code: def test_top_k_categorical_accuracy_accumulates_and_resets_correctly(
      self, device: str
  ):
      accuracy = CategoricalAccuracy(top_k=2)
      predictions = torch.tensor(
          [[0.35, 0.25, 0.1, 0.1, 0.2], [0.1, 0.6, 0.1, 0.2, 0.0]],
          device=device,
      )
      targets = torch.tensor([0, 3], device=device)
      accuracy(predictions, targets)
      accuracy(predictions, targets)
      accuracy(predictions, torch.tensor([4, 4], device=device))
      accuracy(predictions, torch.tensor([4, 4], device=device))
      actual_accuracy = accuracy.get_metric(reset=True)
      assert actual_accuracy == 0.50
      assert accuracy.correct_count == 0.0
      assert accuracy.total_count == 0.0
  
imports: import pytest
  import torch
  from torch.testing import assert_allclose
  from allennlp.common.checks import ConfigurationError
  from allennlp.common.testing import AllenNlpTestCase, multi_device, global_distributed_metric, run_distributed_test
  from allennlp.training.metrics import CategoricalAccuracy
label_desc: Use image node_id 3 for calling the CategoricalAccuracyTest obj's underlying member method code with example usage: obj.test_top_k_categorical_accuracy_accumulates_and_resets_correctly(device) without return types