code: def _num_label_issues_multilabel(
      labels: LabelLike,
      pred_probs: np.ndarray,
      confident_joint: Optional[np.ndarray] = None,
  ) -> int:
      """
      Parameters
      ----------
      labels: list
         Refer to documentation for this argument in ``count.calibrate_confident_joint()`` with `multi_label=True` for details.
  
      pred_probs : np.ndarray
         Predicted-probabilities in the same format expected by the `~cleanlab.count.get_confident_thresholds` function.
  
      Returns
      -------
      num_issues : int
         The estimated number of examples with label issues in the multi-label dataset.
  
      Note: We set the filter_by method as 'confident_learning' to match the non-multilabel case
      (analog to the off_diagonal estimation method)
      """
  
      from cleanlab.filter import find_label_issues
  
      issues_idx = find_label_issues(
          labels=labels,
          pred_probs=pred_probs,
          confident_joint=confident_joint,
          multi_label=True,
          filter_by="confident_learning",  # specified to match num_label_issues
      )
      return sum(issues_idx)
  
imports: from sklearn.linear_model import LogisticRegression
  from sklearn.model_selection import StratifiedKFold
  from sklearn.metrics import confusion_matrix
  import sklearn.base
  import numpy
  import warnings
  from typing import Tuple, Union, Optional
  from cleanlab.typing import LabelLike
  from cleanlab.internal.multilabel_utils import stack_complement, get_onehot_num_classes
  from cleanlab.internal.constants import TINY_VALUE, CONFIDENT_THRESHOLDS_LOWER_BOUND, FLOATING_POINT_COMPARISON
  from cleanlab.internal.util import value_counts_fill_missing_classes, clip_values, clip_noise_rates, round_preserving_row_totals, append_extra_datapoint, train_val_split, get_num_classes, get_unique_classes, is_torch_dataset, is_tensorflow_dataset
  from cleanlab.internal.latent_algebra import compute_inv_noise_matrix, compute_py, compute_noise_matrix_from_inverse
  from cleanlab.internal.validation import assert_valid_inputs, labels_to_array
label_desc: Use image node_id 2 for calling a global function with example usage: _num_label_issues_multilabel(labels, pred_probs, confident_joint) and returns: sum