code: def model(input_shape, n_classes, bitW, bitA):
      in_net = Input(shape=input_shape, name="input")
      net = QuanConv2dWithBN(
          64,
          (5, 5),
          (1, 1),
          act="relu",
          padding="SAME",
          bitW=bitW,
          bitA=bitA,
          name="qcnnbn1",
      )(in_net)
      net = MaxPool2d((3, 3), (2, 2), padding="SAME", name="pool1")(net)
      net = QuanConv2dWithBN(
          64,
          (5, 5),
          (1, 1),
          padding="SAME",
          act="relu",
          bitW=bitW,
          bitA=bitA,
          name="qcnnbn2",
      )(net)
      net = MaxPool2d((3, 3), (2, 2), padding="SAME", name="pool2")(net)
      net = Flatten(name="flatten")(net)
      net = QuanDense(
          384, act=tf.nn.relu, bitW=bitW, bitA=bitA, name="qd1relu"
      )(net)
      net = QuanDense(
          192, act=tf.nn.relu, bitW=bitW, bitA=bitA, name="qd2relu"
      )(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_quanconv_cifar10.py
imports: import multiprocessing
  import time
  import numpy
  import tensorflow
  import tensorlayer
  from tensorlayer.layers import Dense, Flatten, Input, MaxPool2d, QuanConv2dWithBN, QuanDense
  from tensorlayer.models import Model
label_desc: Use image node_id 1 for calling a global function with example usage: model(input_shape, n_classes, bitW, bitA) and returns: net