GraphConvolution example

      Typically matches number of bond types used in the molecule.

    name: string, optional (default="")

      Name of the layer

    Example

    --------

    def create_discriminator(adjacency_tensor, node_tensor):

      vertices = 9

      edges = 5

      dropout_rate = .0

      adjacency_tensor= layers.Input(shape=(vertices, vertices, edges))

      node_tensor = layers.Input(shape=(vertices, nodes))

      graph = GraphEncoderLayer(units = [(128,64),128],

                                dropout_rate= dropout_rate,

                                edges=edges)([adjacency_tensor,node_tensor])

      dense = layers.Dense(units=128, activation='tanh')(graph)

      dense = layers.Dropout(dropout_rate)(dense)

      dense = layers.Dense(units=64, activation='tanh')(dense)

      dense = layers.Dropout(dropout_rate)(dense)

      output = layers.Dense(units=1)(dense)

      return keras.Model(inputs=[adjacency_tensor,node_tensor], outputs=[output])

    """

    super(MolGANEncoderLayer, self).__init__(name=name, **kwargs)