code: def run_evaluate_episode(env, agent):
      eval_rewards = []
      for i in range(5):
          obs = env.reset()
          total_reward = 0
          while True:
              batch_obs = np.expand_dims(obs, axis=0)
              action = agent.predict(batch_obs.astype("float32"))
              action = np.squeeze(action)
  
              next_obs, reward, done, info = env.step(action)
  
              obs = next_obs
              total_reward += reward
  
              if done:
                  break
          eval_rewards.append(total_reward)
      return np.mean(eval_rewards)
  
file_path: PARL/benchmark/fluid/TD3/train.py
imports: from parl.utils import check_version_for_fluid
  import argparse
  import gym
  import numpy
  import time
  import parl
  from mujoco_agent import MujocoAgent
  from mujoco_model import MujocoModel
  from parl.utils import logger, summary, ReplayMemory
  from parl.env.continuous_wrappers import ActionMappingWrapper
label_desc: Use image node_id 2 for calling a global function with example usage: run_evaluate_episode(env, agent) and returns: np