code: def get_img_mask_augments(train_mode, pad_mode):
      if pad_mode == "resize":
          img_mask_aug_train = Compose(
              [
                  RandomHFlipWithMask(),
                  RandomAffineWithMask(
                      10,
                      translate=(0.1, 0.1),
                      scale=(0.9, 1.1),
                      shear=None,
                  ),
              ]
          )
          img_mask_aug_val = None
      else:
          img_mask_aug_train = Compose(
              [
                  PadWithMask((28, 28), padding_mode=pad_mode),
                  RandomHFlipWithMask(),
                  RandomAffineWithMask(
                      10,
                      translate=(0.1, 0.1),
                      scale=(0.9, 1.1),
                      shear=None,
                  ),
                  RandomResizedCropWithMask(
                      128, scale=(1.0, 1.0), ratio=(1.0, 1.0)
                  ),
              ]
          )
          img_mask_aug_val = PadWithMask(
              (13, 14), padding_mode=pad_mode
          )
  
      return img_mask_aug_train, img_mask_aug_val
  
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 for calling a global function with example usage: get_img_mask_augments(train_mode, pad_mode) and returns: img_mask_aug_train,  img_mask_aug_val