class_docstr: Normalize the input image.
  
  Args:
      mean(list): Mean value for normalization.
      std(list): Standard deviation for normalization.
      channel_first(bool): im channel firest or last
class_docstr_tok: ["Normalize","the","input","image",".","Args",":","mean","(","list",")",":","Mean","value","for","normalization",".","std","(","list",")",":","Standard","deviation","for","normalization",".","channel_first","(","bool",")",":","im","channel","firest","or","last"]
code: def __call__(self, im):
      if not self.channel_first:
          mean = np.array(self.mean)[np.newaxis, np.newaxis, :]
          std = np.array(self.std)[np.newaxis, np.newaxis, :]
      else:
          mean = np.array(self.mean)[:, np.newaxis, np.newaxis]
          std = np.array(self.std)[:, np.newaxis, np.newaxis]
      im = F.normalize(im, mean, std)
      return im
  
imports: import random
  from typing import Callable, Union, List, Tuple
  import cv2
  import PIL
  import numpy
  import paddlehub.vision.functional
label_desc: Use image node_id 2 for calling the Normalize obj's underlying member method code with example usage: obj.__call__(im) and returns: im