class_docstr: normalize image
  Args:
      mean (list): im - mean
      std (list): im / std
      is_scale (bool): whether need im / 255
      norm_type (str): type in ['mean_std', 'none']
class_docstr_tok: ["normalize","image","Args",":","mean","(","list",")",":","im","-","mean","std","(","list",")",":","im","\/","std","is_scale","(","bool",")",":","whether","need","im","\/","255","norm_type","(","str",")",":","type","in","[","'mean_std","'",",","'none","'","]"]
code: def __init__(self, mean, std, is_scale=True, norm_type="mean_std"):
      self.mean = mean
      self.std = std
      self.is_scale = is_scale
      self.norm_type = norm_type
  
imports: import cv2
  import numpy
  import imgaug.augmenters
  from keypoint_preprocess import get_affine_transform
  from PIL import Image
label_desc: Use image node_id 1 to create a new NormalizeImage object from inherited base classes: object with example: obj = NormalizeImage(mean, std, is_scale, norm_type)