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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def forward(self, src_seq, tgt_seq, src_valid_length=None, tgt_valid_length=None):
 #pylint: disable=arguments-differ """Generate the prediction given the src_seq and tgt_seq. This is used in training an NMT model. Parameters src_seq : NDArray tgt_seq : NDArray src_valid_length : NDArray or None tgt_valid_length : NDArray or None Returns ------- outputs : NDArray Shape (batch_size, tgt_length, tgt_word_num) additional_outputs : list of list Additional outputs of encoder and decoder, e.g, the attention weights """

        additional_outputs = []
        encoder_outputs, encoder_additional_outputs = self.encode(src_seq,
                                                                  valid_length=src_valid_length)
        decoder_states = self.decoder.init_state_from_encoder(encoder_outputs,
                                                              encoder_valid_length=src_valid_length)
        outputs, _, decoder_additional_outputs =\
            self.decode_seq(tgt_seq, decoder_states, tgt_valid_length)
        additional_outputs.append(encoder_additional_outputs)
        additional_outputs.append(decoder_additional_outputs)
        return outputs, additional_outputs