enforce consistencies there but for unlabeled data if you take two different views and make sure that the logits are close enough for the classifier that's being trained on label dia that enforces a lot of structurally believer so you just make sure that the predictions are roughly similar and if you do this for a lot of legal data with a lot of different data occupations then your classifier is getting very much regularize in generalizable even those training on very little data so that's the idea of label label consistency constrains using