code: def test_distributed_auc(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[:4], predictions[4:]]
      labels = [labels[:4], labels[4:]]
  
      metric_kwargs = {
          "predictions": predictions,
          "gold_labels": labels,
      }
      desired_auc = metrics.auc(
          false_positive_rates, true_positive_rates
      )
      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 5 for calling the AucTest obj's underlying member method code with example usage: obj.test_distributed_auc() without return types