How to use the deephyper.search.nas.model.space.op.op1d.Dense function in deephyper

def test_create_more_nodes(self):
        from deephyper.search.nas.model.space.struct import AutoOutputStructure
        from deephyper.search.nas.model.space.node import VariableNode
        from deephyper.search.nas.model.space.op.op1d import Dense
        struct = AutoOutputStructure((5, ), (1, ))

        vnode1 = VariableNode()
        struct.connect(struct.input_nodes[0], vnode1)

        vnode1.add_op(Dense(10))

        vnode2 = VariableNode()
        vnode2.add_op(Dense(10))

        struct.connect(vnode1, vnode2)

        struct.set_ops([0, 0])

        falias = 'test_direct_structure'
        struct.draw_graphviz(f'{falias}.dot')

        model = struct.create_model()
        from tensorflow.keras.utils import plot_model

        plot_model(model, to_file=f'{falias}.png', show_shapes=True)

def test_create_more_nodes(self):
        from deephyper.search.nas.model.space.struct import DirectStructure
        from deephyper.search.nas.model.space.node import VariableNode
        from deephyper.search.nas.model.space.op.op1d import Dense
        struct = DirectStructure((5, ), (1, ))

        vnode1 = VariableNode()
        struct.connect(struct.input_nodes[0], vnode1)

        vnode1.add_op(Dense(10))

        vnode2 = VariableNode()
        vnode2.add_op(Dense(1))

        struct.connect(vnode1, vnode2)

        struct.set_ops([0, 0])

        falias = 'test_direct_structure'
        struct.draw_graphviz(f'{falias}.dot')

        model = struct.create_model()
        from tensorflow.keras.utils import plot_model

        plot_model(model, to_file=f'{falias}.png', show_shapes=True)

def add_dense_to_(node):
    node.add_op(Identity()) # we do not want to create a layer in this case

    activations = [None, tf.nn.relu, tf.nn.tanh, tf.nn.sigmoid]
    for units in range(16, 97, 16):
        for activation in activations:
            node.add_op(Dense(units=units, activation=activation))

def create_search_space(
    input_shape=(100,), output_shape=[(100), (1,)], num_layers=5, **kwargs
):
    struct = KSearchSpace(input_shape, output_shape)

    inp = struct.input_nodes[0]

    # regressor
    # prev_node = latente_space
    prev_node = inp
    for _ in range(num_layers):
        vnode = VariableNode()
        for i in range(16, 129, 16):
            vnode.add_op(Dense(i, tf.nn.relu))

        struct.connect(prev_node, vnode)
        prev_node = vnode

    out1 = ConstantNode(op=Dense(1, name="output_0"))
    struct.connect(prev_node, out1)

    # auto-encoder
    # units = [128, 64, 32, 16, 8, 16, 32, 64, 128]
    units = [32, 16, 32]
    prev_node = inp
    d = 1
    for i in range(len(units)):
        vnode = VariableNode()
        # vnode.add_op(Identity)
        if d == 1 and units[i] < units[i + 1]:

def create_block():
        # first node of block
        n1 = Node('N1')
        for inpt in input_nodes:
            n1.add_op(Connect(cell.graph, inpt, n1))

        # second node of block
        mlp_op_list = list()
        mlp_op_list.append(Identity())
        mlp_op_list.append(Dense(5, tf.nn.relu))
        mlp_op_list.append(Dense(5, tf.nn.tanh))
        mlp_op_list.append(Dense(10, tf.nn.relu))
        mlp_op_list.append(Dense(10, tf.nn.tanh))
        mlp_op_list.append(Dense(20, tf.nn.relu))
        mlp_op_list.append(Dense(20, tf.nn.tanh))
        n2 = Node('N2')
        for op in mlp_op_list:
            n2.add_op(op)

        # third node of block
        n3 = Node('N3')
        for op in dropout_ops:
            n3.add_op(op)

        block = Block()
        block.add_node(n1)
        block.add_node(n2)
        block.add_node(n3)

        block.add_edge(n1, n2)

d = 1
    for i in range(len(units)):
        vnode = VariableNode()
        # vnode.add_op(Identity)
        if d == 1 and units[i] < units[i + 1]:
            d = -1
            # print(min(1, units[i]), ' - ', max(1, units[i])+1)
            for u in range(min(2, units[i], 2), max(2, units[i]) + 1, 2):
                vnode.add_op(Dense(u, tf.nn.relu))
            latente_space = vnode
        else:
            # print(min(units[i], units[i+d]), ' - ', max(units[i], units[i+d])+1)
            for u in range(
                min(units[i], units[i + d]), max(units[i], units[i + d]) + 1, 2
            ):
                vnode.add_op(Dense(u, tf.nn.relu))
        struct.connect(prev_node, vnode)
        prev_node = vnode

    out2 = ConstantNode(op=Dense(100, name="output_1"))
    struct.connect(prev_node, out2)

    return struct

def create_block():
        # first node of block
        n1 = Node('N1')
        for inpt in input_nodes:
            n1.add_op(Connect(cell.graph, inpt, n1))

        # second node of block
        mlp_op_list = list()
        mlp_op_list.append(Identity())
        mlp_op_list.append(Dense(5, tf.nn.relu))
        mlp_op_list.append(Dense(5, tf.nn.tanh))
        mlp_op_list.append(Dense(10, tf.nn.relu))
        mlp_op_list.append(Dense(10, tf.nn.tanh))
        mlp_op_list.append(Dense(20, tf.nn.relu))
        mlp_op_list.append(Dense(20, tf.nn.tanh))
        n2 = Node('N2')
        for op in mlp_op_list:
            n2.add_op(op)

        # third node of block
        n3 = Node('N3')
        for op in dropout_ops:
            n3.add_op(op)

        block = Block()
        block.add_node(n1)
        block.add_node(n2)
        block.add_node(n3)

def add_dense_to_(node):
    node.add_op(Identity()) # we do not want to create a layer in this case

    activations = [None, tf.nn.relu, tf.nn.tanh, tf.nn.sigmoid]
    for units in range(16, 97, 16):
        for activation in activations:
            node.add_op(Dense(units=units, activation=activation))

"""
    cell = Cell(input_nodes)

    # first node of block
    n1 = VariableNode('N_0')
    for inpt in input_nodes:
        n1.add_op(Connect(cell.graph, inpt, n1))

    # second node of block
    mlp_op_list = list()
    mlp_op_list.append(Identity())
    mlp_op_list.append(Dense(5, tf.nn.relu))
    mlp_op_list.append(Dense(10, tf.nn.relu))
    mlp_op_list.append(Dense(20, tf.nn.relu))
    mlp_op_list.append(Dense(40, tf.nn.relu))
    mlp_op_list.append(Dense(80, tf.nn.relu))
    mlp_op_list.append(Dense(160, tf.nn.relu))
    mlp_op_list.append(Dense(320, tf.nn.relu))
    n2 = VariableNode('N_1')
    for op in mlp_op_list:
        n2.add_op(op)

    # third
    n3 = VariableNode('N_2')
    drop_ops = []
    drop_ops.extend(dropout_ops)
    for op in drop_ops:
        n3.add_op(op)

    # 1 Blocks
    block1 = Block()
    block1.add_node(n1)