code: def test_multiple_distributed_runs(self):
      predictions = [
          torch.tensor(
              [
                  [0.35, 0.25, 0.1, 0.1, 0.2],
                  [0.1, 0.6, 0.1, 0.2, 0.0],
                  [0.1, 0.6, 0.1, 0.2, 0.0],
              ]
          ),
          torch.tensor(
              [
                  [0.1, 0.5, 0.1, 0.2, 0.0],
                  [0.1, 0.2, 0.1, 0.7, 0.0],
                  [0.1, 0.6, 0.1, 0.2, 0.0],
              ]
          ),
      ]
      targets = [torch.tensor([0, 4, 1]), torch.tensor([0, 3, 0])]
      metric_kwargs = {
          "predictions": predictions,
          "gold_labels": targets,
      }
      desired_metrics = {}
      for i, (p, r, f) in enumerate(
          zip(
              self.desired_precisions,
              self.desired_recalls,
              self.desired_fscores,
          )
      ):
          desired_metrics[f"{i}-precision"] = p
          desired_metrics[f"{i}-recall"] = r
          desired_metrics[f"{i}-fscore"] = f
  
      # compute desired averages with sklearn
      num_labels = self.predictions.size(1)
      labels = np.arange(num_labels)
      for avg in ["macro", "micro", "weighted"]:
          (
              avg_precision,
              avg_recall,
              avg_fscore,
              _,
          ) = precision_recall_fscore_support(
              self.targets.cpu().numpy(),
              self.predictions.argmax(dim=1).cpu().numpy(),
              average=avg,
              labels=labels,  # necessary to consider missing labels from 'true' and 'pred'
          )
          desired_metrics[f"{avg}-precision"] = avg_precision
          desired_metrics[f"{avg}-recall"] = avg_recall
          desired_metrics[f"{avg}-fscore"] = avg_fscore
  
      run_distributed_test(
          [-1, -1],
          multiple_runs,
          FBetaVerboseMeasure(),
          metric_kwargs,
          desired_metrics,
          exact=False,
      )
  
imports: from typing import Any, Dict, List, Tuple, Union
  import torch
  import numpy
  from sklearn.metrics import precision_recall_fscore_support
  from torch.testing import assert_allclose
  import pytest
  from allennlp.common.checks import ConfigurationError
  from allennlp.common.testing import AllenNlpTestCase, multi_device, run_distributed_test, global_distributed_metric
  from allennlp.training.metrics import FBetaVerboseMeasure
label_desc: Use image node_id 20 for calling the FBetaVerboseMeasureTest obj's underlying member method code with example usage: obj.test_multiple_distributed_runs() without return types