def predict_quantiles(self, X, quantiles=(2.5, 97.5), Y_metadata=None, kern=None, likelihood=None):
        m, v = self._raw_predict(X,  full_cov=False, kern=kern)
        if self.normalizer is not None:
            m, v = self.normalizer.inverse_mean(m), self.normalizer.inverse_variance(v)
        if likelihood is None:
            likelihood = self.likelihood
        return likelihood.predictive_quantiles(m, v, quantiles, Y_metadata=Y_metadata)
Get the predictive quantiles around the prediction at X
dataset/ETHPy150Open SheffieldML/GPy/GPy/core/gp.py/GP.predict_quantiles