the Q has to do is it has to somehow it's like it's like a it has to take its best guess right so basically the Q is trained to output the expected value of the representation right the expected of the representation f of a of X under all of the different possible image augmentations and that's why it learns to ignore these augmentations so your entire goal with these methods is you learn to ignore these augmentations so you want to learn some method that is independent of the augmentations so by crafting the augmentations in a smart