code: def load_images(
      images_PIL, image_size=256, sharpen=False, im_type="RGB"
  ):
      loaded_images = list()
      for img_pil in images_PIL:
          img = img_pil.convert(im_type)
          if image_size is not None:
              img = img.resize(
                  (image_size, image_size), PIL.Image.LANCZOS
              )
              if sharpen:
                  img = img.filter(ImageFilter.DETAIL)
          img = np.array(img)
          img = np.expand_dims(img, 0)
          loaded_images.append(img)
      loaded_images = np.vstack(loaded_images)
      return loaded_images
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import tensorflow
  import bz2
  import PIL.Image
  from PIL import ImageFilter
  import numpy
  from keras.models import Model
  from keras.applications.vgg16 import VGG16, preprocess_input
  import keras.backend
  import traceback
  import external.stylegan2.dnnlib.tflib
  from core.landmark_handler import Landmark_Handler
  from core import arcface_handler
label_desc: Use image node_id 1 for calling a global function with example usage: load_images(images_PIL, image_size, sharpen, im_type) and returns: loaded_images