code: def test_fbeta_multilabel_with_mask(self, device: str):
      self.predictions = self.predictions.to(device)
      self.targets = self.targets.to(device)
  
      mask = torch.tensor(
          [True, True, True, True, True, False], device=device
      ).unsqueeze(-1)
  
      fbeta = FBetaMultiLabelMeasure()
      fbeta(self.predictions, self.targets, mask)
      metric = fbeta.get_metric()
      precisions = metric["precision"]
      recalls = metric["recall"]
      fscores = metric["fscore"]
  
      assert_allclose(fbeta._pred_sum.tolist(), [3, 3, 3, 4, 1])
      assert_allclose(fbeta._true_sum.tolist(), [4, 5, 2, 4, 0])
      assert_allclose(
          fbeta._true_positive_sum.tolist(), [3, 3, 2, 4, 0]
      )
  
      desired_precisions = [3 / 3, 3 / 3, 2 / 3, 4 / 4, 0 / 1]
      desired_recalls = [3 / 4, 3 / 5, 2 / 2, 4 / 4, 0.00]
      desired_fscores = [
          (2 * p * r) / (p + r) if p + r != 0.0 else 0.0
          for p, r in zip(desired_precisions, desired_recalls)
      ]
      assert_allclose(precisions, desired_precisions)
      assert_allclose(recalls, desired_recalls)
      assert_allclose(fscores, desired_fscores)
  
imports: from typing import Dict, List, Tuple, Union, Any
  import pytest
  import torch
  from allennlp.common.checks import ConfigurationError
  from allennlp.common.testing import AllenNlpTestCase, global_distributed_metric, multi_device, run_distributed_test
  from sklearn.metrics import precision_recall_fscore_support
  from torch.testing import assert_allclose
  from allennlp.training.metrics import FBetaMultiLabelMeasure
label_desc: Use image node_id 7 for calling the FBetaMultiLabelMeasureTest obj's underlying member method code with example usage: obj.test_fbeta_multilabel_with_mask(device) without return types