class_docstr: Linearly scales the image to have zero mean and unit norm.
  ``x = (x - mean) / adjusted_stddev``
  where ``adjusted_stddev = max(stddev, 1.0/sqrt(num_pixels * channels))``
  
  This augmentor always returns float32 images.
class_docstr_tok: ["Linearly","scales","the","image","to","have","zero","mean","and","unit","norm",".","``","x","=","(","x","-","mean",")","\/","adjusted_stddev","``","where","``","adjusted_stddev","=","max","(","stddev",",","1.0\/sqrt","(","num_pixels","*","channels",")",")","``","This","augmentor","always","returns","float32","images","."]
docstr: Args:
      all_channel (bool): if True, normalize all channels together. else separately.
code: def __init__(self, all_channel=True):
      """
      Args:
          all_channel (bool): if True, normalize all channels together. else separately.
      """
      self._init(locals())
  
imports: import numpy
  import cv2
  from ...utils.develop import log_deprecated
  from .base import PhotometricAugmentor
label_desc: Use image node_id 1 to create a new MeanVarianceNormalize object from inherited base classes: PhotometricAugmentor with example: obj = MeanVarianceNormalize(all_channel)