code: def embedding(self, sentences, oov_way='avg'):
          
          data_iter = self.data_loader(sentences=sentences)
          batches = []
          for token_ids, valid_length, token_types in data_iter:
              token_ids = token_ids.as_in_context(self.ctx)
              valid_length = valid_length.as_in_context(self.ctx)
              token_types = token_types.as_in_context(self.ctx)
              sequence_outputs = self.bert(token_ids, token_types,
                                           valid_length.astype(self.dtype))
              for token_id, sequence_output in zip(token_ids.asnumpy(),
                                                   sequence_outputs.asnumpy()):
                  batches.append((token_id, sequence_output))
          return self.oov(batches, oov_way)
docs: Get tokens, tokens embedding
  
          Parameters
          ----------
          sentences : List[str]
              sentences for encoding.
          oov_way : str, default avg.
              use **avg**, **sum** or **last** to get token embedding for those out of
              vocabulary words
  
          Returns
          -------
          List[(List[str], List[ndarray])]
              List of tokens, and tokens embedding