code: def batch_run_evaluation_episodes(
      env,
      agent,
      n_steps,
      n_episodes,
      max_episode_len=None,
      logger=None,
  ):
      """Run multiple evaluation episodes and return returns in a batch manner.
  
      Args:
          env (VectorEnv): Environment used for evaluation.
          agent (Agent): Agent to evaluate.
          n_steps (int): Number of total timesteps to evaluate the agent.
          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.
      """
      assert (n_steps is None) != (n_episodes is None)
  
      logger = logger or logging.getLogger(__name__)
      num_envs = env.num_envs
      episode_returns = dict()
      episode_lengths = dict()
      episode_indices = np.zeros(num_envs, dtype="i")
      episode_idx = 0
      for i in range(num_envs):
          episode_indices[i] = episode_idx
          episode_idx += 1
      episode_r = np.zeros(num_envs, dtype=np.float64)
      episode_len = np.zeros(num_envs, dtype="i")
  
      obss = env.reset()
      rs = np.zeros(num_envs, dtype="f")
  
      termination_conditions = False
      timestep = 0
      while True:
          # a_t
          actions = agent.batch_act(obss)
          timestep += 1
          # o_{t+1}, r_{t+1}
          obss, rs, dones, infos = env.step(actions)
          episode_r += rs
          episode_len += 1
          # Compute mask for done and reset
          if max_episode_len is None:
              resets = np.zeros(num_envs, dtype=bool)
          else:
              resets = episode_len == max_episode_len
          resets = np.logical_or(
              resets, [info.get("needs_reset", False) for info in infos]
          )
  
          # Make mask. 0 if done/reset, 1 if pass
          end = np.logical_or(resets, dones)
          not_end = np.logical_not(end)
  
          for index in range(len(end)):
              if end[index]:
                  episode_returns[episode_indices[index]] = episode_r[
                      index
                  ]
                  episode_lengths[episode_indices[index]] = episode_len[
                      index
                  ]
                  # Give the new episode an a new episode index
                  episode_indices[index] = episode_idx
                  episode_idx += 1
  
          episode_r[end] = 0
          episode_len[end] = 0
  
          # find first unfinished episode
          first_unfinished_episode = 0
          while first_unfinished_episode in episode_returns:
              first_unfinished_episode += 1
  
          # Check for termination conditions
          eval_episode_returns = []
          eval_episode_lens = []
          if n_steps is not None:
              total_time = 0
              for index in range(first_unfinished_episode):
                  total_time += episode_lengths[index]
                  # If you will run over allocated steps, quit
                  if total_time > n_steps:
                      break
                  else:
                      eval_episode_returns.append(
                          episode_returns[index]
                      )
                      eval_episode_lens.append(episode_lengths[index])
              termination_conditions = total_time >= n_steps
              if not termination_conditions:
                  unfinished_index = np.where(
                      episode_indices == first_unfinished_episode
                  )[0]
                  if (
                      total_time + episode_len[unfinished_index]
                      >= n_steps
                  ):
                      termination_conditions = True
                      if first_unfinished_episode == 0:
                          eval_episode_returns.append(
                              episode_r[unfinished_index]
                          )
                          eval_episode_lens.append(
                              episode_len[unfinished_index]
                          )
  
          else:
              termination_conditions = (
                  first_unfinished_episode >= n_episodes
              )
              if termination_conditions:
                  # Get the first n completed episodes
                  for index in range(n_episodes):
                      eval_episode_returns.append(
                          episode_returns[index]
                      )
                      eval_episode_lens.append(episode_lengths[index])
  
          if termination_conditions:
              # If this is the last step, make sure the agent observes reset=True
              resets.fill(True)
  
          # Agent observes the consequences.
          agent.batch_observe(obss, rs, dones, resets)
  
          if termination_conditions:
              break
          else:
              obss = env.reset(not_end)
  
      for i, (epi_len, epi_ret) in enumerate(
          zip(eval_episode_lens, eval_episode_returns)
      ):
          logger.info(
              "evaluation episode %s length: %s R: %s",
              i,
              epi_len,
              epi_ret,
          )
      return [float(r) for r in eval_episode_returns]
  
imports: import logging
  import multiprocessing
  import os
  import statistics
  import time
  import numpy
  import chainerrl
label_desc: Use image node_id 2 for calling a global function with example usage: batch_run_evaluation_episodes(env, agent, n_steps, n_episodes, max_episode_len, logger) and returns: unknown