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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def list_metafeatures(cls, group="all"):
 """ Returns a list of metafeatures computable by the Metafeatures class. """

        # todo make group for intractable metafeatures for wide datasets or
        # datasets with high cardinality categorical columns:
        # PredPCA1, PredPCA2, PredPCA3, PredEigen1, PredEigen2, PredEigen3,
        # PredDet, kNN1NErrRate, kNN1NKappa, LinearDiscriminantAnalysisKappa,
        # LinearDiscriminantAnalysisErrRate
        if group == "all":
            return copy.deepcopy(cls.IDS)
        elif group == "landmarking":
            return list(filter(
                lambda mf_id: "ErrRate" in mf_id or "Kappa" in mf_id, cls.IDS
            ))
        elif group == "target_dependent":
            return list(filter(
                cls._resource_is_target_dependent, cls.IDS
            ))
        else:
            raise ValueError(f"Unknown group {group}")