from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer # create a list of documents corpus = [ 'This is the first document.', 'This is the second document.', 'And this is the third one.', 'Is this the first document?', ] # create a CountVectorizer object to count the frequency of each word in the documents count_vectorizer = CountVectorizer() # fit the CountVectorizer object to the corpus and transform it into a matrix of word counts word_counts = count_vectorizer.fit_transform(corpus) # create a TfidfTransformer object to calculate the importance of each word in the documents tfidf_transformer = TfidfTransformer() # fit the TfidfTransformer object to the word counts matrix and transform it into a matrix of TF-IDF values tfidf_values = tfidf_transformer.fit_transform(word_counts) # print the TF-IDF values of the first document print(tfidf_values[0])In this code, the corpus is first transformed into a matrix of word counts using the CountVectorizer object. Then, the TfidfTransformer object is used to transform the word count matrix into a matrix of TF-IDF values. Finally, the TF-IDF values of the first document are printed. Therefore, it can be concluded that the `sklearn.feature_extraction.text TfidfTransformer` is a part of the Scikit-learn Python library.