code: def cleanlab_data_strategy(draw):
      num_classes = draw(st.integers(min_value=2, max_value=3))
      num_samples = draw(st.integers(min_value=10, max_value=50))
  
      # Generate true labels as one-hot encoded vectors for multi-label
      true_labels = draw(
          arrays(
              dtype=np.int8,
              shape=(num_samples, num_classes),
              elements=st.integers(0, 1),
          )
      )
  
      # Generate noise matrix for multi-label and flip those values
      flip_prob = 0.2
      noisy_labels = flip_labels(true_labels, flip_prob)
  
      # Generate predicted probabilities for each class for each sample
      pred_probs = draw(
          arrays(
              dtype=np.float32,
              shape=(num_samples, num_classes),
              elements=st.floats(
                  min_value=0, max_value=1, width=32
              ),  # Specify width here
          )
      )
  
      for i in range(num_samples):
          for j in range(num_classes):
              if draw(st.floats(min_value=0, max_value=1)) < 0.1:
                  # Set some probability values to exactly 0.5
                  pred_probs[i][j] = 0.5
      return true_labels, noisy_labels, np.array(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 30 for calling a global function with example usage: cleanlab_data_strategy(draw) and returns: true_labels,  noisy_labels,  np