code: def write_summary(self, global_step, delta_train_start, lesson_num=0):
          
          if global_step % self.trainer_parameters['summary_freq'] == 0 and global_step != 0:
              is_training = "Training." if self.is_training and self.get_step <= self.get_max_steps else "Not Training."
              if len(self.stats['Environment/Cumulative Reward']) > 0:
                  mean_reward = np.mean(
                      self.stats['Environment/Cumulative Reward'])
                  LOGGER.info(" {}: {}: Step: {}. "
                              "Time Elapsed: {:0.3f} s "
                              "Mean "
                              "Reward: {"
                              ":0.3f}. Std of Reward: {:0.3f}. {}"
                              .format(self.run_id, self.brain_name,
                                      min(self.get_step, self.get_max_steps),
                                      delta_train_start,
                                      mean_reward, np.std(
                                          self.stats['Environment/Cumulative Reward']),
                                      is_training))
              else:
                  LOGGER.info(" {}: {}: Step: {}. No episode was completed since last summary. {}"
                              .format(self.run_id, self.brain_name, self.get_step, is_training))
              summary = tf.Summary()
              for key in self.stats:
                  if len(self.stats[key]) > 0:
                      stat_mean = float(np.mean(self.stats[key]))
                      summary.value.add(tag='{}'.format(
                          key), simple_value=stat_mean)
                      self.stats[key] = []
              summary.value.add(tag='Environment/Lesson', simple_value=lesson_num)
              self.summary_writer.add_summary(summary, self.get_step)
              self.summary_writer.flush()
docs: Saves training statistics to Tensorboard.
          :param delta_train_start:  Time elapsed since training started.
          :param lesson_num: Current lesson number in curriculum.
          :param global_step: The number of steps the simulation has been going for