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 __init__(self, mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]):
      self.mean = mean
      self.std = std
      if not (
          isinstance(self.mean, list) and isinstance(self.std, list)
      ):
          raise ValueError("{}: input type is invalid.".format(self))
      from functools import reduce
  
      if reduce(lambda x, y: x * y, self.std) == 0:
          raise ValueError("{}: std is invalid!".format(self))
  
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 1 to create a new Normalize object  with example: obj = Normalize(mean, std)