import tensorflow as tf

        for i in range(len(propensity_list)):
            pw_i = propensity_list[0] / propensity_list[i]
            pw_list.append(pw_i)
        propensity_weights = tf.stack(pw_list, axis=1)
        if self.hparams.max_propensity_weight > 0:
            propensity_weights = tf.clip_by_value(propensity_weights, clip_value_min=0, clip_value_max=self.hparams.max_propensity_weight)
