code: def test_distributed_accuracy_unequal_batches(self):
      predictions = [
          torch.tensor(
              [[0.35, 0.25, 0.1, 0.1, 0.2], [0.1, 0.6, 0.1, 0.2, 0.0]]
          ),
          torch.tensor([[0.1, 0.2, 0.5, 0.2, 0.0]]),
      ]
      targets = [torch.tensor([0, 3]), torch.tensor([0])]
      mask = [torch.tensor([False, True]), torch.tensor([True])]
      metric_kwargs = {
          "predictions": predictions,
          "gold_labels": targets,
          "mask": mask,
      }
      desired_accuracy = 0.5
      run_distributed_test(
          [-1, -1],
          global_distributed_metric,
          CategoricalAccuracy(top_k=2),
          metric_kwargs,
          desired_accuracy,
          exact=False,
      )
  
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 12 for calling the CategoricalAccuracyTest obj's underlying member method code with example usage: obj.test_distributed_accuracy_unequal_batches() without return types