code: def multiple_runs(
      global_rank: int,
      world_size: int,
      gpu_id: Union[int, torch.device],
      metric: FBetaMultiLabelMeasure,
      metric_kwargs: Dict[str, List[Any]],
      desired_values: Dict[str, Any],
      exact: Union[bool, Tuple[float, float]] = True,
  ):
      kwargs = {}
      # Use the arguments meant for the process with rank `global_rank`.
      for argname in metric_kwargs:
          kwargs[argname] = metric_kwargs[argname][global_rank]
  
      for i in range(200):
          metric(**kwargs)
  
      metric_values = metric.get_metric()
  
      for key in desired_values:
          assert_allclose(desired_values[key], metric_values[key])
  
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 1 for calling a global function with example usage: multiple_runs(global_rank, world_size, gpu_id, metric, metric_kwargs, desired_values, exact) without return types