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: Make URL clickable.
  
  Args:
      address (str): URL address to make clickable.
docstr_tok: ["Make","URL","clickable",".","Args",":","address","(","str",")",":","URL","address","to","make","clickable","."]
code: def __make_clickable(self, address):
      """Make URL clickable.
  
      Args:
          address (str): URL address to make clickable.
      """
      return '<a href="{0}">{0}</a>'.format(address)
  
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 12 for calling the TfidfRecommender obj's underlying member method code with example usage: obj.__make_clickable(address) and returns: str