import tensorflow as tf

    Args:
    * is_train: whether to restore a model for training
    """

    save_path = tf.train.latest_checkpoint(os.path.dirname(FLAGS.ws_save_path))
    if is_train:
      self.saver_train.restore(self.sess_train, save_path)
    else:
      self.saver_eval.restore(self.sess_eval, save_path)
    tf.logging.info('model restored from ' + save_path)

  def __monitor_progress(self, summary, log_rslt, idx_iter, time_step):
    """Monitor the training progress.
    Args:
    * summary: summary protocol buffer
    * log_rslt: logging operations' results
    * idx_iter: index of the training iteration
    * time_step: time step between two summary operations
