code: def collate_fn(self, batch):
      imgs = [x[0] for x in batch]
      inputs = torch.stack(imgs)
  
      if self.train_mode:
          masks = [x[1] for x in batch]
          labels = torch.stack(masks)
  
          salt_target = [x[2] for x in batch]
          return inputs, labels, torch.FloatTensor(salt_target)
      else:
          return inputs
  
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 the ImageDataset obj's underlying member method code with example usage: obj.collate_fn(batch) and returns: inputs,  labels,  torch, inputs