code: def load_one_queue(
      transform: Callable, requires_pil: bool = False
  ) -> float:
      """Run a given transform repeatedly to simulate a single dataloader worker"""
      start = time.time()
      for _ in range(ITERATIONS):
          transform(RAND_PIL) if requires_pil else transform(
              RAND_TENSOR
          )
  
      return ITERATIONS / (time.time() - start)
  
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 3 for calling a global function with example usage: load_one_queue(transform, requires_pil) and returns: unknown