code: def run_evaluate_episode(env, agent):
      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
      return total_reward
  
file_path: PARL/benchmark/fluid/SAC/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 ActorModel, CriticModel
  from parl.utils import logger, summary, ReplayMemory
label_desc: Use image node_id 2 for calling a global function with example usage: run_evaluate_episode(env, agent) and returns: total_reward