def METHOD_NAME(
    series_uid: str,
    download_dir: PathLike,
    output_dir: PathLike,
    check_md5: bool = False,
    hashes_filename: str = "md5hashes.csv",
    progress: bool = True,
) -> None:
    """
    Download a dicom series from a public The Cancer Imaging Archive (TCIA) dataset.
    The downloaded compressed file will be stored in `download_dir`, and the uncompressed folder will be saved
    in `output_dir`.
    Args:
        series_uid: SeriesInstanceUID of a dicom series.
        download_dir: the path to store the downloaded compressed file. The full path of the file is:
            `os.path.join(download_dir, f"{series_uid}.zip")`.
        output_dir: target directory to save extracted dicom series.
        check_md5: whether to download the MD5 hash values as well. If True, will check hash values for all images in
            the downloaded dicom series.
        hashes_filename: file that contains hashes.
        progress: whether to display progress bar.
    """
    query_name = "getImageWithMD5Hash" if check_md5 else "getImage"
    download_url = f"{BASE_URL}{query_name}?SeriesInstanceUID={series_uid}"
    monai.apps.utils.download_and_extract(
        url=download_url,
        filepath=os.path.join(download_dir, f"{series_uid}.zip"),
        output_dir=output_dir,
        progress=progress,
    )
    if check_md5:
        if not has_pandas:
            raise ValueError("pandas package is necessary, please install it.")
        hashes_df = pd.read_csv(os.path.join(output_dir, hashes_filename))
        for dcm, md5hash in hashes_df.values:
            monai.apps.utils.check_hash(filepath=os.path.join(output_dir, dcm), val=md5hash, hash_type="md5")