code: def run_evaluate_episodes(agent, env):
      obs = env.reset()
      while not env.get_real_done():
          action = agent.predict(obs)
          obs, _, done, _ = env.step(action)
          if EVAL_RENDER:
              env.render()
          if done:
              obs = env.reset()
      return np.mean(env.get_eval_rewards())
  
file_path: PARL/examples/DQN_variant/train.py
imports: import os
  import gym
  import numpy
  import paddle
  import parl
  from parl.utils import logger, summary
  from atari_model import AtariModel
  from atari_agent import AtariAgent
  from replay_memory import ReplayMemory, Experience
  from parl.env.atari_wrappers import wrap_deepmind
  from parl.algorithms import DQN, DDQN
  from tqdm import tqdm
  import argparse
label_desc: Use image node_id 2 for calling a global function with example usage: run_evaluate_episodes(agent, env) and returns: np