code: def hybrid_forward(self, F, inputs, token_types, valid_length=None, masked_positions=None):
          # pylint: disable=arguments-differ
          # pylint: disable=unused-argument
          
          outputs = []
          seq_out, attention_out = self._encode_sequence(F, inputs, token_types, valid_length)
          outputs.append(seq_out)
  
          if self.encoder._output_all_encodings:
              assert isinstance(seq_out, list)
              output = seq_out[-1]
          else:
              output = seq_out
  
          if attention_out:
              outputs.append(attention_out)
  
          if self._use_pooler:
              pooled_out = self._apply_pooling(output)
              outputs.append(pooled_out)
              if self._use_classifier:
                  next_sentence_classifier_out = self.classifier(pooled_out)
                  outputs.append(next_sentence_classifier_out)
          if self._use_decoder:
              assert masked_positions is not None, \
                  'masked_positions tensor is required for decoding masked language model'
              decoder_out = self._decode(output, masked_positions)
              outputs.append(decoder_out)
          return tuple(outputs) if len(outputs) > 1 else outputs[0]
docs: Generate the representation given the inputs.
  
          This is used in training or fine-tuning a static (hybridized) BERT model.