at Berkeley so the wait works since it basically takes an average mass out a rectangular region and encodes that image now with the mask and has a big order that tries to reconstruct the actual image now so that way the model is filling up the details of what's missing in the mask and supervision basically can be constructed from your data itself because you actually knew what part you masked you because you had the complete image so the model is able to learn without any labels by creating its own supervision so that that's