code: def group_class_detection_label(
      detected: np.ndarray, ground_truth: np.ndarray
  ):
      """Group bounding detection and labels by class."""
      class_bounding_boxes = defaultdict(
          lambda: {"detected": [], "ground_truth": []}
      )
      for single_detection in detected:
          class_bounding_boxes[single_detection[5]]["detected"].append(
              single_detection
          )
      for single_ground_truth in ground_truth:
          class_bounding_boxes[single_ground_truth[0]][
              "ground_truth"
          ].append(single_ground_truth)
      return class_bounding_boxes
  
file_path: deepchecks/deepchecks/vision/metrics_utils/iou_utils.py
imports: from collections import defaultdict
  from typing import List
  import numpy
label_desc: Use image node_id 3 for calling a global function with example usage: group_class_detection_label(detected, ground_truth) and returns: class_bounding_boxes