import tensorflow as tf

    current_inputs = text_emb # [num_sentences, max_sentence_length, emb]

    for layer in range(self.config["contextualization_layers"]):
      with tf.variable_scope("layer_{}".format(layer)):
        with tf.variable_scope("fw_cell"):
          cell_fw = util.CustomLSTMCell(self.config["contextualization_size"], num_sentences, self.lstm_dropout)
        with tf.variable_scope("bw_cell"):
          cell_bw = util.CustomLSTMCell(self.config["contextualization_size"], num_sentences, self.lstm_dropout)
        state_fw = tf.contrib.rnn.LSTMStateTuple(tf.tile(cell_fw.initial_state.c, [num_sentences, 1]), tf.tile(cell_fw.initial_state.h, [num_sentences, 1]))
        state_bw = tf.contrib.rnn.LSTMStateTuple(tf.tile(cell_bw.initial_state.c, [num_sentences, 1]), tf.tile(cell_bw.initial_state.h, [num_sentences, 1]))

        (fw_outputs, bw_outputs), _ = tf.nn.bidirectional_dynamic_rnn(
          cell_fw=cell_fw,
          cell_bw=cell_bw,
          inputs=current_inputs,
          sequence_length=text_len,
