code: def test_train_loader():
      train_loader, val_loader = get_train_loaders(
          1,
          batch_size=4,
          dev_mode=False,
          pad_mode="edge",
          meta_version=2,
          pseudo_label=True,
      )
      print(train_loader.num, val_loader.num)
      for i, data in enumerate(train_loader):
          imgs, masks, salt_exists = data
          # pdb.set_trace()
          print(imgs.size(), masks.size(), salt_exists.size())
          print(salt_exists)
          add_depth_channel(imgs, "resize")
          print(masks)
          break
  
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 10 for calling a global function with example usage: test_train_loader() without return types