code: def test_multiple_distributed_runs(self):
      predictions = [
          torch.tensor(
              [
                  [0.55, 0.25, 0.10, 0.10, 0.20],
                  [0.10, 0.60, 0.10, 0.95, 0.00],
                  [0.90, 0.80, 0.75, 0.80, 0.00],
              ]
          ),
          torch.tensor(
              [
                  [0.49, 0.50, 0.95, 0.55, 0.00],
                  [0.60, 0.49, 0.60, 0.65, 0.85],
                  [0.85, 0.40, 0.10, 0.20, 0.00],
              ]
          ),
      ]
      targets = [
          torch.tensor(
              [[1, 1, 0, 0, 0], [0, 1, 0, 1, 0], [1, 1, 0, 1, 0]]
          ),
          torch.tensor(
              [[1, 1, 1, 1, 0], [1, 1, 1, 1, 0], [0, 0, 0, 0, 0]]
          ),
      ]
      metric_kwargs = {
          "predictions": predictions,
          "gold_labels": targets,
      }
      desired_metrics = {
          "precision": self.desired_precisions,
          "recall": self.desired_recalls,
          "fscore": self.desired_fscores,
      }
      run_distributed_test(
          [-1, -1],
          multiple_runs,
          FBetaMultiLabelMeasure(),
          metric_kwargs,
          desired_metrics,
          exact=False,
      )
  
imports: from typing import Dict, List, Tuple, Union, Any
  import pytest
  import torch
  from allennlp.common.checks import ConfigurationError
  from allennlp.common.testing import AllenNlpTestCase, global_distributed_metric, multi_device, run_distributed_test
  from sklearn.metrics import precision_recall_fscore_support
  from torch.testing import assert_allclose
  from allennlp.training.metrics import FBetaMultiLabelMeasure
label_desc: Use image node_id 20 for calling the FBetaMultiLabelMeasureTest obj's underlying member method code with example usage: obj.test_multiple_distributed_runs() without return types