code: def test_input_tensors_cast_before_transfer_to_device(
      strategy, precision, expected_dtype
  ):
      class CustomBoringModel(BoringModel):
          def transfer_batch_to_device(self, batch, *args, **kwargs):
              assert batch.dtype == expected_dtype
              return super().transfer_batch_to_device(
                  batch, *args, **kwargs
              )
  
      model = CustomBoringModel()
      trainer = Trainer(
          strategy=strategy,
          devices=1,
          precision=precision,
          barebones=True,
          max_steps=2,
      )
      trainer.fit(model)
  
imports: import os
  from collections import namedtuple
  from unittest import mock
  from unittest.mock import patch
  import pytest
  import torch
  from lightning.fabric.plugins.environments import TorchElasticEnvironment
  from lightning.fabric.utilities.device_parser import _parse_gpu_ids
  from lightning.pytorch import Trainer, seed_everything
  from lightning.pytorch.accelerators import CPUAccelerator, CUDAAccelerator
  from lightning.pytorch.demos.boring_classes import BoringModel
  from lightning.pytorch.utilities.exceptions import MisconfigurationException
  import tests_pytorch.helpers.pipelines
  from tests_pytorch.helpers.datamodules import ClassifDataModule
  from tests_pytorch.helpers.runif import RunIf
  from tests_pytorch.helpers.simple_models import ClassificationModel
label_desc: Use image node_id 7 for calling a global function with example usage: test_input_tensors_cast_before_transfer_to_device(strategy, precision, expected_dtype) and returns: super