code: def _matching_no_gt(
      iou_thresholds: Sequence[float],
      pred_scores: np.ndarray,
      max_detections: int,
  ) -> dict[str, np.ndarray]:
      """
      Matching result with not ground truth in image
  
      Args:
          iou_thresholds: defined which IoU thresholds should be evaluated
          dt_scores: predicted scores
          max_detections: maximum number of allowed detections per image.
              This functions uses this parameter to stay consistent with
              the actual matching function which needs this limit.
  
      Returns:
          computed matching, a Dict[str, np.ndarray]
  
          - `dtMatches`: matched detections [T, D], where T = number of
            thresholds, D = number of detections
          - `gtMatches`: matched ground truth boxes [T, G], where T = number
            of thresholds, G = number of ground truth
          - `dtScores`: prediction scores [D] detection scores
          - `gtIgnore`: ground truth boxes which should be ignored
            [G] indicate whether ground truth should be ignored
          - `dtIgnore`: detections which should be ignored [T, D],
            indicate which detections should be ignored
      """
      dt_ind = np.argsort(-pred_scores, kind="mergesort")
      dt_ind = dt_ind[:max_detections]
      dt_scores = pred_scores[dt_ind]
  
      num_preds = len(dt_scores)
  
      gt_match: np.ndarray = np.array([[]] * len(iou_thresholds))
      dt_match: np.ndarray = np.zeros((len(iou_thresholds), num_preds))
      dt_ignore: np.ndarray = np.zeros((len(iou_thresholds), num_preds))
  
      return {
          "dtMatches": dt_match,  # [T, D], where T = number of thresholds, D = number of detections
          "gtMatches": gt_match,  # [T, G], where T = number of thresholds, G = number of ground truth
          "dtScores": dt_scores,  # [D] detection scores
          "gtIgnore": np.array([]).reshape(
              -1
          ),  # [G] indicate whether ground truth should be ignored
          "dtIgnore": dt_ignore,  # [T, D], indicate which detections should be ignored
      }
  
imports: from __future__ import annotations
  from collections.abc import Callable, Sequence
  import numpy
label_desc: Use image node_id 2 for calling a global function with example usage: _matching_no_gt(iou_thresholds, pred_scores, max_detections) and returns: dict