code: def __init__(
      self,
      size,
      scale=(0.08, 1.0),
      ratio=(3.0 / 4.0, 4.0 / 3.0),
      interpolation=Image.BILINEAR,
  ):
      super(RandomResizedCropWithMask, self).__init__(
          size, scale, ratio, interpolation
      )
  
imports: import os
  import cv2
  import numpy
  import random
  import torchvision.transforms.functional
  from torchvision.transforms import RandomResizedCrop, ColorJitter, RandomAffine
  import PIL
  from PIL import Image
  import collections
  import settings
label_desc: Use image node_id 1 to create a new RandomResizedCropWithMask object from inherited base classes: RandomResizedCrop with example: obj = RandomResizedCropWithMask(size, scale, ratio, interpolation)