Get the predictive quantiles around the prediction at X

        :param X: The points at which to make a prediction
        :type X: np.ndarray (Xnew x self.input_dim)
        :param quantiles: tuple of quantiles, default is (2.5, 97.5) which is the 95% interval
        :type quantiles: tuple
        :returns: list of quantiles for each X and predictive quantiles for interval combination
        :rtype: [np.ndarray (Xnew x self.input_dim), np.ndarray (Xnew x self.input_dim)]

def predict_quantiles(self, X, quantiles=(2.5, 97.5), Y_metadata=None, likelihood=None, kern=None):
        """
        Get the predictive quantiles around the prediction at X

        :param X: The points at which to make a prediction
        :type X: np.ndarray (Xnew x self.input_dim)
        :param quantiles: tuple of quantiles, default is (2.5, 97.5) which is the 95% interval
        :type quantiles: tuple
        :returns: list of quantiles for each X and predictive quantiles for interval combination
        :rtype: [np.ndarray (Xnew x self.input_dim), np.ndarray (Xnew x self.input_dim)]
        """
        qs = super(WarpedGP, self).predict_quantiles(X, quantiles, Y_metadata=Y_metadata, likelihood=likelihood, kern=kern)
        if self.predict_in_warped_space:
            return [self.warping_function.f_inv(q) for q in qs]
        return qs