layers in those systems don't do much you can kind of get rid of them actually after after training but what it does is that it makes the system sort of fault tolerant if you want so if the learning algorithm somehow kind of gets into a situation where some layers die which can happen it's not catastrophic because you always have the information going through the bypass connection and so that you know a pair of layers just you know kind of checks itself out of the network so it's not it's not used but it doesn't kill the entire entire effort so