code: def test_torchelastic_gpu_parsing(cuda_count_1, devices):
      """Ensure when using torchelastic and nproc_per_node is set to the default of 1 per GPU device that we omit
      sanitizing the gpus as only one of the GPUs is visible."""
      trainer = Trainer(accelerator="cuda", devices=devices)
      assert isinstance(
          trainer._accelerator_connector.cluster_environment,
          TorchElasticEnvironment,
      )
      # when using gpu
      if _parse_gpu_ids(devices, include_cuda=True) is not None:
          assert isinstance(trainer.accelerator, CUDAAccelerator)
          assert (
              trainer.num_devices == len(devices)
              if isinstance(devices, list)
              else devices
          )
          assert trainer.device_ids == _parse_gpu_ids(
              devices, include_cuda=True
          )
      # fall back to cpu
      else:
          assert isinstance(trainer.accelerator, CPUAccelerator)
          assert trainer.num_devices == 1
          assert trainer.device_ids == [0]
  
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 4 for calling a global function with example usage: test_torchelastic_gpu_parsing(cuda_count_1, devices) without return types