def METHOD_NAME(self, X, y, features=None, is_oof=True, **kwargs) -> pd.DataFrame:
    logger.warning(
        "Warning: non-raw feature importance calculation is not valid for weighted ensemble since it does not have features, returning ensemble weights instead..."
    )
    if is_oof:
        fi = pd.Series(self._get_model_weights()).sort_values(ascending=False)
    else:
        logger.warning(
            "Warning: Feature importance calculation is not yet implemented for WeightedEnsembleModel on unseen data, returning generic feature importance..."
        )
        fi = pd.Series(self._get_model_weights()).sort_values(ascending=False)
    fi_df = fi.to_frame(name="importance")
    fi_df["stddev"] = np.nan
    fi_df["p_score"] = np.nan
    fi_df["n"] = np.nan
    # TODO: Rewrite preprocess() in greedy_weighted_ensemble_model to enable
    # fi_df = super().compute_feature_importance(X=X, y=y, features_to_use=features_to_use, preprocess=preprocess, is_oof=is_oof, **kwargs)
    return fi_df