code: def test_distributed_accuracy(self):
      gold = torch.tensor(
          [[2, 4, 8], [1, 2, 3], [7, 1, 1], [11, 14, 17]]
      )
      predictions = torch.tensor(
          [
              [[2, 4, 8], [2, 5, 9]],  # 3/3
              [[-1, 2, 4], [3, 8, -1]],  # 2/2
              [[-1, -1, -1], [7, 2, -1]],  # 1/2
              [[12, 13, 17], [11, 13, 18]],  # 2/2
          ]
      )
      mask = torch.tensor(
          [
              [True, True, True],
              [False, True, True],
              [True, True, False],
              [True, False, True],
          ]
      )
      gold = [gold[:2], gold[2:]]
      predictions = [predictions[:2], predictions[2:]]
      mask = [mask[:2], mask[2:]]
  
      metric_kwargs = {
          "predictions": predictions,
          "gold_labels": gold,
          "mask": mask,
      }
      desired_values = {"unigram_recall": 7 / 8}
      run_distributed_test(
          [-1, -1],
          global_distributed_metric,
          UnigramRecall(),
          metric_kwargs,
          desired_values,
          exact=False,
      )
  
file_path: allennlp/tests/training/metrics/unigram_recall_test.py
imports: from typing import Any, Dict, List, Tuple, Union
  import torch
  from torch.testing import assert_allclose
  from allennlp.common.testing import AllenNlpTestCase, multi_device, global_distributed_metric, run_distributed_test
  from allennlp.training.metrics import UnigramRecall
label_desc: Use image node_id 5 for calling the UnigramRecallTest obj's underlying member method code with example usage: obj.test_distributed_accuracy() without return types