code: def hybrid_forward(self, F, inputs, token_types, valid_length=None):
          # pylint: disable=arguments-differ
          # pylint: disable=unused-argument
          
          bert_output = self.bert(inputs, token_types, valid_length)
          output = self.span_classifier(bert_output)
          return output
docs: Generate the unnormalized score for the given the input sequences.
  
          Parameters
          ----------
          inputs : NDArray, shape (batch_size, seq_length)
              Input words for the sequences.
          token_types : NDArray, shape (batch_size, seq_length)
              Token types for the sequences, used to indicate whether the word belongs to the
              first sentence or the second one.
          valid_length : NDArray or None, shape (batch_size,)
              Valid length of the sequence. This is used to mask the padded tokens.
  
          Returns
          -------
          outputs : NDArray
              Shape (batch_size, seq_length, 2)