def METHOD_NAME():
    parser = argparse.ArgumentParser(description="PyTorch Object Detection Inference")
    parser.add_argument(
        "--config-file",
        default="/private/home/fmassa/github/detectron.pytorch_v2/configs/e2e_faster_rcnn_R_50_C4_1x_caffe2.yaml",
        metavar="FILE",
        help="path to config file",
    )
    parser.add_argument("--local_rank", type=int, default=0)
    parser.add_argument(
        "--ckpt",
        help="The path to the checkpoint for test, default is the latest checkpoint.",
        default=None,
    )
    parser.add_argument(
        "opts",
        help="Modify config options using the command-line",
        default=None,
        nargs=argparse.REMAINDER,
    )
    parser.add_argument('--bf16', action='store_true', default=False,
                        help='enable BF16 by IPEX autocast')
    parser.add_argument('--bf32', action='store_true', default=False,
                        help='enable IPEX bf32 path')
    parser.add_argument('--jit', action='store_true', default=False,
                        help='enable IPEX JIT path')
    parser.add_argument('-i', '--iterations', default=-1, type=int, metavar='N',
                        help='number of total iterations to run')
    parser.add_argument('--iter-warmup', default=-1, type=int, metavar='N',
                        help='number of warm-up iterations to run')
    parser.add_argument("--enable-profiling", action="store_true", default=False)
    args = parser.parse_args()
    num_gpus = int(os.environ["WORLD_SIZE"]) if "WORLD_SIZE" in os.environ else 1
    distributed = num_gpus > 1
    if distributed:
        torch.cuda.set_device(args.local_rank)
        torch.distributed.init_process_group(
            backend="nccl", init_method="env://"
        )
        synchronize()
    cfg.merge_from_file(args.config_file)
    cfg.merge_from_list(args.opts)
    cfg.freeze()
    save_dir = ""
    logger = setup_logger("maskrcnn_benchmark", save_dir, get_rank())
    logger.info("Using {} GPUs".format(num_gpus))
    logger.info(cfg)
    logger.info("Collecting env info (might take some time)")
    logger.info("\n" + collect_env_info())
    model = build_detection_model(cfg)
    model.to(cfg.MODEL.DEVICE)
    # Initialize mixed-precision if necessary
    # use_mixed_precision = cfg.DTYPE == 'float16'
    # amp_handle = amp.init(enabled=use_mixed_precision, verbose=cfg.AMP_VERBOSE)
    output_dir = cfg.OUTPUT_DIR
    checkpointer = DetectronCheckpointer(cfg, model, save_dir=output_dir)
    ckpt = cfg.MODEL.WEIGHT if args.ckpt is None else args.ckpt
    _ = checkpointer.load(ckpt, use_latest=args.ckpt is None)
    iou_types = ("bbox",)
    if cfg.MODEL.MASK_ON:
        iou_types = iou_types + ("segm",)
    if cfg.MODEL.KEYPOINT_ON:
        iou_types = iou_types + ("keypoints",)
    output_folders = [None] * len(cfg.DATASETS.TEST)
    dataset_names = cfg.DATASETS.TEST
    if cfg.OUTPUT_DIR:
        for idx, dataset_name in enumerate(dataset_names):
            output_folder = os.path.join(cfg.OUTPUT_DIR, "inference", dataset_name)
            mkdir(output_folder)
            output_folders[idx] = output_folder
    data_loaders_val = make_data_loader(cfg, is_train=False, is_distributed=distributed)
    for output_folder, dataset_name, data_loader_val in zip(output_folders, dataset_names, data_loaders_val):
        inference(
            model,
            data_loader_val,
            dataset_name=dataset_name,
            ims_per_patch=cfg.TEST.IMS_PER_BATCH,
            iou_types=iou_types,
            box_only=False if cfg.MODEL.RETINANET_ON else cfg.MODEL.RPN_ONLY,
            bbox_aug=cfg.TEST.BBOX_AUG.ENABLED,
            device=cfg.MODEL.DEVICE,
            expected_results=cfg.TEST.EXPECTED_RESULTS,
            expected_results_sigma_tol=cfg.TEST.EXPECTED_RESULTS_SIGMA_TOL,
            output_folder=output_folder,
            bf16=args.bf16,
            bf32=args.bf32,
            jit=args.jit,
            iterations=args.iterations,
            iter_warmup=args.iter_warmup,
            enable_profiling=args.enable_profiling
        )
        synchronize()