class_docstr: First rescale the image for its shorter side to reach the short_size and
  then jitter its aspect ratio, final rescale the shape guaranteed to be
  divided by scale_divisor.
  
  Required Keys:
  
  - img
  - img_shape
  - gt_bboxes (optional)
  - gt_polygons (optional)
  
  
  Modified Keys:
  
  - img
  - img_shape
  - gt_bboxes (optional)
  - gt_polygons (optional)
  
  Added Keys:
  
  - scale
  - scale_factor
  - keep_ratio
  
  Args:
      short_size (int): Target shorter size before jittering the aspect
          ratio. Defaults to 736.
      short_size_jitter_range (tuple(float, float)): Range of the ratio used
          to jitter the target shorter size. Defaults to (0.7, 1.3).
      aspect_ratio_jitter_range (tuple(float, float)): Range of the ratio
          used to jitter its aspect ratio. Defaults to (0.9, 1.1).
      scale_divisor (int): The scale divisor. Defaults to 1.
      resize_type (str): The type of resize class to use. Defaults to
          "Resize".
      **resize_kwargs: Other keyword arguments for the ``resize_type``.
docstr: Short Scale Aspect Jitter.
  Args:
      results (dict): Result dict containing the data to transform.
  
  Returns:
      dict: The transformed data.
docstr_tok: ["Short","Scale","Aspect","Jitter",".","Args",":","results","(","dict",")",":","Result","dict","containing","the","data","to","transform",".","Returns",":","dict",":","The","transformed","data","."]
code: def transform(self, results: Dict) -> Dict:
      """Short Scale Aspect Jitter.
      Args:
          results (dict): Result dict containing the data to transform.
  
      Returns:
          dict: The transformed data.
      """
      h, w = results["img"].shape[:2]
      ratio = self._sample_from_range(self.ratio_range)
      scale = (ratio * self.short_size) / min(h, w)
  
      aspect = self._sample_from_range(self.aspect_ratio_range)
      h_scale = scale * math.sqrt(aspect)
      w_scale = scale / math.sqrt(aspect)
      new_h = round(h * h_scale)
      new_w = round(w * w_scale)
  
      new_h = math.ceil(new_h / self.scale_divisor) * self.scale_divisor
      new_w = math.ceil(new_w / self.scale_divisor) * self.scale_divisor
      self.resize.scale = (new_w, new_h)
      return self.resize(results)
  
imports: import math
  import random
  from typing import Dict, List, Sequence, Tuple, Union
  import cv2
  import mmcv
  import numpy
  from mmcv.transforms import RandomFlip
  from mmcv.transforms.base import BaseTransform
  from mmcv.transforms.utils import avoid_cache_randomness, cache_randomness
  from shapely.geometry import Polygon
  from mmocr.registry import TRANSFORMS
  from mmocr.utils import crop_polygon, poly2bbox, poly_intersection
label_desc: Use image node_id 3 for calling the ShortScaleAspectJitter obj's underlying member method code with example usage: obj.transform(results) and returns: self