assign on a tf.Variable tensor slice
<p>I am trying to do the following</p>

<pre><code>state[0,:] = state[0,:].assign( 0.9*prev_state + 0.1*( tf.matmul(inputs, weights) + biases ) )
for i in xrange(1,BATCH_SIZE):
    state[i,:] = state[i,:].assign( 0.9*state[i-1,:] + 0.1*( tf.matmul(inputs, weights) + biases ) )
prev_state = prev_state.assign( state[BATCH_SIZE-1,:] )
</code></pre>

<p>with </p>

<pre><code>state = tf.Variable(tf.zeros([BATCH_SIZE, HIDDEN_1]), name='inner_state')
prev_state = tf.Variable(tf.zeros([HIDDEN_1]), name='previous_inner_state')
</code></pre>

<p>As a follow-up for <a href="https://stackoverflow.com/q/37566925/1792701">this question</a>. I get an error that <code>Tensor</code> does not have an <code>assign</code> method.</p>

<p>What is the correct way to call the <code>assign</code> method on a slice of a <code>Variable</code> tensor?</p>

<hr>

<p>Full current code:</p>

<pre><code>import tensorflow as tf
import math
import numpy as np

INPUTS = 10
HIDDEN_1 = 20
BATCH_SIZE = 3


def create_graph(inputs, state, prev_state):
    with tf.name_scope('h1'):
        weights = tf.Variable(
        tf.truncated_normal([INPUTS, HIDDEN_1],
                            stddev=1.0 / math.sqrt(float(INPUTS))),
        name='weights')
        biases = tf.Variable(tf.zeros([HIDDEN_1]), name='biases')

        updated_state = tf.scatter_update(state, [0], 0.9 * prev_state + 0.1 * (tf.matmul(inputs[0,:], weights) + biases))
        for i in xrange(1, BATCH_SIZE):
          updated_state = tf.scatter_update(
              updated_state, [i], 0.9 * updated_state[i-1, :] + 0.1 * (tf.matmul(inputs[i,:], weights) + biases))

        prev_state = prev_state.assign(updated_state[BATCH_SIZE-1, :])
        output = tf.nn.relu(updated_state)
    return output

def data_iter():
    while True:
        idxs = np.random.rand(BATCH_SIZE, INPUTS)
        yield idxs

with tf.Graph().as_default():
    inputs = tf.placeholder(tf.float32, shape=(BATCH_SIZE, INPUTS))
    state = tf.Variable(tf.zeros([BATCH_SIZE, HIDDEN_1]), name='inner_state')
    prev_state = tf.Variable(tf.zeros([HIDDEN_1]), name='previous_inner_state')

    output = create_graph(inputs, state, prev_state)

    sess = tf.Session()
    # Run the Op to initialize the variables.
    init = tf.initialize_all_variables()
    sess.run(init)
    iter_ = data_iter()
    for i in xrange(0, 2):
        print ("iteration: ",i)
        input_data = iter_.next()
        out = sess.run(output, feed_dict={ inputs: input_data})
</code></pre>