code: def get_and_convert_images_parallel(
      image_src_locations,
      image_convert_settings,
      n_jobs=-1,
      disk_cache=False,
      threads=False,
      verbose=10,
      allow_truncated_images=False,
      request_timeout=60,
      http_max_retries=2,
  ):
      """Parallelize retrieving and converting image tasks.
  
      Args:
        images: List of source image paths (filepaths, URLs, etc).
        image_convert_settings: ImageConvertSettings object.
        disk_cache:: If True, will cache converted images to disk.
        threads: If true, use threads instead of processes.
        verbose: verbosity level for parallel. See joblib.Parallel documentation.
        allow_truncated_images: If True, PIL will be tolerant of truncated image
                                files and load/process them. Note that this isn't
                                supported on old versions on PIL, just pillow.
        request_timeout: Max secs for http requests before timeout.
        http_max_retries: Max number of attempts we will try to retrive http images
                          due to timeout errors.
  
      Returns:
        List of tuples, where each tuple contains
        (converted Image object or None, status/error message string).
      """
      logging.info("Parallelizing with setting %d jobs" % n_jobs)
  
      backend = None
      if threads:
          logging.debug("Parallelizing using threads.")
          backend = "threading"
  
      outputs = Parallel(
          n_jobs=n_jobs, backend=backend, verbose=verbose
      )(
          delayed(get_and_convert_image)(
              location,
              image_convert_settings,
              allow_truncated_images=allow_truncated_images,
              disk_cache=disk_cache,
              request_timeout=request_timeout,
              http_max_retries=http_max_retries,
          )
          for location in image_src_locations
      )
  
      return outputs
  
imports: from absl import logging
  from joblib import Parallel, delayed
  from PIL import ImageFile
  import atlasmaker_io
  import convert
label_desc: Use image node_id 2 for calling a global function with example usage: get_and_convert_images_parallel(image_src_locations, image_convert_settings, n_jobs, disk_cache, threads, verbose, allow_truncated_images, request_timeout, http_max_retries) and returns: outputs