code: def test_convert_bbox_function_with_ambiguous_combination_of_parameters():
      image_width, image_height = 100, 100
      bbox = torch.tensor([35, 70, 10, 15, 1])
      notation = "whxyl"
  
      # format notation indicates that coordinates are not normalized
      # but image width and height parameters were passed
      assert_that(
          calling(convert_bbox).with_args(
              bbox=bbox,
              notation=notation,
              image_width=image_width,
              image_height=image_height,
          ),
          raises(
              ValueError,
              r"bbox format notation indicates that coordinates of the bbox "
              r"are not normalized but \'image_height\' and \'image_width\' were provided. "
              r"Those parameters are redundant in the case when bbox coordinates are not "
              r"normalized\. Please remove those parameters or add \'n\' element to the format "
              r"notation to indicate that coordinates are indeed normalized\.",
          ),
      )
  
      normalized_bbox = torch.tensor([35, 70, 0.10, 0.15, 1])
      normalized_notation = "whxyln"
  
      # opposite situation
      # format notation indicates that coordinates are normalized
      # but image width and height parameters were not provided
      assert_that(
          calling(convert_bbox).with_args(
              bbox=normalized_bbox, notation=normalized_notation
          ),
          raises(
              ValueError,
              r"bbox format notation indicates that coordinates of the bbox "
              r"are normalized but \'image_height\' and \'image_width\' parameters "
              r"were not provided\. Please pass image height and width parameters "
              r"or remove \'n\' element from the format notation\.",
          ),
      )
  
      # verify that in other cases function does not raise an error
  
      convert_bbox(
          bbox=bbox,
          notation=notation,
      )
      convert_bbox(
          bbox=normalized_bbox,
          notation=normalized_notation,
          image_width=image_width,
          image_height=image_height,
      )
  
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 18 for calling a global function with example usage: test_convert_bbox_function_with_ambiguous_combination_of_parameters() without return types