class_docstr: Performs input / target transformation before feeding the data to a GeneralizedRCNN
  model.
  
  The transformations it performs 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","performs","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"]
code: def __init__(
      self,
      min_size: int,
      max_size: int,
      image_mean: List[float],
      image_std: List[float],
      size_divisible: int = 32,
      fixed_size: Optional[Tuple[int, int]] = None,
      **kwargs: Any,
  ):
      super().__init__()
      if not isinstance(min_size, (list, tuple)):
          min_size = (min_size,)
      self.min_size = min_size
      self.max_size = max_size
      self.image_mean = image_mean
      self.image_std = image_std
      self.size_divisible = size_divisible
      self.fixed_size = fixed_size
      self._skip_resize = kwargs.pop("_skip_resize", False)
  
imports: import math
  from typing import Any, Dict, List, Optional, Tuple
  import torch
  import torchvision
  from torch import nn, Tensor
  from .image_list import ImageList
  from .roi_heads import paste_masks_in_image
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, size_divisible, fixed_size)