code: def test_fbeta_multiclass_with_explicit_label(self):
      # same prediction but with and explicit label
      label = 3
      precision = self._compute(metric="precision", average=label)
      recall = self._compute(metric="recall", average=label)
      fscore = self._compute(metric="fscore", average=label)
  
      desired_precision = self.desired_precisions[label]
      desired_recall = self.desired_recalls[label]
      desired_fscore = self.desired_fscores[label]
      # check value
      numpy.testing.assert_almost_equal(
          precision, desired_precision, decimal=2
      )
      numpy.testing.assert_almost_equal(
          recall, desired_recall, decimal=2
      )
      numpy.testing.assert_almost_equal(
          fscore, desired_fscore, decimal=2
      )
  
imports: from unittest import TestCase
  import numpy
  import torch
  import torch.nn
  from poutyne import F1, BinaryF1, FBeta, Model
label_desc: Use image node_id 11 for calling the BaseFBetaTest obj's underlying member method code with example usage: obj.test_fbeta_multiclass_with_explicit_label() without return types