import tensorflow as tf


    if self.config["coarse_to_fine"]:
      top_antecedents, top_antecedents_mask, top_fast_antecedent_scores, top_antecedent_offsets = self.coarse_to_fine_pruning(top_span_emb, top_span_mention_scores, c)
    else:
      top_antecedents, top_antecedents_mask, top_fast_antecedent_scores, top_antecedent_offsets = self.distance_pruning(top_span_emb, top_span_mention_scores, c)

    dummy_scores = tf.zeros([k, 1]) # [k, 1]
    for i in range(self.config["coref_depth"]):
      with tf.variable_scope("coref_layer", reuse=(i > 0)):
        top_antecedent_emb = tf.gather(top_span_emb, top_antecedents) # [k, c, emb]
        top_antecedent_scores = top_fast_antecedent_scores + self.get_slow_antecedent_scores(top_span_emb, top_antecedents, top_antecedent_emb, top_antecedent_offsets, top_span_speaker_ids, genre_emb) # [k, c]
        top_antecedent_weights = tf.nn.softmax(tf.concat([dummy_scores, top_antecedent_scores], 1)) # [k, c + 1]
