def METHOD_NAME():
    parser = ArgumentParser(add_help=False)
    args = parser.add_argument_group('Options')
    args.add_argument('-h', '--help', action='help', default=SUPPRESS,
                      help='Show this help message and exit.')
    args.add_argument("-m", "--model", type=str, required=True,
                      help="Required. Path to an .xml file with a trained model.")
    args.add_argument("-i", "--input", type=str, required=True,
                      help="Required. Path to an image to infer")
    args.add_argument("-d", "--device", type=str, default="CPU",
                      help="Optional. Specify the target device to infer on; CPU, GPU or HETERO is "
                           "acceptable. The demo will look for a suitable plugin for device specified. Default "
                           "value is CPU")
    args.add_argument("-ni", "--number_iter", type=int, default=1,
                      help="Optional. Number of inference iterations")
    args.add_argument("-cl", "--charlist", type=str, default=str(Path(__file__).resolve().parents[3] / "data/dataset_classes/kondate_nakayosi.txt"),
                      help="Path to the decoding char list file. Default is for Japanese")
    args.add_argument("-dc", "--designated_characters", type=str, default=None, help="Optional. Path to the designated character file")
    args.add_argument("-tk", "--top_k", type=int, default=20, help="Optional. Top k steps in looking up the decoded character, until a designated one is found")
    args.add_argument("-ob", "--output_blob", type=str, default=None, help="Optional. Name of the output layer of the model. Default is None, in which case the demo will read the output name from the model, assuming there is only 1 output layer")
    return parser