code: def get_model(inputs_shape):
      # self defined initialization
      W_init = tl.initializers.truncated_normal(stddev=5e-2)
      W_init2 = tl.initializers.truncated_normal(stddev=0.04)
      b_init2 = tl.initializers.constant(value=0.1)
  
      # build network
      ni = Input(inputs_shape)
      nn = Conv2d(
          64,
          (5, 5),
          (1, 1),
          padding="SAME",
          act=tf.nn.relu,
          W_init=W_init,
          b_init=None,
          name="conv1",
      )(ni)
      nn = MaxPool2d((3, 3), (2, 2), padding="SAME", name="pool1")(nn)
      nn = LocalResponseNorm(
          depth_radius=4,
          bias=1.0,
          alpha=0.001 / 9.0,
          beta=0.75,
          name="norm1",
      )(nn)
  
      nn = Conv2d(
          64,
          (5, 5),
          (1, 1),
          padding="SAME",
          act=tf.nn.relu,
          W_init=W_init,
          b_init=None,
          name="conv2",
      )(nn)
      nn = LocalResponseNorm(
          depth_radius=4,
          bias=1.0,
          alpha=0.001 / 9.0,
          beta=0.75,
          name="norm2",
      )(nn)
      nn = MaxPool2d((3, 3), (2, 2), padding="SAME", name="pool2")(nn)
  
      nn = Flatten(name="flatten")(nn)
      nn = Dense(
          384,
          act=tf.nn.relu,
          W_init=W_init2,
          b_init=b_init2,
          name="dense1relu",
      )(nn)
      nn = Dense(
          192,
          act=tf.nn.relu,
          W_init=W_init2,
          b_init=b_init2,
          name="dense2relu",
      )(nn)
      nn = Dense(10, act=None, W_init=W_init2, name="output")(nn)
  
      M = Model(inputs=ni, outputs=nn, name="cnn")
      return M
  
file_path: TensorLayer/examples/basic_tutorials/tutorial_cifar10_cnn_static.py
imports: import multiprocessing
  import time
  import numpy
  import tensorflow
  import tensorlayer
  from tensorlayer.layers import BatchNorm, Conv2d, Dense, Flatten, Input, LocalResponseNorm, MaxPool2d
  from tensorlayer.models import Model
label_desc: Use image node_id 1 for calling a global function with example usage: get_model(inputs_shape) and returns: M