import numpy as np
import tensorflow as tf
tf.reset_default_graph()
F = lambda x: tf.cumsum(x)
G = lambda x: x[-1]
H = lambda x: x
encoder_emb_inp = tf.placeholder(dtype=tf.float32, shape=[4])
encoder_outputs = F(encoder_emb_inp)
decoder_initial_state = G(encoder_outputs)
decoder_initial_output = H(decoder_initial_state)
def cond(time, unused_state, unused_output):
    return tf.less(time, 3)

def body(time, state, inputs):
    step = lambda s, i: (tf.multiply(s,s), tf.multiply(s,i))
    (next_state, next_output) = step(state, inputs)
    next_grads = tf.gradients(next_output, decoder_initial_state)
    tf.Print(next_grads, next_grads)
    return (time + 1, next_state, next_output)

initial_time = tf.constant(0, dtype=tf.int32)


final_time, final_state, final_outputs = tf.while_loop(cond, body, loop_vars = [initial_time, decoder_initial_state, decoder_initial_output])

<ipython-input-126-5205901211cc> in body(time, state, inputs)
      6     (next_state, next_output) = step(state, inputs)
      7 #    next_grads = tf.gradients(next_output, state)
----> 8     next_grads = tf.gradients(next_output, decoder_initial_state)
      9     tf.Print(next_grads, next_grads)
     10     return (time + 1, next_state, next_output)

/home/pramodkm/tensorflow/local/lib/python2.7/site-packages/tensorflow/python/ops/gradients_impl.pyc in gradients(ys, xs, grad_ys, name, colocate_gradients_with_ops, gate_gradients, aggregation_method, stop_gradients)
    591                 out_grads[i] = loop_state.ZerosLike(op, i)
    592               else:
--> 593                 out_grads[i] = control_flow_ops.ZerosLikeOutsideLoop(op, i)
    594           with ops.name_scope(op.name + "_grad"):
    595             # pylint: disable=protected-access

/home/pramodkm/tensorflow/local/lib/python2.7/site-packages/tensorflow/python/ops/control_flow_ops.pyc in ZerosLikeOutsideLoop(op, index)
   1342     if op_ctxt:
   1343       # We are in a cond context. Use a switch to create zeros only when needed.
-> 1344       pred = op_ctxt.pred
   1345       branch = op_ctxt.branch
   1346       switch_val = switch(op.inputs[0], pred)[1 - branch]

AttributeError: 'WhileContext' object has no attribute 'pred'