code: def __call__(self, *inputs):
      xs = inputs[: len(inputs) // 2]
      ys = inputs[len(inputs) // 2 :]
  
      xs = [x[::-1] for x in xs]
  
      eos = self.xp.zeros(1, self.xp.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
      )
  
      reporter.report({"loss": loss.data}, self)
      n_words = concat_ys_out.shape[0]
      perp = self.xp.exp(loss.data * batch / n_words)
      reporter.report({"perp": perp}, self)
      return loss
  
imports: import argparse
  import math
  import os.path
  import pickle
  import re
  import sys
  import time
  from nltk.translate import bleu_score
  import numpy
  import six
  import chainer
  from chainer import cuda
  import chainer.functions
  import chainer.links
  from chainer import reporter
  from chainer import training
  from chainer.training import extensions
  import chainermn
  import europal
label_desc: Use image node_id 2 for calling the Seq2seq obj's underlying member method code with example usage: obj.__call__() and returns: loss