code: def batch_to_images(self, batch):
      inp = torch.stack(list(batch[0])).numpy().transpose((0, 2, 3, 1))
      mean = [0.485, 0.456, 0.406]
      std = [0.229, 0.224, 0.225]
      inp = std * inp + mean
      inp = np.clip(inp, 0, 1)
      return inp * 255
  
imports: import os
  from functools import partial
  import numpy
  import torch
  from PIL import Image
  import xml.etree.ElementTree
  import torchvision
  from torch import nn
  from torch.utils.data import Dataset, DataLoader
  from torchvision.models.detection import _utils
  from torchvision.models.detection.ssdlite import SSDLiteClassificationHead
  import albumentations
  from albumentations.pytorch import ToTensorV2
  import time
  import math
  from deepchecks.vision.detection_data import DetectionData
  from deepchecks.vision.suites import full_suite
label_desc: Use image node_id 1 for calling the TomatoData obj's underlying member method code with example usage: obj.batch_to_images(batch) and returns: unknown