def predict_quantiles(self, X, quantiles=(<NUM_LIT>, <NUM_LIT>), Y_metadata=None, likelihood=None, kern=None):
qs = super(WarpedGP, self).predict_quantiles(X, quantiles, Y_metadata=Y_metadata, likelihood=likelihood, kern=kern)<EOL>if self.predict_in_warped_space:<EOL><INDENT>return [self.warping_function.f_inv(q) for q in qs]<EOL><DEDENT>return qs<EOL>
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)]