code: def dorefanet_model(input_shape, n_classes):
      in_net = Input(shape=input_shape, name="input")
      net = Conv2d(
          32, (5, 5), (1, 1), act="relu", padding="SAME", name="conv1"
      )(in_net)
      net = MaxPool2d((3, 3), (2, 2), padding="SAME", name="pool1")(net)
      net = LocalResponseNorm(4, 1.0, 0.001 / 9.0, 0.75, name="norm1")(
          net
      )
      net = tl.layers.Sign("sign")(net)
      net = DorefaConv2d(
          8,
          32,
          64,
          (5, 5),
          (1, 1),
          act="relu",
          padding="SAME",
          name="DorefaConv1",
      )(net)
      net = LocalResponseNorm(4, 1.0, 0.001 / 9.0, 0.75, name="norm2")(
          net
      )
      net = MaxPool2d((3, 3), (2, 2), padding="SAME", name="pool2")(net)
      net = Flatten(name="flatten")(net)
      net = DorefaDense(8, 16, 384, act="relu", name="DorefaDense1")(
          net
      )
      net = DorefaDense(8, 16, 192, act="relu", name="DorefaDense2")(
          net
      )
      net = Dense(n_classes, act=None, name="output")(net)
      net = Model(inputs=in_net, outputs=net, name="dorefanet")
      return net
  
file_path: TensorLayer/examples/quantized_net/tutorial_dorefanet_cifar10_tfrecord.py
imports: import multiprocessing
  import time
  import numpy
  import tensorflow
  import tensorlayer
  from tensorlayer.layers import Conv2d, Dense, DorefaConv2d, DorefaDense, Flatten, Input, LocalResponseNorm, MaxPool2d
  from tensorlayer.models import Model
label_desc: Use image node_id 1 for calling a global function with example usage: dorefanet_model(input_shape, n_classes) and returns: net