Tensorflow enqueuing data

Question

I'm wondering why the implementation of x_input_data in this code won't work as intended when using the uncommented version, but the commented version will. Anyone know the reason? I feel it has something to do with pointers to objects/values. Thanks!

import tensorflow as tf
from random import randint

def generate_random_input():
    x = []
    for a in range(6):
        x.append(randint(0,9))
    return x

x_input_data = tf.cast(generate_random_input(), tf.float32)
# x_input_data = tf.random_normal([6], mean=-1, stddev=4)     <----HERE

q = tf.FIFOQueue(capacity=3, dtypes=tf.float32)

x_input_data = tf.Print(x_input_data, data=[x_input_data], message="Raw inputs data generated:", summarize=6)
enqueue_op = q.enqueue_many(x_input_data)

numberOfThreads = 1 
qr = tf.train.QueueRunner(q, [enqueue_op] * numberOfThreads)
tf.train.add_queue_runner(qr) 

input = q.dequeue() 
input = tf.Print(input, data=[q.size(), input], message="Nb elements left, input:")

# fake graph: START
y = input + 1
# fake graph: END 

with tf.Session() as sess:
    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(coord=coord)

    for steps in range(100):
        sess.run(y)

    coord.request_stop()
    coord.join(threads)