class_docstr: Normalize the input images.
  
  Args:
      mean(list): Mean values for normalization, default is [0.5, 0.5, 0.5].
      std(list): Standard deviation for normalization, default is [0.5, 0.5, 0.5].
  
  Returns:
      img(np.ndarray): Preprocessed image.
      data(dict): Image info and label info, default is None.
class_docstr_tok: ["Normalize","the","input","images",".","Args",":","mean","(","list",")",":","Mean","values","for","normalization",",","default","is","[","0.5",",","0.5",",","0.5","]",".","std","(","list",")",":","Standard","deviation","for","normalization",",","default","is","[","0.5",",","0.5",",","0.5","]",".","Returns",":","img","(","np.ndarray",")",":","Preprocessed","image",".","data","(","dict",")",":","Image","info","and","label","info",",","default","is","None","."]
code: def __call__(self, im, data=None):
      mean = np.array(self.mean)[np.newaxis, np.newaxis, :]
      std = np.array(self.std)[np.newaxis, np.newaxis, :]
      im = F.normalize(im, mean, std)
  
      if data is not None:
          return im, data
      else:
          return im
  
imports: import os
  import random
  from typing import Callable
  import cv2
  import PIL
  import numpy
  import paddlehub.vision.transforms.functional
  from paddlehub.vision.utils import box_crop, box_iou_xywh
label_desc: Use image node_id 2 for calling the Normalize obj's underlying member method code with example usage: obj.__call__(im, data) and returns: im,  data, im