The RandomFourierFeatures layer can be used to \"kernelize\" linear models by applying a non-linear transformation to the input features and then training a linear model on top of the transformed features. Depending on the loss function of the linear model, the composition of this layer and the linear model results to models that are equivalent (up to approximation) to kernel SVMs (for hinge loss), kernel logistic regression (for logistic loss), kernel linear regression (for MSE loss), etc.