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 stop words excluded in the TF-IDF vectorizer.
  
  Returns:
      list: Frozenset of stop words used by the TF-IDF vectorizer (can be converted to list).
docstr_tok: ["Return","the","stop","words","excluded","in","the","TF-IDF","vectorizer",".","Returns",":","list",":","Frozenset","of","stop","words","used","by","the","TF-IDF","vectorizer","(","can","be","converted","to","list",")","."]
code: def get_stop_words(self):
      """Return the stop words excluded in the TF-IDF vectorizer.
  
      Returns:
          list: Frozenset of stop words used by the TF-IDF vectorizer (can be converted to list).
      """
      try:
          self.stop_words = self.tf.get_stop_words()
      except Exception:
          self.stop_words = (
              "Run .tokenize_text() and .fit_tfidf() first"
          )
      return self.stop_words
  
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 7 for calling the TfidfRecommender obj's underlying member method code with example usage: obj.get_stop_words() and returns: self