class DeePredict:
    def __init__(self, model=None, bridge=None):
        if model:
            self._model = model
        if bridge:
            self._bridge = bridge

    def model(self, *args, **kwargs):
        if self.__getattribute__('_model'):
            return self._model(*args, **kwargs)
        else:
            raise ValueError('缺少模型')

    def bridge(self, *args, **kwargs):
        return self._bridge(*args, **kwargs)

    def predict(self, *args, **kwargs):
        x = self.model(*args, **kwargs)
        if self.__getattribute__('_bridge'):
            y = self.bridge(x)
            return y
        else:
            return x

if __name__ == '__main__':
    def f1(x):
        return x


    dp = DeePredict(f1, f1)
    print(dp.predict([1]))