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

def main():
    def ide(x, y, int_mat):
        rhs = tf.matmul(int_mat, y)
        lhs1 = tf.gradients(y, x)[0]
        return (lhs1 + y)[: tf.size(rhs)] - rhs

    def kernel(x, s):
        return np.exp(s - x)

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

    def func(x):
        return np.exp(-x) * np.cosh(x)

    geom = dde.geometry.Interval(0, 5)
    bc = dde.DirichletBC(geom, func, boundary)

    quad_deg = 20
    data = dde.data.IDE(
        geom,
        ide,
        bc,
        quad_deg,
        kernel=kernel,
        num_domain=10,
        num_boundary=2,
        train_distribution="uniform",
    )

    layer_size = [1] + [20] * 3 + [1]
    activation = "tanh"

def main():
    def pde(x, y):
        dy_x = tf.gradients(y, x)[0]
        dy_xx = tf.gradients(dy_x, x)[0]
        return -dy_xx - np.pi ** 2 * tf.sin(np.pi * x)

    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

def main():
    def pde(x, y):
        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])
    )

def main():
    def func_lo(x):
        A, B, C = 0.5, 10, -5
        return A * (6 * x - 2) ** 2 * np.sin(12 * x - 4) + B * (x - 0.5) + C

    def func_hi(x):
        return (6 * x - 2) ** 2 * np.sin(12 * x - 4)

    geom = dde.geometry.Interval(0, 1)
    num_test = 1000
    data = dde.data.MfFunc(geom, func_lo, func_hi, 100, 6, num_test)

    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"])

def main():
    def pde(x, y):
        dy_x = tf.gradients(y, x)[0]
        dy_xx = tf.gradients(dy_x, x)[0]
        return -dy_xx - np.pi ** 2 * tf.sin(np.pi * x)

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

    def boundary_r(x, on_boundary):
        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)