import tensorflow as tf

    with tf.variable_scope("lm_aggregation"):
      self.lm_weights = tf.nn.softmax(tf.get_variable("lm_scores", [lm_num_layers], initializer=tf.constant_initializer(0.0)))
      self.lm_scaling = tf.get_variable("lm_scaling", [], initializer=tf.constant_initializer(1.0))
    flattened_lm_emb = tf.reshape(lm_emb, [num_sentences * max_sentence_length * lm_emb_size, lm_num_layers])
    flattened_aggregated_lm_emb = tf.matmul(flattened_lm_emb, tf.expand_dims(self.lm_weights, 1)) # [num_sentences * max_sentence_length * emb, 1]
    aggregated_lm_emb = tf.reshape(flattened_aggregated_lm_emb, [num_sentences, max_sentence_length, lm_emb_size])
