def METHOD_NAME(modelname, target_dir):
    """
    Downloads a DeepLabCut Model Zoo Project
    """
    import urllib.request
    import tarfile
    from tqdm import tqdm
    def show_progress(count, block_size, total_size):
        pbar.update(block_size)
    def tarfilenamecutting(tarf):
        """' auxfun to extract folder path
        ie. /xyz-trainsetxyshufflez/
        """
        for memberid, member in enumerate(tarf.getmembers()):
            if memberid == 0:
                parent = str(member.path)
                l = len(parent) + 1
            if member.path.startswith(parent):
                member.path = member.path[l:]
                yield member
    dlc_path = auxiliaryfunctions.get_deeplabcut_path()
    neturls = auxiliaryfunctions.read_plainconfig(
        os.path.join(
            dlc_path,
            "pose_estimation_tensorflow",
            "models",
            "pretrained",
            "pretrained_model_urls.yaml",
        )
    )
    if modelname in neturls.keys():
        url = neturls[modelname]
        response = urllib.request.urlopen(url)
        print(
            "Downloading the model from the DeepLabCut server @Harvard -> Go Crimson!!! {}....".format(
                url
            )
        )
        total_size = int(response.getheader("Content-Length"))
        pbar = tqdm(unit="B", total=total_size, position=0)
        filename, _ = urllib.request.urlretrieve(url, reporthook=show_progress)
        with tarfile.open(filename, mode="r:gz") as tar:
            tar.extractall(target_dir, members=tarfilenamecutting(tar))
    else:
        models = [
            fn
            for fn in neturls.keys()
            if "resnet_" not in fn
            and "efficientnet" not in fn
            and "mobilenet_" not in fn
        ]
        print("Model does not exist: ", modelname)
        print("Pick one of the following: ", models)