code: def test_step_with_optimizer_closure_2(tmpdir):
      class TestModel(BoringModel):
          def __init__(self):
              super().__init__()
              self.automatic_optimization = False
  
          def training_step(self, batch, batch_idx):
              opt = self.optimizers()
              x = batch[0]
              loss = self(x).sum()
  
              def optimizer_closure():
                  # emulate bayesian optimization.
                  num_backward = 1
                  for backward_idx in range(num_backward + 1):
                      retain_graph = num_backward != backward_idx
                      self.manual_backward(
                          loss, retain_graph=retain_graph
                      )
  
              weight_before = self.layer.weight.clone()
              opt.step(closure=optimizer_closure)
              weight_after = self.layer.weight.clone()
              assert not torch.equal(weight_before, weight_after)
  
      model = TestModel()
      limit_train_batches = 4
      trainer = Trainer(
          default_root_dir=tmpdir,
          limit_train_batches=limit_train_batches,
          limit_val_batches=0,
          max_epochs=1,
          log_every_n_steps=1,
      )
  
      with mock.patch.object(
          Strategy, "backward", wraps=trainer.strategy.backward
      ) as bwd_mock:
          trainer.fit(model)
      assert bwd_mock.call_count == limit_train_batches * 2
      assert trainer.global_step == limit_train_batches
  
imports: import collections
  import contextlib
  from copy import deepcopy
  from unittest import mock
  from unittest.mock import ANY, call, patch
  import pytest
  import torch
  import torch.distributed
  import torch.nn.functional
  from lightning.fabric.utilities.exceptions import MisconfigurationException
  from lightning.fabric.utilities.imports import _TORCH_GREATER_EQUAL_2_0
  from lightning.pytorch import Trainer, seed_everything
  from lightning.pytorch.demos.boring_classes import BoringModel, ManualOptimBoringModel
  from lightning.pytorch.strategies import Strategy
  from tests_pytorch.helpers.runif import RunIf
label_desc: Use image node_id 9 for calling a global function with example usage: test_step_with_optimizer_closure_2(tmpdir) without return types