code: def test_labels_repartition(self):
      metric = LabelsRepartition()
      f = metric.result()
      self.assertEqual(f, {})
      metric.update([0, 0, 1, 0, 2, 1, 2], [1, 1, 2, 2, 3, 3, 5])
  
      metric.update([0, 3], [7, 8])
  
      f = metric.result()
      reference_dict = {
          0: {1: 2, 2: 1, 7: 1},
          1: {2: 1, 3: 1},
          2: {3: 1, 5: 1},
          3: {8: 1},
      }
      self.assertDictEqual(reference_dict, f)
      metric.update_order([7, 8, 9, 10, 0, 2, 1, 5, 3])
      f = metric.result()
      self.assertDictEqual(reference_dict, f)
      self.assertSequenceEqual(list(f[0].keys()), [7, 2, 1])
      self.assertSequenceEqual(list(f[1].keys()), [2, 3])
      self.assertSequenceEqual(list(f[2].keys()), [5, 3])
      self.assertSequenceEqual(list(f[3].keys()), [8])
  
      # Should not return a defaultdict
      with self.assertRaises(Exception):
          a = f[4]
  
      # Missing class in order
      metric.update_order([7, 8, 9, 10, 0, 2, 1, 5])
      f2 = metric.result()
      reference_dict2 = {
          0: {1: 2, 2: 1, 7: 1},
          1: {
              2: 1,
          },
          2: {5: 1},
          3: {8: 1},
      }
      self.assertDictEqual(reference_dict2, f2)
  
      # Check reset
      metric.reset()
      self.assertEqual(metric.result(), {})
  
imports: import unittest
  import torch
  from avalanche.evaluation.metrics import TaskAwareAccuracy, TopkAccuracy, AverageMeanClassAccuracy, MultiStreamAMCA, ClassAccuracy, TaskAwareLoss, ConfusionMatrix, DiskUsage, MAC, CPUUsage, MaxGPU, MaxRAM, Mean, Sum, ElapsedTime, Forgetting, ForwardTransfer, CumulativeAccuracy, LabelsRepartition
  from tests.unit_tests_utils import FAST_TEST, is_github_action
label_desc: Use image node_id 27 for calling the GeneralMetricTests obj's underlying member method code with example usage: obj.test_labels_repartition() without return types