code: def test_public_label_quality_scores(labels, pred_probs):
      formatted_labels = onehot2int(labels)
      assert isinstance(formatted_labels, list)
      scores1 = ml_classification.get_label_quality_scores(
          formatted_labels, pred_probs
      )
      assert len(scores1) == len(labels)
      assert (scores1 >= 0).all() and (scores1 <= 1).all()
      scores2 = ml_classification.get_label_quality_scores(
          formatted_labels,
          pred_probs,
          method="confidence_weighted_entropy",
      )
      assert not np.isclose(scores1, scores2).all()
      scores3 = ml_classification.get_label_quality_scores(
          formatted_labels, pred_probs, adjust_pred_probs=True
      )
      assert not np.isclose(scores1, scores3).all()
      scores4 = ml_classification.get_label_quality_scores(
          formatted_labels,
          pred_probs,
          method="normalized_margin",
          adjust_pred_probs=True,
          aggregator_kwargs={"method": "exponential_moving_average"},
      )
      assert not np.isclose(scores1, scores4).all()
      scores5 = ml_classification.get_label_quality_scores(
          formatted_labels,
          pred_probs,
          method="normalized_margin",
          adjust_pred_probs=True,
          aggregator_kwargs={"method": "softmin"},
      )
      assert not np.isclose(scores4, scores5).all()
      scores6 = ml_classification.get_label_quality_scores(
          formatted_labels,
          pred_probs,
          method="normalized_margin",
          adjust_pred_probs=True,
          aggregator_kwargs={"method": "softmin", "temperature": 0.002},
      )
      assert not np.isclose(scores5, scores6).all()
      scores7 = ml_classification.get_label_quality_scores(
          formatted_labels,
          pred_probs,
          method="normalized_margin",
          adjust_pred_probs=True,
          aggregator_kwargs={"method": np.min},
      )
      assert np.isclose(scores6, scores7, rtol=1e-3).all()
  
      with pytest.raises(ValueError) as e:
          _ = ml_classification.get_label_quality_scores(
              formatted_labels, pred_probs, method="badchoice"
          )
          assert "Invalid method name: badchoice" in str(e.value)
  
      with pytest.raises(ValueError) as e:
          _ = ml_classification.get_label_quality_scores(
              formatted_labels,
              pred_probs,
              aggregator_kwargs={"method": "invalid"},
          )
          assert (
              "Invalid aggregation method specified: 'invalid'"
              in str(e.value)
          )
  
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 9 for calling a global function with example usage: test_public_label_quality_scores(labels, pred_probs) without return types