class_docstr: Test the MultilabelScorer class.
class_docstr_tok: ["Test","the","MultilabelScorer","class","."]
code: def test_call(
      self, base_scorer, aggregator, strict, labels, pred_probs
  ):
      scorer = ml_scorer.MultilabelScorer(
          base_scorer, aggregator, strict=strict
      )
      assert callable(scorer)
  
      test_scores = scorer(labels, pred_probs)
      assert isinstance(test_scores, np.ndarray)
      assert test_scores.shape == (labels.shape[0],)
  
      # Test base_scorer_kwargs
      base_scorer_kwargs = {"adjust_pred_probs": True}
      if (
          scorer.base_scorer
          is not ml_scorer.ClassLabelScorer.CONFIDENCE_WEIGHTED_ENTROPY
      ):
          test_scores = scorer(
              labels, pred_probs, base_scorer_kwargs=base_scorer_kwargs
          )
          assert isinstance(test_scores, np.ndarray)
          assert test_scores.shape == (labels.shape[0],)
      else:
          with pytest.raises(ValueError) as e:
              scorer(
                  labels,
                  pred_probs,
                  base_scorer_kwargs=base_scorer_kwargs,
              )
              assert (
                  "adjust_pred_probs is not currently supported for"
                  in str(e)
              )
  
imports: import itertools
  import typing
  import numpy
  import pytest
  import sklearn
  from hypothesis import given, settings
  from hypothesis import strategies
  from hypothesis.strategies import composite
  from hypothesis.extra.numpy import arrays
  from sklearn.linear_model import LogisticRegression
  from sklearn.multiclass import OneVsRestClassifier
  from cleanlab import multilabel_classification
  from cleanlab.internal import multilabel_scorer
  from cleanlab.internal.multilabel_utils import get_onehot_num_classes, onehot2int, stack_complement
  from cleanlab.multilabel_classification import filter
  from cleanlab.multilabel_classification.dataset import common_multilabel_issues, multilabel_health_summary, overall_multilabel_health_score, rank_classes_by_multilabel_quality
  from cleanlab.multilabel_classification.rank import get_label_quality_scores_per_class
label_desc: Use image node_id 4 for calling the TestMultilabelScorer obj's underlying member method code with example usage: obj.test_call(base_scorer, aggregator, strict, labels, pred_probs) without return types