code: def test_find_label_issues_multi_label(
      multi_label,
      use_dataset_function,
      filter_by,
      return_indices_ranked_by,
  ):
      """Note: argmax_not_equal method is not compatible with multi_label == True"""
      dataset = multilabel_data if multi_label else data
      if multi_label:
          if use_dataset_function:
              noise_idx = cleanlab.multilabel_classification.filter.find_label_issues(
                  labels=dataset["labels"],
                  pred_probs=dataset["pred_probs"],
                  filter_by=filter_by,
                  return_indices_ranked_by=return_indices_ranked_by,
              )
          else:
              with pytest.warns(DeprecationWarning):
                  noise_idx = filter.find_label_issues(
                      labels=dataset["labels"],
                      pred_probs=dataset["pred_probs"],
                      filter_by=filter_by,
                      multi_label=multi_label,
                      return_indices_ranked_by=return_indices_ranked_by,
                  )
      else:
          noise_idx = filter.find_label_issues(
              labels=dataset["labels"],
              pred_probs=dataset["pred_probs"],
              filter_by=filter_by,
              multi_label=multi_label,
              return_indices_ranked_by=return_indices_ranked_by,
          )
  
      if return_indices_ranked_by is not None:
          noise_bool = np.zeros(len(dataset["labels"])).astype(bool)
          noise_bool[noise_idx] = True
          noise_idx = noise_bool
      acc = np.mean(
          (
              np.array(dataset["labels"], dtype=np.object_)
              != np.array(
                  dataset["true_labels_train"], dtype=np.object_
              )
          )
          == noise_idx
      )
      # Make sure cleanlab does reasonably well finding the errors.
      # acc is the accuracy of detecting a label error.
      assert acc > 0.85
  
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 19 for calling a global function with example usage: test_find_label_issues_multi_label(multi_label, use_dataset_function, filter_by, return_indices_ranked_by) without return types