How to use the lightfm.evaluation.reciprocal_rank function in lightfm

with pytest.raises(ValueError):
        evaluation.reciprocal_rank(
            model, train, train_interactions=train, check_intersections=True
        )

    # check no errors raised when train and test have no interactions in common
    evaluation.auc_score(
        model, test, train_interactions=train, check_intersections=True
    )
    evaluation.recall_at_k(
        model, test, train_interactions=train, check_intersections=True
    )
    evaluation.precision_at_k(
        model, test, train_interactions=train, check_intersections=True
    )
    evaluation.reciprocal_rank(
        model, test, train_interactions=train, check_intersections=True
    )

    # check no error is raised when there are intersections but flag is False
    evaluation.auc_score(
        model, train, train_interactions=train, check_intersections=False
    )
    evaluation.recall_at_k(
        model, train, train_interactions=train, check_intersections=False
    )
    evaluation.precision_at_k(
        model, train, train_interactions=train, check_intersections=False
    )
    evaluation.reciprocal_rank(
        model, train, train_interactions=train, check_intersections=False
    )

)
    evaluation.reciprocal_rank(
        model, test, train_interactions=train, check_intersections=True
    )

    # check no error is raised when there are intersections but flag is False
    evaluation.auc_score(
        model, train, train_interactions=train, check_intersections=False
    )
    evaluation.recall_at_k(
        model, train, train_interactions=train, check_intersections=False
    )
    evaluation.precision_at_k(
        model, train, train_interactions=train, check_intersections=False
    )
    evaluation.reciprocal_rank(
        model, train, train_interactions=train, check_intersections=False
    )

def reciprocal_rank_on_ranks(
        ranks, test_interactions, train_interactions=None, preserve_rows=False):
    return reciprocal_rank(
        model=ModelMockRanksCacher(ranks.copy()),
        test_interactions=test_interactions,
        train_interactions=train_interactions,
        preserve_rows=preserve_rows,
    )