class_docstr: 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: ["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)
      """
      return F.normalize(clip, self.mean, self.std, self.inplace)
  
imports: import numbers
  import random
  from torchvision.transforms import RandomCrop, RandomResizedCrop
  import lavis.processors.functional_video
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: F