def _build_rnn_graph_lstm

def _build_rnn_graph_lstm(self, inputs, config, is_training):
    """Build the inference graph using canonical LSTM cells."""
    # Slightly better results can be obtained with forget gate biases
    # initialized to 1 but the hyperparameters of the model would need to be
    # different than reported in the paper.
    def make_cell():
        cell = self._get_lstm_cell(config, is_training)
        if is_training and config.keep_prob < 1:
            cell = tf.contrib.rnn.DropoutWrapper(cell, output_keep_prob=config.keep_prob)
        return cell

    cell = tf.contrib.rnn.MultiRNNCell([make_cell() for _ in range(config.num_layers)], state_is_tuple=True)

    self._initial_state = cell.zero_state(config.batch_size, data_type())
    state = self._initial_state
    # Simplified version of tf.nn.static_rnn().
    # This builds an unrolled LSTM for tutorial purposes only.
    # In general, use tf.nn.static_rnn() or tf.nn.static_state_saving_rnn().
    #
    # The alternative version of the code below is:
    #
    # inputs = tf.unstack(inputs, num=self.num_steps, axis=1)
    # outputs, state = tf.nn.static_rnn(cell, inputs,
    # initial_state=self._initial_state)
    outputs = []
    with tf.variable_scope("RNN"):
        for time_step in range(self.num_steps):
            if time_step > 0: tf.get_variable_scope().reuse_variables()
            (cell_output, state) = cell(inputs[:, time_step, :], state)
            outputs.append(cell_output)
    output = tf.reshape(tf.concat(outputs, 1), [-1, config.hidden_size])
    return output, state