text: import tensorflow.keras as ks
  
  from kgcnn.layers.casting import ChangeTensorType
  from kgcnn.layers.conv.gin_conv import GIN
  from kgcnn.layers.modules import DenseEmbedding, OptionalInputEmbedding
  from kgcnn.layers.mlp import GraphMLP, MLP
  from kgcnn.layers.pooling import PoolingNodes
  from kgcnn.utils.models import update_model_kwargs
  
  # How Powerful are Graph Neural Networks?
  # Keyulu Xu, Weihua Hu, Jure Leskovec, Stefanie Jegelka
  # https://arxiv.org/abs/1810.00826
  
  model_default = {'name': "GIN",
                   'inputs': [{'shape': (None,), 'name': "node_attributes", 'dtype': 'float32', 'ragged': True},
                              {'shape': (None, 2), 'name': "edge_indices", 'dtype': 'int64', 'ragged': True}],
                   'input_embedding': {"node": {"input_dim": 95, "output_dim": 64}},
                   'gin_args': {"units": [64, 64], "use_bias": True, "activation": ['relu', 'linear'],
                                "use_normalization": True, "normalization_technique": "batch"},
                   'depth': 3, "dropout": 0.0, 'verbose': 10,
                   'last_mlp': {"use_bias": [True, True, True], "units": [64, 64, 64],
                                "activation": ['relu', 'relu', 'linear']},
                   'output_embedding': 'graph',
                   'output_mlp': {"use_bias": True, "units": 1,
                                  "activation": "softmax"}
                   }
  
  
  @update_model_kwargs(model_default)
  def make_model(inputs=None,
                 input_embedding=None,
                 depth=None,
                 gin_args=None,
                 last_mlp=None,
                 dropout=None,
                 name=None,
                 verbose=None,
                 output_embedding=None,
                 output_mlp=None
                 ):
      """Make GIN graph network via functional API. Default parameters can be found in :obj:`model_default`.
  
      Args:
          inputs (list): List of dictionaries unpacked in :obj:`tf.keras.layers.Input`. Order must match model definition.
          input_embedding (dict): Dictionary of embedding arguments for nodes etc. unpacked in `Embedding` layers.
          depth (int): Number of graph embedding units or depth of the network.
          gin_args (dict): Dictionary of layer arguments unpacked in `GIN` convolutional layer.
          last_mlp (dict): Dictionary of layer arguments unpacked in last `MLP` layer before output or pooling.
          dropout (float): Dropout to use.
          name (str): Name of the model.
          verbose (int): Level of print output.
          output_embedding (str): Main embedding task for graph network. Either "node", ("edge") or "graph".
          output_mlp (dict): Dictionary of layer arguments unpacked in the final classification `MLP` layer block.
              Defines number of model outputs and activation.
  
      Returns:
          tf.keras.models.Model
      """
  
      # Make input
      assert len(inputs) == 2
      node_input = ks.layers.Input(**inputs[0])
      edge_index_input = ks.layers.Input(**inputs[1])
  
      # Embedding, if no feature dimension
      n = OptionalInputEmbedding(**input_embedding['node'],
                                 use_embedding=len(inputs[0]['shape']) < 2)(node_input)
      edi = edge_index_input
  
      # Model
      # Map to the required number of units.
      n = DenseEmbedding(gin_args["units"][0], use_bias=True, activation='linear')(n)
      # n = MLP(gin_args["units"], use_bias=gin_args["use_bias"], activation=gin_args["activation"])(n)
      list_embeddings = [n]
      for i in range(0, depth):
          n = GIN()([n, edi])
          n = GraphMLP(**gin_args)(n)
          list_embeddings.append(n)
  
      # Output embedding choice
      if output_embedding == "graph":
          out = [PoolingNodes()(x) for x in list_embeddings]  # will return tensor
          out = [MLP(**last_mlp)(x) for x in out]
          out = [ks.layers.Dropout(dropout)(x) for x in out]
          out = ks.layers.Add()(out)
          out = MLP(**output_mlp)(out)
      elif output_embedding == "node":  # Node labeling
          out = n
          out = GraphMLP(**last_mlp)(out)
          out = GraphMLP(**output_mlp)(out)
          # no ragged for distribution supported atm
          out = ChangeTensorType(input_tensor_type='ragged', output_tensor_type="tensor")(out)
      else:
          raise ValueError("Unsupported graph embedding for mode `GIN`")
  
      model = ks.models.Model(inputs=[node_input, edge_index_input], outputs=out)
      return model
  