How to use the ngboost.scores.CRPS_surv function in ngboost

def main():
    mu, logstd = 0., np.log(1.)
    print('True mu=%.03f std=%.03f' % (mu, np.exp(logstd)))
    for frac in [0.01, 0.05, 0.1, 0.5, 0.9, 0.95, 0.99]:
        Y, C = generate_data(10000, frac_cens=frac, mu=mu, logstd=logstd)
        #print(Y[C==0].mean(), Y[C==1].mean())
        print('==== Censoring fraction %.2f ====' % torch.mean(C))
        mle_mu, mle_std = fit(Y, C, MLE_surv, mu_init=0., logstd_init=0.)
        print('MLE  mu=%.03f std=%.03f' % (mle_mu, mle_std))
        crps_mu, crps_std = fit(Y, C, CRPS_surv, mu_init=0., logstd_init=0.)
        print('CRPS mu=%.03f std=%.03f' % (crps_mu, crps_std))

def cv_n_estimators(X, y, C, cv_list, n_folds=10, distrib = HomoskedasticNormal, quadrant = False, s = CRPS_surv):
    kf = KFold(n_splits=n_folds)
    kf.get_n_splits(X)
    mse_list = []
    lkh_list = []
    for param in cv_list:
        print("Cross validating with parameter %.2f" % (param))
        mse = 0
        lkh = 0
        for train_index, val_index in kf.split(X):
            X_train_cv, X_val_cv = X[train_index], X[val_index]
            y_train_cv, y_val_cv = y[train_index], y[val_index]
            C_train_cv, C_val_cv = C[train_index], C[val_index]

            base_learner = lambda: DecisionTreeRegressor(criterion='friedman_mse', \
                          min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_depth=3)

print(optimal_het_q_crps)


    print("*"*6 + "  Homoskedastic Distributions with CRPS [Orthan Search]  " + "*"*6)
    hom_q_crps = cv_n_estimators(X_train, y_train, C_train, cv_list = n_estimators_list, \
                                n_folds=fold_num, distrib = HomoskedasticNormal, quadrant = True, s = CRPS_surv)
    optimal_hom_q_crps = n_estimators_list[np.argmax(hom_q_crps)]
    print("--- Cross Validation MSE ---")
    print(hom_q_crps)
    print("--- Optimal parameter for Heteroskedastic Distributions with CRPS [Orthan Search] ---")
    print(optimal_hom_q_crps)

    
    print("*"*6 + "  Heteroskedastic Distributions with CRPS [Line Search]  " + "*"*6)
    het_l_crps = cv_n_estimators(X_train, y_train, C_train, cv_list = n_estimators_list, \
                                n_folds=fold_num, distrib = Normal, quadrant = False, s=CRPS_surv)
    optimal_het_l_crps = n_estimators_list[np.argmax(het_l_crps)]

    print("--- Cross Validation MSE ---")
    print(het_l_crps)
    print("--- Optimal parameter for Heteroskedastic Distributions with CRPS [Line Search] ---")
    print(optimal_het_l_crps)

    
    print("*"*6 + "  Homoskedastic Distributions with CRPS [Line Search]  " + "*"*6)
    hom_l_crps = cv_n_estimators(X_train, y_train, C_train, cv_list = n_estimators_list, \
                                n_folds=fold_num, distrib = HomoskedasticNormal, quadrant = False, s = CRPS_surv)
    optimal_hom_l_crps = n_estimators_list[np.argmax(hom_l_crps)]

    print("--- Cross Validation MSE ---")
    print(hom_l_crps)
    print("--- Optimal parameter for Heteroskedastic Distributions with CRPS [Line Search] ---")

print(optimal_het_l_mle)

    print("*"*6 + "  Homoskedastic Distributions with MLE [Line Search]  " + "*"*6)
    hom_l_mle = cv_n_estimators(X_train, y_train, C_train, cv_list = n_estimators_list, \
                                n_folds=fold_num, distrib = HomoskedasticNormal, quadrant = False, s = MLE_surv)
    optimal_hom_l_mle = n_estimators_list[np.argmax(hom_l_mle)]

    print("--- Cross Validation MSE ---")
    print(hom_l_mle)
    print("--- Optimal parameter for Heteroskedastic Distributions with MLE [Line Search] ---")
    print(optimal_hom_l_mle)


    print("*"*6 + "  Heteroskedastic Distributions with CRPS [Orthan Search]  " + "*"*6)
    het_q_crps = cv_n_estimators(X_train, y_train, C_train, cv_list = n_estimators_list, \
                                n_folds=fold_num, distrib = Normal, quadrant = True, s=CRPS_surv)
    optimal_het_q_crps = n_estimators_list[np.argmax(het_q_crps)]
    print("--- Cross Validation MSE ---")
    print(het_q_crps)
    print("--- Optimal parameter for Heteroskedastic Distributions with CRPS [Orthan Search] ---")
    print(optimal_het_q_crps)


    print("*"*6 + "  Homoskedastic Distributions with CRPS [Orthan Search]  " + "*"*6)
    hom_q_crps = cv_n_estimators(X_train, y_train, C_train, cv_list = n_estimators_list, \
                                n_folds=fold_num, distrib = HomoskedasticNormal, quadrant = True, s = CRPS_surv)
    optimal_hom_q_crps = n_estimators_list[np.argmax(hom_q_crps)]
    print("--- Cross Validation MSE ---")
    print(hom_q_crps)
    print("--- Optimal parameter for Heteroskedastic Distributions with CRPS [Orthan Search] ---")
    print(optimal_hom_q_crps)