code: def model(x, is_train=True, reuse=False):
      with tf.variable_scope("model", reuse=reuse):
          n = tl.layers.InputLayer(x, name="in")
          n = tl.layers.Conv2d(n, n_filter=80, name="conv2d_1")
          n = tl.layers.BatchNormLayer(
              n, is_train=is_train, name="norm_batch"
          )
          n = tl.layers.Conv2d(n, n_filter=80, name="conv2d_2")
          n = tl.layers.LocalResponseNormLayer(n, name="norm_local")
          n = tl.layers.LayerNormLayer(
              n, reuse=reuse, name="norm_layer"
          )
          n = tl.layers.InstanceNormLayer(n, name="norm_instance")
          # n = tl.layers.GroupNormLayer(n, groups=40, name='groupnorm')
          n.outputs = tf.reshape(n.outputs, [-1, 80, 100, 100])
          n = tl.layers.GroupNormLayer(
              n,
              groups=40,
              data_format="channels_first",
              name="groupnorm",
          )
          n.outputs = tf.reshape(n.outputs, [-1, 100, 100, 80])
          n = tl.layers.SwitchNormLayer(n, name="switchnorm")
          n = tl.layers.QuanConv2dWithBN(
              n, n_filter=3, is_train=is_train, name="quan_cnn_with_bn"
          )
          n = tl.layers.FlattenLayer(n, name="flatten")
          n = tl.layers.QuanDenseLayerWithBN(
              n, n_units=10, name="quan_dense_with_bn"
          )
      return n
  
file_path: TensorLayer/tests/pending/test_layers_normalization.py
imports: import os
  import unittest
  import tensorflow
  import tensorlayer
  from tests.utils import CustomTestCase
label_desc: Use image node_id 1 for calling a global function with example usage: model(x, is_train, reuse) and returns: n