code: def test_fbeta_multilabel_handles_batch_size_of_one(
      self, device: str
  ):
      predictions = torch.tensor(
          [[0.2862, 0.5479, 0.1627, 0.2033]], device=device
      )
      targets = torch.tensor([[0, 1, 0, 0]], device=device)
      mask = torch.tensor([[True]], device=device)
  
      fbeta = FBetaMultiLabelMeasure()
      fbeta(predictions, targets, mask)
      metric = fbeta.get_metric()
      precisions = metric["precision"]
      recalls = metric["recall"]
  
      assert_allclose(precisions, [0.0, 1.0, 0.0, 0.0])
      assert_allclose(recalls, [0.0, 1.0, 0.0, 0.0])
  
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 14 for calling the FBetaMultiLabelMeasureTest obj's underlying member method code with example usage: obj.test_fbeta_multilabel_handles_batch_size_of_one(device) without return types