如何在机器学习管道中添加自定义中间预处理器来处理scikit-learn中的n-gram列？

from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.feature_extraction.text import CountVectorizer

class NGramPreprocessor(BaseEstimator, TransformerMixin):
    def __init__(self, ngram_range=(1, 1)):
        self.ngram_range = ngram_range
        self.vectorizer = CountVectorizer(ngram_range=ngram_range)
    
    def fit(self, X, y=None):
        self.vectorizer.fit(X)
        return self
    
    def transform(self, X):
        return self.vectorizer.transform(X)

from sklearn.pipeline import Pipeline
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.svm import SVC

pipeline = Pipeline([
    ('ngram_preprocessor', NGramPreprocessor(ngram_range=(1, 2))),
    ('tfidf_transformer', TfidfTransformer()),
    ('classifier', SVC())
])