How to use the autokeras.DenseBlock function in autokeras

def test_merge(tmp_dir):
    x_train = np.random.rand(100, 33)
    y_train = np.random.rand(100, 1)

    input_node1 = ak.Input()
    input_node2 = ak.Input()
    output_node1 = ak.DenseBlock()(input_node1)
    output_node2 = ak.DenseBlock()(input_node2)
    output_node = ak.Merge()([output_node1, output_node2])
    output_node = ak.RegressionHead()(output_node)

    graph = ak.GraphAutoModel([input_node1, input_node2],
                              output_node,
                              directory=tmp_dir,
                              max_trials=1)
    graph.fit([x_train, x_train], y_train,
              epochs=1,
              batch_size=100,
              verbose=False,
              validation_split=0.5)
    result = graph.predict([x_train, x_train])

    assert result.shape == (100, 1)

def test_preprocessing(_, tmp_dir):
    x_train = np.random.rand(100, 33)
    y_train = np.random.rand(100, 1)

    input_node1 = ak.Input()
    temp_node1 = ak.Normalization()(input_node1)
    output_node1 = ak.DenseBlock()(temp_node1)

    output_node3 = ak.Normalization()(temp_node1)
    output_node3 = ak.DenseBlock()(output_node3)

    input_node2 = ak.Input()
    output_node2 = ak.Normalization()(input_node2)
    output_node2 = ak.DenseBlock()(output_node2)

    output_node = ak.Merge()([output_node1, output_node2, output_node3])
    output_node = ak.RegressionHead()(output_node)

    graph = ak.GraphAutoModel([input_node1, input_node2],
                              output_node,
                              directory=tmp_dir,
                              max_trials=1)
    graph.fit([x_train, x_train], y_train,

def test_preprocessing(_, tmp_dir):
    x_train = np.random.rand(100, 33)
    y_train = np.random.rand(100, 1)

    input_node1 = ak.Input()
    temp_node1 = ak.Normalization()(input_node1)
    output_node1 = ak.DenseBlock()(temp_node1)

    output_node3 = ak.Normalization()(temp_node1)
    output_node3 = ak.DenseBlock()(output_node3)

    input_node2 = ak.Input()
    output_node2 = ak.Normalization()(input_node2)
    output_node2 = ak.DenseBlock()(output_node2)

    output_node = ak.Merge()([output_node1, output_node2, output_node3])
    output_node = ak.RegressionHead()(output_node)

    graph = ak.GraphAutoModel([input_node1, input_node2],
                              output_node,
                              directory=tmp_dir,
                              max_trials=1)
    graph.fit([x_train, x_train], y_train,
              epochs=1,
              batch_size=100,
              validation_data=([x_train, x_train], y_train),
              validation_split=0.5,
              verbose=False)

def test_input_missing():
    input_node1 = ak.Input()
    input_node2 = ak.Input()
    output_node1 = ak.DenseBlock()(input_node1)
    output_node2 = ak.DenseBlock()(input_node2)
    output_node = ak.Merge()([output_node1, output_node2])
    output_node = ak.RegressionHead()(output_node)

    with pytest.raises(ValueError) as info:
        ak.hypermodel.graph.GraphHyperModel(input_node1, output_node)
    assert 'A required input is missing for HyperModel' in str(info.value)

def test_input_missing():
    input_node1 = ak.Input()
    input_node2 = ak.Input()
    output_node1 = ak.DenseBlock()(input_node1)
    output_node2 = ak.DenseBlock()(input_node2)
    output_node = ak.Merge()([output_node1, output_node2])
    output_node = ak.RegressionHead()(output_node)

    with pytest.raises(ValueError) as info:
        ak.hypermodel.graph.GraphHyperModel(input_node1, output_node)
    assert 'A required input is missing for HyperModel' in str(info.value)

def test_input_output_disconnect():
    input_node1 = ak.Input()
    output_node = input_node1
    _ = ak.DenseBlock()(output_node)

    input_node = ak.Input()
    output_node = input_node
    output_node = ak.DenseBlock()(output_node)
    output_node = ak.RegressionHead()(output_node)

    with pytest.raises(ValueError) as info:
        ak.hypermodel.graph.GraphHyperModel(input_node1, output_node)
    assert 'Inputs and outputs not connected.' in str(info.value)

trial = kerastuner.engine.trial.Trial()
    trial.hyperparameters = kerastuner.HyperParameters()
    get_trials.return_value = [trial]
    input_shape = (32,)
    num_instances = 100
    num_classes = 10
    x = common.generate_data(num_instances=num_instances,
                             shape=input_shape,
                             dtype='dataset')
    y = common.generate_one_hot_labels(num_instances=num_instances,
                                       num_classes=num_classes,
                                       dtype='dataset')

    input_node = ak.Input(shape=input_shape)
    output_node = input_node
    output_node = ak.DenseBlock()(output_node)
    output_node = ak.ClassificationHead(output_shape=(num_classes,))(output_node)
    hypermodel = ak.hypermodel.graph.HyperBuiltGraphHyperModel(input_node,
                                                               output_node)
    tuner = ak.tuner.RandomSearch(
        hypermodel=hypermodel,
        objective='val_loss',
        max_trials=1,
        directory=tmp_dir,
        seed=common.SEED)
    tuner.search(x=tf.data.Dataset.zip((x, y)),
                 validation_data=(x, y),
                 epochs=20,
                 callbacks=[])

    _, kwargs = run_trial.call_args_list[0]
    callbacks = kwargs['callbacks']

def functional_api():
    (x_train, y_train), (x_test, y_test) = mnist.load_data()
    input_node = ak.ImageInput()
    output_node = input_node
    output_node = ak.Normalization()(output_node)
    output_node = ak.ConvBlock()(output_node)
    output_node = ak.SpatialReduction()(output_node)
    output_node = ak.DenseBlock()(output_node)
    output_node = ak.ClassificationHead()(output_node)
    clf = ak.AutoModel(input_node, output_node, seed=5, max_trials=3)
    clf.fit(x_train, y_train, validation_split=0.2)
    return clf.evaluate(x_test, y_test)

y_test = y_test[:data_slice]
x_image = x_train.reshape(x_train.shape + (1,))
x_test = x_test.reshape(x_test.shape + (1,))

x_structured = np.random.rand(x_train.shape[0], 100)
y_regression = np.random.rand(x_train.shape[0], 1)
y_classification = y_classification.reshape(-1, 1)

# Build model and train.
inputs = ak.ImageInput(shape=(28, 28, 1))
outputs1 = ak.ResNetBlock(version='next')(inputs)
outputs2 = ak.XceptionBlock()(inputs)
image_outputs = ak.Merge()((outputs1, outputs2))

structured_inputs = ak.StructuredDataInput()
structured_outputs = ak.DenseBlock()(structured_inputs)
merged_outputs = ak.Merge()((structured_outputs, image_outputs))

classification_outputs = ak.ClassificationHead()(merged_outputs)
regression_outputs = ak.RegressionHead()(merged_outputs)
automodel = ak.GraphAutoModel(inputs=[inputs, structured_inputs],
                              outputs=[regression_outputs,
                                       classification_outputs])

automodel.fit((x_image, x_structured),
              (y_regression, y_classification),
              # trials=100,
              validation_split=0.2,
              epochs=200,
              callbacks=[tf.keras.callbacks.EarlyStopping()])