class_docstr: Normalize an image.
  
  Args:
      mean (list, optional): The mean value of a data set. Default: [0.5, 0.5, 0.5].
      std (list, optional): The standard deviation of a data set. Default: [0.5, 0.5, 0.5].
  
  Raises:
      ValueError: When mean/std is not list or any value in std is 0.
class_docstr_tok: ["Normalize","an","image",".","Args",":","mean","(","list",",","optional",")",":","The","mean","value","of","a","data","set",".","Default",":","[","0.5",",","0.5",",","0.5","]",".","std","(","list",",","optional",")",":","The","standard","deviation","of","a","data","set",".","Default",":","[","0.5",",","0.5",",","0.5","]",".","Raises",":","ValueError",":","When","mean\/std","is","not","list","or","any","value","in","std","is","0","."]
code: def __init__(
      self,
      mean: Union[List[float], Tuple[float]] = (0.5, 0.5, 0.5),
      std: Union[List[float], Tuple[float]] = (0.5, 0.5, 0.5),
  ):
      self.mean = mean
      self.std = std
      if not (
          isinstance(self.mean, (list, tuple))
          and isinstance(self.std, (list, tuple))
      ):
          raise ValueError(
              "{}: input type is invalid. It should be list or tuple".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 random
  import base64
  from typing import Callable, Union, List, Tuple
  import cv2
  import numpy
  import paddle
  import paddle.nn.functional
  from paddleseg.transforms import functional
  from PIL import Image
label_desc: Use image node_id 1 to create a new Normalize object  with example: obj = Normalize(mean, std)