How to use the nni.utils.OptimizeMode function in nni

def __init__(self, optimize_mode="maximize", population_size=32):
        """
        Parameters
        ----------
        optimize_mode : str, default 'maximize'
        population_size : int
            initial population size. The larger population size,
        the better evolution performance.
        """
        self.optimize_mode = OptimizeMode(optimize_mode)
        self.population_size = population_size

        self.trial_result = []
        self.searchspace_json = None
        self.total_data = {}
        self.random_state = None
        self.population = None
        self.space = None

ent_coef : float
            Policy entropy coefficient in the optimization objective
        lr : float
            Learning rate of the model (lstm network), constant
        vf_coef : float
            Value function loss coefficient in the optimization objective
        max_grad_norm : float
            Gradient norm clipping coefficient
        gamma : float
            Discounting factor
        lam : float
            Advantage estimation discounting factor (lambda in the paper)
        cliprange : float
            Cliprange in the PPO algorithm, constant
        """
        self.optimize_mode = OptimizeMode(optimize_mode)
        self.model_config = ModelConfig()
        self.model = None
        self.search_space = None
        self.running_trials = {}                  # key: parameter_id, value: actions/states/etc.
        self.inf_batch_size = trials_per_update   # number of trials to generate in one inference
        self.first_inf = True                     # indicate whether it is the first time to inference new trials
        self.trials_result = [None for _ in range(self.inf_batch_size)] # results of finished trials

        self.credit = 0 # record the unsatisfied trial requests
        self.param_ids = []
        self.finished_trials = 0
        self.chosen_arch_template = {}

        self.actions_spaces = None
        self.actions_to_config = None
        self.full_act_space = None

def receive_trial_result(self, parameter_id, parameters, value, **kwargs):
        """
        Method invoked when a trial reports its final result.

        Override of the abstract method in :class:`~nni.tuner.Tuner`.
        """
        value = extract_scalar_reward(value)
        if self._optimize_mode == OptimizeMode.Minimize:
            value = -value

        logger.info("Received trial result.")
        logger.info("value :%s", value)
        logger.info("parameter : %s", parameters)
        self._space.register(parameters, value)

def receive_trial_result(self, parameter_id, parameters, value, **kwargs):
        """
        Tuner receive result from trial.

        Parameters
        ----------
        parameter_id : int
            The id of parameters, generated by nni manager.
        parameters : dict
            A group of parameters that trial has tried.
        value : dict/float
            if value is dict, it should have "default" key.
        """
        value = extract_scalar_reward(value)
        if self.optimize_mode == OptimizeMode.Maximize:
            value = -value

        logger.info("Received trial result.")
        logger.info("value is : %s", str(value))
        logger.info("parameter is : %s", str(parameters))

        # parse parameter to sample_x
        sample_x = [0 for i in range(len(self.key_order))]
        for key in parameters:
            idx = self.key_order.index(key)
            sample_x[idx] = parameters[key]

        # parse value to sample_y
        temp_y = []
        if sample_x in self.samples_x:
            idx = self.samples_x.index(sample_x)

self.optimal_y = reward
                else:
                    if self.constant_liar_type == 'mean':
                        _sum = self.optimal_y[0] + reward
                        _number = self.optimal_y[1] + 1
                        self.optimal_y = [_sum, _number]
                    elif self.constant_liar_type == 'min':
                        self.optimal_y = min(self.optimal_y, reward)
                    elif self.constant_liar_type == 'max':
                        self.optimal_y = max(self.optimal_y, reward)
                logger.debug("Update optimal_y with reward, optimal_y = %s", self.optimal_y)
        else:
            rval = self.rval


        if self.optimize_mode is OptimizeMode.Maximize:
            reward = -reward

        domain = rval.domain
        trials = rval.trials

        new_id = len(trials)

        rval_specs = [None]
        rval_results = [domain.new_result()]
        rval_miscs = [dict(tid=new_id, cmd=domain.cmd, workdir=domain.workdir)]

        vals = params
        idxs = dict()

        out_y = dict()
        json2vals(self.json, vals, out_y)

def __init__(self, optimize_mode = 'maximize', feature_percent = 0.6):
        """Initlization function
        count : 
        optimize_mode : contains "Maximize" or "Minimize" mode.
        search_space : define which features that tuner need to search
        feature_percent : @mengjiao
        default_space : @mengjiao 
        epoch_importance : @mengjiao
        estimate_sample_prob : @mengjiao
        """
        self.count = -1
        self.optimize_mode = OptimizeMode(optimize_mode)
        self.search_space = None
        self.feature_percent = feature_percent
        self.default_space = []
        self.epoch_importance = []
        self.estimate_sample_prob = None

        logger.debug('init aufo-fe done.')

