def METHOD_NAME(model_index, data_index):
    model_pretrain, dataset = CLIP_MODELS[model_index], DATA_FOLDER[data_index]
    data, labels = load_data(dataset)
    model, processor = load_model(model_pretrain)
    res, acc = classify_imagenetr(
        data, labels, model, processor, device='cuda')
    m_rep, d_rep = model_pretrain.replace('/', '-'), dataset.replace('/', '-')
    # if exist some folder
    if not os.path.exists('clip_res/'):
        os.makedirs('clip_res/')
    fname = f'clip_res/{m_rep}_{d_rep}'
    np.savetxt(fname + '.txt', res, fmt='%d')
    with open(fname + '.txt', 'w') as fp:
        fp.write(fname + ',' + str(acc))
    return res, acc