How to use gradient_override_map in Tensorflow 2.0?

I'm trying to use gradient_override_map with Tensorflow 2.0. There is an example in the documentation, which I will use as the example here as well.

In 2.0, GradientTape can be used to compute gradients as follows:

import tensorflow as tf
print(tf.version.VERSION)  # 2.0.0-alpha0

x = tf.Variable(5.0)
with tf.GradientTape() as tape:
    s_1 = tf.square(x)
print(tape.gradient(s_1, x))

There is also the tf.custom_gradient decorator, which can be used to define the gradient for a new function (again, using the example from the docs):

import tensorflow as tf
print(tf.version.VERSION)  # 2.0.0-alpha

@tf.custom_gradient
def log1pexp(x):
    e = tf.exp(x)

    def grad(dy):
        return dy * (1 - 1 / (1 + e))

    return tf.math.log(1 + e), grad

x = tf.Variable(100.)

with tf.GradientTape() as tape:
    y = log1pexp(x)

print(tape.gradient(y, x))

However, I would like to replace the gradient for standard functions such as tf.square. I tried to use the following code:

@tf.RegisterGradient("CustomSquare")
def _custom_square_grad(op, grad):
  return tf.constant(0)

with tf.Graph().as_default() as g:
    x = tf.Variable(5.0)
    with g.gradient_override_map({"Square": "CustomSquare"}):
        with tf.GradientTape() as tape:
            s_2 = tf.square(x, name="Square")

    with tf.compat.v1.Session() as sess:
        sess.run(tf.compat.v1.global_variables_initializer())            
        print(sess.run(tape.gradient(s_2, x)))

However, there are two issues: The gradient replacement does not seem to work (it is evaluated to 10.0 instead of 0.0) and I need to resort to session.run() to execute the graph. Is there a way to achieve this in "native" TensorFlow 2.0?

In TensorFlow 1.12.0, the following produces the desired output:

import tensorflow as tf
print(tf.__version__)  # 1.12.0

@tf.RegisterGradient("CustomSquare")
def _custom_square_grad(op, grad):
  return tf.constant(0)

x = tf.Variable(5.0)

g = tf.get_default_graph()
with g.gradient_override_map({"Square": "CustomSquare"}):
    s_2 = tf.square(x, name="Square")
grad = tf.gradients(s_2, x)

with tf.Session() as sess:
  sess.run(tf.global_variables_initializer())
  print(sess.run(grad))

from How to use gradient_override_map in Tensorflow 2.0?