code: def load_vgg_weights(model, path):
      url = "https://s3-us-west-1.amazonaws.com/nervana-modelzoo/"
      filename = "VGG_ILSVRC_16_layers_fc_reduced_fused_conv_bias.p"
      size = 86046032
  
      workdir, filepath = Dataset._valid_path_append(path, "", filename)
      if not os.path.exists(filepath):
          Dataset.fetch_dataset(url, filename, filepath, size)
  
      print(
          "De-serializing the pre-trained VGG16 model with dilated convolutions..."
      )
      pdict = load_obj(filepath)
  
      model_layers = [l for l in model.layers.layers[0].layers]
      # convert source model into dictionary with layer name as keys
      src_layers = {
          layer["config"]["name"]: layer
          for layer in pdict["model"]["config"]["layers"]
      }
  
      i = 0
      for layer in model_layers:
          if layer.classnm == "Convolution_bias" and i < 15:
              # no states in above parameter file
              layer.load_weights(
                  src_layers["Convolution_bias_" + str(i)],
                  load_states=False,
              )
              print("{} loaded from source file".format(layer.name))
              i += 1
          elif hasattr(layer, "W"):
              print("Skipping {} layer".format(layer.name))
  
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 3 for calling a global function with example usage: load_vgg_weights(model, path) without return types