class_docstr: Augmenter that leverage pre-defined spelling mistake dictionary to simulate spelling mistake.
  
  :param str dict_path: Path of misspelling dictionary
  :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.SpellingAug(dict_path='./spelling_en.txt')
code: def skip_aug(self, token_idxes, tokens):
      results = []
      for token_idx in token_idxes:
          # Some words do not exit. It will be excluded in lucky draw.
          token = tokens[token_idx]
          if (
              token in self.model.dict
              and len(self.model.dict[token]) > 0
          ):
              results.append(token_idx)
  
      return results
  
file_path: nlpaug/nlpaug/augmenter/word/spelling.py
imports: import os
  import nlpaug
  import nlpaug.model.word_dict
  from nlpaug.augmenter.word import WordAugmenter
  from nlpaug.util import Action, Doc, LibraryUtil
label_desc: Use image node_id 2 for calling the SpellingAug obj's underlying member method code with example usage: obj.skip_aug(token_idxes, tokens) and returns: results