code: def model(x, is_train):
      with tf.variable_scope("model", reuse=tf.AUTO_REUSE):
          net = InputLayer(x, name="input")
          net = Conv2d(
              net,
              64,
              (5, 5),
              (1, 1),
              padding="SAME",
              b_init=None,
              name="cnn1",
          )
          net = BatchNormLayer(
              net,
              decay=0.99,
              is_train=is_train,
              act=tf.nn.relu,
              name="batch1",
          )
          net = MaxPool2d(
              net, (3, 3), (2, 2), padding="SAME", name="pool1"
          )
  
          net = Conv2d(
              net,
              64,
              (5, 5),
              (1, 1),
              padding="SAME",
              b_init=None,
              name="cnn2",
          )
          net = BatchNormLayer(
              net,
              decay=0.99,
              is_train=is_train,
              act=tf.nn.relu,
              name="batch2",
          )
          net = MaxPool2d(
              net, (3, 3), (2, 2), padding="SAME", name="pool2"
          )
  
          net = FlattenLayer(net, name="flatten")
          net = DenseLayer(net, 384, act=tf.nn.relu, name="d1relu")
          net = DenseLayer(net, 192, act=tf.nn.relu, name="d2relu")
          net = DenseLayer(net, 10, act=None, name="output")
      return net
  
file_path: TensorLayer/examples/distributed_training/tutorial_cifar10_distributed_trainer.py
imports: import multiprocessing
  import numpy
  import tensorflow
  import tensorlayer
  from tensorlayer.layers import BatchNormLayer, Conv2d, DenseLayer, FlattenLayer, InputLayer, MaxPool2d
label_desc: Use image node_id 4 for calling a global function with example usage: model(x, is_train) and returns: net