code: def test_top_1_accuracy_distributed(self):
      # Simulate test on 2 process DDP execution
      labels = torch.tensor([[0, 0, 2], [2, 0, 0]], dtype=torch.long)
  
      accuracy = TopKClassificationAccuracy(top_k=None)
      proc1_acc = accuracy(logits=self.top_k_logits, labels=labels[0])
      correct1, total1 = (
          accuracy.correct_counts_k,
          accuracy.total_counts_k,
      )
  
      accuracy.reset()
      proc2_acc = accuracy(
          logits=torch.flip(self.top_k_logits, dims=[1]),
          labels=labels[1],
      )  # reverse logits
      correct2, total2 = (
          accuracy.correct_counts_k,
          accuracy.total_counts_k,
      )
  
      correct = torch.stack([correct1, correct2])
      total = torch.stack([total1, total2])
  
      assert correct.shape == torch.Size([2, 1])
      assert total.shape == torch.Size([2, 1])
  
      assert abs(proc1_acc[0] - 0.667) < 1e-3  # 2/3
      assert abs(proc2_acc[0] - 0.333) < 1e-3  # 1/3
  
      accuracy.reset()
      accuracy.correct_counts_k = torch.tensor([correct.sum()])
      accuracy.total_counts_k = torch.tensor([total.sum()])
      acc_topk = accuracy.compute()
      acc_top1 = acc_topk[0]
  
      assert abs(acc_top1 - 0.5) < 1e-3
  
imports: import pytest
  import torch
  from nemo.collections.common.metrics.classification_accuracy import TopKClassificationAccuracy
  from nemo.collections.common.metrics.punct_er import DatasetPunctuationErrorRate, OccurancePunctuationErrorRate, punctuation_error_rate
  from .loss_inputs import ALL_NUM_MEASUREMENTS_ARE_ZERO, NO_ZERO_NUM_MEASUREMENTS, SOME_NUM_MEASUREMENTS_ARE_ZERO
  from .perplexity_inputs import NO_PROBS_NO_LOGITS, ONLY_LOGITS1, ONLY_LOGITS100, ONLY_PROBS, PROBS_AND_LOGITS
  from .pl_utils import LossTester, PerplexityTester
label_desc: Use image node_id 3 for calling the TestCommonMetrics obj's underlying member method code with example usage: obj.test_top_1_accuracy_distributed() without return types