code: def test_auto_transfer_data_from_host_to_device(
      ray_start_1_cpu_1_gpu, device_choice, auto_transfer
  ):
      import numpy as np
      import torch
  
      def compute_average_runtime(func):
          device = torch.device(device_choice)
          start = torch.cuda.Event(enable_timing=True)
          end = torch.cuda.Event(enable_timing=True)
          runtime = []
          for _ in range(10):
              torch.cuda.synchronize()
              start.record()
              func(device)
              end.record()
              torch.cuda.synchronize()
          runtime.append(start.elapsed_time(end))
          return np.mean(runtime)
  
      small_dataloader = [
          (torch.randn((1024 * 4, 1024 * 4), device="cpu"),)
          for _ in range(10)
      ]
  
      def host_to_device(device):
          for (x,) in small_dataloader:
              x = x.to(device)
              torch.matmul(x, x)
  
      def host_to_device_auto_pipeline(device):
          wrapped_dataloader = (
              ray.train.torch.train_loop_utils._WrappedDataLoader(
                  small_dataloader, device, auto_transfer
              )
          )
          for (x,) in wrapped_dataloader:
              torch.matmul(x, x)
  
      # test if all four configurations are okay
      with_auto_transfer = compute_average_runtime(
          host_to_device_auto_pipeline
      )
  
      if device_choice == "cuda" and auto_transfer:
          assert (
              compute_average_runtime(host_to_device)
              >= with_auto_transfer
          )
  
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 1 for calling a global function with example usage: test_auto_transfer_data_from_host_to_device(ray_start_1_cpu_1_gpu, device_choice, auto_transfer) and returns: np