code: def test_scorer_metric_classification(mnist_visiondata_test):
      scorer = make_scorer(jaccard_score, average=None, zero_division=0)
      metric_dict = get_scorers_dict(
          mnist_visiondata_test, {"kappa": scorer}
      )
      res = calculate_metrics(metric_dict, mnist_visiondata_test)
      assert_that(
          np.mean(list(res["kappa"].values())), close_to(0.976, 0.001)
      )
  
imports: from typing import Dict
  import numpy
  from hamcrest import assert_that, close_to, has_items, has_length
  from ignite.engine import Engine
  from ignite.metrics import Metric
  from numpy import nanmean
  from sklearn.metrics import jaccard_score, make_scorer
  from deepchecks.vision import VisionData
  from deepchecks.vision.metrics_utils import get_scorers_dict
  from deepchecks.vision.metrics_utils.detection_precision_recall import ObjectDetectionAveragePrecision
  from deepchecks.vision.metrics_utils.semantic_segmentation_metrics import MeanDice, MeanIoU, per_sample_dice
  from deepchecks.vision.vision_data.utils import sequence_to_numpy
label_desc: Use image node_id 10 for calling a global function with example usage: test_scorer_metric_classification(mnist_visiondata_test) without return types