code: def replace_random_tokens_bow(self,
                                    n_samples,  # type: int
                                    replacement='',  # type: str
                                    random_state=None,
                                    min_replace=1,  # type: Union[int, float]
                                    max_replace=1.0, # type: Union[int, float]
                                    ):
          # type: (...) -> List[Tuple[str, int, np.ndarray]]
          
          if not self.vocab:
              nomask = np.array([], dtype=int)
              return [('', 0, nomask)] * n_samples
  
          min_replace, max_replace = self._get_min_max(min_replace, max_replace,
                                                       len(self.vocab))
          rng = check_random_state(random_state)
          replace_sizes = rng.randint(low=min_replace, high=max_replace + 1,
                                      size=n_samples)
          res = []
          for num_to_replace in replace_sizes:
              tokens_to_replace = set(rng.choice(self.vocab, num_to_replace,
                                                 replace=False))
              idx_to_replace = [idx for idx, token in enumerate(self.tokens)
                                if token in tokens_to_replace]
              mask = indices_to_bool_mask(idx_to_replace, len(self.tokens))
              s = self.split.masked(idx_to_replace, replacement)
              res.append((s.text, num_to_replace, mask))
          return res
docs: Return a list of ``(text, replaced_words_count, mask)`` tuples with
          n_samples versions of text with some words replaced.
          If a word is replaced, all duplicate words are also replaced
          from the text. By default words are replaced with '', i.e. removed.