when restoring from a checkpoint, how can I change the data type of the parameters?

I have a pre-trained Tensorflow checkpoint, where the parameters are all of float32 data type.

How can I load checkpoint parameters as float16? Or is there a way to modify data types of a checkpoint?

Followings is my code snippet that tries to load float32 checkpoint into a float16 graph, and I got the type mismatch error.

import tensorflow as tf

A = tf.get_variable(name='foo', shape=[3, 3], dtype=tf.float32)
dense = tf.layers.dense(inputs=A, units=3)
varis = tf.trainable_variables(scope=None)
print(varis[1])  # <tf.Variable 'dense/kernel:0' shape=(3, 3) dtype=float32_ref>
assign = dict([(vari.name, vari) for vari in varis])
saver = tf.train.Saver(assign)

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    print(sess.run(dense))
    save_path = saver.save(sess, "tmp.ckpt")

tf.reset_default_graph()
A = tf.get_variable(name='foo', shape=[3, 3], dtype=tf.float16)
dense = tf.layers.dense(inputs=A, units=3)
varis = tf.trainable_variables(scope=None)
print(varis[1])  # <tf.Variable 'dense/kernel:0' shape=(3, 3) dtype=float16_ref>
assign = dict([(vari.name, vari) for vari in varis])
saver = tf.train.Saver(assign)

with tf.Session() as sess:
    saver.restore(sess, "tmp.ckpt")
    print(sess.run(dense))
    pass

# errors:
# tensor_name = dense/bias:0; expected dtype half does not equal original dtype float
# tensor_name = dense/kernel:0; expected dtype half does not equal original dtype float
# tensor_name = foo:0; expected dtype half does not equal original dtype float

from when restoring from a checkpoint, how can I change the data type of the parameters?