code: def test_apply_batch_transfer_handler(
      model_getter_mock, accelerator, expected_device_str
  ):
      expected_device = torch.device(expected_device_str)
  
      class CustomBatch:
          def __init__(self, data):
              self.samples = data[0]
              self.targets = data[1]
  
      class CurrentTestModel(BoringModel):
          rank = 0
          transfer_batch_to_device_hook_rank = None
          on_after_batch_transfer_hook_rank = None
  
          def on_after_batch_transfer(self, batch, dataloader_idx):
              assert dataloader_idx == 0
              assert (
                  batch.samples.device
                  == batch.targets.device
                  == expected_device
              )
              self.on_after_batch_transfer_hook_rank = self.rank
              self.rank += 1
              batch.targets *= 2
              return batch
  
          def transfer_batch_to_device(
              self, batch, device, dataloader_idx
          ):
              assert dataloader_idx == 0
              self.transfer_batch_to_device_hook_rank = self.rank
              self.rank += 1
              batch.samples = batch.samples.to(device)
              batch.targets = batch.targets.to(device)
              return batch
  
      model = CurrentTestModel()
      batch = CustomBatch(
          (torch.zeros(5, 32), torch.ones(5, 1, dtype=torch.long))
      )
  
      trainer = Trainer(accelerator=accelerator, devices=1)
      # running .fit() would require us to implement custom data loaders, we mock the model reference instead
  
      model_getter_mock.return_value = model
      batch_gpu = trainer.strategy.batch_to_device(
          batch, expected_device
      )
  
      assert model.transfer_batch_to_device_hook_rank == 0
      assert model.on_after_batch_transfer_hook_rank == 1
      assert (
          batch_gpu.samples.device
          == batch_gpu.targets.device
          == expected_device
      )
      assert torch.allclose(batch_gpu.samples.cpu(), torch.zeros(5, 32))
      assert torch.allclose(
          batch_gpu.targets.cpu(),
          torch.ones(5, 1, dtype=torch.long) * 2,
      )
  
imports: from functools import partial, update_wrapper
  from inspect import getmembers, isfunction
  from unittest import mock
  from unittest.mock import ANY, PropertyMock
  import pytest
  import torch
  from lightning.fabric.utilities.imports import _TORCH_GREATER_EQUAL_2_0
  from lightning.pytorch import Callback, LightningDataModule, LightningModule, Trainer, __version__
  from lightning.pytorch.demos.boring_classes import BoringDataModule, BoringModel, RandomDataset
  from lightning.pytorch.utilities.model_helpers import is_overridden
  from torch import Tensor
  from torch.utils.data import DataLoader
  from tests_pytorch.helpers.runif import RunIf
label_desc: Use image node_id 3 for calling a global function with example usage: test_apply_batch_transfer_handler(model_getter_mock, accelerator, expected_device_str) and returns: batch, batch