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"]
docstr: Args:
      clip (torch.tensor): video clip to be normalized. Size is (C, T, H, W)
docstr_tok: ["Args",":","clip","(","torch.tensor",")",":","video","clip","to","be","normalized",".","Size","is","(","C",",","T",",","H",",","W",")"]
code: def __call__(self, clip):
      """
      Args:
          clip (torch.tensor): video clip to be normalized. Size is (C, T, H, W)
      """
      assert clip.ndimension() == 4
      if not self.inplace:
          clip = clip.clone()
      mean = torch.as_tensor(
          self.mean, dtype=clip.dtype, device=clip.device
      )
      std = torch.as_tensor(
          self.std, dtype=clip.dtype, device=clip.device
      )
      clip.sub_(mean[:, None, None, None]).div_(
          std[:, None, None, None]
      )
      return clip
  
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 2 for calling the NormalizeVideo obj's underlying member method code with example usage: obj.__call__(clip) and returns: clip