How to use the deepxde.geometry.GeometryXTime function in DeepXDE

dy_x = tf.gradients(y, x)[0]
        dy_x, dy_t = dy_x[:, 0:1], dy_x[:, 1:]
        dy_xx = tf.gradients(dy_x, x)[0][:, 0:1]
        return (
            dy_t
            - C * dy_xx
            + tf.exp(-x[:, 1:])
            * (tf.sin(np.pi * x[:, 0:1]) - np.pi ** 2 * tf.sin(np.pi * x[:, 0:1]))
        )

    def func(x):
        return np.sin(np.pi * x[:, 0:1]) * np.exp(-x[:, 1:])

    geom = dde.geometry.Interval(-1, 1)
    timedomain = dde.geometry.TimeDomain(0, 1)
    geomtime = dde.geometry.GeometryXTime(geom, timedomain)

    bc = dde.DirichletBC(geomtime, func, lambda _, on_boundary: on_boundary)
    ic = dde.IC(geomtime, func, lambda _, on_initial: on_initial)

    observe_x = np.vstack((np.linspace(-1, 1, num=10), np.full((10), 1))).T
    ptset = dde.bc.PointSet(observe_x)
    observe_y = dde.DirichletBC(
        geomtime, ptset.values_to_func(func(observe_x)), lambda x, _: ptset.inside(x)
    )

    data = dde.data.TimePDE(
        geomtime,
        1,
        pde,
        [bc, ic, observe_y],
        num_domain=40,

dy_x = tf.gradients(y, x)[0]
        dy_x, dy_t = dy_x[:, 0:1], dy_x[:, 1:]
        dy_xx = tf.gradients(dy_x, x)[0][:, 0:1]
        return (
            dy_t
            - dy_xx
            + tf.exp(-x[:, 1:])
            * (tf.sin(np.pi * x[:, 0:1]) - np.pi ** 2 * tf.sin(np.pi * x[:, 0:1]))
        )

    def func(x):
        return np.sin(np.pi * x[:, 0:1]) * np.exp(-x[:, 1:])

    geom = dde.geometry.Interval(-1, 1)
    timedomain = dde.geometry.TimeDomain(0, 1)
    geomtime = dde.geometry.GeometryXTime(geom, timedomain)

    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)

dy_x = tf.gradients(y, x)[0]
        dy_x, dy_t = dy_x[:, 0:1], dy_x[:, 1:]
        dy_xx = tf.gradients(dy_x, x)[0][:, 0:1]
        return (
            dy_t
            - dy_xx
            + tf.exp(-x[:, 1:])
            * (tf.sin(np.pi * x[:, 0:1]) - np.pi ** 2 * tf.sin(np.pi * x[:, 0:1]))
        )

    def func(x):
        return np.sin(np.pi * x[:, 0:1]) * np.exp(-x[:, 1:])

    geom = dde.geometry.Interval(-1, 1)
    timedomain = dde.geometry.TimeDomain(0, 1)
    geomtime = dde.geometry.GeometryXTime(geom, timedomain)

    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,
        num_boundary=20,
        num_initial=10,
        func=func,
        num_test=10000,
    )

    layer_size = [2] + [32] * 3 + [1]