class_docstr: Performs input / target transformation before feeding the data to a GeneralizedRCNN
  model.
  
  The transformations it perform are:
      - input normalization (mean subtraction and std division)
      - input / target resizing to match min_size / max_size
  
  It returns a ImageList for the inputs, and a List[Dict[Tensor]] for the targets
class_docstr_tok: ["Performs","input","\/","target","transformation","before","feeding","the","data","to","a","GeneralizedRCNN","model",".","The","transformations","it","perform","are",":","-","input","normalization","(","mean","subtraction","and","std","division",")","-","input","\/","target","resizing","to","match","min_size","\/","max_size","It","returns","a","ImageList","for","the","inputs",",","and","a","List","[","Dict","[","Tensor","]","]","for","the","targets"]
docstr: 标准化处理
docstr_tok: ["\u6807\u51c6\u5316\u5904\u7406"]
code: def normalize(self, image):
      """标准化处理"""
      dtype, device = image.dtype, image.device
      mean = torch.as_tensor(
          self.image_mean, dtype=dtype, device=device
      )
      std = torch.as_tensor(self.image_std, dtype=dtype, device=device)
      # [:, None, None]: shape [3] -> [3, 1, 1]
      return (image - mean[:, None, None]) / std[:, None, None]
  
imports: import math
  from typing import List, Tuple, Dict, Optional
  import torch
  from torch import nn, Tensor
  import torch.nn.functional
  import torchvision
  from .image_list import ImageList
label_desc: Use image node_id 2 for calling the GeneralizedRCNNTransform obj's underlying member method code with example usage: obj.normalize(image) and returns: unknown