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 batch_images(
      self, images: List[Tensor], size_divisible: int = 32
  ) -> Tensor:
      if torchvision._is_tracing():
          # batch_images() does not export well to ONNX
          # call _onnx_batch_images() instead
          return self._onnx_batch_images(images, size_divisible)
  
      max_size = self.max_by_axis([list(img.shape) for img in images])
      stride = float(size_divisible)
      max_size = list(max_size)
      max_size[1] = int(math.ceil(float(max_size[1]) / stride) * stride)
      max_size[2] = int(math.ceil(float(max_size[2]) / stride) * stride)
  
      batch_shape = [len(images)] + max_size
      batched_imgs = images[0].new_full(batch_shape, 0)
      for i in range(batched_imgs.shape[0]):
          img = images[i]
          batched_imgs[
              i, : img.shape[0], : img.shape[1], : img.shape[2]
          ].copy_(img)
  
      return batched_imgs
  
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 8 for calling the GeneralizedRCNNTransform obj's underlying member method code with example usage: obj.batch_images(images, size_divisible) and returns: batched_imgs, self