def METHOD_NAME():
    parser = make_parser()
    args = options.parse_args_and_arch(parser)
    with open(
        args.config,
        "r",
    ) as f:
        config = yaml.load(f, Loader=yaml.FullLoader)
    dict_args = vars(args)
    dict_args.update(config["general"])
    args = Namespace(**dict_args)
    i = 1
    stage_args = []
    while i <= 3:
        var = f"stage{i}"
        tmp_args = copy.deepcopy(dict_args)
        if var in config:
            tmp_args.update(config[var])
            stage_args.append(Namespace(**tmp_args))
            i += 1
        else:
            break
    if args.model_type == "S2S" or args.model_type == "TTS":
        model = SpeechGeneration(stage_args[0])
    elif args.model_type == "S2UT":
        model = S2UT(stage_args[0], stage_args[1] if len(stage_args) > 1 else None)
    elif args.model_type == "MT" or args.model_type == "S2T":
        model = Processing(stage_args[0])
    elif args.model_type == "2StageS2ST":
        model = Cascaded2StageS2ST(stage_args[0], stage_args[1])
    elif args.model_type == "3StageS2ST":
        model = Cascaded3StageS2ST(stage_args[0], stage_args[2], stage_args[1])
    else:
        raise Exception(f"Currently unsupported model type {args.model_type}")
    print(f"Evaluating on dataset - {args.dataset_path}\n")
    if args.dataset_type == "npy":
        dataset = load_dataset_npy(args.dataset_path, dataset_size=args.dataset_size)
    elif args.dataset_type == "raw":
        dataset = load_dataset_raw_to_waveforms(
            args.dataset_path,
            dataset_size=args.dataset_size,
            read_using_soundfile=args.read_using_sf,
        )
    else:
        raise Exception(f"Invalid dataset type {args.dataset_type}")
    model.warm_up(sample=dataset[0], repeat=2)
    run_time, memory, flops = model.gather_all_metrics(dataset, repeat=1)
    print(f"run_time = {run_time}sec \tmemory = {memory}MiB \tflops = {flops}")
    if args.dump_speech_waveforms_dir:
        model.dump_final_speech_output(
            dataset,
            args.dump_speech_waveforms_dir,
            lambda x: x,
            args.target_sr,
            prefix=args.dump_waveform_file_prefix,
        )