about and so if you plug it's back coding to the D quantizer to get those extra bits and then altogether the code length you get is the variational D quantization bound which is what you explicit you train to to be small on on the data set so it ends up being a reasonable you and so with all this stuff we tried it for one of the models that we trained and we found that we were able to get some code links that are very close to what is predicted by the variational T quantization bound and this sort of holds across all these data sets