code: def deepchecks_collate_fn(batch) -> BatchOutputFormat:
      """Return a batch of images, labels and predictions for a batch of data. The expected format is a dictionary with
      the following keys: 'images', 'labels' and 'predictions', each value is in the deepchecks format for the task.
      You can also use the BatchOutputFormat class to create the output.
      """
      # batch received as iterable of tuples of (image, label) and transformed to tuple of iterables of images and labels:
      batch = tuple(zip(*batch))
  
      # images:
      inp = (
          torch.stack(batch[0]).detach().numpy().transpose((0, 2, 3, 1))
      )
      mean = [0.485, 0.456, 0.406]
      std = [0.229, 0.224, 0.225]
      inp = std * inp + mean
      images = np.clip(inp, 0, 1) * 255
  
      # labels:
      labels = batch[1]
  
      # predictions:
      logits = model.to(device)(torch.stack(batch[0]).to(device))
      predictions = nn.Softmax(dim=1)(logits)
      return BatchOutputFormat(
          images=images, labels=labels, predictions=predictions
      )
  
file_path: deepchecks/docs/source/vision/tutorials/quickstarts/plot_classification_tutorial.py
imports: import os
  import urllib.request
  import zipfile
  import albumentations
  import numpy
  import PIL.Image
  import torch
  import torchvision
  from albumentations.pytorch import ToTensorV2
  from torch import nn
  from torch.utils.data import DataLoader
  from deepchecks.vision.vision_data import BatchOutputFormat
  from deepchecks.vision import VisionData
  from deepchecks.vision.suites import train_test_validation
label_desc: Use image node_id 1 for calling a global function with example usage: deepchecks_collate_fn(batch) and returns: BatchOutputFormat