code: def __init__(
      self, comm, n_layers, n_source_vocab, n_target_vocab, n_units
  ):
      super(Decoder, self).__init__(
          embed_y=L.EmbedID(n_target_vocab, n_units),
          # Corresponding encoder LSTM will be invoked on process 0.
          mn_decoder=chainermn.links.create_multi_node_n_step_rnn(
              L.NStepLSTM(n_layers, n_units, n_units, 0.1),
              comm,
              rank_in=0,
              rank_out=None,
          ),
          W=L.Linear(n_units, n_target_vocab),
      )
      self.comm = comm
      self.n_layers = n_layers
      self.n_units = n_units
  
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 chainermn.functions
  import chainermn.links
  import europal
label_desc: Use image node_id 1 to create a new Decoder object from inherited base classes: chainer with example: obj = Decoder(comm, n_layers, n_source_vocab, n_target_vocab, n_units)