code: def VGG_static(
      layer_type, batch_norm=False, end_with="outputs", name=None
  ):
      ni = Input([None, 224, 224, 3])
      n = Lambda(
          lambda x: x * 255
          - np.array(
              [123.68, 116.779, 103.939], dtype=np.float32
          ).reshape([1, 1, 1, 3]),
          name="scale",
      )(ni)
  
      config = cfg[mapped_cfg[layer_type]]
      layers = make_layers(config, batch_norm, end_with)
  
      nn = layers(n)
  
      M = Model(inputs=ni, outputs=nn, name=name)
      return M
  
file_path: TensorLayer/tensorlayer/models/vgg.py
imports: import os
  import numpy
  import tensorflow
  import tensorlayer
  from tensorlayer import logging
  from tensorlayer.files import assign_weights, maybe_download_and_extract
  from tensorlayer.layers import BatchNorm, Conv2d, Dense, Flatten, Input, Lambda, LayerList, MaxPool2d
  from tensorlayer.models import Model
label_desc: Use image node_id 3 for calling a global function with example usage: VGG_static(layer_type, batch_norm, end_with, name) and returns: M