code: def test_lightning_optimizer_automatic_optimization_optimizer_zero_grad(
      tmpdir,
  ):
      """Test overriding zero_grad works in automatic_optimization."""
  
      class TestModel(BoringModel):
          def optimizer_zero_grad(self, epoch, batch_idx, optimizer):
              if batch_idx % 2 == 0:
                  optimizer.zero_grad()
  
          def configure_optimizers(self):
              optimizer_1 = torch.optim.SGD(
                  self.layer.parameters(), lr=0.1
              )
              lr_scheduler = torch.optim.lr_scheduler.StepLR(
                  optimizer_1, step_size=1
              )
              return [optimizer_1], [lr_scheduler]
  
      model = TestModel()
      trainer = Trainer(
          default_root_dir=tmpdir,
          limit_train_batches=20,
          limit_val_batches=1,
          max_epochs=1,
          enable_model_summary=False,
      )
  
      with patch("torch.optim.SGD.zero_grad") as sgd_zero_grad:
          trainer.fit(model)
      assert sgd_zero_grad.call_count == 10
  
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 6 for calling a global function with example usage: test_lightning_optimizer_automatic_optimization_optimizer_zero_grad(tmpdir) and returns: list,  list