text: """GCN using DGL nn package
  
  References:
  - Semi-Supervised Classification with Graph Convolutional Networks
  - Paper: https://arxiv.org/abs/1609.02907
  - Code: https://github.com/tkipf/gcn
  """
  import tensorflow as tf
  from tensorflow.keras import layers
  from dgl.nn.tensorflow import GraphConv
  
  class GCN(tf.keras.Model):
      def __init__(self,
                   g,
                   in_feats,
                   n_hidden,
                   n_classes,
                   n_layers,
                   activation,
                   dropout):
          super(GCN, self).__init__()
          self.g = g
          self.layer_list = []
          # input layer
          self.layer_list.append(GraphConv(in_feats, n_hidden, activation=activation))
          # hidden layers
          for i in range(n_layers - 1):
              self.layer_list.append(GraphConv(n_hidden, n_hidden, activation=activation))
          # output layer
          self.layer_list.append(GraphConv(n_hidden, n_classes))
          self.dropout = layers.Dropout(dropout)
  
      def call(self, features):
          h = features
          for i, layer in enumerate(self.layer_list):
              if i != 0:
                  h = self.dropout(h)
              h = layer(self.g, h)
          return h
  