class_docstr: SSD model is like a Tree, except with additional handling for the output layer, and
  for the prior boxes
class_docstr_tok: ["SSD","model","is","like","a","Tree",",","except","with","additional","handling","for","the","output","layer",",","and","for","the","prior","boxes"]
docstr: Append leafs to trunk_layers at the branch_node. If normalize, add a Normalize layer.
docstr_tok: ["Append","leafs","to","trunk_layers","at","the","branch_node",".","If","normalize",",","add","a","Normalize","layer","."]
code: def create_normalize_branch(self, leafs, branch_node, layer):
      """
      Append leafs to trunk_layers at the branch_node. If normalize, add a Normalize layer.
      """
  
      tree_branch = BranchNode(name=layer + "_norm_branch")
      branch1 = [
          Normalize(init=Constant(20.0), name=layer + "_norm"),
          tree_branch,
          leafs["mbox_loc"],
      ]
  
      branch2 = [tree_branch, leafs["mbox_conf"]]
      new_tree = Tree([branch1, branch2])
  
      return [branch_node, new_tree]
  
imports: from neon.initializers import Constant, Xavier
  from neon.transforms import Rectlin
  from neon.layers import Conv, Pooling, BranchNode
  from layer import Normalize, PriorBox, DetectionOutput, ConcatTranspose
  import numpy
  from neon.util.persist import load_obj
  from neon.layers.container import Tree
  from neon.layers.layer import Layer
  from collections import OrderedDict
  import inspect
  import re
  from neon.data.datasets import Dataset
  import os
label_desc: Use image node_id 13 for calling the SSD obj's underlying member method code with example usage: obj.create_normalize_branch(leafs, branch_node, layer) and returns: list