code: 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 += f 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
docs: Take one step forward on the annealing schedule for the optim params.