如何在scikit-learn中归一化TF*IDF或计数？

from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.preprocessing import Normalizer

# 示例文本数据
texts = [
    "This is the first document.",
    "This document is the second document.",
    "And this is the third one.",
    "Is this the first document?"
]

# 创建TF-IDF向量化器
tfidf_vectorizer = TfidfVectorizer()
tfidf_matrix = tfidf_vectorizer.fit_transform(texts)

# 创建归一化器
normalizer = Normalizer(norm='l2')

# 归一化TF-IDF矩阵
normalized_tfidf_matrix = normalizer.fit_transform(tfidf_matrix)

# 创建计数向量化器
count_vectorizer = CountVectorizer()
count_matrix = count_vectorizer.fit_transform(texts)

# 归一化计数矩阵
normalized_count_matrix = normalizer.fit_transform(count_matrix)