code: def load_test(
      filenames,
      short=600,
      max_size=1000,
      mean=(0.485, 0.456, 0.406),
      std=(0.229, 0.224, 0.225),
  ):
      """A util function to load all images, transform them to tensor by applying
      normalizations. This function support 1 filename or list of filenames.
  
      Parameters
      ----------
      filenames : str or list of str
          Image filename(s) to be loaded.
      short : int, optional, default is 600
          Resize image short side to this `short` and keep aspect ratio.
      max_size : int, optional, default is 1000
          Maximum longer side length to fit image.
          This is to limit the input image shape, avoid processing too large image.
      mean : iterable of float
          Mean pixel values.
      std : iterable of float
          Standard deviations of pixel values.
  
      Returns
      -------
      (mxnet.NDArray, numpy.ndarray) or list of such tuple
          A (1, 3, H, W) mxnet NDArray as input to network, and a numpy ndarray as
          original un-normalized color image for display.
          If multiple image names are supplied, return two lists. You can use
          `zip()`` to collapse it.
  
      """
      if isinstance(filenames, str):
          filenames = [filenames]
      imgs = [mx.image.imread(f) for f in filenames]
      return transform_test(imgs, short, max_size, mean, std)
  
imports: from __future__ import absolute_import
  import copy
  from random import randint
  import mxnet
  from ..None import bbox
  from ..None import image
  from ..None import mask
label_desc: Use image node_id 2 for calling a global function with example usage: load_test(filenames, short, max_size, mean, std) and returns: transform_test