def predictive_quantiles(self, mu, var, quantiles, Y_metadata=None):
        _s = self.variance[Y_metadata['output_index'].flatten()]
        return  [stats.norm.ppf(100./q)*np.sqrt(var + _s) + mu for q in quantiles]
dataset/ETHPy150Open SheffieldML/GPy/GPy/likelihoods/gaussian.py/HeteroscedasticGaussian.predictive_quantiles/swapped operands of `/`