def forward(self, inputs, token_types, valid_length=None, masked_positions=None):  
outputs = []<EOL>seq_out, attention_out = self._encode_sequence(inputs, token_types, valid_length)<EOL>outputs.append(seq_out)<EOL>if self.encoder._output_all_encodings:<EOL><INDENT>assert isinstance(seq_out, list)<EOL>output = seq_out[-<NUM_LIT:1>]<EOL><DEDENT>else:<EOL><INDENT>output = seq_out<EOL><DEDENT>if attention_out:<EOL><INDENT>outputs.append(attention_out)<EOL><DEDENT>if self._use_pooler:<EOL><INDENT>pooled_out = self._apply_pooling(output)<EOL>outputs.append(pooled_out)<EOL>if self._use_classifier:<EOL><INDENT>next_sentence_classifier_out = self.classifier(pooled_out)<EOL>outputs.append(next_sentence_classifier_out)<EOL><DEDENT><DEDENT>if self._use_decoder:<EOL><INDENT>assert masked_positions is not None,'<STR_LIT>'<EOL>decoder_out = self._decode(output, masked_positions)<EOL>outputs.append(decoder_out)<EOL><DEDENT>return tuple(outputs) if len(outputs) > <NUM_LIT:1> else outputs[<NUM_LIT:0>]<EOL>
Generate the representation given the inputs.

        This is used in training or fine-tuning a BERT model.