class_docstr: Normalize a clip with mean and standard deviation.
  Given mean: ``(M1,...,Mn)`` and std: ``(S1,..,Sn)`` for ``n`` channels, this transform
  will normalize each channel of the input ``torch.*Tensor`` i.e.
  ``input[channel] = (input[channel] - mean[channel]) / std[channel]``
  
  .. note::
      This transform acts out of place, i.e., it does not mutates the input tensor.
  
  Args:
      mean (sequence): Sequence of means for each channel.
      std (sequence): Sequence of standard deviations for each channel.
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docstr: Args:
      clip (Tensor): Tensor clip of size (T, C, H, W) to be normalized.
  
  Returns:
      Tensor: Normalized Tensor clip.
docstr_tok: ["Args",":","clip","(","Tensor",")",":","Tensor","clip","of","size","(","T",",","C",",","H",",","W",")","to","be","normalized",".","Returns",":","Tensor",":","Normalized","Tensor","clip","."]
code: def __call__(self, clip):
      """
      Args:
          clip (Tensor): Tensor clip of size (T, C, H, W) to be normalized.
  
      Returns:
          Tensor: Normalized Tensor clip.
      """
      return F.normalize(clip, self.mean, self.std)
  
imports: import numbers
  import random
  import numpy
  import PIL
  import torch
  import torchvision
  from .None import functional
label_desc: Use image node_id 2 for calling the Normalize obj's underlying member method code with example usage: obj.__call__(clip) and returns: F