code: def _has_overlap(bbox_list, labels):
      """This function determines whether each labeled box overlaps with another box of a different class (i.e. virtually the same box having multiple conflicting annotations). It returns a boolean array."""
      iou_matrix = _get_overlap_matrix(bbox_list, bbox_list)
      results_overlap = []
      for i in range(0, len(iou_matrix)):
          is_overlap = False
          for j in range(0, len(iou_matrix)):
              if i != j:
                  if iou_matrix[i][j] >= LABEL_OVERLAP_THRESHOLD:
                      lab_1 = labels[i]
                      lab_2 = labels[j]
                      if lab_1 != lab_2:
                          is_overlap = True
          results_overlap.append(is_overlap)
      return np.array(results_overlap)
  
imports: import warnings
  from cleanlab.internal.constants import ALPHA, CUSTOM_SCORE_WEIGHT_BADLOC, CUSTOM_SCORE_WEIGHT_OVERLOOKED, CUSTOM_SCORE_WEIGHT_SWAP, EUC_FACTOR, HIGH_PROBABILITY_THRESHOLD, LOW_PROBABILITY_THRESHOLD, MAX_ALLOWED_BOX_PRUNE, TINY_VALUE, TEMPERATURE, LABEL_OVERLAP_THRESHOLD
  import copy
  from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, TypeVar
  import numpy
  from cleanlab.internal.object_detection_utils import softmin1d, assert_valid_aggregation_weights, assert_valid_inputs
label_desc: Use image node_id 15 for calling a global function with example usage: _has_overlap(bbox_list, labels) and returns: np