code: def on_train_begin(self, **kwargs:Any)->None:
          "Prepare the master model."
          #Get a copy of the model params in FP32
          self.model_params, self.master_params = get_master(self.learn.layer_groups, self.flat_master)
          #Changes the optimizer so that the optimization step is done in FP32.
          new_opt = self.learn.opt.new_with_params(self.master_params)
          if self.opt is not None:
              self.opt.lr,self.opt.wd = self.learn.opt.lr,self.learn.opt.wd
              new_opt.load_state_dict(self.opt)
          self.learn.opt.opt = new_opt.opt
          self.noskip = f on_train_begin(self, **kwargs:Any)->None:
          "Prepare the master model."
          #Get a copy of the model params in FP32
          self.model_params, self.master_params = get_master(self.learn.layer_groups, self.flat_master)
          #Changes the optimizer so that the optimization step is done in FP32.
          new_opt = self.learn.opt.new_with_params(self.master_params)
          if self.opt is not None:
              self.opt.lr,self.opt.wd = self.learn.opt.lr,self.learn.opt.wd
              new_opt.load_state_dict(self.opt)
          self.learn.opt.opt = new_opt.opt
          self.noskip = 0
docs: Prepare the master model.