which tf version is this project implemented?

if i run with tf 1.0.0, exception arise as `tf.nn.ops have no attribution of rnn`

i run the notebook with tf 0.12 got issue below:
`---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
in
----> 1 pointer_network = PointerNetwork(FLAGS.max_steps, 1, FLAGS.rnn_size, 1, 5, FLAGS.batch_size, 1e-2, 0.95)
2 dataset = DataGenerator()
3 pointer_network.step()

in __init__(self, max_len, input_size, size, num_layers, max_gradient_norm, batch_size, learning_rate, learning_rate_decay_factor)
59 with tf.variable_scope("decoder"):
60 outputs, states, _ = pointer_decoder(
---> 61 self.decoder_inputs, final_state, attention_states, cell)
62
63 with tf.variable_scope("decoder", reuse=True):

~/tutorial/TensorFlow-Pointer-Networks/pointer.py in pointer_decoder(decoder_inputs, initial_state, attention_states, cell, feed_prev, dtype, scope)
121
122 # Merge input and previous attentions into one vector of the right size.
--> 123 x = core_rnn_cell_impl._linear([inp, attns], cell.output_size, True)
124 # Run the RNN.
125 cell_output, new_state = cell(x, states[-1])

/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/tensorflow/contrib/rnn/python/ops/core_rnn_cell_impl.py in _linear(args, output_size, bias, bias_start, scope)
749 res = math_ops.matmul(args[0], weights)
750 else:
--> 751 res = math_ops.matmul(array_ops.concat(args, 1), weights)
752 if not bias:
753 return res

/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/tensorflow/python/ops/array_ops.py in concat(concat_dim, values, name)
1073 ops.convert_to_tensor(concat_dim,
1074 name="concat_dim",
-> 1075 dtype=dtypes.int32).get_shape(
1076 ).assert_is_compatible_with(tensor_shape.scalar())
1077 return identity(values[0], name=scope)

/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/tensorflow/python/framework/ops.py in convert_to_tensor(value, dtype, name, as_ref, preferred_dtype)
667
668 if ret is None:
--> 669 ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
670
671 if ret is NotImplemented:

/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/tensorflow/python/framework/constant_op.py in _constant_tensor_conversion_function(v, dtype, name, as_ref)
174 as_ref=False):
175 _ = as_ref
--> 176 return constant(v, dtype=dtype, name=name)
177
178

/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/tensorflow/python/framework/constant_op.py in constant(value, dtype, shape, name, verify_shape)
163 tensor_value = attr_value_pb2.AttrValue()
164 tensor_value.tensor.CopyFrom(
--> 165 tensor_util.make_tensor_proto(value, dtype=dtype, shape=shape, verify_shape=verify_shape))
166 dtype_value = attr_value_pb2.AttrValue(type=tensor_value.tensor.dtype)
167 const_tensor = g.create_op(

/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/tensorflow/python/framework/tensor_util.py in make_tensor_proto(values, dtype, shape, verify_shape)
365 nparray = np.empty(shape, dtype=np_dt)
366 else:
--> 367 _AssertCompatible(values, dtype)
368 nparray = np.array(values, dtype=np_dt)
369 # check to them.

/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/tensorflow/python/framework/tensor_util.py in _AssertCompatible(values, dtype)
300 else:
301 raise TypeError("Expected %s, got %s of type '%s' instead." %
--> 302 (dtype.name, repr(mismatch), type(mismatch).__name__))
303
304

TypeError: Expected int32, got list containing Tensors of type '_Message' instead.`

Start with the notebook and pointer.py, then compare the TensorFlow 0.12 and 1.0.0 behavior described in the report. Determine which TensorFlow version the project supports and whether the reported RNN and concat errors can be reproduced; done means the supported version and compatibility status are clear.

Written by the indexing model from the issue text.