code: def test_gpu(self):
      if torch.cuda.is_available():
          metric = MaxGPU(0)
          self.assertEqual(metric.result(), 0)
          metric.start_thread()  # start thread
          self.assertGreaterEqual(metric.result(), 0)
          metric.stop_thread()  # stop thread
          metric.reset()  # stop thread
          self.assertEqual(metric.result(), 0)
  
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 17 for calling the GeneralMetricTests obj's underlying member method code with example usage: obj.test_gpu() without return types