<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:

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 = 0