class_docstr: Augmenter that apply word splitting for augmentation.
  
  :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 int min_char: If word less than this value, do not draw word for augmentation
  :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.SplitAug()
code: def split(self, data):
      if not data or not data.strip():
          return data
  
      change_seq = 0
      doc = Doc(data, self.tokenizer(data))
  
      aug_idxes = self._get_aug_idxes(doc.get_original_tokens())
      aug_idxes.sort(reverse=True)
  
      if aug_idxes is None or len(aug_idxes) == 0:
          if self.include_detail:
              return data, []
          return data
  
      for aug_idx in aug_idxes:
          target_token = doc.get_token(aug_idx).get_latest_token().token
          separate_pos = self.sample(len(target_token), 1)
          prev_token = target_token[:separate_pos]
          next_token = target_token[separate_pos:]
  
          change_seq += 1
          doc.add_change_log(
              aug_idx,
              new_token=next_token,
              action=Action.SPLIT,
              change_seq=self.parent_change_seq + change_seq,
          )
          doc.add_token(
              aug_idx,
              token=prev_token,
              action=Action.SPLIT,
              change_seq=self.parent_change_seq + change_seq,
          )
  
      if self.include_detail:
          return (
              self.reverse_tokenizer(doc.get_augmented_tokens()),
              doc.get_change_logs(),
          )
      else:
          return self.reverse_tokenizer(doc.get_augmented_tokens())
  
imports: from nlpaug.augmenter.word import WordAugmenter
  from nlpaug.util import Action, Doc
label_desc: Use image node_id 3 for calling the SplitAug obj's underlying member method code with example usage: obj.split(data) and returns: data, data, self,  doc, self, data,  list