code: def _separate_prediction(
      prediction, prediction_type="single_pred"
  ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
      """Returns bbox, label and pred_prob values for prediction."""
  
      if prediction_type == "all_pred":
          boxes, labels, pred_probs = _separate_prediction_all_preds(
              prediction
          )
      else:
          boxes, labels, pred_probs = _separate_prediction_single_box(
              prediction
          )
      return boxes, labels, pred_probs
  
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 10 for calling a global function with example usage: _separate_prediction(prediction, prediction_type) and returns: boxes,  labels,  pred_probs