code: def test_segmentation_many_scorers(
      segmentation_coco_visiondata_train,
  ):
      check = SingleDatasetPerformance(
          scorers=["dice_per_class", "dice_macro", "iou_micro"]
      )
      result = check.run(
          segmentation_coco_visiondata_train, with_display=False
      ).value
  
      assert_that(
          result[result["Metric"] == "dice"]["Value"].mean(),
          close_to(0.649, 0.006),
      )
      assert_that(
          result[result["Metric"] == "dice_macro"]["Value"],
          close_to(0.649, 0.006),
      )
      assert_that(
          result[result["Metric"] == "iou_micro"]["Value"],
          close_to(0.948, 0.001),
      )
  
file_path: deepchecks/tests/vision/checks/model_evaluation/single_dataset_performance_test.py
imports: from hamcrest import assert_that, close_to, equal_to, has_length
  from ignite.metrics import Accuracy, Precision
  from sklearn.metrics import jaccard_score, make_scorer
  from deepchecks.core import ConditionCategory
  from deepchecks.vision.checks import SingleDatasetPerformance
  from deepchecks.vision.metrics_utils import ObjectDetectionTpFpFn
  from deepchecks.vision.metrics_utils.custom_scorer import CustomClassificationScorer
  from tests.base.utils import equal_condition_result
label_desc: Use image node_id 7 for calling a global function with example usage: test_segmentation_many_scorers(segmentation_coco_visiondata_train) without return types