class_docstr: normalize image such as substract mean, divide std
      
class_docstr_tok: ["normalize","image","such","as","substract","mean",",","divide","std"]
code: def __init__(
      self,
      scale=None,
      mean=None,
      std=None,
      order="chw",
      output_fp16=False,
      channel_num=3,
  ):
      if isinstance(scale, str):
          scale = eval(scale)
      assert channel_num in [
          3,
          4,
      ], "channel number of input image should be set to 3 or 4."
      self.channel_num = channel_num
      self.output_dtype = "float16" if output_fp16 else "float32"
      self.scale = np.float32(
          scale if scale is not None else 1.0 / 255.0
      )
      self.order = order
      mean = mean if mean is not None else [0.485, 0.456, 0.406]
      std = std if std is not None else [0.229, 0.224, 0.225]
  
      shape = (3, 1, 1) if self.order == "chw" else (1, 1, 3)
      self.mean = np.array(mean).reshape(shape).astype("float32")
      self.std = np.array(std).reshape(shape).astype("float32")
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  import base64
  import inspect
  import math
  import os
  import random
  import sys
  from functools import partial
  import cv2
  import numpy
  import paddle
  import paddle.nn.functional
  import six
  from paddle.vision.transforms import ColorJitter
  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(scale, mean, std, order, output_fp16, channel_num)