code: def scale_bbreg_weights(model, means, stds, num_classes):
      means = np.array(num_classes * means)
      stds = np.array(num_classes * stds)
  
      means_be = model.be.array(means)
      stds_be = model.be.array(stds)
      model.layers.layers[2].layers[1].layers[1].layers[-3].W[:] = (
          model.layers.layers[2].layers[1].layers[1].layers[-3].W
          * stds_be
      )
      model.layers.layers[2].layers[1].layers[1].layers[-2].W[:] = (
          model.layers.layers[2].layers[1].layers[1].layers[-2].W
          * stds_be
          + means_be
      )
      return model
  
imports: from __future__ import division
  from __future__ import print_function
  from builtins import zip
  import numpy
  import os
  from neon.util.persist import load_obj
  from neon.data.datasets import Dataset
  from neon.initializers import Constant, Xavier
  from neon.transforms import Rectlin
  from neon.layers import Conv, Pooling
label_desc: Use image node_id 2 for calling a global function with example usage: scale_bbreg_weights(model, means, stds, num_classes) and returns: model