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])
    aggregated_lm_emb *= self.lm_scaling
    context_emb_list.append(aggregated_lm_emb)

    context_emb = tf.concat(context_emb_list, 2) # [num_sentences, max_sentence_length, emb]
    head_emb = tf.concat(head_emb_list, 2) # [num_sentences, max_sentence_length, emb]
    context_emb = tf.nn.dropout(context_emb, self.lexical_dropout) # [num_sentences, max_sentence_length, emb]
    head_emb = tf.nn.dropout(head_emb, self.lexical_dropout) # [num_sentences, max_sentence_length, emb]

    text_len_mask = tf.sequence_mask(text_len, maxlen=max_sentence_length) # [num_sentence, max_sentence_length]

    context_outputs = self.lstm_contextualize(context_emb, text_len, text_len_mask) # [num_words, emb]
    num_words = util.shape(context_outputs, 0)

    genre_emb = tf.gather(tf.get_variable("genre_embeddings", [len(self.genres), self.config["feature_size"]]), genre) # [emb]

    sentence_indices = tf.tile(tf.expand_dims(tf.range(num_sentences), 1), [1, max_sentence_length]) # [num_sentences, max_sentence_length]
