我正在嘗試為 sklearn 管道創建一個自定義轉換器，它將提取特定文本的平均字長，然后在其上應用標準縮放器以標準化數據集。我正在將一系列文本傳遞給管道。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))然后我創建了一個這樣的管道。pipeline = Pipeline(['text_length', AverageWordLengthExtractor(),                          'scale', StandardScaler()])當我在此管道上執行 fit_transform 時，出現錯誤， 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)TypeError: zip argument #2 must support iteration