code: def __call__(self, img, mask):
      scale_amt = random.uniform(self.scale_min, self.scale_max)
      w, h = [int(i * scale_amt) for i in img.size]
  
      resized_img = img.resize((w, h), Image.BICUBIC)
      resized_mask = mask.resize((w, h), Image.NEAREST)
      img, mask = self.crop(resized_img, resized_mask)
      return img, mask
  
imports: import random
  import os
  import math
  import mmcv
  import torch
  import numpy
  import torchvision.transforms
  from torchvision import datasets
  from torch.utils.data import Dataset
  from megatron import print_rank_0
  from megatron import get_args
  from PIL import Image, ImageOps, ImageEnhance
  import torchvision.transforms
label_desc: Use image node_id 2 for calling the RandomSizeAndCrop obj's underlying member method code with example usage: obj.__call__(img, mask) and returns: img,  mask