code: def test_rank_classes_by_multilabel_quality(
      pred_probs_multilabel, labels_multilabel, class_names
  ):
      df_ranked = rank_classes_by_multilabel_quality(
          pred_probs=pred_probs_multilabel,
          labels=labels_multilabel,
          class_names=class_names,
      )
      expected_Label_Issues = [1, 0, 0, 0, 0]
  
      expected_Label_Noise = [0.14285714285714285, 0.0, 0.0, 0.0, 0.0]
  
      expected_Label_Quality_Score = [
          0.8571428571428572,
          1.0,
          1.0,
          1.0,
          1.0,
      ]
  
      expected_Inverse_Label_Issues = [0, 1, 0, 0, 0]
  
      expected_Inverse_Label_Noise = [
          0.0,
          0.14285714285714285,
          0.0,
          0.0,
          0.0,
      ]
      assert list(df_ranked["Label Issues"]) == expected_Label_Issues
  
      assert np.isclose(
          np.array(expected_Label_Noise), df_ranked["Label Noise"]
      ).all()
      assert np.isclose(
          np.array(expected_Label_Quality_Score),
          df_ranked["Label Quality Score"],
      ).all()
      assert (
          list(df_ranked["Inverse Label Issues"])
          == expected_Inverse_Label_Issues
      )
      assert np.isclose(
          np.array(expected_Inverse_Label_Noise),
          df_ranked["Inverse Label Noise"],
      ).all()
      if class_names:
          expected_res = [
              "Dog",
              "Apple",
              "Cat",
              "Peach",
              "Bird",
          ]
          assert list(df_ranked["Class Name"]) == expected_res
      else:
          assert "Class Name" not in df_ranked.columns
  
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 17 for calling a global function with example usage: test_rank_classes_by_multilabel_quality(pred_probs_multilabel, labels_multilabel, class_names) without return types