tensorflow rnn variable seq length

import tensorflow as tf    
import numpy as np

if __name__ == '__main__':
  np.random.seed(1)  
  # the size of the hidden state for the lstm (notice the lstm uses 2x of this amount so actually lstm will have state of size 2)
  size = 1
  # 2 different sequences total
  batch_size= 2
  # the maximum steps for both sequences is 10
  n_steps = 10
  # each element of the sequence has dimension of 2
  seq_width = 2

  # the first input is to be stopped at 4 steps, the second at 6 steps
  e_stop = np.array([4,6])

  initializer = tf.random_uniform_initializer(-1,1) 

  # the sequences, has n steps of maximum size
  seq_input = tf.placeholder(tf.float32, [n_steps, batch_size, seq_width])
  # what timesteps we want to stop at, notice it's different for each batch hence dimension of [batch]
  early_stop = tf.placeholder(tf.int32, [batch_size])

  # inputs for rnn needs to be a list, each item being a timestep. 
  # we need to split our input into each timestep, and reshape it because split keeps dims by default  
  inputs = [tf.reshape(i, (batch_size, seq_width)) for i in tf.split(0, n_steps, seq_input)]

  cell = tf.nn.rnn_cell.LSTMCell(size, seq_width, initializer=initializer)  
  initial_state = cell.zero_state(batch_size, tf.float32)

  # ========= This is the most important part ==========
  # output will be of length 4 and 6
  # the state is the final state at termination (stopped at step 4 and 6)  
  outputs, state = tf.nn.rnn(cell, inputs, initial_state=initial_state, sequence_length=early_stop)

  # usual crap
  iop = tf.initialize_all_variables()
  session = tf.Session()
  session.run(iop)
  feed = {early_stop:e_stop, seq_input:np.random.rand(n_steps, batch_size, seq_width).astype('float32')}

  print "outputs, should be 2 things one of length 4 and other of 6"
  outs = session.run(outputs, feed_dict=feed)
  for xx in outs:
    print xx

  print "states, 2 things total both of size 2, which is the size of the hidden state"
  st = session.run(state, feed_dict=feed)
  print st