code: def test_manual_optimization_and_accumulated_gradient(tmpdir):
      """This test verify that in `automatic_optimization=False`, step is being called only when we shouldn't
      accumulate."""
      seed_everything(234)
  
      class ExtendedModel(BoringModel):
          count = 1
          called = collections.defaultdict(int)
          detach = False
  
          def __init__(self):
              super().__init__()
              self.automatic_optimization = False
  
          @property
          def should_update(self):
              return self.count % 2 == 0
  
          @property
          def should_have_updated(self):
              return self.count % 4 == 0
  
          @property
          def has_gradient(self):
              return self.layer.weight.grad is not None
  
          def on_train_batch_start(self, batch, batch_idx):
              self.called["on_train_batch_start"] += 1
              self.weight_before = self.layer.weight.clone()
  
          def training_step(self, batch, batch_idx):
              self.called["training_step"] += 1
              opt = self.optimizers()
              loss = self.step(batch)
              loss /= loss.clone().detach()
              loss *= 0.1
  
              if self.should_update:
                  self.manual_backward(loss)
                  if self.should_have_updated:
                      opt.step()
                      opt.zero_grad()
  
              return loss.detach() if self.detach else loss
  
          def on_train_batch_end(self, *_):
              self.called["on_train_batch_end"] += 1
              after_before = self.layer.weight.clone()
              if self.should_update and self.should_have_updated:
                  assert not torch.equal(
                      self.weight_before, after_before
                  ), self.count
                  assert_emtpy_grad(self.layer.weight.grad)
              else:
                  assert torch.equal(self.weight_before, after_before)
                  if self.count > 1:
                      if self.count % 4 == 1:
                          assert_emtpy_grad(self.layer.weight.grad)
                      else:
                          assert torch.sum(self.layer.weight.grad) != 0
              self.count += 1
  
          def on_train_epoch_end(self, *_, **__):
              assert self.called["training_step"] == 20
              assert self.called["on_train_batch_start"] == 20
              assert self.called["on_train_batch_end"] == 20
  
      model = ExtendedModel()
      trainer = Trainer(
          max_epochs=1,
          default_root_dir=tmpdir,
          limit_train_batches=20,
          limit_test_batches=0,
          limit_val_batches=0,
          precision="16-mixed",
          accelerator="gpu",
          devices=1,
      )
      trainer.fit(model)
  
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 6 for calling a global function with example usage: test_manual_optimization_and_accumulated_gradient(tmpdir) and returns: unknown, unknown, unknown, unknown