code: def test_fbeta_multiclass_metric(self, device: str):
      self.predictions = self.predictions.to(device)
      self.targets = self.targets.to(device)
  
      fbeta = FBetaVerboseMeasure()
      fbeta(self.predictions, self.targets)
      metric = fbeta.get_metric()
      precisions = [
          metric[f"{i}-precision"]
          for i in range(self.predictions.size(1))
      ]
      recalls = [
          metric[f"{i}-recall"] for i in range(self.predictions.size(1))
      ]
      fscores = [
          metric[f"{i}-fscore"] for i in range(self.predictions.size(1))
      ]
  
      # check value
      assert_allclose(precisions, self.desired_precisions)
      assert_allclose(recalls, self.desired_recalls)
      assert_allclose(fscores, self.desired_fscores)
  
      # check type
      assert isinstance(precisions, List)
      assert isinstance(recalls, List)
      assert isinstance(fscores, List)
  
imports: from typing import Any, Dict, List, Tuple, Union
  import torch
  import numpy
  from sklearn.metrics import precision_recall_fscore_support
  from torch.testing import assert_allclose
  import pytest
  from allennlp.common.checks import ConfigurationError
  from allennlp.common.testing import AllenNlpTestCase, multi_device, run_distributed_test, global_distributed_metric
  from allennlp.training.metrics import FBetaVerboseMeasure
label_desc: Use image node_id 5 for calling the FBetaVerboseMeasureTest obj's underlying member method code with example usage: obj.test_fbeta_multiclass_metric(device) without return types