import tensorflow as tf

                click_feature = [tf.expand_dims(tf.zeros_like(self.labels[i]) , -1) for _ in range(4*list_size)]
                click_feature[i] = tf.expand_dims(tf.ones_like(self.labels[i]) , -1)
#                 click_feature[list_size:]=[tf.expand_dims(tf.zeros_like(self.labels[i]) , -1) for _ in range(3*list_size)]
                click_feature[list_size:list_size+i] =[tf.expand_dims(self.labels[k] , -1) for k in range(i-1,-1,-1)]
                click_feature[2*list_size:2*list_size+i+1]=[tf.expand_dims(self.types[k] , -1) for k in range(i,-1,-1)]
                click_feature[3*list_size:3*list_size+list_size-i-1]=[tf.expand_dims(self.types[k] , -1) for k in range(i+1,list_size)]
                # Predict propensity with a simple network
                output_propensity_list.append(propensity_network(tf.concat(click_feature, 1), i))
