code: def test_lightning_optimizer_manual_optimization_and_accumulated_gradients(
      tmpdir,
  ):
      """Test that the user can use our LightningOptimizer.
  
      Not recommended.
  
      """
  
      class TestModel(BoringModel):
          def __init__(self):
              super().__init__()
              self.automatic_optimization = False
  
          def training_step(self, batch, batch_idx):
              opt_1, opt_2 = self.optimizers()
  
              assert isinstance(opt_1, LightningOptimizer)
              assert isinstance(opt_2, LightningOptimizer)
  
              def closure(opt):
                  loss = self.step(batch)
                  opt.zero_grad()
                  self.manual_backward(loss)
  
              if batch_idx % 2 == 0:
                  closure(opt_1)
                  opt_1.step()
  
              closure(opt_2)
              step_output = opt_2.step()
              # check that the step output is returned with manual optimization
              # since the optimizer is mocked, the step output is a Mock
              assert isinstance(step_output, Mock)
  
          def configure_optimizers(self):
              optimizer_1 = torch.optim.SGD(
                  self.layer.parameters(), lr=0.1
              )
              optimizer_2 = torch.optim.Adam(
                  self.layer.parameters(), lr=0.1
              )
              lr_scheduler = torch.optim.lr_scheduler.StepLR(
                  optimizer_1, step_size=1
              )
              return [optimizer_1, optimizer_2], [lr_scheduler]
  
      model = TestModel()
      trainer = Trainer(
          default_root_dir=tmpdir,
          limit_train_batches=8,
          limit_val_batches=1,
          max_epochs=1,
          enable_model_summary=False,
      )
  
      with patch.multiple(
          torch.optim.SGD, zero_grad=DEFAULT, step=DEFAULT
      ) as sgd, patch.multiple(
          torch.optim.Adam, zero_grad=DEFAULT, step=DEFAULT
      ) as adam:
          trainer.fit(model)
  
      assert sgd["step"].call_count == 4
      assert adam["step"].call_count == 8
  
      assert sgd["zero_grad"].call_count == 4
      assert adam["zero_grad"].call_count == 8
  
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 3 for calling a global function with example usage: test_lightning_optimizer_manual_optimization_and_accumulated_gradients(tmpdir) and returns: list,  list