class_docstr: Optimizer that implements the YellowFin algorithm.
  
  See [Zhang et. al., 2017](https://arxiv.org/abs/1706.03471) for details.
docstr: Distance to optimum.
  
  Returns:
    D_t ops
docstr_tok: ["Distance","to","optimum",".","Returns",":","D_t","ops"]
code: 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
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow.compat.v1
label_desc: Use image node_id 4 for calling the YellowFinOptimizer obj's underlying member method code with example usage: obj._dist_to_opt() and returns: dist_to_opt_ops