code: def evaluate(env, agent, render=False):
      eval_reward = []
      for i in range(5):
          obs = env.reset()
          total_reward = 0
          steps = 0
          while True:
              batch_obs = np.expand_dims(obs, axis=0)
              action = agent.predict(batch_obs.astype("float32"))
              action = np.clip(action, -1.0, 1.0)
  
              steps += 1
              next_obs, reward, done, info = env.step(action)
  
              obs = next_obs
              total_reward += reward
  
              if render:
                  env.render()
              if done or steps >= 200:
                  break
          eval_reward.append(total_reward)
      return np.mean(eval_reward)
  
file_path: PARL/examples/tutorials/lesson5/ddpg/train.py
imports: import gym
  import parl
  import paddle
  import gym
  import numpy
  import parl
  from parl.utils import logger
  from agent import Agent
  from model import Model
  from algorithm import DDPG
  from env import ContinuousCartPoleEnv
  from replay_memory import ReplayMemory
label_desc: Use image node_id 2 for calling a global function with example usage: evaluate(env, agent, render) and returns: np