class_docstr: Transforms an input by replacing its tokens with words of mask language
  predicted.
  To accelerate transformation for long text, input single sentence to
  language model rather than whole text.
docstr: :param str masked_model: masked language model to predicate candidates
  :param str device: indicate utilize cpu or which gpu device to run
      neural network
  :param int accrue_threshold: threshold of Bert results to pick
  :param max_sent_size: max_sent_size
  :param int trans_min: Minimum number of character will be augmented.
  :param int trans_max: Maximum number of character will be augmented.
      If None is passed, number of augmentation is calculated via aup_char_p.
      If calculated result from aug_p is smaller than aug_max, will use
      calculated result from aup_char_p. Otherwise, using aug_max.
  :param float trans_p: Percentage of character (per token) will be
      augmented.
  :param list stop_words: List of words which will be skipped from augment
      operation.
code: def __init__(
      self,
      trans_min=1,
      trans_max=10,
      trans_p=0.1,
      stop_words=None,
      islist=False,
      **kwargs,
  ):
      r"""
      :param str masked_model: masked language model to predicate candidates
      :param str device: indicate utilize cpu or which gpu device to run
          neural network
      :param int accrue_threshold: threshold of Bert results to pick
      :param max_sent_size: max_sent_size
      :param int trans_min: Minimum number of character will be augmented.
      :param int trans_max: Maximum number of character will be augmented.
          If None is passed, number of augmentation is calculated via aup_char_p.
          If calculated result from aug_p is smaller than aug_max, will use
          calculated result from aup_char_p. Otherwise, using aug_max.
      :param float trans_p: Percentage of character (per token) will be
          augmented.
      :param list stop_words: List of words which will be skipped from augment
          operation.
  
      """
      super().__init__(
          trans_min=trans_min,
          trans_max=trans_max,
          trans_p=trans_p,
          stop_words=stop_words,
      )
      self.islist = islist
  
file_path: textflint/textflint/generation/transformation/UTCN/cn_nasal.py
imports: from ...transformation import CnWordSubstitute
  from pypinyin import lazy_pinyin
  from Pinyin2Hanzi import DefaultHmmParams
  from Pinyin2Hanzi import viterbi
label_desc: Use image node_id 1 to create a new CnNasal object from inherited base classes: CnWordSubstitute with example: obj = CnNasal(trans_min, trans_max, trans_p, stop_words, islist)