class_docstr: Test class collection different tests for different properties parametrized by backend argument.
class_docstr_tok: ["Test","class","collection","different","tests","for","different","properties","parametrized","by","backend","argument","."]
docstr: Test empty predictions in cxcywh format.
docstr_tok: ["Test","empty","predictions","in","cxcywh","format","."]
code: def test_empty_preds_cxcywh(self, backend):
      """Test empty predictions in cxcywh format."""
      metric = MeanAveragePrecision(
          box_format="cxcywh", backend=backend
      )
  
      metric.update(
          [
              {
                  "boxes": Tensor([]),
                  "scores": Tensor([]),
                  "labels": IntTensor([]),
              }
          ],
          [
              {
                  "boxes": Tensor(
                      [[388.2800, 163.1800, 348.2600, 243.7800]]
                  ),
                  "labels": IntTensor([4]),
              }
          ],
      )
      metric.compute()
  
imports: import contextlib
  import io
  import json
  from copy import deepcopy
  from functools import partial
  from itertools import product
  import numpy
  import pytest
  import torch
  from lightning_utilities import apply_to_collection
  from pycocotools.coco import COCO
  from pycocotools.cocoeval import COCOeval
  from torch import IntTensor, Tensor
  from torchmetrics.detection.mean_ap import MeanAveragePrecision
  from torchmetrics.utilities.imports import _FASTER_COCO_EVAL_AVAILABLE, _PYCOCOTOOLS_AVAILABLE, _TORCHVISION_GREATER_EQUAL_0_8
  from unittests.detection import _DETECTION_BBOX, _DETECTION_SEGM, _DETECTION_VAL
  from unittests.helpers.testers import MetricTester
label_desc: Use image node_id 7 for calling the TestMapProperties obj's underlying member method code with example usage: obj.test_empty_preds_cxcywh(backend) without return types