code: def run_evaluate_episode(env, agent):
      obs = env.reset()
      done = False
      total_reward = 0
      while not done:
          action = agent.predict(np.array(obs))
          obs, reward, done, _ = env.step(action)
          total_reward += reward
      return total_reward
  
file_path: PARL/benchmark/torch/td3/train.py
imports: import gym
  import argparse
  import numpy
  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 TD3
label_desc: Use image node_id 2 for calling a global function with example usage: run_evaluate_episode(env, agent) and returns: total_reward