code: def __init__(
      self,
      degrees,
      translate=None,
      scale=None,
      shear=None,
      resample="edge",
  ):
      super(RandomAffineWithMask, self).__init__(
          degrees, translate, scale, shear, resample
      )
  
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 RandomAffineWithMask object from inherited base classes: RandomAffine with example: obj = RandomAffineWithMask(degrees, translate, scale, shear, resample)