code: def test_stack_complement():
      # Toy example
      pred_probs_class = np.array([0.1, 0.9, 0.3, 0.8])
      pred_probs_extended = stack_complement(pred_probs_class)
      pred_probs_expected = np.array(
          [
              [0.9, 0.1],
              [0.1, 0.9],
              [0.7, 0.3],
              [0.2, 0.8],
          ]
      )
      assert np.isclose(pred_probs_extended, pred_probs_expected).all()
  
      # Check preservation of probabilities
      pred_probs_class = np.random.rand(100)
      pred_probs_extended = stack_complement(pred_probs_class)
      assert np.sum(pred_probs_extended, axis=1).all() == 1
  
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 22 for calling a global function with example usage: test_stack_complement() without return types