After the IPs are dynamically learned through gradient descent and maintained within the valid normalized range, they are mapped back into the original data domain by the scaling and log transformations. At this stage, Euclidean distances are computed between each inducing point and all original data points, and for every inducing point the nearest real candidate is identified. The target value of this nearest candidate is then assigned to the corresponding inducing point, effectively anchoring our sparse approximation to actual observations. This step ensures that the learned inducing points accurately capture the underlying data distribution.