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: Return the tokens generated by the TF-IDF vectorizer.
  
  Returns:
      dict: Dictionary of tokens generated by the TF-IDF vectorizer.
docstr_tok: ["Return","the","tokens","generated","by","the","TF-IDF","vectorizer",".","Returns",":","dict",":","Dictionary","of","tokens","generated","by","the","TF-IDF","vectorizer","."]
code: def get_tokens(self):
      """Return the tokens generated by the TF-IDF vectorizer.
  
      Returns:
          dict: Dictionary of tokens generated by the TF-IDF vectorizer.
      """
      try:
          self.tokens = self.tf.vocabulary_
      except Exception:
          self.tokens = "Run .tokenize_text() and .fit_tfidf() first"
      return self.tokens
  
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 6 for calling the TfidfRecommender obj's underlying member method code with example usage: obj.get_tokens() and returns: self