code: def test_distributed_fbeta_measure(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 = {
          "precision": 1.0,
          "recall": 0.333333333,
          "f1": 0.499999999,
      }
      run_distributed_test(
          [-1, -1],
          global_distributed_metric,
          F1Measure(positive_label=0),
          metric_kwargs,
          desired_metrics,
          exact=False,
      )
  
file_path: allennlp/tests/training/metrics/f1_measure_test.py
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, run_distributed_test, global_distributed_metric
  from allennlp.training.metrics import F1Measure
label_desc: Use image node_id 6 for calling the F1MeasureTest obj's underlying member method code with example usage: obj.test_distributed_fbeta_measure() without return types