code: def pred_probs():
      return np.array(
          [
              [0.9, 0.1, 0.2],
              [0.5, 0.6, 0.4],
              [0.75, 0.80, 0.85],
              [0.9, 0.85, 0.2],
              [0.9, 0.1, 0.85],
              [0.5, 0.6, 0.85],
              [0.9, 0.85, 0.85],
              [0.8, 0.4, 0.2],
              [0.9, 0.1, 0.85],
              [0.15, 0.95, 0.05],
          ]
      )
  
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 3 for calling a global function with example usage: pred_probs() and returns: np