code: def collate_without_model(
      data,
  ) -> t.Tuple[t.List[np.ndarray], t.List[int]]:
      """Collate function for the mnist dataset returning images and labels in correct format as tuple."""
      raw_images = torch.stack([x[0] for x in data])
      labels = [x[1] for x in data]
      images = raw_images.permute(0, 2, 3, 1)
      images = un_normalize_batch(images, mean=(0.1307,), std=(0.3081,))
      return images, labels
  
imports: import logging
  import os
  import pathlib
  import pickle
  import typing
  import warnings
  from itertools import cycle
  from urllib.error import URLError
  import albumentations
  import numpy
  import torch
  import torch.nn.functional
  from albumentations.pytorch import ToTensorV2
  from torch import nn
  from torch.utils.data import DataLoader
  from torch.utils.data.dataset import IterableDataset
  from typing_extensions import Literal
  from deepchecks.utils.logger import get_logger
  from deepchecks.vision.utils.test_utils import get_data_loader_sequential, hash_image, un_normalize_batch
  from deepchecks.vision.vision_data import BatchOutputFormat, VisionData
  from deepchecks.vision.vision_data.utils import object_to_numpy
label_desc: Use image node_id 2 for calling a global function with example usage: collate_without_model(data) and returns: images,  labels