class_docstr: Augmenter that apply randomly behavior for augmentation.
  
  :param str mode: Shuffle sentence to left, right, neighbor or random position. For `left`, target sentence
      will be swapped with left sentnece. For `right`, target sentence will be swapped with right sentnece.
      For `neighbor`, target sentence will be swapped with left or right sentnece radomly. For `random`, 
      target sentence will be swapped with any sentnece randomly.
  :param float aug_p: Percentage of sentence will be augmented. 
  :param int aug_min: Minimum number of sentence will be augmented.
  :param int aug_max: Maximum number of sentence 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 func tokenizer: Customize tokenization process
  :param str name: Name of this augmenter
  
  >>> import nlpaug.augmenter.sentence as nas
  >>> aug = nas.RandomSentAug()
code: def swap(self, data):
      if not data:
          return data
  
      if isinstance(data, list):
          all_data = data
      else:
          if data.strip() == "":
              return data
          all_data = [data]
  
      for i, d in enumerate(all_data):
          sentences = self.model.tokenize(d)
          aug_idxes = self._get_random_aug_idxes(sentences)
          for aug_idx in aug_idxes:
              sentences = self.model.predict(sentences, aug_idx)
          all_data[i] = " ".join(sentences)
  
      # TODO: always return array
      if isinstance(data, list):
          return all_data
      else:
          return all_data[0]
  
file_path: nlpaug/nlpaug/augmenter/sentence/random.py
imports: import os
  from nlpaug.augmenter.sentence import SentenceAugmenter
  import nlpaug.model.word_rule
  from nlpaug.util import Action, Doc
label_desc: Use image node_id 3 for calling the RandomSentAug obj's underlying member method code with example usage: obj.swap(data) and returns: data, all_data, all_data, data