code: def global_distributed_metric(
      global_rank: int,
      world_size: int,
      gpu_id: Union[int, torch.device],
      metric: Metric,
      metric_kwargs: Dict[str, List[Any]],
      desired_values: Dict[str, Any],
      exact: Union[bool, Tuple[float, float]] = True,
      number_of_runs: int = 1,
  ):
      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 _ in range(number_of_runs):
          metric(**kwargs)
  
      metrics = metric.get_metric(False)
      if not isinstance(metrics, Dict) and not isinstance(
          desired_values, Dict
      ):
          metrics = {"metric_value": metrics}
          desired_values = {"metric_value": desired_values}
  
      # Call `assertion_metrics_values` to check if the metrics have the desired values.
      if isinstance(exact, bool):
          if exact:
              rtol = 0.0
              atol = 0.0
          else:
              rtol = 0.0001
              atol = 1e-05
      else:
          rtol = exact[0]
          atol = exact[1]
  
      assert_metrics_values(metrics, desired_values, rtol, atol)
  
file_path: allennlp/allennlp/common/testing/__init__.py
imports: from typing import Dict, Any, Optional, Union, Tuple, List
  import torch
  from torch.testing import assert_allclose
  import pytest
  from allennlp.common.testing.test_case import AllenNlpTestCase
  from allennlp.common.testing.model_test_case import ModelTestCase
  from allennlp.common.testing.distributed_test import run_distributed_test
  from allennlp.modules.transformer import TransformerModule
  from allennlp.training.metrics import Metric
label_desc: Use image node_id 6 for calling a global function with example usage: global_distributed_metric(global_rank, world_size, gpu_id, metric, metric_kwargs, desired_values, exact, number_of_runs) without return types