Are there any easy ways to apply weight constraints and regularization?

[Bpoole908]

I was just trying to see if there was an easy way to add weight constraints and regularization methods to the training loop. For instance, TensorFlow has parameters attached to its Layer class called kernel_regularization and kernel_constraint (ref) which allows regularization and constraint of weights fairly easily. So let's say if I wanted to use max norm to constrain my weights directly I could then just feed the MaxNorm class (ref) which inherits from their Constraint class (ref) and during training this constraint would automatically be applied after the weights are updated.

As I am transferring from TensorFlow I have been trying to find something similar in PyTorch and PyTorch Lighting but I haven't been able to find anything. Does anyone have any ideas?