code: def test_get_cross_validated_multilabel_pred_probs(
      dummy_features, labels, cv, pred_probs_gold
  ):
      clf = OneVsRestClassifier(LogisticRegression(random_state=0))
      pred_probs = ml_scorer.get_cross_validated_multilabel_pred_probs(
          dummy_features,
          labels,
          clf=clf,
          cv=cv,
      )
      assert isinstance(pred_probs, np.ndarray)
      assert pred_probs.shape == labels.shape
      assert np.all(pred_probs >= 0) and np.all(pred_probs <= 1)
      assert np.all(np.isfinite(pred_probs))
  
      # Gold master test - Ensure output is consistent
      assert dummy_features.shape == (10, 2)
      assert np.allclose(pred_probs, pred_probs_gold, atol=5e-4)
  
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 28 for calling a global function with example usage: test_get_cross_validated_multilabel_pred_probs(dummy_features, labels, cv, pred_probs_gold) without return types