code: def test_state():
      model = torch.nn.Linear(3, 4)
      optimizer = torch.optim.Adam(model.parameters())
      lightning_optimizer = LightningOptimizer(optimizer)
  
      # test state
      assert optimizer.state == lightning_optimizer.state
      lightning_optimizer.state = optimizer.state
      assert optimizer.state == lightning_optimizer.state
  
      # test param_groups
      assert optimizer.param_groups == lightning_optimizer.param_groups
      lightning_optimizer.param_groups = optimizer.param_groups
      assert optimizer.param_groups == lightning_optimizer.param_groups
  
      # test defaults
      assert optimizer.defaults == lightning_optimizer.defaults
      lightning_optimizer.defaults = optimizer.defaults
      assert optimizer.defaults == lightning_optimizer.defaults
  
      assert isinstance(lightning_optimizer, LightningOptimizer)
      assert isinstance(lightning_optimizer, Adam)
      assert isinstance(lightning_optimizer, Optimizer)
  
      assert optimizer.state_dict() == lightning_optimizer.state_dict()
  
imports: from copy import deepcopy
  from unittest.mock import DEFAULT, Mock, patch
  import pytest
  import torch
  from lightning.pytorch import Trainer
  from lightning.pytorch.core.optimizer import LightningOptimizer
  from lightning.pytorch.demos.boring_classes import BoringModel
  from lightning.pytorch.loops.optimization.automatic import Closure
  from lightning.pytorch.tuner.tuning import Tuner
  from torch.optim import SGD, Adam, Optimizer
label_desc: Use image node_id 4 for calling a global function with example usage: test_state() without return types