code: def get_tta_transforms(index, pad_mode):
      tta_transforms = {
          0: [],
          1: [transforms.RandomHorizontalFlip(p=2.0)],
          2: [transforms.RandomVerticalFlip(p=2.0)],
          3: [
              transforms.RandomHorizontalFlip(p=2.0),
              transforms.RandomVerticalFlip(p=2.0),
          ],
      }
      if pad_mode == "resize":
          return transforms.Compose(
              [
                  transforms.Resize((H, W)),
                  *(tta_transforms[index]),
                  *img_transforms,
              ]
          )
      else:
          return transforms.Compose(
              [*(tta_transforms[index]), *img_transforms]
          )
  
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 2 for calling a global function with example usage: get_tta_transforms(index, pad_mode) and returns: transforms, transforms