How to use the pyranges.concat function in pyranges

grs = []

    for _, gdf in natsorted(df.groupby(groupby)):
        grs.append(pr.PyRanges(gdf))

    clusters = [gr.cluster(strand=strand) for gr in grs]
    i = 1
    new_clusters = []
    for c in clusters:
        print("c")
        print(c)
        c.Cluster = i
        i += 1
        new_clusters.append(c)

    expected = pr.concat(new_clusters).df
    expected.loc[:, "Cluster"] = expected.Cluster.astype(np.int32)
    # expected = expected.drop_duplicates()

    print(expected)
    print(result)

    assert_df_equal(result, expected)

def test_merge_by(gr, strand):

    print(gr)
    result = gr.merge(by="ID").df.drop("ID", axis=1)

    df = gr.df

    grs = []
    for _, gdf in df.groupby("ID"):
        grs.append(pr.PyRanges(gdf))

    expected = pr.concat([gr.merge() for gr in grs]).df

    print(expected)
    print(result)

    assert_df_equal(result, expected)

| (category)   |   (int32) |   (int32) |   (int32) |   (int32) |   (int32) |
    |--------------+-----------+-----------+-----------+-----------+-----------|
    | chr1         |         0 |        10 |         1 |         0 |         1 |
    | chr1         |        10 |        20 |         2 |         2 |         2 |
    | chr1         |        20 |        30 |         2 |         2 |         0 |
    | chr1         |        30 |        40 |         0 |         1 |         1 |
    +--------------+-----------+-----------+-----------+-----------+-----------+
    Unstranded PyRanges object has 4 rows and 6 columns from 1 chromosomes.
    For printing, the PyRanges was sorted on Chromosome.
    """

    kwargs = {"as_pyranges": False, "nb_cpu": nb_cpu, "strandedness": strandedness, "how": how, "nb_cpu": nb_cpu}
    names = list(grs.keys())

    if features is None:
        features = pr.concat(grs.values()).split(between=True)

    from pyranges.methods.intersection import _count_overlaps

    for name, gr in grs.items():

        gr = gr.drop()

        res = features.apply_pair(gr, _count_overlaps, **kwargs)

        setattr(features, name, res)

        setattr(features, name, getattr(features, name).fillna(0))


    def to_int(df):
        df.loc[:, names] = df[names].astype(np.int32)