def METHOD_NAME(sample_features):
    # test the ordering of feature importance computation when feature impotance computation comes from mix of current and previous fit models,
    # and some feature are unevaluated
    length = len(sample_features)
    using_prev_fit_fi = set(sample_features[: length // 3])
    evaluated_rows, unevaluated_rows = evaluated_fi_df_template(sample_features[: length // 2]), unevaluated_fi_df_template(sample_features[length // 2 :])
    prev_importance_df = pd.concat([evaluated_rows, unevaluated_rows])
    sorted_features = sort_features_by_priority(features=sample_features, prev_importance_df=prev_importance_df, using_prev_fit_fi=using_prev_fit_fi)
    unevaluated_features = unevaluated_rows.index.tolist()
    sorted_prev_fit_features = (
        evaluated_rows[(~evaluated_rows.index.isin(sample_features[length // 2 :])) & (evaluated_rows.index.isin(using_prev_fit_fi))]
        .sort_values("importance")
        .index.tolist()
    )
    sorted_curr_fit_features = (
        evaluated_rows[(~evaluated_rows.index.isin(sample_features[length // 2 :])) & (~evaluated_rows.index.isin(using_prev_fit_fi))]
        .sort_values("importance")
        .index.tolist()
    )
    expected_features = unevaluated_features + sorted_prev_fit_features + sorted_curr_fit_features
    assert sorted_features == expected_features