def METHOD_NAME(from_path='result/result_transformer/libri_sd1337_fmllrBase960-F-N-K-RA/model-1000000.ckpt', display_settings=False):
    ''' Wrapper that loads the transformer model from checkpoint path '''
    # load config and paras
    all_states = torch.load(from_path, map_location='cpu')
    config = all_states['Settings']['Config']
    paras = all_states['Settings']['Paras']
    # handling older checkpoints
    if not hasattr(paras, 'multi_gpu'):
        setattr(paras, 'multi_gpu', False)
    if 'prune_headids' not in config['transformer']:
        config['transformer']['prune_headids'] = None
    # display checkpoint settings
    if display_settings:
        for cluster in config:
            print(cluster + ':')
            for item in config[cluster]:
                print('\t' + str(item) + ': ', config[cluster][item])
        print('paras:')
        v_paras = vars(paras)
        for item in v_paras:
            print('\t' + str(item) + ': ', v_paras[item])
    # load model with Tester
    from transformer.solver import Tester
    tester = Tester(config, paras)
    tester.set_model(inference=True, with_head=False, from_path=from_path)
    return tester