In this problem we will show that the existence of an efficient mistake$-$bounded learner for a class C implies an efficient PAC learner for C$.$ Concretely, let C be a function class with domain X E$\{-1,1\}"$ and binary labels Y E$\{-1,1\}.$ Assume that C can be learned by algorithm$/$learner A with some mistake bound t$.$ You may assume you know the value t$.$ You may also assume that at each iteration, A runs in time polynomial in n and that A only updates its state when it gets an example wrong. The concrete goal of this problem is to create a PAC$-$learning algorithm, B$,$ that can PAC$-$ learn concept class C with respect to an arbitrary distribution D over $\{-1,1\}"$ using algorithm A as a sub$-$routine.