code: def test_accelerate_base(ray_2_node_2_gpu, use_gpu):
      def train_func(config):
          accelerator = Accelerator(cpu=not use_gpu)
          assert accelerator.device == train.torch.get_device()
          assert (
              accelerator.process_index
              == train.get_context().get_world_rank()
          )
          if accelerator.device.type != "cpu":
              assert (
                  accelerator.local_process_index
                  == train.get_context().get_local_rank()
              )
          result = linear_train_func(accelerator, config)
          assert len(result) == epochs
          assert result[-1]["loss"] < result[0]["loss"]
  
      epochs = 3
      scaling_config = ScalingConfig(num_workers=2, use_gpu=use_gpu)
      config = {
          "lr": 1e-2,
          "hidden_size": 1,
          "batch_size": 4,
          "epochs": epochs,
      }
  
      trainer = TorchTrainer(
          train_loop_per_worker=train_func,
          train_loop_config=config,
          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 3 for calling a global function with example usage: test_accelerate_base(ray_2_node_2_gpu, use_gpu) without return types