class_docstr: Resize the short side of an image to given size, and then scale the other side proportionally.
  
  Args:
      short_size (int): The target size of short side.
class_docstr_tok: ["Resize","the","short","side","of","an","image","to","given","size",",","and","then","scale","the","other","side","proportionally",".","Args",":","short_size","(","int",")",":","The","target","size","of","short","side","."]
code: def __call__(self, data: dict) -> dict:
      data["trans_info"].append(("resize", data["img"].shape[0:2]))
      data["img"] = functional.resize_short(
          data["img"], self.short_size
      )
      for key in data.get("gt_fields", []):
          data[key] = functional.resize_short(
              data[key], self.short_size
          )
      return data
  
imports: import random
  import base64
  from typing import Callable, Union, List, Tuple
  import cv2
  import numpy
  import paddle
  import paddle.nn.functional
  from paddleseg.transforms import functional
  from PIL import Image
label_desc: Use image node_id 2 for calling the ResizeByShort obj's underlying member method code with example usage: obj.__call__(data) and returns: data