code: def train_torch(
      num_workers: int,
      use_gpu: bool = False,
      config: Optional[Dict] = None,
  ):
      trainer = get_trainer(
          num_workers=num_workers, use_gpu=use_gpu, config=config
      )
      trainer.fit()
  
file_path: ray/release/air_tests/air_benchmarks/workloads/tune_torch_benchmark.py
imports: import json
  import os
  import time
  import timeit
  from typing import Optional, Dict
  import click
  import numpy
  import ray
  from ray.train import ScalingConfig
  from ray.train.torch import TorchTrainer
label_desc: Use image node_id 3 for calling a global function with example usage: train_torch(num_workers, use_gpu, config) without return types