code: def _compute_badloc_box_scores_for_image(
      alpha: float,
      low_probability_threshold: float,
      label: Optional[Dict[str, Any]] = None,
      prediction: Optional[np.ndarray] = None,
      pred_labels: Optional[np.ndarray] = None,
      pred_label_probs: Optional[np.ndarray] = None,
      pred_bboxes: Optional[np.ndarray] = None,
      lab_labels: Optional[np.ndarray] = None,
      lab_bboxes: Optional[np.ndarray] = None,
      similarity_matrix: Optional[np.ndarray] = None,
      iou_matrix: Optional[np.ndarray] = None,
      min_possible_similarity: Optional[float] = None,
  ) -> np.ndarray:
      """This method returns one score per labeled box in an image. Score from 0 to 1 ranking how badly located the box is."""
  
      auxiliary_input_dict = (
          _get_valid_inputs_for_compute_scores_per_image(
              alpha=alpha,
              label=label,
              prediction=prediction,
              pred_labels=pred_labels,
              pred_label_probs=pred_label_probs,
              pred_bboxes=pred_bboxes,
              lab_labels=lab_labels,
              lab_bboxes=lab_bboxes,
              similarity_matrix=similarity_matrix,
              iou_matrix=iou_matrix,
              min_possible_similarity=min_possible_similarity,
          )
      )
      pred_labels = auxiliary_input_dict["pred_labels"]
      pred_label_probs = auxiliary_input_dict["pred_label_probs"]
      lab_labels = auxiliary_input_dict["lab_labels"]
      similarity_matrix = auxiliary_input_dict["similarity_matrix"]
      iou_matrix = auxiliary_input_dict["iou_matrix"]
  
      scores_badloc = np.empty(len(lab_labels))
  
      for iid, k in enumerate(lab_labels):
          k_similarity = similarity_matrix[iid, pred_labels == k]
          k_pred = pred_label_probs[pred_labels == k]
          k_iou = iou_matrix[iid, pred_labels == k]
  
          if (
              len(k_pred) == 0
              or np.max(k_pred) <= low_probability_threshold
          ):
              scores_badloc[iid] = 1.0
              continue
  
          idx_at_least_low_probability_threshold = np.where(
              k_pred > low_probability_threshold
          )[0]
          idx_at_least_intersection_threshold = np.where(k_iou > 0)[0]
          combined_idx = np.intersect1d(
              idx_at_least_low_probability_threshold,
              idx_at_least_intersection_threshold,
          )
  
          k_similarity = k_similarity[combined_idx]
          k_pred = k_pred[combined_idx]
  
          scores_badloc[iid] = (
              np.max(k_similarity) if len(k_pred) > 0 else 1.0
          )
      return scores_badloc
  
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 26 for calling a global function with example usage: _compute_badloc_box_scores_for_image(alpha, low_probability_threshold, label, prediction, pred_labels, pred_label_probs, pred_bboxes, lab_labels, lab_bboxes, similarity_matrix, iou_matrix, min_possible_similarity) and returns: scores_badloc