code: def _pool_box_scores_per_image(
      is_issue_per_box: List[np.ndarray],
  ) -> np.ndarray:
      """Takes in a list of size ``N`` where each index is a boolean array of length number of boxes per image `n `
      marking if a specific box is an issue - 1 or not - 0.
  
      Returns a list of size ``N`` where each index marks if the image contains an issue - 1 or not - 0.
      Images are marked as issues if 1 or more bounding boxes in the image is an issue.
      """
      is_issue = np.zeros(
          shape=[
              len(
                  is_issue_per_box,
              )
          ]
      )
      for idx, issue_per_box in enumerate(is_issue_per_box):
          if np.sum(issue_per_box) > 0:
              is_issue[idx] = 1
      return is_issue
  
file_path: cleanlab/cleanlab/object_detection/filter.py
imports: from collections import defaultdict
  from multiprocessing import Pool
  from typing import Any, Dict, List, Optional, Tuple, Union
  import numpy
  from cleanlab.internal.constants import ALPHA, HIGH_PROBABILITY_THRESHOLD, LOW_PROBABILITY_THRESHOLD, OVERLOOKED_THRESHOLD_FACTOR, BADLOC_THRESHOLD_FACTOR, SWAP_THRESHOLD_FACTOR, AP_SCALE_FACTOR
  from cleanlab.internal.object_detection_utils import assert_valid_inputs
  from cleanlab.object_detection.rank import _get_valid_inputs_for_compute_scores, _separate_label, _separate_prediction, compute_badloc_box_scores, compute_overlooked_box_scores, compute_swap_box_scores, get_label_quality_scores, issues_from_scores, _get_overlap_matrix
label_desc: Use image node_id 4 for calling a global function with example usage: _pool_box_scores_per_image(is_issue_per_box) and returns: is_issue