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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def _dist_to_opt(self):
 """Distance to optimum. Returns: D_t ops """

    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