code: def crop_resize_normalize(
      img,
      bbox_list,
      output_size,
      mean=(0.485, 0.456, 0.406),
      std=(0.229, 0.224, 0.225),
  ):
      output_list = []
      transform_test = transforms.Compose(
          [transforms.ToTensor(), transforms.Normalize(mean, std)]
      )
      for bbox in bbox_list:
          x0 = max(int(bbox[0]), 0)
          y0 = max(int(bbox[1]), 0)
          x1 = min(int(bbox[2]), int(img.shape[1]))
          y1 = min(int(bbox[3]), int(img.shape[0]))
          w = x1 - x0
          h = y1 - y0
          res_img = image.fixed_crop(
              nd.array(img),
              x0,
              y0,
              w,
              h,
              (output_size[1], output_size[0]),
          )
          res_img = transform_test(res_img)
          output_list.append(res_img)
      output_array = nd.stack(*output_list)
      return output_array
  
imports: from __future__ import absolute_import
  from __future__ import division
  import math
  import random
  import numpy
  import mxnet
  from mxnet import nd, image
  from mxnet.gluon.data.vision import transforms
  from ...utils.filesystem import try_import_cv2
label_desc: Use image node_id 13 for calling a global function with example usage: crop_resize_normalize(img, bbox_list, output_size, mean, std) and returns: output_array