class_docstr: A handler class for implementing `MixUp` style scheduling
class_docstr_tok: ["A","handler","class","for","implementing","`","MixUp","`","style","scheduling"]
docstr: Set the loss function back to the previous loss
docstr_tok: ["Set","the","loss","function","back","to","the","previous","loss"]
code: def after_train(self):
      "Set the loss function back to the previous loss"
      if self.stack_y:
          self.learn.loss_func = self.old_lf
  
imports: from __future__ import annotations
  from ..basics import *
  from torch.distributions.beta import Beta
label_desc: Use image node_id 3 for calling the MixHandler obj's underlying member method code with example usage: obj.after_train() without return types