def METHOD_NAME(raw_dir, processed_dir):
    r"""
    Description
    -----------
    Preprocess Elliptic dataset like the EvolveGCN official instruction:
    github.com/IBM/EvolveGCN/blob/master/elliptic_construction.md
    The main purpose is to convert original idx to contiguous idx start at 0.
    """
    oid_nid_path = os.path.join(processed_dir, "oid_nid.npy")
    id_label_path = os.path.join(processed_dir, "id_label.npy")
    id_time_features_path = os.path.join(processed_dir, "id_time_features.npy")
    src_dst_time_path = os.path.join(processed_dir, "src_dst_time.npy")
    if (
        os.path.exists(oid_nid_path)
        and os.path.exists(id_label_path)
        and os.path.exists(id_time_features_path)
        and os.path.exists(src_dst_time_path)
    ):
        print(
            "The preprocessed data already exists, skip the preprocess stage!"
        )
        return
    print("starting process raw data in {}".format(raw_dir))
    id_label = pandas.read_csv(
        os.path.join(raw_dir, "elliptic_txs_classes.csv")
    )
    src_dst = pandas.read_csv(
        os.path.join(raw_dir, "elliptic_txs_edgelist.csv")
    )
    # elliptic_txs_features.csv has no header, and it has the same order idx with elliptic_txs_classes.csv
    id_time_features = pandas.read_csv(
        os.path.join(raw_dir, "elliptic_txs_features.csv"), header=None
    )
    # get oldId_newId
    oid_nid = id_label.loc[:, ["txId"]]
    oid_nid = oid_nid.rename(columns={"txId": "originalId"})
    oid_nid.insert(1, "newId", range(len(oid_nid)))
    # map classes unknown,1,2 to -1,1,0 and construct id_label. type 1 means illicit.
    id_label = pandas.concat(
        [
            oid_nid["newId"],
            id_label["class"].map({"unknown": -1.0, "1": 1.0, "2": 0.0}),
        ],
        axis=1,
    )
    # replace originalId to newId.
    # Attention: the timestamp in features start at 1.
    id_time_features[0] = oid_nid["newId"]
    # construct originalId2newId dict
    oid_nid_dict = oid_nid.set_index(["originalId"])["newId"].to_dict()
    # construct newId2timestamp dict
    nid_time_dict = id_time_features.set_index([0])[1].to_dict()
    # Map id in edgelist to newId, and add a timestamp to each edge.
    # Attention: From the EvolveGCN official instruction, the timestamp with edgelist start at 0, rather than 1.
    # see: github.com/IBM/EvolveGCN/blob/master/elliptic_construction.md
    # Here we dose not follow the official instruction, which means timestamp with edgelist also start at 1.
    # In EvolveGCN example, the edge timestamp will not be used.
    #
    # Note: in the dataset, src and dst node has the same timestamp, so it's easy to set edge's timestamp.
    new_src = src_dst["txId1"].map(oid_nid_dict).rename("newSrc")
    new_dst = src_dst["txId2"].map(oid_nid_dict).rename("newDst")
    edge_time = new_src.map(nid_time_dict).rename("timestamp")
    src_dst_time = pandas.concat([new_src, new_dst, edge_time], axis=1)
    # save oid_nid, id_label, id_time_features, src_dst_time to disk. we can convert them to numpy.
    # oid_nid: type int.  id_label: type int.  id_time_features: type float.  src_dst_time: type int.
    oid_nid = oid_nid.to_numpy(dtype=int)
    id_label = id_label.to_numpy(dtype=int)
    id_time_features = id_time_features.to_numpy(dtype=float)
    src_dst_time = src_dst_time.to_numpy(dtype=int)
    numpy.save(oid_nid_path, oid_nid)
    numpy.save(id_label_path, id_label)
    numpy.save(id_time_features_path, id_time_features)
    numpy.save(src_dst_time_path, src_dst_time)
    print(
        "Process Elliptic raw data done, data has saved into {}".format(
            processed_dir
        )
    )