code: def load_vgg_weights(model, path):
      url = "https://s3-us-west-1.amazonaws.com/nervana-modelzoo/VGG/"
      filename = "VGG_D_Conv_fused_conv_bias.p"
      size = 58867537
  
      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...")
      pdict = load_obj(filepath)
  
      param_layers = [l for l in model.layers.layers[0].layers]
      param_dict_list = pdict["model"]["config"]["layers"]
  
      i = 0
      for layer, ps in zip(param_layers, param_dict_list):
          # finished loading param_dict_list[00 - 29] and param_layers[00-29]
          if i == 30:
              break
          layer.load_weights(ps, load_states=False)
          i += 1
          print(layer.name + " <-- " + ps["config"]["name"])
  
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 4 for calling a global function with example usage: load_vgg_weights(model, path) without return types