code: def batch_to_labels(self, batch):
      tensor_annotations = batch[1]
      label = []
      for annotation in tensor_annotations:
          if len(annotation["boxes"]):
              bbox = torch.stack(annotation["boxes"])
              bbox[:, 2:] = bbox[:, 2:] - bbox[:, :2]
              label.append(
                  torch.concat(
                      [
                          torch.stack(annotation["labels"]).reshape(
                              (-1, 1)
                          ),
                          bbox,
                      ],
                      dim=1,
                  )
              )
          else:
              label.append(torch.tensor([]))
      return label
  
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 2 for calling the TomatoData obj's underlying member method code with example usage: obj.batch_to_labels(batch) and returns: label