code: def run_evaluate_episode(env, agent):
      agent.reset_agent()
      episode_reward = 0.0
      episode_step = 0
      terminated = False
      state, obs = env.reset()
  
      while not terminated:
          available_actions = env.get_available_actions()
          actions = agent.predict(obs, available_actions)
          state, obs, reward, terminated = env.step(actions)
          episode_step += 1
          episode_reward += reward
  
      is_win = env.win_counted
      return episode_reward, episode_step, is_win
  
file_path: PARL/benchmark/torch/qmix/train.py
imports: from smac.env import StarCraft2Env
  from env_wrapper import SC2EnvWrapper
  from replay_buffer import EpisodeExperience, EpisodeReplayBuffer
  from qmixer_model import QMixerModel
  from rnn_model import RNNModel
  from parl.algorithms import QMIX
  from qmix_agent import QMixAgent
  from parl.utils import logger, summary
  import numpy
  from copy import deepcopy
  from qmix_config import QMixConfig
label_desc: Use image node_id 2 for calling a global function with example usage: run_evaluate_episode(env, agent) and returns: episode_reward,  episode_step,  is_win