class BFNet(EconomicNetwork):
    result_type: ClassVar[type] = EconomicIntermediaries
    num_mask_steps: ClassVar[int] = 1

    def get_baseline(self, impacts: ImpactsIntermediaries, training=False) -> tuple[tf.Tensor, dict]:  # noqa: U100
        """Getting baseline from the previous week actual sales by removing impacts

        Args:
            impacts (ImpactsIntermediaries): Intermediaries object which contains additive and multiplicative impacts.
            training (bool, optional):  Flag to set the trainable params. Defaults to False.

        Returns:
            tuple[tf.Tensor, dict[str, tf.Tensor]] : Returning the baseline by removing the additive and
             multiplicative effect of previous week
        """
        additive_shifted_impacts = [impact[:, :-1] for impact in impacts.additive_impacts]
        multiplicative_shifted_impacts = [impact[:, :-1] for impact in impacts.multiplicative_impacts]
        baseline = apply_inverse_impacts(
            self.yph[:, :-1],
            additive_impacts=additive_shifted_impacts,
            multiplicative_impacts=multiplicative_shifted_impacts,
        )
        return tf.concat([tf.zeros_like(baseline[:, :1]), baseline], axis=1), {}