code: def test_multiple_distributed_runs(self):
      predictions = [
          torch.Tensor([[0, 1, 3, 5, 2, 4]]),
          torch.Tensor([[0, 3, 2, 1, 0, 0]]),
      ]
  
      gold_indices = [
          torch.Tensor([[0, 1, 3, 5, 2, 4]]),
          torch.Tensor([[0, 3, 2, 1, 0, 0]]),
      ]
  
      label_predictions = [
          torch.Tensor([[0, 5, 2, 3, 3, 3]]),
          torch.Tensor([[7, 4, 8, 2, 0, 0]]),
      ]
  
      gold_labels = [
          torch.Tensor([[0, 5, 2, 1, 4, 2]]),
          torch.Tensor([[0, 4, 8, 2, 0, 0]]),
      ]
  
      mask = [
          torch.tensor([[True, True, True, True, True, True]]),
          torch.tensor([[True, True, True, True, False, False]]),
      ]
  
      metric_kwargs = {
          "predicted_indices": predictions,
          "gold_indices": gold_indices,
          "predicted_labels": label_predictions,
          "gold_labels": gold_labels,
          "mask": mask,
      }
  
      desired_metrics = {
          "UAS": 1.0,
          "LAS": 0.6,
          "UEM": 1.0,
          "LEM": 0.0,
      }
      run_distributed_test(
          [-1, -1],
          multiple_runs,
          AttachmentScores(),
          metric_kwargs,
          desired_metrics,
          exact=True,
      )
  
file_path: allennlp/tests/training/metrics/attachment_scores_test.py
imports: from typing import Any, Dict, List, Tuple, Union
  import torch
  from allennlp.common.testing import AllenNlpTestCase, multi_device, run_distributed_test, global_distributed_metric
  from allennlp.training.metrics import AttachmentScores
label_desc: Use image node_id 8 for calling the AttachmentScoresTest obj's underlying member method code with example usage: obj.test_multiple_distributed_runs() without return types