code: def run_evaluate_episode(env, agent):
      obs = env.reset()
      total_reward = 0
      while True:
          action = agent.predict(obs)
          obs, reward, isOver, info = env.step(action)
          total_reward += reward
          if isOver:
              break
      return total_reward
  
imports: from parl.utils import check_version_for_fluid
  import argparse
  import os
  import pickle
  from collections import deque
  from datetime import datetime
  import gym
  import numpy
  import paddle.fluid
  from tqdm import tqdm
  import parl
  from atari_agent import AtariAgent
  from atari_model import AtariModel
  from parl.utils import logger, summary
  from per_alg import PrioritizedDoubleDQN, PrioritizedDQN
  from proportional_per import ProportionalPER
  from utils import get_player
label_desc: Use image node_id 4 for calling a global function with example usage: run_evaluate_episode(env, agent) and returns: total_reward