How to use the deepxde.Model function in DeepXDE

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)

col_x=(0,),
        col_y=(1,),
    )

    activation = "tanh"
    initializer = "Glorot uniform"
    regularization = ["l2", 0.01]
    net = dde.maps.MfNN(
        [1] + [20] * 4 + [1],
        [10] * 2 + [1],
        activation,
        initializer,
        regularization=regularization,
    )

    model = dde.Model(data, net)
    model.compile("adam", lr=0.001, metrics=["l2 relative error"])
    losshistory, train_state = model.train(epochs=80000)

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

def boundary(x, on_boundary):
        return on_boundary

    def func(x):
        return np.sin(np.pi * x)

    geom = dde.geometry.Interval(-1, 1)
    bc = dde.DirichletBC(geom, func, boundary)
    data = dde.data.PDE(geom, 1, pde, bc, 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"])

    checkpointer = dde.callbacks.ModelCheckpoint(
        "./model/model.ckpt", verbose=1, save_better_only=True
    )
    movie = dde.callbacks.MovieDumper(
        "model/movie", [-1], [1], period=100, save_spectrum=True, y_reference=func
    )
    losshistory, train_state = model.train(
        epochs=10000, callbacks=[checkpointer, movie]
    )

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

    # Plot PDE residue
    x = geom.uniform_points(1000, True)

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)

bc = dde.DirichletBC(geomtime, func, lambda _, on_boundary: on_boundary)
    ic = dde.IC(geomtime, func, lambda _, on_initial: on_initial)
    data = dde.data.TimePDE(
        geomtime, 1, pde, [bc, ic], num_domain=40, func=func, num_test=10000
    )

    layer_size = [2] + [32] * 3 + [1]
    activation = "tanh"
    initializer = "Glorot uniform"
    net = dde.maps.FNN(layer_size, activation, initializer)
    net.outputs_modify(
        lambda x, y: x[:, 1:2] * (1 - x[:, 0:1] ** 2) * y + tf.sin(np.pi * x[:, 0:1])
    )

    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)

return -dy_xx - dy_yy - 1

    def boundary(x, on_boundary):
        return on_boundary

    def func(x):
        return np.zeros([len(x), 1])

    geom = dde.geometry.Polygon([[0, 0], [1, 0], [1, -1], [-1, -1], [-1, 1], [0, 1]])
    bc = dde.DirichletBC(geom, func, boundary)

    data = dde.data.PDE(
        geom, 1, pde, bc, num_domain=1200, num_boundary=120, num_test=1500
    )
    net = dde.maps.FNN([2] + [50] * 4 + [1], "tanh", "Glorot uniform")
    model = dde.Model(data, net)

    model.compile("adam", lr=0.001)
    model.train(epochs=50000)
    model.compile("L-BFGS-B")
    losshistory, train_state = model.train()
    dde.saveplot(losshistory, train_state, issave=True, isplot=True)

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

    def func(x):
        return np.sin(np.pi * x)

    geom = dde.geometry.Interval(-1, 1)
    bc1 = dde.DirichletBC(geom, func, boundary_l)
    bc2 = dde.PeriodicBC(geom, 0, boundary_r)
    data = dde.data.PDE(geom, 1, pde, [bc1, bc2], 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)