code: def load_normalized_test_case(
      input_shape, test_image, pagelocked_buffer, normalization_hint
  ):
      def normalize_image(image):
          c, h, w = input_shape
          image_arr = (
              np.asarray(image.resize((w, h), Image.ANTIALIAS))
              .transpose([2, 0, 1])
              .astype(trt.nptype(trt.float32))
              .ravel()
          )
          if normalization_hint == 0:
              return (image_arr / 255.0 - 0.45) / 0.225
          elif normalization_hint == 1:
              return image_arr / 256.0 - 0.5
  
      np.copyto(
          pagelocked_buffer, normalize_image(Image.open(test_image))
      )
      return test_image
  
imports: import os
  import unittest
  from PIL import Image
  import numpy
  import torch
  import torchvision.models
  import pycuda.driver
  import pycuda.autoinit
  import tensorrt
label_desc: Use image node_id 2 for calling a global function with example usage: load_normalized_test_case(input_shape, test_image, pagelocked_buffer, normalization_hint) and returns: test_image, unknown, unknown