code: def test_mean_teacher_hook(self):
      device = "cuda:0" if torch.cuda.is_available() else "cpu"
      model = ToyModel2().to(device)
      runner = Runner(
          model=model,
          train_dataloader=dict(
              dataset=DummyDataset(),
              sampler=dict(type="DefaultSampler", shuffle=True),
              batch_size=3,
              num_workers=0,
          ),
          val_dataloader=dict(
              dataset=DummyDataset(),
              sampler=dict(type="DefaultSampler", shuffle=False),
              batch_size=3,
              num_workers=0,
          ),
          val_evaluator=[ToyMetric1()],
          work_dir=self.temp_dir.name,
          default_scope="mmdet",
          optim_wrapper=OptimWrapper(
              torch.optim.Adam(ToyModel().parameters())
          ),
          train_cfg=dict(by_epoch=True, max_epochs=2, val_interval=1),
          val_cfg=dict(),
          default_hooks=dict(logger=None),
          custom_hooks=[dict(type="MeanTeacherHook")],
          experiment_name="test1",
      )
      runner.train()
  
      self.assertTrue(
          osp.exists(osp.join(self.temp_dir.name, "epoch_2.pth"))
      )
      # checkpoint = torch.load(osp.join(self.temp_dir.name, 'epoch_2.pth'))
  
      # load and testing
      runner = Runner(
          model=model,
          test_dataloader=dict(
              dataset=DummyDataset(),
              sampler=dict(type="DefaultSampler", shuffle=True),
              batch_size=3,
              num_workers=0,
          ),
          test_evaluator=[ToyMetric1()],
          test_cfg=dict(),
          work_dir=self.temp_dir.name,
          default_scope="mmdet",
          load_from=osp.join(self.temp_dir.name, "epoch_2.pth"),
          default_hooks=dict(logger=None),
          custom_hooks=[dict(type="MeanTeacherHook")],
          experiment_name="test2",
      )
      runner.test()
  
      @MODEL_WRAPPERS.register_module()
      class DummyWrapper(BaseModel):
          def __init__(self, model):
              super().__init__()
              self.module = model
  
          def forward(self, *args, **kwargs):
              return self.module(*args, **kwargs)
  
      # with model wrapper
      runner = Runner(
          model=DummyWrapper(ToyModel2()),
          test_dataloader=dict(
              dataset=DummyDataset(),
              sampler=dict(type="DefaultSampler", shuffle=True),
              batch_size=3,
              num_workers=0,
          ),
          test_evaluator=[ToyMetric1()],
          test_cfg=dict(),
          work_dir=self.temp_dir.name,
          default_scope="mmdet",
          load_from=osp.join(self.temp_dir.name, "epoch_2.pth"),
          default_hooks=dict(logger=None),
          custom_hooks=[dict(type="MeanTeacherHook")],
          experiment_name="test3",
      )
      runner.test()
  
imports: import os.path
  import tempfile
  from unittest import TestCase
  import torch
  import torch.nn
  from mmengine.evaluator import BaseMetric
  from mmengine.model import BaseModel
  from mmengine.optim import OptimWrapper
  from mmengine.registry import MODEL_WRAPPERS
  from mmengine.runner import Runner
  from torch.utils.data import Dataset
  from mmdet.registry import DATASETS
  from mmdet.utils import register_all_modules
label_desc: Use image node_id 3 for calling the TestMeanTeacherHook obj's underlying member method code with example usage: obj.test_mean_teacher_hook() and returns: self