import tensorflow as tf

      indices = tf.transpose(
          tf.stack([orig_indices, tf.squeeze(relabel_indices)], axis=0))
      relabel_q_vals = tf.gather_nd(logits_vec, indices)
      tf.compat.v2.summary.scalar(
          name="relabel_q_vals",
          data=tf.reduce_mean(relabel_q_vals),
          step=global_step,
      )

      max_q = tf.reduce_max(logits_vec, axis=1)
