code: def test_mean_absolute_error_computation(self, device: str):
      mae = MeanAbsoluteError()
      predictions = torch.tensor(
          [
              [1.0, 1.5, 1.0],
              [2.0, 3.0, 3.5],
              [4.0, 5.0, 5.5],
              [6.0, 7.0, 7.5],
          ],
          device=device,
      )
      targets = torch.tensor(
          [
              [0.0, 1.0, 0.0],
              [2.0, 2.0, 0.0],
              [4.0, 5.0, 0.0],
              [7.0, 7.0, 0.0],
          ],
          device=device,
      )
      mae(predictions, targets)
      actual_mae_value = mae.get_metric()["mae"]
      assert isinstance(actual_mae_value, float)
      assert actual_mae_value == 21.0 / 12.0
  
      mask = torch.tensor(
          [
              [True, True, False],
              [True, True, False],
              [True, True, False],
              [True, True, False],
          ],
          device=device,
      )
      mae(predictions, targets, mask)
      actual_mae_value = mae.get_metric()["mae"]
      assert isinstance(actual_mae_value, float)
      assert actual_mae_value == (21.0 + 3.5) / (12.0 + 8.0)
  
      new_targets = torch.tensor(
          [
              [2.0, 2.0, 0.0],
              [0.0, 1.0, 0.0],
              [7.0, 7.0, 0.0],
              [4.0, 5.0, 0.0],
          ],
          device=device,
      )
      mae(predictions, new_targets)
      actual_mae_value = mae.get_metric()["mae"]
      assert isinstance(actual_mae_value, float)
      assert actual_mae_value == (21.0 + 3.5 + 32.0) / (
          12.0 + 8.0 + 12.0
      )
  
      mae.reset()
      mae(predictions, new_targets)
      actual_mae_value = mae.get_metric()["mae"]
      assert isinstance(actual_mae_value, float)
      assert actual_mae_value == 32.0 / 12.0
  
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 1 for calling the MeanAbsoluteErrorTest obj's underlying member method code with example usage: obj.test_mean_absolute_error_computation(device) without return types