class_docstr: Original code from torchvision: https://github.com/pytorch/vision/tree/main/torchvision/transforms
  
  Normalize the video clip by mean subtraction and division by standard deviation
  Args:
      mean (3-tuple): pixel RGB mean
      std (3-tuple): pixel RGB standard deviation
      inplace (boolean): whether do in-place normalization
class_docstr_tok: ["Original","code","from","torchvision",":","https",":","\/\/github.com\/pytorch\/vision\/tree\/main\/torchvision\/transforms","Normalize","the","video","clip","by","mean","subtraction","and","division","by","standard","deviation","Args",":","mean","(","3-tuple",")",":","pixel","RGB","mean","std","(","3-tuple",")",":","pixel","RGB","standard","deviation","inplace","(","boolean",")",":","whether","do","in-place","normalization"]
code: def __init__(self, mean, std, inplace=False):
      self.mean = mean
      self.std = std
      self.inplace = inplace
  
imports: import os
  import logging
  import numpy
  import numbers
  import torch
  from torch import nn
  from torchvision.transforms import Compose
label_desc: Use image node_id 1 to create a new NormalizeVideo object  with example: obj = NormalizeVideo(mean, std, inplace)