code: def __init__(self, n_layers, n_source_vocab, n_target_vocab, n_units):
      super(Seq2seq, self).__init__()
      with self.init_scope():
          self.embed_x = L.EmbedID(n_source_vocab, n_units)
          self.embed_y = L.EmbedID(n_target_vocab, n_units)
          self.encoder = L.NStepLSTM(n_layers, n_units, n_units, 0.1)
          self.decoder = L.NStepLSTM(n_layers, n_units, n_units, 0.1)
          self.W = L.Linear(n_units, n_target_vocab)
  
      self.n_layers = n_layers
      self.n_units = n_units
  
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 1 to create a new Seq2seq object from inherited base classes: chainer with example: obj = Seq2seq(n_layers, n_source_vocab, n_target_vocab, n_units)