code: def test_trainer_accumulate_grad_batches_with_callback(
      tmpdir, accumulate_grad_batches, expected_call_count
  ):
      with patch("torch.optim.SGD.zero_grad") as sgd_zero_grad:
          model = BoringModel()
          trainer = Trainer(
              default_root_dir=tmpdir,
              limit_train_batches=10,
              limit_val_batches=1,
              max_epochs=4,
              enable_model_summary=False,
              callbacks=GradientAccumulationScheduler(
                  accumulate_grad_batches
              ),
          )
          assert (
              trainer.accumulate_grad_batches == 1
          )  # initial value of Trainer argument
          trainer.fit(model)
  
          assert (
              sum(
                  isinstance(cb, GradientAccumulationScheduler)
                  for cb in trainer.callbacks
              )
              == 1
          )
          assert sgd_zero_grad.call_count == expected_call_count
  
file_path: pytorch-lightning/tests/tests_pytorch/callbacks/test_gradient_accumulation_scheduler.py
imports: import math
  from unittest.mock import Mock, patch
  import pytest
  from lightning.pytorch import Trainer
  from lightning.pytorch.callbacks import GradientAccumulationScheduler
  from lightning.pytorch.demos.boring_classes import BoringModel
  from lightning.pytorch.strategies import DeepSpeedStrategy
  from lightning.pytorch.utilities.exceptions import MisconfigurationException
  from lightning.pytorch.utilities.imports import _LIGHTNING_COLOSSALAI_AVAILABLE
label_desc: Use image node_id 2 for calling a global function with example usage: test_trainer_accumulate_grad_batches_with_callback(tmpdir, accumulate_grad_batches, expected_call_count) without return types