modelscope中， 为啥不使用gpu，只使用cpu？

"modelscope中， 为啥不使用gpu，只使用cpu？
def t2t_generator_result(input_filepath, output_filepath):
os.makedirs(INPUT_DIR, exist_ok=True) # noqa: F405
os.makedirs(OUTPUT_DIR, exist_ok=True) # noqa: F405
task = Tasks.text2text_generation

# model = r""damo/nlp_mt5_zero-shot-augment_chinese-base""
model = os.path.join(MODEL_DIR, ModelFile.TRAIN_OUTPUT_DIR)  # noqa: F405
if torch.cuda.is_available():
    device = ""cuda""
    print(""CUDA is available. You can use GPU."")
else:
    device = ""cpu""
    print(""CUDA is not available. You can only use CPU."")
pipeline_ins = pipeline(task=task, model=model,device=""gpu"")

df = pd.read_csv(input_filepath, encoding=""utf-8"")
labels = ""城中村,居民小区,聚类市场,农村非住宅,农村住宅,企事业单位,商务楼宇,沿街商铺""

outputs = []
for index, row in tqdm(
    df.iterrows(),
    total=len(df),
    desc=""Processing"",
    bar_format=""{l_bar}{bar} | {n_fmt}/{total_fmt} [{elapsed}<{remaining}]"",
):
    output = {}
    full_name = row[""full_name""]
    first_label = row[""first_lable""]
    text_str = f""文本分类。\n候选标签：{labels}\n文本内容：{full_name}""
    result = pipeline_ins(text_str)
    print(result)
    output[""full_name""] = full_name
    output[""first_lable""] = first_label
    output[""new_labels""] = result
    outputs.append(output)

matched_df = pd.DataFrame(outputs)
matched_df.to_csv(output_filepath, index=False, encoding=""utf-8"")"