code: def test_torch_auto_gpu_to_cpu(ray_start_4_cpus_2_gpus):
      """Tests if GPU tensors are auto converted to CPU on driver."""
      num_workers = 2
      assert os.environ["CUDA_VISIBLE_DEVICES"] == ""
  
      def train_func():
          model = torch.nn.Linear(1, 1)
  
          # Move to GPU device.
          model = ray.train.torch.prepare_model(model)
  
          assert next(model.parameters()).is_cuda
  
          with TemporaryDirectory() as tmpdir:
              state_dict = {
                  k.replace("module.", ""): v
                  for k, v in model.state_dict().items()
              }
              torch.save(
                  state_dict, os.path.join(tmpdir, "checkpoint.pt")
              )
              train.report(
                  {}, checkpoint=Checkpoint.from_directory(tmpdir)
              )
  
      trainer = TorchTrainer(
          train_func,
          scaling_config=ScalingConfig(
              num_workers=num_workers, use_gpu=True
          ),
      )
      results = trainer.fit()
  
      with results.checkpoint.as_directory() as tmpdir:
          state_dict = torch.load(os.path.join(tmpdir, "checkpoint.pt"))
          checkpoint_model = torch.nn.Linear(1, 1)
          checkpoint_model.load_state_dict(state_dict)
  
      assert not next(checkpoint_model.parameters()).is_cuda
  
      # Test the same thing for state dict.
  
      def train_func():
          model = torch.nn.Linear(1, 1)
  
          # Move to GPU device.
          model = ray.train.torch.prepare_model(model)
  
          assert next(model.parameters()).is_cuda
  
          state_dict = model.state_dict()
  
          for tensor in state_dict.values():
              assert tensor.is_cuda
  
          with TemporaryDirectory() as tmpdir:
              torch.save(
                  model.state_dict(),
                  os.path.join(tmpdir, "checkpoint.pt"),
              )
              train.report(
                  {}, checkpoint=Checkpoint.from_directory(tmpdir)
              )
  
      trainer = TorchTrainer(
          train_func,
          scaling_config=ScalingConfig(
              num_workers=num_workers, use_gpu=True
          ),
      )
      results = trainer.fit()
  
      with results.checkpoint.as_directory() as tmpdir:
          state_dict_checkpoint = torch.load(
              os.path.join(tmpdir, "checkpoint.pt")
          )
  
      for tensor in state_dict_checkpoint.values():
          assert tensor.is_cuda
  
file_path: ray/python/ray/train/tests/test_gpu_auto_transfer.py
imports: import os
  from tempfile import TemporaryDirectory
  from unittest.mock import patch
  import pytest
  import torch
  import ray
  import ray.train.torch.train_loop_utils
  from ray import train
  from ray.train import Checkpoint, ScalingConfig
  from ray.train.torch import TorchTrainer
label_desc: Use image node_id 3 for calling a global function with example usage: test_torch_auto_gpu_to_cpu(ray_start_4_cpus_2_gpus) without return types