code: def forward(self, xs, ys):
      xs = [x[::-1] for x in xs]
  
      eos = self.xp.array([EOS], numpy.int32)
      ys_in = [F.concat([eos, y], axis=0) for y in ys]
      ys_out = [F.concat([y, eos], axis=0) for y in ys]
  
      # Both xs and ys_in are lists of arrays.
      exs = sequence_embed(self.embed_x, xs)
      eys = sequence_embed(self.embed_y, ys_in)
  
      batch = len(xs)
      # None represents a zero vector in an encoder.
      hx, cx, _ = self.encoder(None, None, exs)
      _, _, os = self.decoder(hx, cx, eys)
  
      # It is faster to concatenate data before calculating loss
      # because only one matrix multiplication is called.
      concat_os = F.concat(os, axis=0)
      concat_ys_out = F.concat(ys_out, axis=0)
      loss = (
          F.sum(
              F.softmax_cross_entropy(
                  self.W(concat_os), concat_ys_out, reduce="no"
              )
          )
          / batch
      )
  
      chainer.report({"loss": loss}, self)
      n_words = concat_ys_out.shape[0]
      perp = self.xp.exp(loss.array * batch / n_words)
      chainer.report({"perp": perp}, self)
      return loss
  
imports: import argparse
  import datetime
  import io
  from nltk.translate import bleu_score
  import numpy
  import progressbar
  import six
  import chainer
  import chainer.functions
  import chainer.links
  from chainer import training
  from chainer.training import extensions
  import chainerx
label_desc: Use image node_id 2 for calling the Seq2seq obj's underlying member method code with example usage: obj.forward(xs, ys) and returns: loss