code: def _compute_overlooked_box_scores_for_image(
      alpha: float,
      high_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 predicted box (above threshold) in an image. Score from 0 to 1 ranking how overlooked 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,
              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"]
      min_possible_similarity = auxiliary_input_dict[
          "min_possible_similarity"
      ]
      iou_matrix = auxiliary_input_dict["iou_matrix"]
  
      scores_overlooked = np.empty(
          len(pred_labels)
      )  # same length as num of predicted boxes
  
      for iid, k in enumerate(pred_labels):
          if pred_label_probs[
              iid
          ] < high_probability_threshold or np.any(
              iou_matrix[:, iid] > 0
          ):
              scores_overlooked[iid] = np.nan
              continue
  
          k_similarity = similarity_matrix[lab_labels == k, iid]
  
          if (
              len(k_similarity) == 0
          ):  # if there are no annotated boxes of class k
              score = min_possible_similarity * (
                  1 - pred_label_probs[iid]
              )
          else:
              closest_annotated_box = np.argmax(k_similarity)
              score = k_similarity[closest_annotated_box]
  
          scores_overlooked[iid] = score
  
      return scores_overlooked
  
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 24 for calling a global function with example usage: _compute_overlooked_box_scores_for_image(alpha, high_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_overlooked