import tensorflow as tf

            for i, (grad, var) in enumerate(gradients):
                if grad is not None:
                    gradients[i] = (tf.clip_by_norm(grad, grad_norm_clipping), var)

    with tf.variable_scope("input_info", reuse=False):
        tf.summary.scalar('rewards', tf.reduce_mean(rew_t_ph))
        tf.summary.scalar('importance_weights', tf.reduce_mean(importance_weights_ph))

        if full_tensorboard_log:
            tf.summary.histogram('rewards', rew_t_ph)
            tf.summary.histogram('importance_weights', importance_weights_ph)
            if tf_util.is_image(obs_phs[0]):
