如何在optuna中对参数进行无重复采样？

import optuna
from optuna.samplers import BaseSampler

class NoDuplicateSampler(BaseSampler):
    def __init__(self, seed=None):
        super().__init__(seed)
        self._previous_trials = set()

    def sample_independent(self, study, trial, param_name, param_distribution):
        if param_name in self._previous_trials:
            return None  # 返回None表示跳过该参数的采样
        else:
            return super().sample_independent(study, trial, param_name, param_distribution)

    def sample_relative(self, study, trial, param_name, param_distribution):
        return super().sample_relative(study, trial, param_name, param_distribution)

    def sample_categorical(self, study, trial, param_name, choices):
        return super().sample_categorical(study, trial, param_name, choices)

    def sample_loguniform(self, study, trial, param_name, low, high):
        return super().sample_loguniform(study, trial, param_name, low, high)

    def sample_discrete_uniform(self, study, trial, param_name, low, high, q):
        return super().sample_discrete_uniform(study, trial, param_name, low, high, q)

    def sample_int(self, study, trial, param_name, low, high):
        return super().sample_int(study, trial, param_name, low, high)

sampler = NoDuplicateSampler()
study = optuna.create_study(sampler=sampler)