code: def random_short_side_scale_with_boxes(
      images: torch.Tensor,
      boxes: torch.Tensor,
      min_size: int,
      max_size: int,
      interpolation: str = "bilinear",
      backend: str = "pytorch",
  ) -> Tuple[torch.Tensor, torch.Tensor]:
      """
      Perform a spatial short scale jittering on the given images and
      corresponding boxes.
      Args:
          images (tensor): images to perform scale jitter. Dimension is
              `channel` x `num frames` x `height` x `width`.
          boxes (tensor): Corresponding boxes to images.
              Dimension is `num boxes` x 4.
          min_size (int): the minimal size to scale the frames.
          max_size (int): the maximal size to scale the frames.
          interpolation (str): Algorithm used for upsampling,
              options: nearest' | 'linear' | 'bilinear' | 'bicubic' | 'trilinear' | 'area'
          backend (str): backend used to perform interpolation. Options includes
              `pytorch` as default, and `opencv`. Note that opencv and pytorch behave
              differently on linear interpolation on some versions.
              https://discuss.pytorch.org/t/pytorch-linear-interpolation-is-different-from-pil-opencv/71181
      Returns:
          (tensor): the scaled images with dimension of
              `channel` x `num frames` x `height` x `width`.
          (tensor): the scaled boxes with dimension of
              `num boxes` x 4.
      """
      size = torch.randint(min_size, max_size + 1, (1,)).item()
      return short_side_scale_with_boxes(
          images, boxes, size, interpolation, backend
      )
  
imports: import copy
  import math
  from typing import Tuple
  import numpy
  import torch
label_desc: Use image node_id 7 for calling a global function with example usage: random_short_side_scale_with_boxes(images, boxes, min_size, max_size, interpolation, backend) and returns: short_side_scale_with_boxes