code: def get_test_loader(
      batch_size=16, index=0, dev_mode=False, pad_mode="edge"
  ):
      test_meta = get_test_meta()
      if dev_mode:
          test_meta = test_meta.iloc[:10]
      test_set = ImageDataset(
          False,
          test_meta,
          image_augment=None
          if pad_mode == "resize"
          else transforms.Pad((13, 13, 14, 14), padding_mode=pad_mode),
          image_transform=get_tta_transforms(index, pad_mode),
      )
      test_loader = data.DataLoader(
          test_set,
          batch_size=batch_size,
          shuffle=False,
          num_workers=4,
          collate_fn=test_set.collate_fn,
          drop_last=False,
      )
      test_loader.num = len(test_set)
      test_loader.meta = test_set.meta
  
      return test_loader
  
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 7 for calling a global function with example usage: get_test_loader(batch_size, index, dev_mode, pad_mode) and returns: test_loader