How to use the hyperactive.BayesianOptimizer function in hyperactive

def test_BayesianOptimizer():
    from hyperactive import BayesianOptimizer

    opt0 = BayesianOptimizer(
        search_config, n_iter_0, random_state=random_state, verbosity=0, cv=cv, n_jobs=1
    )
    opt0.fit(X, y)

    opt1 = BayesianOptimizer(
        search_config,
        n_iter_1,
        random_state=random_state,
        verbosity=0,
        cv=cv,
        n_jobs=n_jobs,
    )
    opt1.fit(X, y)

    assert opt0.score_best < opt1.score_best

def test_BayesianOptimizer():
    from hyperactive import BayesianOptimizer

    opt0 = BayesianOptimizer(
        search_config, n_iter_0, random_state=random_state, verbosity=0, cv=cv, n_jobs=1
    )
    opt0.fit(X, y)

    opt1 = BayesianOptimizer(
        search_config,
        n_iter_1,
        random_state=random_state,
        verbosity=0,
        cv=cv,
        n_jobs=n_jobs,
    )
    opt1.fit(X, y)

    assert opt0.score_best < opt1.score_best

EvolutionStrategyOptimizer,
        BayesianOptimizer,
    )

    _ = HillClimbingOptimizer(search_config, 1)
    _ = StochasticHillClimbingOptimizer(search_config, 1)
    _ = TabuOptimizer(search_config, 1)
    _ = RandomSearchOptimizer(search_config, 1)
    _ = RandomRestartHillClimbingOptimizer(search_config, 1)
    _ = RandomAnnealingOptimizer(search_config, 1)
    _ = SimulatedAnnealingOptimizer(search_config, 1)
    _ = StochasticTunnelingOptimizer(search_config, 1)
    _ = ParallelTemperingOptimizer(search_config, 1)
    _ = ParticleSwarmOptimizer(search_config, 1)
    _ = EvolutionStrategyOptimizer(search_config, 1)
    _ = BayesianOptimizer(search_config, 1)