code: def get_img_mask_augments(pad_mode, depths_channel=False):
      if depths_channel:
          affine_aug = aug.RandomAffineWithMask(
              5, translate=(0.1, 0.0), scale=(0.9, 1.1), shear=None
          )
      else:
          affine_aug = aug.RandomAffineWithMask(
              15, translate=(0.1, 0.1), scale=(0.9, 1.1), shear=None
          )
  
      if pad_mode == "resize":
          img_mask_aug_train = aug.Compose(
              [aug.RandomHFlipWithMask(), affine_aug]
          )
          img_mask_aug_val = None
      else:
          img_mask_aug_train = aug.Compose(
              [
                  aug.PadWithMask((28, 28), padding_mode=pad_mode),
                  aug.RandomHFlipWithMask(),
                  affine_aug,
                  aug.RandomResizedCropWithMask(
                      H, scale=(1.0, 1.0), ratio=(1.0, 1.0)
                  ),
              ]
          )
          img_mask_aug_val = aug.PadWithMask(
              (13, 13, 14, 14), padding_mode=pad_mode
          )
  
      return img_mask_aug_train, img_mask_aug_val
  
imports: import os, cv2, glob
  import numpy
  from PIL import Image
  import torch
  import torch.utils.data
  from torchvision import datasets, models, transforms
  from utils import read_masks, get_test_meta, get_nfold_split
  import augmentation
  from settings import *
label_desc: Use image node_id 5 for calling a global function with example usage: get_img_mask_augments(pad_mode, depths_channel) and returns: img_mask_aug_train,  img_mask_aug_val