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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:

def on_batch_end(self, train, **kwargs:Any)->None:
        "Take one step forward on the annealing schedule for the optim params."
        if train:
            if self.idx_s >= len(self.lr_scheds): return {'stop_training': True, 'stop_epoch': True}
            self.opt.lr = self.lr_scheds[self.idx_s].step()
            self.opt.mom = self.mom_scheds[self.idx_s].step()
            # when the current schedule is complete we move onto the next
            # schedule. (in 1-cycle there are two schedules)
            if self.lr_scheds[self.idx_s].is_done:
                self.idx_s += 1