do instead is learn some meta parameters theta so we're going to use D meta train to learn theta and theta will basically contain all the information that we need for solving new tasks that we've extracted from D meta train so whatever we need to know about D meta train is going to be baked into theta via a meta learning process and that's essentially the essence of the meta learning problem now if we want to treat this probabilistically what we can do now is we can say well we can write out our p of phi given d common