How to use the deepxde.DirichletBC function in DeepXDE

* (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,
        num_boundary=20,
        num_initial=10,
        anchors=observe_x,
        func=func,
        num_test=10000,
    )

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)

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

    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 (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)

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]
    activation = "tanh"
    initializer = "Glorot uniform"

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"
    initializer = "Glorot uniform"

dy2_x = tf.gradients(y2, x)[0]
        return [dy1_x - y2, dy2_x + y1]

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

    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)

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

    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 (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)

lhs2 = tf.gradients(y, x)[0]
        rhs = 2 * np.pi * tf.cos(2 * np.pi * x) + tf.sin(np.pi * x) ** 2 / np.pi
        return lhs1 + (lhs2 - rhs)[: tf.size(lhs1)]

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

    def func(x):
        """
        x: array_like, N x D_in
        y: array_like, N x D_out
        """
        return np.sin(2 * np.pi * x)

    geom = dde.geometry.Interval(0, 1)
    bc = dde.DirichletBC(geom, func, boundary)

    quad_deg = 16
    data = dde.data.IDE(geom, ide, bc, quad_deg, num_domain=16, num_boundary=2)

    layer_size = [1] + [20] * 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)
    model.train(epochs=10000)

    X = geom.uniform_points(100, True)
    y_true = func(X)
    y_pred = model.predict(X)

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,
        num_boundary=20,
        num_initial=10,

def main():
    def pde(x, y):
        dy_x = tf.gradients(y, x)[0]
        dy_x, dy_y = dy_x[:, 0:1], dy_x[:, 1:]
        dy_xx = tf.gradients(dy_x, x)[0][:, 0:1]
        dy_yy = tf.gradients(dy_y, x)[0][:, 1:]
        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)