code: def new_evaluation_result(self, has_improved: bool) -> bool:
          
          logger.info("Checkpoint learning rate: %1.2e (%d/%d updates)",
                      self.current_rate,
                      self.latest_t - self.current_step_started_at,
                      self.current_step_num_updates)
          if self.latest_t >= self.next_step_at:
              self.current_step += 1
              self._update_rate(self.current_step)
          return False
docs: Returns true if the parameters should be reset to the ones with the best validation score.
  
          :param has_improved: Whether the model improved on held-out validation data.
          :return: True if parameters should be reset to the ones with best validation score.