TensorFlow scatter_nd function is not working with placeholder and complex input - python-2.7

Question

I am using tf.scatter_nd to update complex values at some index.
It seems the real and imaginary parts are somehow getting added together by this function. My question is how to make it work with placeholders. Here is the minimal working example where the variables b and e should have same values.
import tensorflow as tf
import numpy as np
tf.reset_default_graph()
update=np.asarray([1.+2j])
idx=tf.constant( [[0]])
shp=tf.constant([1])
# works with constants
a=tf.constant(update)
b=tf.scatter_nd(idx,a,shp)
with tf.Session() as sess:
print sess.run(b) # correct output: 1.+2j
#Does not work with placeholders
d=tf.placeholder(tf.complex128)
e=tf.scatter_nd(idx,d,shp)
with tf.Session() as sess:
print sess.run(e,feed_dict={d:update}) # WRONG output: 3.+0j
I am using Anaconda python 2.7 + TensorFlow 1.7 GPU version installed using the conda command.
Edit:
The issue occurs when running the code on GPU. CPU version works correctly.
Here is the updated code to reproduce the issue in TensorFlow-GPU 1.8 installed using Anaconda Python 2.7.
import tensorflow as tf
import numpy as np
tf.reset_default_graph()
update=np.asarray([1.+2j])
idx=tf.constant( [[0]])
shp=tf.constant([1])
a=tf.placeholder(tf.complex128)
with tf.device("/cpu:0"):
b=tf.scatter_nd(idx,a,shp)
with tf.device("/gpu:0"):
c=tf.scatter_nd(idx,a,shp)
with tf.Session() as sess:
print 'Correct output on CPU', sess.run(b,feed_dict={a:update})
print 'Wrong output on GPU',sess.run(c,feed_dict={a:update})
I saw this thread and this thread but could not find how to resolve it. Is there any alternative to tf.scatter_nd that will run on GPU?