code: def yolo_eval(
      yolo_outputs,
      anchors,
      num_classes,
      image_shape,
      max_boxes=20,
      score_threshold=0.6,
      iou_threshold=0.5,
  ):
      num_layers = len(yolo_outputs)
      anchor_mask = (
          [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
          if num_layers == 3
          else [[3, 4, 5], [1, 2, 3]]
      )
      input_shape = K.shape(yolo_outputs[0])[1:3] * 32
      boxes = []
      box_scores = []
      for l in range(num_layers):
          _boxes, _box_scores = yolo_boxes_and_scores(
              yolo_outputs[l],
              anchors[anchor_mask[l]],
              num_classes,
              input_shape,
              image_shape,
          )
          boxes.append(_boxes)
          box_scores.append(_box_scores)
      boxes = K.concatenate(boxes, axis=0)
      box_scores = K.concatenate(box_scores, axis=0)
  
      mask = box_scores >= score_threshold
      max_boxes_tensor = K.constant(max_boxes, dtype="int32")
      boxes_ = []
      scores_ = []
      classes_ = []
      for c in range(num_classes):
          class_boxes = tf.boolean_mask(boxes, mask[:, c])
          class_box_scores = tf.boolean_mask(
              box_scores[:, c], mask[:, c]
          )
          nms_index = tf.image.non_max_suppression(
              class_boxes,
              class_box_scores,
              max_boxes_tensor,
              iou_threshold=iou_threshold,
          )
          class_boxes = K.gather(class_boxes, nms_index)
          class_box_scores = K.gather(class_box_scores, nms_index)
          classes = K.ones_like(class_box_scores, "int32") * c
          boxes_.append(class_boxes)
          scores_.append(class_box_scores)
          classes_.append(classes)
      boxes_ = K.concatenate(boxes_, axis=0)
      scores_ = K.concatenate(scores_, axis=0)
      classes_ = K.concatenate(classes_, axis=0)
  
      return boxes_, scores_, classes_
  
imports: import tensorflow
  from keras import backend
  import numpy
  from PIL import Image
  import cv2
label_desc: Use image node_id 4 for calling a global function with example usage: yolo_eval(yolo_outputs, anchors, num_classes, image_shape, max_boxes, score_threshold, iou_threshold) and returns: boxes_,  scores_,  classes_