我正在嘗試創建一個 sklearn 管道，該管道將首先提取文本中的平均字長，然后使用StandardScaler.定制變壓器class AverageWordLengthExtractor(BaseEstimator, TransformerMixin):    def __init__(self):        pass    def average_word_length(self, text):        return np.mean([len(word) for word in text.split( )])    def fit(self, x, y=None):        return self    def transform(self, x , y=None):        return pd.DataFrame(pd.Series(x).apply(self.average_word_length))我的目標是實現這一目標。X 是一個帶有文本值的熊貓系列。這行得通。    extractor=AverageWordLengthExtractor()    print(extractor.transform(X[:10]))    sc=StandardScaler()    print(sc.fit_transform(extractor.transform(X[:10])))我為此創建的管道是。pipeline = Pipeline([('text_length', AverageWordLengthExtractor(), 'scale', StandardScaler())])但pipeline.fit_transform()產生以下錯誤。Traceback (most recent call last):  File "custom_transformer.py", line 48, in <module>    main()  File "custom_transformer.py", line 43, in main    'scale', StandardScaler())])  File "/opt/conda/lib/python3.6/site-packages/sklearn/pipeline.py", line 114, in __init__    self._validate_steps()  File "/opt/conda/lib/python3.6/site-packages/sklearn/pipeline.py", line 146, in _validate_steps    names, estimators = zip(*self.steps)ValueError: too many values to unpack (expected 2)