How to use the suod.models.utils.utility.get_estimators function in suod

print("\n... Processing", mat_file_name, '...')
    mat = sp.io.loadmat(os.path.join('', 'datasets', mat_file))

    X = mat['X']
    y = mat['y']


    X_train, X_test, y_train, y_test = \
        train_test_split(X, y, test_size=0.4, random_state=42)
        
    # standardize data to be digestible for most algorithms
    X_train, X_test = standardizer(X_train, X_test)

    contamination = y.sum() / len(y)

    base_estimators = deepcopy(get_estimators(contamination=contamination))

    ##########################################################################
    model = SUOD(base_estimators=base_estimators, rp_flag_global=True, 
                 approx_clf=RandomForestRegressor(),
                 n_jobs=n_jobs, bps_flag=True, contamination=contamination,
                 approx_flag_global=True)

    start = time.time()
    model.fit(X_train)  # fit all models with X
    print('Fit time:', time.time() - start)
    print()

    start = time.time()
    model.approximate(X_train)  # conduct model approximation if it is enabled
    print('Approximation time:', time.time() - start)
    print()