Tensorflow 1.1 Error : Attempt to reuse RNNCell with a different variable scope than its first use - deep-learning

Question

I am trying to run the seq2seq translate example present on Tensorflow website and getting following error. I am using tensorflow-gpu==1.1.0
ValueError: Attempt to reuse RNNCell
with a different variable scope than its first use.
First use of cell was with scope
'embedding_attention_seq2seq/embedding_attention_decoder/attention_decoder/gru_cell',
this attempt is with scope 'embedding_attention_seq2seq/rnn/gru_cell'.
Please create a new instance of the cell if you would like it to use a
different set of weights. If before you were using:
MultiRNNCell([GRUCell(...)] * num_layers), change to:
MultiRNNCell([GRUCell(...) for _ in range(num_layers)]). If before
you were using the same cell instance as both the forward and reverse
cell of a bidirectional RNN, simply create two instances (one for
forward, one for reverse). In May 2017, we will start transitioning
this cell's behavior to use existing stored weights, if any, when it
is called with scope=None (which can lead to silent model degradation,
so this error will remain until then.)
On github people were suggesting to change the add the reuse argument to cell as follows :
def single_cell():
return tf.contrib.rnn.GRUCell(size, reuse = tf.get_variable_scope().reuse)
if use_lstm:
def single_cell():
return tf.contrib.rnn.BasicLSTMCell(size, reuse = tf.get_variable_scope().reuse)
cell = single_cell()
if num_layers > 1:
cell = tf.contrib.rnn.MultiRNNCell([single_cell() for _ in range(num_layers)])
But still I am getting the same error. What's the issue and how to resolve it ?
Any help is highly appreciated.
P.S: A similar post was there on stackoverflow,but that solution didn't work for me and since the version of TF is different, I created a new post.