code: def test_find_label_issues_overlapping_labels(
      overlapping_label_check,
  ):
      bboxes = np.array(
          [
              [359.0, 146.0, 472.0, 360.0],
              [340.0, 22.0, 494.0, 323.0],
              [472.0, 173.0, 508.0, 221.0],
              [486.0, 183.0, 517.0, 218.0],
              [359.0, 144.0, 470.0, 358.0],
              [340.0, 22.0, 494.0, 323.0],
          ]
      )
      label_classes = np.array([0, 1, 1, 1, 1, 1])
      perfect_pred = [[], []]
      for i in range(0, len(label_classes)):
          perfect_pred[label_classes[i]].append(
              list(bboxes[i]) + [0.95]
          )
      prediction = [np.array(p) for p in perfect_pred]
      prediction = np.array(prediction, dtype=object)
      label = {"bboxes": bboxes, "labels": label_classes}
      is_issue = find_label_issues(
          [label],
          [prediction],
          overlapping_label_check=overlapping_label_check,
      )[0]
      if overlapping_label_check:
          assert is_issue == True
      else:
          assert is_issue == False
  
imports: import os
  from collections import Counter, defaultdict
  from multiprocessing import Pool
  import numpy
  from cleanlab.internal.constants import ALPHA, BADLOC_THRESHOLD_FACTOR, CUSTOM_SCORE_WEIGHT_BADLOC, CUSTOM_SCORE_WEIGHT_OVERLOOKED, CUSTOM_SCORE_WEIGHT_SWAP, HIGH_PROBABILITY_THRESHOLD, LOW_PROBABILITY_THRESHOLD, OVERLOOKED_THRESHOLD_FACTOR, SWAP_THRESHOLD_FACTOR, TEMPERATURE
  from cleanlab.internal.object_detection_utils import bbox_xyxy_to_xywh, softmax, softmin1d
  from cleanlab.object_detection.filter import _calculate_true_positives_false_positives, _filter_by_class, _find_label_issues, _find_label_issues_per_box, _get_per_class_ap, _pool_box_scores_per_image, _process_class_list, find_label_issues
  from cleanlab.object_detection.rank import _compute_label_quality_scores, _get_aggregation_weights, _get_dist_matrix, _get_min_pred_prob, _get_overlap_matrix, _get_prediction_type, _get_valid_inputs_for_compute_scores, _get_valid_inputs_for_compute_scores_per_image, _get_valid_score, _get_valid_subtype_score_params, _has_overlap, _prune_by_threshold, _separate_label, _separate_prediction, compute_badloc_box_scores, compute_overlooked_box_scores, compute_swap_box_scores, get_label_quality_scores, issues_from_scores
  from cleanlab.object_detection.summary import bounding_box_size_distribution, class_label_distribution, get_sorted_bbox_count_idxs, object_counts_per_image, plot_class_distribution, plot_class_size_distributions, visualize, calculate_per_class_metrics, get_average_per_class_confusion_matrix
  import copy
  import warnings
  import matplotlib.pyplot
  import numpy
  import pytest
  from PIL import Image
label_desc: Use image node_id 42 for calling a global function with example usage: test_find_label_issues_overlapping_labels(overlapping_label_check) without return types