code: def benchmark(
      transform: Callable, title: str, requires_pil: bool = False
  ) -> float:
      """Given a transform, simulate a real-world load by creating multiple workers"""
  
      #  Test the multi-queue speed
      global benchmark_results
      benchmark_results = []
      pool = torch.multiprocessing.Pool(N_QUEUES)
      pool.starmap_async(
          load_one_queue,
          [(transform, requires_pil)] * N_QUEUES,
          callback=store_result,
      ).get()
  
      # Test the mono-queue speed
      fps_mono = load_one_queue(transform, requires_pil)
  
      print(
          "{: <25}: {: >10.1f} fps single queue, {: >10.1f} fps multi queue".format(
              title, fps_mono, sum(benchmark_results) / N_QUEUES
          )
      )
  
imports: import time
  from typing import Callable, List
  import torch
  import torchvision.transforms
  from PIL import Image
  from vissl.data.ssl_transforms.img_cv_color_distortion import ColorDistortionSettings, ImgOpenCVColorDistortion
  from vissl.data.ssl_transforms.img_pil_color_distortion import ImgPilColorDistortion
  from vissl.data.ssl_transforms.img_pil_gaussian_blur import ImgPilGaussianBlur
  from vissl.data.ssl_transforms.img_pil_random_photometric import ImgPilRandomPhotometric
  from vissl.data.ssl_transforms.img_pil_to_lab_tensor import ImgPil2LabTensor
  from vissl.data.ssl_transforms.img_pil_to_multicrop import ImgPilToMultiCrop
  from vissl.data.ssl_transforms.img_pil_to_patches_and_image import ImgPilToPatchesAndImage
  from vissl.data.ssl_transforms.img_pil_to_tensor import ImgToTensor
  from vissl.data.ssl_transforms.img_rotate_pil import ImgRotatePil
label_desc: Use image node_id 1 for calling a global function with example usage: benchmark(transform, title, requires_pil) without return types