code: def get_train_loaders(
      ifold,
      batch_size=8,
      dev_mode=False,
      pad_mode="edge",
      meta_version=1,
      pseudo_label=False,
      depths=False,
  ):
      train_shuffle = True
      train_meta, val_meta = get_nfold_split(
          ifold, nfold=10, meta_version=meta_version
      )
  
      if pseudo_label:
          test_meta = get_test_meta()
          train_meta = train_meta.append(test_meta, sort=True)
  
      if dev_mode:
          train_shuffle = False
          train_meta = train_meta.iloc[:10]
          val_meta = val_meta.iloc[:10]
      # print(val_meta[X_COLUMN].values[:5])
      # print(val_meta[Y_COLUMN].values[:5])
      print(train_meta.shape, val_meta.shape)
      img_mask_aug_train, img_mask_aug_val = get_img_mask_augments(
          pad_mode, depths
      )
  
      train_set = ImageDataset(
          True,
          train_meta,
          augment_with_target=img_mask_aug_train,
          image_augment=transforms.ColorJitter(0.2, 0.2, 0.2, 0.2),
          image_transform=get_image_transform(pad_mode),
          mask_transform=get_mask_transform(pad_mode),
      )
  
      train_loader = data.DataLoader(
          train_set,
          batch_size=batch_size,
          shuffle=train_shuffle,
          num_workers=4,
          collate_fn=train_set.collate_fn,
          drop_last=True,
      )
      train_loader.num = len(train_set)
  
      val_set = ImageDataset(
          True,
          val_meta,
          augment_with_target=img_mask_aug_val,
          image_augment=None,
          image_transform=get_image_transform(pad_mode),
          mask_transform=get_mask_transform(pad_mode),
      )
      val_loader = data.DataLoader(
          val_set,
          batch_size=batch_size,
          shuffle=False,
          num_workers=4,
          collate_fn=val_set.collate_fn,
      )
      val_loader.num = len(val_set)
      val_loader.y_true = read_masks(val_meta[ID_COLUMN].values)
  
      return train_loader, val_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 6 for calling a global function with example usage: get_train_loaders(ifold, batch_size, dev_mode, pad_mode, meta_version, pseudo_label, depths) and returns: train_loader,  val_loader