Keras disconnected graphs example when using multiple models

connected_graph.py

def test_connected_models(self):
    input1 = Input(shape=(100,))
    dense1 = Dense(1)(input1)
    model1 = Model(input1, dense1)

    input2 = Input(shape=(200,))
    dense2 = Dense(2)(input2)
    model2 = Model(input2, dense2)

    # This will work, because there are no intermediate Inputs.
    # The best solution for a complex graph is not to use intermediate Models
    # but to use the Funtional API and only produce one model at the end
    dense3 = Concatenate()([model1.layers[-1].output, model2.layers[-1].output])
    model3 = Model(inputs=[model1.input, model2.input], outputs=dense3)
    print(model3.summary())

disconnected_graph.py

def test_disconnected_graph(self):

    input1 = Input(shape=(100,))
    dense1 = Dense(1)(input1)
    model1 = Model(input1, dense1)

    input2 = Input(shape=(200,))
    dense2 = Dense(2)(input2)
    model2 = Model(input2, dense2)

    input11 = Input(shape=(1,))
    input12 = Input(shape=(2,))
    dense3 = Concatenate()([input11, input12])
    model3 = Model(inputs=[input11, input12], outputs=dense3)
    print(model3.summary())

    # We cannot use model3.output here, because the model3 graph is disconnected
    # from this graph by defining its own inputs input11 and input12
    model123 = model3.layers[-1].output
    model123 = Model(inputs=[model1.input, model2.input], outputs=model123)
    print(model123.summary())