code: def pred_probs_gold(labels):
      pred_probs = np.array(
          [
              [0.203, 0.465, 0.612],
              [0.802, 0.596, 0.43],
              [0.776, 0.649, 0.391],
              [0.201, 0.439, 0.633],
              [0.203, 0.443, 0.584],
              [0.814, 0.572, 0.332],
              [0.201, 0.388, 0.544],
              [0.778, 0.646, 0.392],
              [0.796, 0.611, 0.387],
              [0.199, 0.381, 0.58],
          ]
      )
      assert pred_probs.shape == labels.shape
      return pred_probs
  
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 2 for calling a global function with example usage: pred_probs_gold(labels) and returns: pred_probs