code: def test_find_label_issues_multi_label_small(
      confident_joint, return_indices_ranked_by
  ):
      pred_probs = np.array(
          [
              [0.9, 0.1, 0.0, 0.4],
              [0.7, 0.8, 0.2, 0.3],
              [0.9, 0.8, 0.4, 0.2],
              [0.1, 0.1, 0.8, 0.3],
              [0.4, 0.5, 0.1, 0.1],
          ]
      )
      labels = [[0], [0, 1], [0, 1], [2], [0, 2, 3]]
      cj = count.compute_confident_joint(
          labels=labels, pred_probs=pred_probs, multi_label=True
      )
      assert cj.shape == (4, 2, 2)
      noise_idx = filter.find_label_issues(
          labels=labels,
          pred_probs=pred_probs,
          multi_label=True,
          confident_joint=np.array(confident_joint)
          if confident_joint
          else None,
          return_indices_ranked_by=return_indices_ranked_by,
      )
      noise_idx2 = filter.find_label_issues(
          labels=labels,
          pred_probs=pred_probs,
          multi_label=True,
          confident_joint=cj,
          return_indices_ranked_by=return_indices_ranked_by,
      )
      if confident_joint is not None:
          noise_idx3 = filter.find_label_issues(
              labels=labels,
              pred_probs=pred_probs,
              multi_label=True,
              confident_joint=cj[::-1],
              return_indices_ranked_by=return_indices_ranked_by,
          )
  
      def _idx_to_bool(idx):
          noise_bool = np.zeros(len(labels)).astype(bool)
          noise_bool[idx] = True
          return noise_bool
  
      if return_indices_ranked_by is not None:
          noise_idx = _idx_to_bool(noise_idx)
          noise_idx2 = _idx_to_bool(noise_idx2)
          if confident_joint is not None:
              noise_idx3 = _idx_to_bool(noise_idx3)
      expected_output = [False, False, False, False, True]
      assert (
          noise_idx.tolist() == noise_idx2.tolist() == expected_output
      )
      if confident_joint is not None:
          assert noise_idx3.tolist() != expected_output
  
imports: import cleanlab.multilabel_classification.dataset
  from cleanlab import count, filter
  from cleanlab.count import get_confident_thresholds, estimate_py_and_noise_matrices_from_probabilities
  from cleanlab.internal.latent_algebra import compute_inv_noise_matrix
  from cleanlab.benchmarking.noise_generation import generate_noise_matrix_from_trace
  from cleanlab.benchmarking.noise_generation import generate_noisy_labels
  from cleanlab.internal.util import value_counts
  from cleanlab.internal.multilabel_utils import int2onehot
  from cleanlab.experimental.label_issues_batched import find_label_issues_batched
  import numpy
  import scipy
  import pytest
  from sklearn.multioutput import MultiOutputClassifier
  from sklearn.linear_model import LogisticRegression
  from sklearn.model_selection import cross_val_predict
  from tempfile import mkdtemp
  import os.path
label_desc: Use image node_id 20 for calling a global function with example usage: test_find_label_issues_multi_label_small(confident_joint, return_indices_ranked_by) and returns: noise_bool