code: def get_untransformed_images(original_images):
      """
      Convert a batch of data to images in the expected format. The expected format is an iterable of images,
      where each image is a numpy array of shape (height, width, channels). The numbers in the array should be in the
      range [0, 255] in a uint8 format.
      """
      inp = (
          torch.stack(list(original_images))
          .cpu()
          .detach()
          .numpy()
          .transpose((0, 2, 3, 1))
      )
      mean = [0.485, 0.456, 0.406]
      std = [0.229, 0.224, 0.225]
      # Un-normalize the images
      inp = std * inp + mean
      inp = np.clip(inp, 0, 1)
      return inp * 255
  
imports: import os
  import numpy
  import torch
  from torch.utils.data import DataLoader, Dataset
  import albumentations
  from albumentations.pytorch import ToTensorV2
  from PIL import Image
  import xml.etree.ElementTree
  import urllib.request
  import zipfile
  from functools import partial
  from torch import nn
  import torchvision
  from torchvision.models.detection import _utils
  from torchvision.models.detection.ssdlite import SSDLiteClassificationHead
  from deepchecks.vision.vision_data import BatchOutputFormat
  from deepchecks.vision.vision_data import VisionData
  from deepchecks.vision.suites import model_evaluation
label_desc: Use image node_id 1 for calling a global function with example usage: get_untransformed_images(original_images) and returns: unknown