code: def _get_set_state(self, grad_values):
      config = self._get_config()
  
      opt1 = build_optimizer(config)
      opt1.set_param_groups(self._parameters(), lr=1, momentum=0.9)
      self.assertIsInstance(opt1, self._instance_to_test())
  
      self._set_gradient(self._parameters(), grad_values)
      opt1.step(where=0)
  
      if config["name"] == "zero":
          opt1.consolidate_state_dict()
  
      state = opt1.get_classy_state()
  
      opt2 = build_optimizer(config)
      opt2.set_param_groups(self._parameters(), lr=2)
  
      self.assertNotEqual(opt1.options_view.lr, opt2.options_view.lr)
      opt2.set_classy_state(state)
      self.assertEqual(opt1.options_view.lr, opt2.options_view.lr)
  
      for i in range(len(opt1.optimizer.param_groups[0]["params"])):
          self.assertTrue(
              torch.allclose(
                  opt1.optimizer.param_groups[0]["params"][i],
                  opt2.optimizer.param_groups[0]["params"][i],
              )
          )
  
      if config["name"] == "zero":
          opt2.consolidate_state_dict()
  
      self._compare_momentum_values(
          opt1.get_classy_state()["optim"],
          opt2.get_classy_state()["optim"],
      )
  
      # check if the optimizers behave the same on params update
      mock_classy_vision_model1 = self._parameters()
      mock_classy_vision_model2 = self._parameters()
      self._set_gradient(mock_classy_vision_model1, grad_values)
      self._set_gradient(mock_classy_vision_model2, grad_values)
      opt1 = build_optimizer(config)
      opt1.set_param_groups(mock_classy_vision_model1)
      opt2 = build_optimizer(config)
      opt2.set_param_groups(mock_classy_vision_model2)
      opt1.step(where=0)
      opt2.step(where=0)
      for i in range(len(opt1.optimizer.param_groups[0]["params"])):
          print(opt1.optimizer.param_groups[0]["params"][i])
          self.assertTrue(
              torch.allclose(
                  opt1.optimizer.param_groups[0]["params"][i],
                  opt2.optimizer.param_groups[0]["params"][i],
              )
          )
  
      if config["name"] == "zero":
          opt1.consolidate_state_dict()
          opt2.consolidate_state_dict()
  
      self._compare_momentum_values(
          opt1.get_classy_state()["optim"],
          opt2.get_classy_state()["optim"],
      )
  
imports: from abc import ABC, abstractmethod
  from unittest.mock import MagicMock
  import torch
  import torch.nn
  from classy_vision.generic.util import split_batchnorm_params
  from classy_vision.models import ClassyModel
  from classy_vision.optim import build_optimizer, build_optimizer_schedulers
  from classy_vision.optim.param_scheduler import LinearParamScheduler
label_desc: Use image node_id 7 for calling the TestOptimizer obj's underlying member method code with example usage: obj._get_set_state(grad_values) without return types