code: def test_lightning_optimizer(tmpdir, auto):
      """Test that optimizer are correctly wrapped by our LightningOptimizer."""
  
      class TestModel(BoringModel):
          def configure_optimizers(self):
              optimizer = torch.optim.SGD(
                  self.layer.parameters(), lr=0.1
              )
              if not auto:
                  # note: this is not recommended, only done for coverage
                  optimizer = LightningOptimizer(optimizer)
              lr_scheduler = torch.optim.lr_scheduler.StepLR(
                  optimizer, step_size=1
              )
              return [optimizer], [lr_scheduler]
  
      model = TestModel()
      trainer = Trainer(
          default_root_dir=tmpdir,
          limit_train_batches=1,
          limit_val_batches=1,
          max_epochs=1,
          enable_model_summary=False,
      )
      trainer.fit(model)
  
      lightning_opt = model.optimizers()
      assert str(lightning_opt) == "Lightning" + str(
          lightning_opt.optimizer
      )
  
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 1 for calling a global function with example usage: test_lightning_optimizer(tmpdir, auto) and returns: list,  list