Tensorflow: error when computing gradients for complex variables - tensorflow

Question

I am trying to compute the gradients of a complex function in Tensorflow, but I have some trouble.
Here is my code:
import numpy as np
import tensorflow as tf
def CompSEQ(seq, rho0):
def EvolveRHO(prev, input):
return tf.mul(tf.complex(input,0.0),tf.matmul(rho0,prev))
def ComputeP(p, rho):
return p * tf.real(tf.trace(rho))
rhos = tf.scan(EvolveRHO, seq, initializer=rho0)
p = tf.scan(ComputeP, rhos, initializer=tf.constant(1.0))
return tf.gather(p,[tf.size(seq)-1])[0]
N = 4
seq = tf.placeholder(tf.float32, shape=[5])
x = tf.Variable(tf.zeros([2*N*N], dtype=tf.float32))
seqP = CompSEQ(seq, tf.complex(tf.reshape(x[0:N*N],[N,N]),
tf.reshape(x[N*N:2*N*N],[N,N])))
#seqPp = tf.gradients([seqP], [x]) # THIS LINE CAUSES THE PROBLEM!!!
sess = tf.Session()
sess.run(tf.initialize_all_variables())
v = np.random.rand(2*N*N).astype(np.float32)
s0 = np.random.rand(5).astype(np.float32)
p = sess.run(seqP, feed_dict={seq:s0, x:v})
print('seqP',p);
I use an input float32 vector x that will be transformed into a complex matrix. All the computations are performed using complex numbers and the last tf.scan in the CompSEQ function transforms all the results into float32 by taking the real part.
If I comment the call to tf.gradients (as in the code) everything works fine, but when I try to compute the gradients I get the following error:
Traceback (most recent call last):
File "error.1.py", line 24, in <module>
seqPp = tf.gradients([seqP], [x]) # THIS LINE CAUSES THE PROBLEM!!!
File "/Users/tamburin/Library/Python/2.7/lib/python/site-packages/tensorflow/python/ops/gradients.py", line 486, in gradients
_VerifyGeneratedGradients(in_grads, op)
File "/Users/tamburin/Library/Python/2.7/lib/python/site-packages/tensorflow/python/ops/gradients.py", line 264, in _VerifyGeneratedGradients
dtypes.as_dtype(inp.dtype).name))
ValueError: Gradient type float32 generated for op name: "scan/while/Switch_1"
op: "Switch"
input: "scan/while/Merge_1"
input: "scan/while/LoopCond"
attr {
key: "T"
value {
type: DT_COMPLEX64
}
}
attr {
key: "_class"
value {
list {
s: "loc:#scan/while/Merge_1"
}
}
}
does not match input type complex64
Converting all the computations into float32 variables resolves the problem, but I need to maintain the computation on complex variables (this is a simplified example w.r.t. my real problem).