code: 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 likelihood is None:
              likelihood = self.likelihood
  
          quantiles = likelihood.predictive_quantiles(m, v, quantiles, Y_metadata=Y_metadata)
  
          if self.normalizer is not None:
              quantiles = [self.normalizer.inverse_mean(q) for q in quantiles]
          return quantiles
docs: 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
          :param kern: optional kernel to use for prediction
          :type predict_kw: dict
          :returns: list of quantiles for each X and predictive quantiles for interval combination
          :rtype: [np.ndarray (Xnew x self.output_dim), np.ndarray (Xnew x self.output_dim)]