reveice the metric data and update Bayesian optimization with final result

        Parameters
        ----------
        data:
            it is an object which has keys 'parameter_id', 'value', 'trial_job_id', 'type', 'sequence'.

        Raises
        ------
        ValueError
            Data type not supported

def handle_report_metric_data(self, data):
        """reveice the metric data and update Bayesian optimization with final result

        Parameters
        ----------
        data:
            it is an object which has keys 'parameter_id', 'value', 'trial_job_id', 'type', 'sequence'.

        Raises
        ------
        ValueError
            Data type not supported
        """
        logger.debug('handle report metric data = %s', data)

        assert 'value' in data
        value = extract_scalar_reward(data['value'])
        if self.optimize_mode is OptimizeMode.Maximize:
            reward = -value
        else:
            reward = value
        assert 'parameter_id' in data
        s, i, _ = data['parameter_id'].split('_')

        logger.debug('bracket id = %s, metrics value = %s, type = %s', s, value, data['type'])
        s = int(s)

        assert 'type' in data
        if data['type'] == 'FINAL':
            # and PERIODICAL metric are independent, thus, not comparable.
            assert 'sequence' in data
            self.brackets[s].set_config_perf(
                int(i), data['parameter_id'], sys.maxsize, value)
            self.completed_hyper_configs.append(data)
       
            _parameters = self.parameters[data['parameter_id']]
            _parameters.pop(_KEY)
            # update BO with loss, max_s budget, hyperparameters
            self.cg.new_result(loss=reward, budget=data['sequence'], parameters=_parameters, update_model=True)
        elif data['type'] == 'PERIODICAL':
            self.brackets[s].set_config_perf(
                int(i), data['parameter_id'], data['sequence'], value)
        else:
            raise ValueError(
                'Data type not supported: {}'.format(data['type']))