import tensorflow as tf

#         loss = None
        with tf.name_scope(name, "click_loglikelihood"):
            ob_prob=tf.nn.softmax(propensity)
            rel_prob=tf.nn.softmax(train_output)
            click_prob=ob_prob*rel_prob
            click_prob_norm=click_prob/tf.reduce_sum(click_prob,axis=1,keep_dims=True)
            label_dis = labels/ tf.reduce_sum(labels, 1, keep_dims=True)
            entropy = tf.reduce_sum(tf.math.log(click_prob_norm)*label_dis,1)
        return tf.reduce_mean(entropy)
    def click_weighted_pairwise_loss(self, output, labels, propensity_weights, name=None):
        """Computes pairwise entropy loss with propensity weighting.

        Args:
            output: (tf.Tensor) A tensor with shape [batch_size, list_size]. Each value is
