code: def test_distributed_auc_unequal_batches(self):
      predictions = torch.randn(8)
      labels = torch.randint(3, 5, (8,), dtype=torch.long)
      # We make sure that the positive label is always present.
      labels[0] = 4
      labels[4] = 4
  
      false_positive_rates, true_positive_rates, _ = metrics.roc_curve(
          labels.cpu().numpy(), predictions.cpu().numpy(), pos_label=4
      )
  
      predictions = [predictions[:2], predictions[2:]]
      labels = [labels[:2], labels[2:]]
  
      metric_kwargs = {
          "predictions": predictions,
          "gold_labels": labels,
      }
      desired_auc = metrics.auc(
          false_positive_rates, true_positive_rates
      )
      with pytest.raises(Exception) as _:
          run_distributed_test(
              [-1, -1],
              global_distributed_metric,
              Auc(positive_label=4),
              metric_kwargs,
              desired_auc,
              exact=False,
          )
  
file_path: allennlp/tests/training/metrics/auc_test.py
imports: import pytest
  import torch
  from sklearn import metrics
  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 Auc
label_desc: Use image node_id 6 for calling the AucTest obj's underlying member method code with example usage: obj.test_distributed_auc_unequal_batches() without return types