How to use the deepxde.maps.FNN function in DeepXDE

def boundary_r(x, on_boundary):
        return on_boundary and np.isclose(x[0], 1)

    def func(x):
        return (x + 1) ** 2

    geom = dde.geometry.Interval(-1, 1)
    bc_l = dde.DirichletBC(geom, func, boundary_l)
    bc_r = dde.RobinBC(geom, lambda X, y: y, boundary_r)
    data = dde.data.PDE(geom, 1, pde, [bc_l, bc_r], 16, 2, func=func, num_test=100)

    layer_size = [1] + [50] * 3 + [1]
    activation = "tanh"
    initializer = "Glorot uniform"
    net = dde.maps.FNN(layer_size, activation, initializer)

    model = dde.Model(data, net)
    model.compile("adam", lr=0.001, metrics=["l2 relative error"])
    losshistory, train_state = model.train(epochs=10000)

    dde.saveplot(losshistory, train_state, issave=True, isplot=True)

x: array_like, N x D_in
        y: array_like, N x D_out
        """
        return x * np.sin(5 * x)

    geom = dde.geometry.Interval(-1, 1)
    num_train = 10
    num_test = 1000
    data = dde.data.Func(geom, func, num_train, num_test)

    layer_size = [1] + [50] * 3 + [1]
    activation = "tanh"
    initializer = "Glorot uniform"
    regularization = ["l2", 1e-5]
    dropout_rate = 0.01
    net = dde.maps.FNN(
        layer_size,
        activation,
        initializer,
        regularization=regularization,
        dropout_rate=dropout_rate,
    )

    model = dde.Model(data, net)
    model.compile("adam", lr=0.001, metrics=["l2 relative error"])
    losshistory, train_state = model.train(epochs=30000, uncertainty=True)

    dde.saveplot(losshistory, train_state, issave=True, isplot=True)

def boundary_r(x, on_boundary):
        return on_boundary and np.isclose(x[0], 1)

    def func(x):
        return (x + 1) ** 2

    geom = dde.geometry.Interval(-1, 1)
    bc_l = dde.DirichletBC(geom, func, boundary_l)
    bc_r = dde.NeumannBC(geom, lambda X: 2 * (X + 1), boundary_r)
    data = dde.data.PDE(geom, 1, pde, [bc_l, bc_r], 16, 2, func=func, num_test=100)

    layer_size = [1] + [50] * 3 + [1]
    activation = "tanh"
    initializer = "Glorot uniform"
    net = dde.maps.FNN(layer_size, activation, initializer)

    model = dde.Model(data, net)
    model.compile("adam", lr=0.001, metrics=["l2 relative error"])
    losshistory, train_state = model.train(epochs=10000)

    dde.saveplot(losshistory, train_state, issave=True, isplot=True)

def func(x):
        """
        y1 = sin(x)
        y2 = cos(x)
        """
        return np.hstack((np.sin(x), np.cos(x)))

    geom = dde.geometry.Interval(0, 10)
    bc1 = dde.DirichletBC(geom, np.sin, boundary, component=0)
    bc2 = dde.DirichletBC(geom, np.cos, boundary, component=1)
    data = dde.data.PDE(geom, 2, ode_system, [bc1, bc2], 35, 2, func=func, num_test=100)

    layer_size = [1] + [50] * 3 + [2]
    activation = "tanh"
    initializer = "Glorot uniform"
    net = dde.maps.FNN(layer_size, activation, initializer)

    model = dde.Model(data, net)
    model.compile("adam", lr=0.001, metrics=["l2 relative error"])
    losshistory, train_state = model.train(epochs=20000)

    dde.saveplot(losshistory, train_state, issave=True, isplot=True)