import tensorflow as tf

    return 1 - (tf.to_float(is_training) * dropout_rate)

  def coarse_to_fine_pruning(self, top_span_emb, top_span_mention_scores, c):
    k = util.shape(top_span_emb, 0)
    top_span_range = tf.range(k) # [k]
    antecedent_offsets = tf.expand_dims(top_span_range, 1) - tf.expand_dims(top_span_range, 0) # [k, k]
    antecedents_mask = antecedent_offsets >= 1 # [k, k]
    fast_antecedent_scores = tf.expand_dims(top_span_mention_scores, 1) + tf.expand_dims(top_span_mention_scores, 0) # [k, k]
