code: def _dist_to_opt(self):
      
      dist_to_opt_ops = []
      # Running average of the norm of gradient
      self._grad_norm = tf.sqrt(self._grad_norm_squared)
      avg_op = self._moving_averager.apply([self._grad_norm,])
      dist_to_opt_ops.append(avg_op)
      with tf.control_dependencies([avg_op]):
        self._grad_norm_avg = self._moving_averager.average(self._grad_norm)
        # Single iteration distance estimation, note here
        # self._grad_norm_avg is per variable
        self._d_t = self._grad_norm_avg / self._grad_norm_squared_avg
      # Running average of distance
      avg_op = self._moving_averager.apply([self._d_t])
      dist_to_opt_ops.append(avg_op)
      with tf.control_dependencies([avg_op]):
        self._dist_to_opt_avg = tf.identity(
            self._moving_averager.average(self._d_t))
        if self._sparsity_debias:
          self._dist_to_opt_avg /= tf.sqrt(self._sparsity_avg)
      return dist_to_opt_ops
docs: Distance to optimum.
  
      Returns:
        D_t ops