code: def test_bleu_batch_micro():
      bleu = Bleu(ngram=4, average="micro")
  
      # Batch size 3
      hypotheses = [corpus.cand_1, corpus.cand_2a, corpus.cand_2b]
      refs = [
          corpus.references_1,
          corpus.references_2,
          corpus.references_2,
      ]
      bleu.update((hypotheses, refs))
  
      with warnings.catch_warnings():
          warnings.simplefilter("ignore")
          reference_bleu_score = corpus_bleu(refs, hypotheses)
      assert pytest.approx(bleu.compute()) == reference_bleu_score
  
      assert (
          pytest.approx(bleu._corpus_bleu(refs, hypotheses))
          == reference_bleu_score
      )
  
imports: import os
  import warnings
  from collections import Counter
  import pytest
  import torch
  from nltk.translate.bleu_score import corpus_bleu, sentence_bleu, SmoothingFunction
  import ignite.distributed
  from ignite.exceptions import NotComputableError
  from ignite.metrics.nlp import Bleu
  from .None import CorpusForTest
label_desc: Use image node_id 14 for calling a global function with example usage: test_bleu_batch_micro() without return types