code: def run_evaluation_episodes(
      env,
      agent,
      n_steps,
      n_episodes,
      max_episode_len=None,
      logger=None,
  ):
      """Run multiple evaluation episodes and return returns.
  
      Args:
          env (Environment): Environment used for evaluation
          agent (Agent): Agent to evaluate.
          n_steps (int): Number of timesteps to evaluate for.
          n_episodes (int): Number of evaluation runs.
          max_episode_len (int or None): If specified, episodes longer than this
              value will be truncated.
          logger (Logger or None): If specified, the given Logger object will be
              used for logging results. If not specified, the default logger of
              this module will be used.
      Returns:
          List of returns of evaluation runs.
      """
      with agent.eval_mode():
          return _run_episodes(
              env=env,
              agent=agent,
              n_steps=n_steps,
              n_episodes=n_episodes,
              max_episode_len=max_episode_len,
              logger=logger,
          )
  
imports: import logging
  import multiprocessing
  import os
  import statistics
  import time
  import numpy
  import pfrl
label_desc: Use image node_id 2 for calling a global function with example usage: run_evaluation_episodes(env, agent, n_steps, n_episodes, max_episode_len, logger) and returns: _run_episodes