code: def test_fbeta_handles_no_prediction_true_other_class(
      self, device: str
  ):
      predictions = torch.tensor(
          [[0.65, 0.35], [0.0, 0.0]], device=device
      )
      # preds = [0, NA]
      targets = torch.tensor([1, 0], device=device)
  
      fbeta = FBetaVerboseMeasure()
      fbeta(predictions, targets)
      metric = fbeta.get_metric()
      precisions = [
          metric[f"{i}-precision"] for i in range(predictions.size(1))
      ]
      recalls = [
          metric[f"{i}-recall"] for i in range(predictions.size(1))
      ]
      fscores = [
          metric[f"{i}-fscore"] for i in range(predictions.size(1))
      ]
  
      assert_allclose(precisions, [0.0, 0.0])
      assert_allclose(recalls, [0.0, 0.0])
      assert_allclose(fscores, [0.0, 0.0])
  
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 17 for calling the FBetaVerboseMeasureTest obj's underlying member method code with example usage: obj.test_fbeta_handles_no_prediction_true_other_class(device) without return types