code: def __init__(self, docs, LEMMA=True, TOKENIZER=None):
      self._tokenize = TOKENIZER if TOKENIZER else word_tokenize
      self._process = (
          lambda x: word_process(self._tokenize(x))
          if LEMMA
          else self._tokenize
      )
      from sklearn.feature_extraction.text import TfidfVectorizer
  
      self._vectorizer = TfidfVectorizer(tokenizer=self._process)
      self._vectorizer.fit_transform(docs)
      self._idf_dict = dict(
          zip(
              self._vectorizer.get_feature_names_out(),
              self._vectorizer.idf_,
          )
      )
  
imports: from sematch.utility import FileIO
  from nltk.stem import WordNetLemmatizer
  from nltk.corpus import wordnet
  import nltk
  from collections import Counter
  import itertools
  import string
  import re
label_desc: Use image node_id 1 to create a new TFIDF object  with example: obj = TFIDF(docs, LEMMA, TOKENIZER)