Create the various optimizers.

def on_train_begin(self, **kwargs):
        "Create the various optimizers."
        self.G_A,self.G_B = self.learn.model.G_A,self.learn.model.G_B
        self.D_A,self.D_B = self.learn.model.D_A,self.learn.model.D_B
        self.crit = self.learn.loss_func.crit
        self.opt_G = self.learn.opt.new([nn.Sequential(*flatten_model(self.G_A), *flatten_model(self.G_B))])
        self.opt_D_A = self.learn.opt.new([nn.Sequential(*flatten_model(self.D_A))])
        self.opt_D_B = self.learn.opt.new([nn.Sequential(*flatten_model(self.D_B))])
        self.learn.opt.opt = self.opt_G.opt
        self._set_trainable()
        self.names = ['idt_loss', 'gen_loss', 'cyc_loss', 'da_loss', 'db_loss']
        self.learn.recorder.no_val=True
        self.learn.recorder.add_metric_names(self.names)
        self.smootheners = {n:SmoothenValue(0.98) for n in self.names}