code: def test_fbeta_multiclass_macro_average_metric_multireturn(self):
      fbeta = FBeta(
          average="macro", make_deterministic=self.make_deterministic
      )
      fbeta.update(self.predictions, self.targets)
      fscore, precision, recall = fbeta.compute()
  
      macro_precision = numpy.mean(self.desired_precisions)
      macro_recall = numpy.mean(self.desired_recalls)
      macro_fscore = numpy.mean(self.desired_fscores)
  
      # check type
      assert isinstance(precision, float)
      assert isinstance(recall, float)
      assert isinstance(fscore, float)
  
      # check value
      numpy.testing.assert_almost_equal(
          precision, macro_precision, decimal=2
      )
      numpy.testing.assert_almost_equal(recall, macro_recall, decimal=2)
      numpy.testing.assert_almost_equal(fscore, macro_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 12 for calling the BaseFBetaTest obj's underlying member method code with example usage: obj.test_fbeta_multiclass_macro_average_metric_multireturn() without return types