code: def model(inputs_shape, n_class=10):
      net_in = Input(inputs_shape, name="input")
  
      net = QuanConv2dWithBN(
          n_filter=32,
          filter_size=(5, 5),
          strides=(1, 1),
          padding="SAME",
          act=tl.nn.relu,
          name="qconvbn1",
      )(net_in)
      net = MaxPool2d(
          filter_size=(2, 2),
          strides=(2, 2),
          padding="SAME",
          name="pool1",
      )(net)
  
      net = QuanConv2dWithBN(
          n_filter=64,
          filter_size=(5, 5),
          strides=(1, 1),
          padding="SAME",
          act=tl.nn.relu,
          name="qconvbn2",
      )(net)
      net = MaxPool2d(
          filter_size=(2, 2),
          strides=(2, 2),
          padding="SAME",
          name="pool2",
      )(net)
  
      net = Flatten(name="ft")(net)
  
      # net = QuanDense(256, act="relu", name='qdbn')(net)
      # net = QuanDense(n_class, name='qdbn_out')(net)
  
      net = QuanDenseLayerWithBN(256, act="relu", name="qdbn")(net)
      net = QuanDenseLayerWithBN(n_class, name="qdbn_out")(net)
  
      # net = Dense(256, act='relu', name='Dense1')(net)
      # net = Dense(n_class, name='Dense2')(net)
  
      net = Model(inputs=net_in, outputs=net, name="quan")
      return net
  
file_path: TensorLayer/examples/quantized_net/tutorial_quanconv_mnist.py
imports: import time
  import numpy
  import tensorflow
  import tensorlayer
  from tensorlayer.layers import Dense, Dropout, Flatten, Input, MaxPool2d, QuanConv2d, QuanConv2dWithBN, QuanDense, QuanDenseLayerWithBN
  from tensorlayer.models import Model
label_desc: Use image node_id 1 for calling a global function with example usage: model(inputs_shape, n_class) and returns: net