code: def test_batch_of_bboxes_convertion():
      # Arrange
      loader = coco_torch.load_dataset()
      _, input_bboxes = batch = loader.dataset[
          9
      ]  # it should be always the same sample
  
      # Act
      output_bboxes = convert_batch_of_bboxes([batch], "xywhl")[0]
  
      # Assert
      assert_that(len(output_bboxes) == len(input_bboxes))
  
      for in_bbox, out_bbox in zip(input_bboxes, output_bboxes):
          assert_that(
              (
                  out_bbox == np.asarray([in_bbox[-1], *in_bbox[:-1]])
              ).all(),
              f"Input bbox: {in_bbox}; Output bbox: {out_bbox}",
          )
  
imports: from itertools import permutations
  from typing import Union
  import numpy
  import torch
  from hamcrest import assert_that, calling, close_to, equal_to, raises
  from torch.utils.data import DataLoader, Dataset
  from deepchecks.vision.datasets.detection import coco_torch
  from deepchecks.vision.utils import image_properties
  from deepchecks.vision.utils.detection_formatters import convert_batch_of_bboxes, convert_bbox, verify_bbox_format_notation
  from deepchecks.vision.vision_data import VisionData
label_desc: Use image node_id 15 for calling a global function with example usage: test_batch_of_bboxes_convertion() without return types