code: def _train(self):
      def _tokenizer(text, token_pattern=r"(?u)\b\w\w+\b"):
          token_pattern = re.compile(token_pattern)
          return token_pattern.findall(text)
  
      train_data = sklearn.datasets.fetch_20newsgroups(
          subset="train", remove=("headers", "footers", "quotes")
      )
      train_x = train_data.data
  
      train_x_tokens = [_tokenizer(x) for x in train_x]
  
      tfidf_model = nmw.TfIdf()
      tfidf_model.train(train_x_tokens)
      tfidf_model.save(".")
  
      aug = naw.TfIdfAug(model_path=".", tokenizer=_tokenizer)
  
      texts = [
          "The quick brown fox jumps over the lazy dog",
          "asdasd test apple dog asd asd",
      ]
  
      for text in texts:
          augmented_text = aug.augment(text)
          self.assertNotEqual(text, augmented_text)
  
      self.assertLess(0, len(texts))
  
file_path: nlpaug/test/augmenter/word/test_tfidf.py
imports: import unittest
  import os
  import re
  from dotenv import load_dotenv
  import sklearn.datasets
  import nlpaug.augmenter.word
  import nlpaug.model.word_stats
  from nlpaug.util import Action
label_desc: Use image node_id 4 for calling the TestTfIdf obj's underlying member method code with example usage: obj._train() and returns: token_pattern