code: def test_fbeta_multiclass_micro_average_metric(self):
      precision = self._compute(metric="precision", average="micro")
      recall = self._compute(metric="recall", average="micro")
      fscore = self._compute(metric="fscore", average="micro")
  
      true_positives = [1, 1, 0, 1, 0]
      false_positives = [0, 3, 0, 0, 0]
      false_negatives = [2, 0, 0, 0, 1]
      mean_true_positive = numpy.mean(true_positives)
      mean_false_positive = numpy.mean(false_positives)
      mean_false_negative = numpy.mean(false_negatives)
  
      micro_precision = mean_true_positive / (
          mean_true_positive + mean_false_positive
      )
      micro_recall = mean_true_positive / (
          mean_true_positive + mean_false_negative
      )
      micro_fscore = (2 * micro_precision * micro_recall) / (
          micro_precision + micro_recall
      )
      # check value
      numpy.testing.assert_almost_equal(
          precision, micro_precision, decimal=2
      )
      numpy.testing.assert_almost_equal(recall, micro_recall, decimal=2)
      numpy.testing.assert_almost_equal(fscore, micro_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 10 for calling the BaseFBetaTest obj's underlying member method code with example usage: obj.test_fbeta_multiclass_micro_average_metric() without return types