code: def setup_method(self):
      super().setup_method()
      self.predictions = torch.tensor(
          [
              [0.55, 0.25, 0.10, 0.10, 0.20],
              [0.10, 0.60, 0.10, 0.95, 0.00],
              [0.90, 0.80, 0.75, 0.80, 0.00],
              [0.49, 0.50, 0.95, 0.55, 0.00],
              [0.60, 0.49, 0.60, 0.65, 0.85],
              [0.85, 0.40, 0.10, 0.20, 0.00],
          ]
      )
      self.targets = torch.tensor(
          [
              [1, 1, 0, 0, 0],
              [0, 1, 0, 1, 0],
              [1, 1, 0, 1, 0],
              [1, 1, 1, 1, 0],
              [1, 1, 1, 1, 0],
              [0, 0, 0, 0, 0],
          ]
      )
  
      # detailed target state
      self.pred_sum = [4, 3, 3, 4, 1]
      self.true_sum = [4, 5, 2, 4, 0]
      self.true_positive_sum = [3, 3, 2, 4, 0]
      self.true_negative_sum = [1, 1, 3, 2, 5]
      self.total_sum = [30, 30, 30, 30, 30]
  
      # true_positive_sum / pred_sum
      desired_precisions = [3 / 4, 3 / 3, 2 / 3, 4 / 4, 0 / 1]
      # true_positive_sum / true_sum
      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)
      ]
      self.desired_precisions = desired_precisions
      self.desired_recalls = desired_recalls
      self.desired_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 1 for calling the FBetaMultiLabelMeasureTest obj's underlying member method code with example usage: obj.setup_method() without return types