How to use the nncf.dynamic_graph.patch_torch_operators function in nncf

def main():
    """Creates a cl parser"""
    parser = argparse.ArgumentParser(description='Evaluation script for landmarks detection network')
    parser.add_argument('--device', '-d', default=0, type=int)
    parser.add_argument('--val_data_root', dest='val', required=True, type=str, help='Path to val data.')
    parser.add_argument('--val_list', dest='v_list', required=False, type=str, help='Path to test data image list.')
    parser.add_argument('--val_landmarks', dest='v_land', default='', required=False, type=str,
                        help='Path to landmarks for test images.')
    parser.add_argument('--val_batch_size', type=int, default=1, help='Validation batch size.')
    parser.add_argument('--snapshot', type=str, default=None, help='Snapshot to evaluate.')
    parser.add_argument('--dataset', choices=['vgg', 'celeb', 'ngd'], type=str, default='vgg', help='Dataset.')
    parser.add_argument('-c', '--compr_config', help='Path to a file with compression parameters', required=False)
    args = parser.parse_args()

    if args.compr_config:
        patch_torch_operators()

    with torch.cuda.device(args.device):
        start_evaluation(args)

parser.add_argument('--show_failed', action='store_true', help='Show misclassified pairs.')
    parser.add_argument('--model', choices=models_backbones.keys(), type=str, default='rmnet', help='Model type.')
    parser.add_argument('--engine', choices=['pt', 'ie'], type=str, default='pt', help='Framework to use for eval.')

    # IE-related options
    parser.add_argument('--fr_model', type=str, required=False)
    parser.add_argument('--lm_model', type=str, required=False)
    parser.add_argument('-pp', '--plugin_dir', type=str, default=None, help='Path to a plugin folder')
    parser.add_argument('-c', '--compr_config', help='Path to a file with compression parameters', required=False)
    args = parser.parse_args()

    if args.engine == 'pt':
        assert args.snap is not None, 'To evaluate PyTorch snapshot, please, specify --snap option.'

        if args.compr_config:
            patch_torch_operators()

        with torch.cuda.device(args.devices[0]):
            data, embeddings_fun = load_test_dataset(args)
            model = models_backbones[args.model](embedding_size=args.embed_size, feature=True)

            if args.compr_config:
                config = Config.from_json(args.compr_config)
                compression_algo = create_compression_algorithm(model, config)
                model = compression_algo.model

            model = load_model_state(model, args.snap, args.devices[0])
            evaluate(args, data, model, embeddings_fun, args.val_batch_size, args.dump_embeddings,
                     args.roc_fname, args.snap, True, args.show_failed)

            if args.compr_config and "sparsity_level" in compression_algo.statistics():
                log.info("Sparsity level: {0:.2f}".format(

#other parameters
    parser.add_argument('--devices', type=int, nargs='+', default=[0], help='CUDA devices to use.')
    parser.add_argument('--val_batch_size', type=int, default=20, help='Validation batch size.')
    parser.add_argument('--snap_folder', type=str, default='./snapshots/', help='Folder to save snapshots.')
    parser.add_argument('--snap_prefix', type=str, default='FaceReidNet', help='Prefix for snapshots.')
    parser.add_argument('--snap_to_resume', type=str, default=None, help='Snapshot to resume.')
    parser.add_argument('--weighted', action='store_true')
    parser.add_argument('-c', '--compr_config', help='Path to a file with compression parameters', required=False)
    parser.add_argument('--to-onnx', type=str, metavar='PATH', default=None, help='Export to ONNX model by given path')

    args = parser.parse_args()
    log.info('Arguments:\n' + pformat(args.__dict__))

    if args.compr_config:
        patch_torch_operators()

    with torch.cuda.device(args.devices[0]):
        train(args)