code: def run_evaluate_episodes(agent, env, eval_episodes):
      avg_reward = 0.0
      for _ in range(eval_episodes):
          obs = env.reset()
          done = False
          while not done:
              action = agent.predict(obs)
              obs, reward, done, _ = env.step(action)
              avg_reward += reward
      avg_reward /= eval_episodes
      return avg_reward
  
file_path: PARL/benchmark/torch/sac/train.py
imports: import numpy
  import gym
  import argparse
  from parl.utils import logger, summary, ReplayMemory
  from parl.env import ActionMappingWrapper, CompatWrapper
  from mujoco_model import MujocoModel
  from mujoco_agent import MujocoAgent
  from parl.algorithms import SAC
label_desc: Use image node_id 2 for calling a global function with example usage: run_evaluate_episodes(agent, env, eval_episodes) and returns: avg_reward