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.
class_docstr_tok: ["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","."]
code: def __repr__(self):
      return self.__class__.__name__ + "(mean={0}, std={1})".format(
          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 3 for calling the Normalize obj's underlying member method code with example usage: obj.__repr__() and returns: unknown