class_docstr: Normalizes an image by subtracting the mean and dividing by the
  standard deviation, separately for each channel.
class_docstr_tok: ["Normalizes","an","image","by","subtracting","the","mean","and","dividing","by","the","standard","deviation",",","separately","for","each","channel","."]
code: def __init__(self, means: List[float], stds: List[float]):
      super().__init__()
      assert len(means) == len(stds)
      self.means = torch.tensor(means, dtype=torch.float32)
      self.stds = torch.tensor(stds, dtype=torch.float32)
  
file_path: allennlp/allennlp/modules/vision/image2image.py
imports: import torch
  from torch import nn, FloatTensor, IntTensor
  from typing import List
  from allennlp.common.registrable import Registrable
label_desc: Use image node_id 1 to create a new NormalizeImage object from inherited base classes: Image2ImageModule with example: obj = NormalizeImage(means, stds)