If we have generated new trials after this trial end, we will return a new trial parameters.
            Otherwise, we will return None.
        """
        global _KEY
        self.num_finished_configs[i] += 1
        logger.debug('bracket id: %d, round: %d %d, finished: %d, all: %d',
                     self.s, self.i, i, self.num_finished_configs[i], self.num_configs_to_run[i])
        if self.num_finished_configs[i] >= self.num_configs_to_run[i] and self.no_more_trial is False:
            # choose candidate configs from finished configs to run in the next round
            assert self.i == i + 1
            # finish this bracket
            if self.i > self.s:
                self.no_more_trial = True
                return None
            this_round_perf = self.configs_perf[i]
            if self.optimize_mode is OptimizeMode.Maximize:
                sorted_perf = sorted(this_round_perf.items(
                ), key=lambda kv: kv[1][1], reverse=True)  # reverse
            else:
                sorted_perf = sorted(
                    this_round_perf.items(), key=lambda kv: kv[1][1])
            logger.debug(
                'bracket %s next round %s, sorted hyper configs: %s', self.s, self.i, sorted_perf)
            next_n, next_r = self.get_n_r()
            logger.debug('bracket %s next round %s, next_n=%d, next_r=%d',
                         self.s, self.i, next_n, next_r)
            hyper_configs = dict()
            for k in range(next_n):
                params_id = sorted_perf[k][0]
                params = self.hyper_configs[i][params_id]
                params[_KEY] = next_r  # modify r
                # generate new id

def __init__(self, optimize_mode="maximize", config_dedup=False):
        """
        Parameters
        ----------
        optimize_mode : str
            Optimize mode, 'maximize' or 'minimize', by default 'maximize'
        config_dedup : bool
            If True, the tuner will not generate a configuration that has been already generated.
            If False, a configuration may be generated twice, but it is rare for relatively large search space.
        """
        self.logger = logger
        self.optimize_mode = OptimizeMode(optimize_mode)
        self.total_data = {}
        self.optimizer = None
        self.smbo_solver = None
        self.first_one = True
        self.update_ss_done = False
        self.loguniform_key = set()
        self.categorical_dict = {}
        self.cs = None
        self.dedup = config_dedup

for key, value in _params.items():
                if key in self.loguniform_key:
                    _params[key] = np.log(value)
                elif key in self.categorical_dict:
                    if value in self.categorical_dict[key]:
                        _params[key] = self.categorical_dict[key].index(value)
                    else:
                        self.logger.info("The value %s of key %s is not in search space.", str(value), key)
                        valid_entry = False
                        break
            if not valid_entry:
                continue
            # start import this data entry
            _completed_num += 1
            config = Configuration(self.cs, values=_params)
            if self.optimize_mode is OptimizeMode.Maximize:
                _value = -_value
            if self.first_one:
                self.smbo_solver.nni_smac_receive_first_run(config, _value)
                self.first_one = False
            else:
                self.smbo_solver.nni_smac_receive_runs(config, _value)
        self.logger.info("Successfully import data to smac tuner, total data: %d, imported data: %d.", len(data), _completed_num)

Parameters
        ----------
        parameter_id : int
        parameters : dict
        value : dict/float
            if value is dict, it should have "default" key.
            value is final metrics of the trial.
        """
        reward = extract_scalar_reward(value)

        if parameter_id not in self.total_data:
            raise RuntimeError('Received parameter_id not in total_data.')
        # restore the paramsters contains "_index"
        params = self.total_data[parameter_id]

        if self.optimize_mode == OptimizeMode.Minimize:
            reward = -reward

        indiv = Individual(config=params, result=reward)
        self.population.append(indiv)