have this loss function you might say why do we need why do we need all this well it turns out that if you make a small mistake and your prediction for the next state then you might say nah Bob just a small mistake no big deal but if you make a small mistake the problem is that you land in a new latent state for which your model might not have been trained and when you make the next prediction to go to time T plus 2 you're doing it from a time T plus 1 latent state that you're not familiar with that doesn't lie in