code: def test_predicted_neq_given_filter_multilabel():
      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]]
      label_issues = filter.find_predicted_neq_given(
          labels, pred_probs, multi_label=True
      )
      assert all(label_issues == [False, False, False, False, True])
  
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 24 for calling a global function with example usage: test_predicted_neq_given_filter_multilabel() without return types