code: def _process_class_list(
      class_list: List[np.ndarray], class_dict: Dict[int, float]
  ) -> List:
      """
      Converts a list of classes represented as numpy arrays using a class-to-float dictionary,
      and returns a list where each class is replaced by its corresponding float value from the dictionary.
  
      Args:
          class_list (List[np.ndarray]): A list of classes represented as numpy arrays.
          class_dict (Dict[int, float]): A dictionary mapping class indices to their corresponding float values.
  
      Returns:
          List[float]: A list of float values corresponding to the classes in the input list.
      """
      class_l2 = []
      for i in class_list:
          l3 = [class_dict[j] for j in i]
          class_l2.append(l3)
      return class_l2
  
file_path: cleanlab/cleanlab/object_detection/filter.py
imports: from collections import defaultdict
  from multiprocessing import Pool
  from typing import Any, Dict, List, Optional, Tuple, Union
  import numpy
  from cleanlab.internal.constants import ALPHA, HIGH_PROBABILITY_THRESHOLD, LOW_PROBABILITY_THRESHOLD, OVERLOOKED_THRESHOLD_FACTOR, BADLOC_THRESHOLD_FACTOR, SWAP_THRESHOLD_FACTOR, AP_SCALE_FACTOR
  from cleanlab.internal.object_detection_utils import assert_valid_inputs
  from cleanlab.object_detection.rank import _get_valid_inputs_for_compute_scores, _separate_label, _separate_prediction, compute_badloc_box_scores, compute_overlooked_box_scores, compute_swap_box_scores, get_label_quality_scores, issues_from_scores, _get_overlap_matrix
label_desc: Use image node_id 5 for calling a global function with example usage: _process_class_list(class_list, class_dict) and returns: class_l2