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: 自定义输出实例化对象的信息，可通过print打印实例信息
docstr_tok: ["\u81ea\u5b9a\u4e49\u8f93\u51fa\u5b9e\u4f8b\u5316\u5bf9\u8c61\u7684\u4fe1\u606f\uff0c\u53ef\u901a\u8fc7print\u6253\u5370\u5b9e\u4f8b\u4fe1\u606f"]
code: def __repr__(self):
      """自定义输出实例化对象的信息，可通过print打印实例信息"""
      format_string = self.__class__.__name__ + "("
      _indent = "\n    "
      format_string += "{0}Normalize(mean={1}, std={2})".format(
          _indent, self.image_mean, self.image_std
      )
      format_string += "{0}Resize(min_size={1}, max_size={2}, mode='bilinear')".format(
          _indent, self.min_size, self.max_size
      )
      format_string += "\n)"
      return format_string
  
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 9 for calling the GeneralizedRCNNTransform obj's underlying member method code with example usage: obj.__repr__() and returns: format_string