code: def train_func(config):
      data_size = config.get("data_size", 4096 * 50)
      batch_size = config.get("batch_size", 4096)
      hidden_size = config.get("hidden_size", 1)
      use_auto_transfer = config.get("use_auto_transfer", False)
      lr = config.get("lr", 1e-2)
      epochs = config.get("epochs", 10)
  
      train_dataset = BenchmarkDataset(4096, size=data_size)
      train_loader = torch.utils.data.DataLoader(
          train_dataset, batch_size=batch_size, shuffle=True
      )
  
      train_loader = train.torch.prepare_data_loader(
          data_loader=train_loader,
          move_to_device=True,
          auto_transfer=use_auto_transfer,
      )
  
      model = Net(in_d=4096, hidden=[4096] * hidden_size)
      model = train.torch.prepare_model(model)
  
      loss_fn = nn.MSELoss()
      optimizer = torch.optim.SGD(model.parameters(), lr=lr)
  
      start = torch.cuda.Event(enable_timing=True)
      end = torch.cuda.Event(enable_timing=True)
  
      choice = "with" if use_auto_transfer else "without"
      print(
          f"Starting the torch data prefetch benchmark {choice} auto pipeline..."
      )
  
      torch.cuda.synchronize()
      start.record()
      for epoch in range(epochs):
          train_epoch(epoch, train_loader, model, loss_fn, optimizer)
      end.record()
      torch.cuda.synchronize()
  
      print(
          f"Finished the torch data prefetch benchmark {choice} "
          f"auto pipeline: {start.elapsed_time(end)} ms."
      )
  
      return "Experiment done."
  
file_path: ray/python/ray/train/examples/pytorch/torch_data_prefetch_benchmark/auto_pipeline_for_host_to_device_data_transfer.py
imports: import argparse
  import warnings
  import numpy
  import torch
  import torch.nn
  import ray.train
  from ray.train import ScalingConfig
  from ray.train.torch import TorchTrainer
label_desc: Use image node_id 2 for calling a global function with example usage: train_func(config) and returns: str