Tensorflow save final state of LSTM in dynamic_rnn for prediction

I want to save the final state of my LSTM such that it's included when I restore the model and can be used for prediction. As explained below, the Saver only has knowledge of the final state when I use tf.assign. However, this throws an error (also explained below).

During training I always feed the final LSTM state back into the network, as explained in this post. Here are the important parts of the code:

When building the graph:

            self.init_state = tf.placeholder(tf.float32, [
                self.n_layers, 2, self.batch_size, self.n_hidden
            ])

            state_per_layer_list = tf.unstack(self.init_state, axis=0)

            rnn_tuple_state = tuple([
                tf.contrib.rnn.LSTMStateTuple(state_per_layer_list[idx][0],
                                              state_per_layer_list[idx][1])

                for idx in range(self.n_layers)
            ])

            outputs, self.final_state = tf.nn.dynamic_rnn(
                cell, inputs=self.inputs, initial_state=rnn_tuple_state)

And during training:

        _current_state = np.zeros((self.n_layers, 2, self.batch_size,
                                   self.n_hidden))

            _train_step, _current_state, _loss, _acc, summary = self.sess.run(
                [
                    self.train_step, self.final_state,
                    self.merged
                ],
                feed_dict={self.inputs: _inputs,
                           self.labels:_labels, 
                           self.init_state: _current_state})

When I later restore my model from a checkpoint, the final state is not restored as well. As outlined in this post the problem is that the Saver has no knowledge of the new state. The post also suggests a solution, based on tf.assign. Regrettably, I cannot use the suggested

            assign_op = tf.assign(self.init_state, _current_state)
            self.sess.run(assign_op)

because self.init state is not a Variable but a placeholder. I get the error

AttributeError: 'Tensor' object has no attribute 'assign'

I have tried to solve this problem for several hours now but I can't get it to work.

Any help is appreciated!

EDIT:

I have changed self.init_state to

            self.init_state = tf.get_variable('saved_state', shape=
            [self.n_layers, 2, self.batch_size, self.n_hidden])

            state_per_layer_list = tf.unstack(self.init_state, axis=0)

            rnn_tuple_state = tuple([
                tf.contrib.rnn.LSTMStateTuple(state_per_layer_list[idx][0],
                                              state_per_layer_list[idx][1])

                for idx in range(self.n_layers)
            ])

            outputs, self.final_state = tf.nn.dynamic_rnn(
                cell, inputs=self.inputs, initial_state=rnn_tuple_state)

And during training I don't feed a value for self.init_state:

            _train_step, _current_state, _loss, _acc, summary = self.sess.run(
                [
                    self.train_step, self.final_state,
                    self.merged
                ],
                feed_dict={self.inputs: _inputs,
                           self.labels:_labels})

However, I still can't run the assignment op. Know I get

TypeError: Expected float32 passed to parameter 'value' of op 'Assign', got (LSTMStateTuple(c=array([[ 0.07291573, -0.06366599, -0.23425588, ..., 0.05307654,