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 that map works with custom iou thresholds.
docstr_tok: ["Test","that","map","works","with","custom","iou","thresholds","."]
code: def test_map_with_custom_thresholds(self, backend):
      """Test that map works with custom iou thresholds."""
      metric = MeanAveragePrecision(
          iou_thresholds=[0.1, 0.2], backend=backend
      )
      metric = metric.to("cuda")
      for preds, targets in zip(
          deepcopy(_inputs["preds"]), deepcopy(_inputs["target"])
      ):
          metric.update(
              apply_to_collection(
                  preds, Tensor, lambda x: x.to("cuda")
              ),
              apply_to_collection(
                  targets, Tensor, lambda x: x.to("cuda")
              ),
          )
      res = metric.compute()
      assert res["map_50"].item() == -1
      assert res["map_75"].item() == -1
  
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 9 for calling the TestMapProperties obj's underlying member method code with example usage: obj.test_map_with_custom_thresholds(backend) without return types