falls across time so I would still do a stochastic the augmentation stochastic Network I would get an embedding every time in the forward pass but I would say that those embeddings should be close to some historical a version of the same same same samples embedding the past and so that way gives to enforcing some kind of data augmentation constraints because you would have done a different organization in the past but you're going to keep an estimator of it for every single sample separately so this is very similar to those ideas we talked