code: def yolo_head(
      feats, anchors, num_classes, input_shape, calc_loss=False
  ):
      num_anchors = len(anchors)
  
      anchors_tensor = K.reshape(
          K.constant(anchors), [1, 1, 1, num_anchors, 2]
      )
  
      grid_shape = K.shape(feats)[1:3]
      grid_y = K.tile(
          K.reshape(K.arange(0, stop=grid_shape[0]), [-1, 1, 1, 1]),
          [1, grid_shape[1], 1, 1],
      )
      grid_x = K.tile(
          K.reshape(K.arange(0, stop=grid_shape[1]), [1, -1, 1, 1]),
          [grid_shape[0], 1, 1, 1],
      )
      grid = K.concatenate([grid_x, grid_y])
      grid = K.cast(grid, K.dtype(feats))
  
      feats = K.reshape(
          feats,
          [
              -1,
              grid_shape[0],
              grid_shape[1],
              num_anchors,
              num_classes + 5,
          ],
      )
  
      box_xy = (K.sigmoid(feats[..., :2]) + grid) / K.cast(
          grid_shape[::-1], K.dtype(feats)
      )
      box_wh = (
          K.exp(feats[..., 2:4])
          * anchors_tensor
          / K.cast(input_shape[::-1], K.dtype(feats))
      )
      box_confidence = K.sigmoid(feats[..., 4:5])
      box_class_probs = K.sigmoid(feats[..., 5:])
  
      if calc_loss == True:
          return grid, feats, box_xy, box_wh
      return box_xy, box_wh, box_confidence, box_class_probs
  
imports: import tensorflow
  from keras import backend
  import numpy
  from PIL import Image
  import cv2
label_desc: Use image node_id 1 for calling a global function with example usage: yolo_head(feats, anchors, num_classes, input_shape, calc_loss) and returns: box_xy,  box_wh,  box_confidence,  box_class_probs, grid,  feats,  box_xy,  box_wh