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","."]
code: def _augment(self, img, _):
      img = img.astype("float32")
      if self.all_channel:
          mean = np.mean(img)
          std = np.std(img)
      else:
          mean = np.mean(img, axis=(0, 1), keepdims=True)
          std = np.std(img, axis=(0, 1), keepdims=True)
      std = np.maximum(std, 1.0 / np.sqrt(np.prod(img.shape)))
      img = (img - mean) / std
      return img
  
imports: import numpy
  import cv2
  from ...utils.develop import log_deprecated
  from .base import PhotometricAugmentor
label_desc: Use image node_id 2 for calling the MeanVarianceNormalize obj's underlying member method code with example usage: obj._augment(img, _) and returns: img