class_docstr: A handler class for implementing `MixUp` style scheduling
class_docstr_tok: ["A","handler","class","for","implementing","`","MixUp","`","style","scheduling"]
docstr: Determine whether to stack y
docstr_tok: ["Determine","whether","to","stack","y"]
code: def before_train(self):
      "Determine whether to stack y"
      self.stack_y = getattr(self.learn.loss_func, "y_int", False)
      if self.stack_y:
          self.old_lf, self.learn.loss_func = (
              self.learn.loss_func,
              self.lf,
          )
  
imports: from __future__ import annotations
  from ..basics import *
  from torch.distributions.beta import Beta
label_desc: Use image node_id 2 for calling the MixHandler obj's underlying member method code with example usage: obj.before_train() without return types