code: def test_multiple_distributed_runs(self):
      predictions = [
          torch.tensor(
              [
                  [1.0, 1.5, 1.0],
                  [2.0, 3.0, 3.5],
              ]
          ),
          torch.tensor(
              [
                  [4.0, 5.0, 5.5],
                  [6.0, 7.0, 7.5],
              ]
          ),
      ]
      targets = [
          torch.tensor(
              [
                  [0.0, 1.0, 0.0],
                  [2.0, 2.0, 0.0],
              ]
          ),
          torch.tensor(
              [
                  [4.0, 5.0, 0.0],
                  [7.0, 7.0, 0.0],
              ]
          ),
      ]
      metric_kwargs = {
          "predictions": predictions,
          "gold_labels": targets,
      }
      desired_values = {"mae": 21.0 / 12.0}
      run_distributed_test(
          [-1, -1],
          multiple_runs,
          MeanAbsoluteError(),
          metric_kwargs,
          desired_values,
          exact=True,
      )
  
file_path: allennlp/tests/training/metrics/mean_absolute_error_test.py
imports: from typing import Any, Dict, List, Tuple, Union
  import torch
  from allennlp.common.testing import AllenNlpTestCase, global_distributed_metric, multi_device, run_distributed_test
  from allennlp.training.metrics import MeanAbsoluteError
label_desc: Use image node_id 3 for calling the MeanAbsoluteErrorTest obj's underlying member method code with example usage: obj.test_multiple_distributed_runs() without return types