code: def _compute_num_issues_calibrated(
      arg: Tuple[np.ndarray, bool]
  ) -> Tuple[Any, int, int]:
      """
      Helper function for `_update_num_label_issues` multiprocessing with calibration.
      """
      ind = arg[0]
      thorough = arg[1]
      label = labels_shared[ind]
      pred_prob = pred_probs_shared[ind, :]
      batch_size = len(label)
  
      pred_class = np.argmax(pred_prob, axis=-1)
      if thorough:
          pred_gt_thresholds = (
              pred_prob >= adj_confident_thresholds_shared
          )
          max_ind = np.argmax(pred_prob * pred_gt_thresholds, axis=-1)
          to_inc = (
              pred_prob[np.arange(batch_size), max_ind]
              >= adj_confident_thresholds_shared[max_ind]
          )
  
          prune_count_batch = to_inc & (max_ind != label)
          normalization_batch = to_inc
      else:
          to_inc = (
              pred_prob[np.arange(batch_size), pred_class]
              >= adj_confident_thresholds_shared[pred_class]
          )
          normalization_batch = to_inc
          prune_count_batch = to_inc & (pred_class != label)
  
      return (label, normalization_batch, prune_count_batch)
  
imports: import numpy
  from typing import Optional, List, Tuple, Any
  from cleanlab.count import get_confident_thresholds, _reduce_issues
  from cleanlab.rank import find_top_issues, _compute_label_quality_scores
  from cleanlab.typing import LabelLike
  from cleanlab.internal.util import value_counts_fill_missing_classes
  from cleanlab.internal.constants import CONFIDENT_THRESHOLDS_LOWER_BOUND, FLOATING_POINT_COMPARISON, CLIPPING_LOWER_BOUND
  import platform
  import multiprocessing
label_desc: Use image node_id 4 for calling a global function with example usage: _compute_num_issues_calibrated(arg) and returns: label,  normalization_batch,  prune_count_batch