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 __init__(
      self,
      dict_path=None,
      name="Spelling_Aug",
      aug_min=1,
      aug_max=10,
      aug_p=0.3,
      stopwords=None,
      tokenizer=None,
      reverse_tokenizer=None,
      include_reverse=True,
      stopwords_regex=None,
      verbose=0,
  ):
      super().__init__(
          action=Action.SUBSTITUTE,
          name=name,
          aug_p=aug_p,
          aug_min=aug_min,
          aug_max=aug_max,
          stopwords=stopwords,
          tokenizer=tokenizer,
          reverse_tokenizer=reverse_tokenizer,
          device="cpu",
          verbose=verbose,
          stopwords_regex=stopwords_regex,
          include_detail=False,
      )
  
      self.dict_path = (
          dict_path
          if dict_path
          else os.path.join(
              LibraryUtil.get_res_dir(),
              "word",
              "spelling",
              "spelling_en.txt",
          )
      )
      self.include_reverse = include_reverse
      self.model = self.get_model(force_reload=False)
  
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 1 to create a new SpellingAug object from inherited base classes: WordAugmenter with example: obj = SpellingAug(dict_path, name, aug_min, aug_max, aug_p, stopwords, tokenizer, reverse_tokenizer, include_reverse, stopwords_regex, verbose)