code: def __call__(self, trainer):
      device = self.device
  
      with chainer.no_backprop_mode():
          references = []
          hypotheses = []
          for i in range(0, len(self.test_data), self.batch):
              sources, targets = zip(
                  *self.test_data[i : i + self.batch]
              )
              references.extend([[t.tolist()] for t in targets])
  
              sources = [device.send(x) for x in sources]
              ys = [
                  y.tolist()
                  for y in self.model.translate(
                      sources, self.max_length
                  )
              ]
              hypotheses.extend(ys)
  
      bleu = bleu_score.corpus_bleu(
          references,
          hypotheses,
          smoothing_function=bleu_score.SmoothingFunction().method1,
      )
      chainer.report({self.key: bleu})
  
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 CalculateBleu obj's underlying member method code with example usage: obj.__call__(trainer) without return types