code: def forward(self, inputs, token_types, valid_length=None):  # pylint: disable=arguments-differ
          
          _, pooler_out = self.bert(inputs, token_types, valid_length)
          return self.classifier(pooler_out)
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, num_classes)