class_docstr: Term Frequency - Inverse Document Frequency (TF-IDF) Recommender
  
  This class provides content-based recommendations using TF-IDF vectorization in combination with cosine similarity.
docstr: Fit TF-IDF vectorizer to the cleaned and tokenized text.
  
  Args:
      tf (TfidfVectorizer): sklearn.feature_extraction.text.TfidfVectorizer object defined in .tokenize_text().
      vectors_tokenized (pandas.Series): Each row contains tokens for respective documents separated by spaces.
docstr_tok: ["Fit","TF-IDF","vectorizer","to","the","cleaned","and","tokenized","text",".","Args",":","tf","(","TfidfVectorizer",")",":","sklearn.feature_extraction.text.TfidfVectorizer","object","defined","in",".tokenize_text","(",")",".","vectors_tokenized","(","pandas.Series",")",":","Each","row","contains","tokens","for","respective","documents","separated","by","spaces","."]
code: def fit(self, tf, vectors_tokenized):
      """Fit TF-IDF vectorizer to the cleaned and tokenized text.
  
      Args:
          tf (TfidfVectorizer): sklearn.feature_extraction.text.TfidfVectorizer object defined in .tokenize_text().
          vectors_tokenized (pandas.Series): Each row contains tokens for respective documents separated by spaces.
      """
      self.tfidf_matrix = tf.fit_transform(vectors_tokenized)
  
imports: from sklearn.feature_extraction.text import TfidfVectorizer
  from sklearn.metrics.pairwise import linear_kernel
  from transformers import BertTokenizer
  import re
  import unicodedata
  import pandas
  import numpy
  import nltk
  from nltk.stem.porter import PorterStemmer
label_desc: Use image node_id 5 for calling the TfidfRecommender obj's underlying member method code with example usage: obj.fit(tf, vectors_tokenized) without return types