewards.append(eval_reward)
      return np.mean(eval_episode_rewards)
  
file_path: PARL/examples/PPO/train.py
imports: import argparse
  import numpy
  from parl.utils import logger, summary
  from mujoco_config import mujoco_config
  from atari_config import atari_config
  from env_utils import ParallelEnv, LocalEnv
  from storage import RolloutStorage
  from atari_model import AtariModel
  from mujoco_model import MujocoModel
  from parl.algorithms import PPO
  from agent import PPOAgent
label_desc: Use image node_id 1 for calling a global function with example usage: run_evaluate_episodes(agent, eval_env, eval_episodes) and returns: np