code: def _test_micro_distrib_integration(device):
      from ignite.engine import Engine
  
      rank = idist.get_rank()
  
      size = len(corpus.chunks)
  
      data = []
      for c in corpus.chunks:
          data += idist.get_world_size() * [c]
  
      def update(_, i):
          return data[i + size * rank]
  
      def _test(metric_device):
          engine = Engine(update)
          m = Bleu(ngram=4, smooth="smooth2", average="micro")
          m.attach(engine, "bleu")
  
          engine.run(data=list(range(size)), max_epochs=1)
  
          assert "bleu" in engine.state.metrics
  
          ref_bleu = 0
          references = []
          candidates = []
          for _candidates, _references in data:
              references.append(_references[0])
              candidates.append(_candidates[0])
          with warnings.catch_warnings():
              warnings.simplefilter("ignore")
              ref_bleu += corpus_bleu(
                  references,
                  candidates,
                  weights=[0.25, 0.25, 0.25, 0.25],
                  smoothing_function=SmoothingFunction().method2,
              )
  
          assert pytest.approx(engine.state.metrics["bleu"]) == ref_bleu
  
      _test("cpu")
  
      if device.type != "xla":
          _test(idist.device())
  
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 17 for calling a global function with example usage: _test_micro_distrib_integration(device) and returns: data