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
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code: def normalize(self, image: Tensor) -> Tensor:
      if not image.is_floating_point():
          raise TypeError(
              f"Expected input images to be of floating type (in range [0, 1]), "
              f"but found type {image.dtype} instead"
          )
      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)
      return (image - mean[:, None, None]) / std[:, None, None]
  
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 3 for calling the GeneralizedRCNNTransform obj's underlying member method code with example usage: obj.normalize(image) and returns: unknown