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
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code: def __init__(self, min_size, max_size, image_mean, image_std):
      super(GeneralizedRCNNTransform, self).__init__()
      if not isinstance(min_size, (list, tuple)):
          min_size = (min_size,)
      self.min_size = min_size  # 指定图像的最小边长范围
      self.max_size = max_size  # 指定图像的最大边长范围
      self.image_mean = image_mean  # 指定图像在标准化处理中的均值
      self.image_std = image_std
  
imports: import math
  from typing import List, Tuple, Dict, Optional
  import torch
  from torch import nn, Tensor
  import torchvision
  from .image_list import ImageList
label_desc: Use image node_id 1 to create a new GeneralizedRCNNTransform object from inherited base classes: nn with example: obj = GeneralizedRCNNTransform(min_size, max_size, image_mean, image_std)