class_docstr: Augmenter that leverage TF-IDF statistics to insert or substitute word.
  
  :param str model_path: Downloaded model directory. Either model_path or model is must be provided
  :param str action: Either 'insert or 'substitute'. If value is 'insert', a new word will be injected to random
      position according to TF-IDF calculation. If value is 'substitute', word will be replaced according
      to TF-IDF calculation
  :param int top_k: Controlling lucky draw pool. Top k score token will be used for augmentation. Larger k, more
      token can be used. Default value is 5. If value is None which means using all possible tokens.
  :param float aug_p: Percentage of word will be augmented.
  :param int aug_min: Minimum number of word will be augmented.
  :param int aug_max: Maximum number of word will be augmented. If None is passed, number of augmentation is
      calculated via aup_p. If calculated result from aug_p is smaller than aug_max, will use calculated result
      from aug_p. Otherwise, using aug_max.
  :param list stopwords: List of words which will be skipped from augment operation.
  :param str stopwords_regex: Regular expression for matching words which will be skipped from augment operation.
  :param func tokenizer: Customize tokenization process
  :param func reverse_tokenizer: Customize reverse of tokenization process
  :param str name: Name of this augmenter
  
  >>> import nlpaug.augmenter.word as naw
  >>> aug = naw.TfIdfAug(model_path='.')
code: def get_model(self, force_reload=False):
      return init_tfidf_model(self.model_path, force_reload)
  
file_path: nlpaug/nlpaug/augmenter/word/tfidf.py
imports: from nlpaug.augmenter.word import WordAugmenter
  from nlpaug.util import Action, Doc, WarningException, WarningName, WarningCode, WarningMessage
  import nlpaug.model.word_stats
label_desc: Use image node_id 6 for calling the TfIdfAug obj's underlying member method code with example usage: obj.get_model(force_reload) and returns: init_tfidf_model