code: def __call__(self, img: PIL.Image.Image, target: dict):
      w = random.randint(self.min_size, min(img.width, self.max_size))
      h = random.randint(self.min_size, min(img.height, self.max_size))
      region = T.RandomCrop.get_params(img, [h, w])
      return crop(img, target, region)
  
imports: import random
  import PIL
  import torch
  import torchvision.transforms
  import torchvision.transforms.functional
  from util.box_ops import box_xyxy_to_cxcywh
  from util.misc import interpolate
label_desc: Use image node_id 2 for calling the RandomSizeCrop obj's underlying member method code with example usage: obj.__call__(img, target) and returns: crop