code: def test_class_accuracy(self):
      metric = ClassAccuracy()
      self.assertDictEqual(metric.result(), {})
  
      metric.update(self.out, self.y, 0)
      result = metric.result()
  
      for task_id, task_classes in result.items():
          self.assertIsInstance(task_id, int)
          self.assertIsInstance(task_classes, dict)
  
          self.assertEqual(task_id, 0)
          expected_n_classes = len(torch.unique(self.y))
          self.assertEqual(len(task_classes), expected_n_classes)
          for class_id, class_accuracy in task_classes.items():
              self.assertLess(class_id, self.n_classes)
              self.assertGreaterEqual(class_id, 0)
              self.assertLessEqual(class_accuracy, 1)
              self.assertGreaterEqual(class_accuracy, 0)
  
      metric.reset()
      self.assertDictEqual(
          metric.result(),
          {0: {int(c): 0.0 for c in torch.unique(self.y)}},
      )
  
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 5 for calling the GeneralMetricTests obj's underlying member method code with example usage: obj.test_class_accuracy() without return types