code: def transform_labels_to_cxywh(original_labels):
      """
      Convert a batch of data to labels in the expected format. The expected format is an iterator of arrays, each array
      corresponding to a sample. Each array element is in a shape of [B, 5], where B is the number of bboxes
      in the image, and each bounding box is in the structure of [class_id, x, y, w, h].
      """
      label = []
      for annotation in original_labels:
          if len(annotation["boxes"]):
              bbox = torch.stack(annotation["boxes"])
              # Convert the Pascal VOC xyxy format to xywh format
              bbox[:, 2:] = bbox[:, 2:] - bbox[:, :2]
              # The label shape is [class_id, x, y, w, h]
              label.append(
                  torch.concat(
                      [
                          torch.stack(annotation["labels"]).reshape(
                              (-1, 1)
                          ),
                          bbox,
                      ],
                      dim=1,
                  )
              )
          else:
              # If it's an empty image, we need to add an empty label
              label.append(torch.tensor([]))
      return label
  
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 2 for calling a global function with example usage: transform_labels_to_cxywh(original_labels) and returns: label