class_docstr: Normalize images with mean and standard deviation.
  
  Given mean `(m1, ..., mn)` and std `(s1, ..., sn)` for `n` channels,
  this transform normalizes each channel of the input tensor with::
  
      output[i] = (input[i] - mi) / si
  
  If mean or std is scalar, the same value will be applied to all channels.
  
  Parameters
  ----------
  mean : float or tuple of floats
      The mean values.
  std : float or tuple of floats
      The standard deviation values.
  
  Inputs:
      - **data**: a list of frames with shape [C x H x W]
  
  Outputs:
      - **out**: a list of normalized frames with shape [C x H x W]
class_docstr_tok: ["Normalize","images","with","mean","and","standard","deviation",".","Given","mean","`","(","m1",",","...",",","mn",")","`","and","std","`","(","s1",",","...",",","sn",")","`","for","`","n","`","channels",",","this","transform","normalizes","each","channel","of","the","input","tensor","with",":",":","output","[","i","]","=","(","input","[","i","]","-","mi",")","\/","si","If","mean","or","std","is","scalar",",","the","same","value","will","be","applied","to","all","channels",".","Parameters","--","--","--","--","--","mean",":","float","or","tuple","of","floats","The","mean","values",".","std",":","float","or","tuple","of","floats","The","standard","deviation","values",".","Inputs",":","-","*","*","data","*","*",":","a","list","of","frames","with","shape","[","C","x","H","x","W","]","Outputs",":","-","*","*","out","*","*",":","a","list","of","normalized","frames","with","shape","[","C","x","H","x","W","]"]
code: def forward(self, clips):
      new_clips = []
      for cur_img in clips:
          new_clips.append((cur_img - self.mean) / self.std)
      return new_clips
  
imports: from __future__ import division
  import random
  import math
  import numbers
  import numpy
  from mxnet.gluon import Block
label_desc: Use image node_id 2 for calling the VideoNormalize obj's underlying member method code with example usage: obj.forward(clips) and returns: new_clips