code: def yolo_boxes_and_scores(
      feats, anchors, num_classes, input_shape, image_shape
  ):
      """Process Conv layer output"""
      # print("feats,anchors, num_classes, input_shape", feats, anchors, num_classes, input_shape)
      box_xy, box_wh, box_confidence, box_class_probs = yolo_head(
          feats, anchors, num_classes, input_shape
      )
      # print(box_xy, box_wh, box_confidence, box_class_probs)
      boxes = yolo_correct_boxes(
          box_xy, box_wh, input_shape, image_shape
      )
      boxes = K.reshape(boxes, [-1, 4])
      box_scores = box_confidence * box_class_probs
      box_scores = K.reshape(box_scores, [-1, num_classes])
      return boxes, box_scores
  
file_path: MMdnn/mmdnn/conversion/examples/keras/utils.py
imports: import tensorflow
  from keras import backend
  from PIL import Image
label_desc: Use image node_id 4 for calling a global function with example usage: yolo_boxes_and_scores(feats, anchors, num_classes, input_shape, image_shape) and returns: boxes,  box_scores