code: def test_accelerate_e2e(ray_start_4_cpus, num_workers):
      def train_func():
          accelerator = Accelerator(cpu=True)
          assert accelerator.device == train.torch.get_device()
          assert (
              accelerator.process_index
              == train.get_context().get_world_rank()
          )
          model = torch.nn.Linear(3, 1)
          model = accelerator.prepare(model)
          with TemporaryDirectory() as tmpdir:
              torch.save(model, os.path.join(tmpdir, "checkpoint.pt"))
              train.report(
                  {}, checkpoint=Checkpoint.from_directory(tmpdir)
              )
  
      scaling_config = ScalingConfig(num_workers=num_workers)
      trainer = TorchTrainer(
          train_loop_per_worker=train_func,
          scaling_config=scaling_config,
      )
      trainer.fit()
  
file_path: ray/python/ray/train/tests/test_torch_accelerate.py
imports: import os
  from tempfile import TemporaryDirectory
  import pytest
  import torch
  import torch.nn
  from accelerate import Accelerator
  import ray
  import ray.train
  from ray.train import Checkpoint, ScalingConfig
  from ray.train.examples.pytorch.torch_linear_example import LinearDataset
  from ray.train.torch import TorchTrainer
label_desc: Use image node_id 5 for calling a global function with example usage: test_accelerate_e2e(ray_start_4_cpus, num_workers) without return types