def METHOD_NAME():
    args = get_args()
    log_file = args.log_file
    preprocessed_data_dir = args.preprocessed_data_dir
    output_folder = args.postprocessed_data_dir
    ground_truths = args.label_data_dir
    output_dtype = dtype_map[args.output_dtype]
    num_threads_nifti_save = args.num_threads_nifti_save
    all_in_gpu = "None"
    force_separate_z = None
    interp_order = 3
    interp_order_z = 0
    # Load necessary metadata.
    print("Loading necessary metadata...")
    with open(os.path.join(preprocessed_data_dir, "preprocessed_files.pkl"), "rb") as f:
        preprocessed_files = pickle.load(f)
    dictionaries = []
    for preprocessed_file in preprocessed_files:
        with open(os.path.join(preprocessed_data_dir, preprocessed_file + ".pkl"), "rb") as f:
            dct = pickle.load(f)[1]
            dictionaries.append(dct)
    # Load predictions from loadgen accuracy log.
    print("Loading loadgen accuracy log...")
    predictions = load_loadgen_log(log_file, output_dtype, dictionaries)
    # Save predictions
    # This runs in multiprocess
    print("Running postprocessing with multiple threads...")
    save_predictions_MLPerf(predictions, output_folder, preprocessed_files, dictionaries, num_threads_nifti_save, all_in_gpu, force_separate_z, interp_order, interp_order_z)
    # Run evaluation
    print("Running evaluation...")
    evaluate_regions(output_folder, ground_truths, get_brats_regions())
    # Load evaluation summary
    print("Loading evaluation summary...")
    with open(os.path.join(output_folder, "summary.csv")) as f:
        for line in f:
            words = line.split(",")
            if words[0] == "mean":
                whole = float(words[1])
                core = float(words[2])
                enhancing = float(words[3])
                mean = (whole + core + enhancing) / 3
                print("Accuracy: mean = {:.5f}, whole tumor = {:.4f}, tumor core = {:.4f}, enhancing tumor = {:.4f}".format(mean, whole, core, enhancing))
                break
    print("Done!")