def METHOD_NAME():
    parser = argparse.ArgumentParser(
        description="Paddle Slim Static with acoustic model & vocoder.")
    parser.add_argument(
        "--batch_size", type=int, default=1, help="Minibatch size.")
    parser.add_argument("--batch_num", type=int, default=1, help="Batch number")
    parser.add_argument(
        "--ngpu", type=int, default=1, help="if ngpu=0, use cpu.")
    # model_path save_path
    parser.add_argument(
        "--inference_dir", type=str, help="dir to save inference models")
    parser.add_argument(
        '--model_name',
        type=str,
        default='fastspeech2_csmsc',
        choices=[
            'speedyspeech_csmsc',
            'fastspeech2_csmsc',
            'fastspeech2_aishell3',
            'fastspeech2_ljspeech',
            'fastspeech2_vctk',
            'fastspeech2_mix',
            'pwgan_csmsc',
            'pwgan_aishell3',
            'pwgan_ljspeech',
            'pwgan_vctk',
            'mb_melgan_csmsc',
            'hifigan_csmsc',
            'hifigan_aishell3',
            'hifigan_ljspeech',
            'hifigan_vctk',
            'pwgan_opencpop',
            'hifigan_opencpop',
        ],
        help='Choose model type of tts task.')
    parser.add_argument(
        "--algo", type=str, default='avg', help="calibration algorithm.")
    parser.add_argument(
        "--round_type",
        type=str,
        default='round',
        help="The method of converting the quantized weights.")
    parser.add_argument(
        "--hist_percent",
        type=float,
        default=0.9999,
        help="The percentile of algo:hist.")
    parser.add_argument(
        "--is_full_quantize",
        type=str2bool,
        default=False,
        help="Whether is full quantization or not.")
    parser.add_argument(
        "--bias_correction",
        type=str2bool,
        default=False,
        help="Whether to use bias correction.")
    parser.add_argument(
        "--ce_test", type=str2bool, default=False, help="Whether to CE test.")
    parser.add_argument(
        "--onnx_format",
        type=str2bool,
        default=False,
        help="Whether to export the quantized model with format of ONNX.")
    parser.add_argument(
        "--phones-dict", type=str, default=None, help="phone vocabulary file.")
    parser.add_argument(
        "--speaker-dict",
        type=str,
        default=None,
        help="speaker id map file for multiple speaker model.")
    parser.add_argument("--dev-metadata", type=str, help="dev data.")
    parser.add_argument(
        "--quantizable_op_type",
        type=list,
        nargs='+',
        default=[
            "conv2d_transpose", "conv2d", "depthwise_conv2d", "mul", "matmul",
            "matmul_v2"
        ],
        help="The list of op types that will be quantized.")
    args = parser.METHOD_NAME()
    return args