def new_evaluation_result(self, has_improved: bool) -> bool:
        """
        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.
        """
        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
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.