code: def test_num_label_issues_multilabel(confident_joint):
      dataset = multilabel_data
      n = count.num_label_issues(
          labels=dataset["labels"],
          pred_probs=dataset["pred_probs"],
          confident_joint=dataset["cj"] if confident_joint else None,
          estimation_method="off_diagonal",
          multi_label=True,
      )
      f = filter.find_label_issues(
          labels=dataset["labels"],
          pred_probs=dataset["pred_probs"],
          confident_joint=dataset["cj"] if confident_joint else None,
          filter_by="confident_learning",
          multi_label=True,
      )
      assert sum(f) == n
  
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 30 for calling a global function with example usage: test_num_label_issues_multilabel(confident_joint) without return types