Importantly, the IPs are treated as learnable parameters within the GP model. They are updated via gradient descent during training alongside the kernel hyperparameters, with a clamping step ensuring that they remain within the valid range of the normalized data. This dynamic learning of IPs allows the model to refine its approximation of the full covariance structure continuously.