To implement translation and rotation invariant attention kernels in practice, programmatic functions are defined for the Gaussian, Boxcar, Constant, and Epanechikov kernels. These functions take scalar distance inputs and return the corresponding attention weights ($\alpha$), providing distinct computational notions of range and smoothness for attention pooling operations.