def _grad_variance(self):
    """Estimate of gradient Variance.

    Returns:
      C_t ops.
    """
    grad_var_ops = []
    tensor_to_avg = []
    for t, g in zip(self._vars, self._grad):
      if isinstance(g, tf.IndexedSlices):
        tensor_to_avg.append(
            tf.reshape(tf.unsorted_segment_sum(g.values,
                                               g.indices,
                                               g.dense_shape[0]),
                       shape=t.get_shape()))
      else:
        tensor_to_avg.append(g)
    avg_op = self._moving_averager.apply(tensor_to_avg)
    grad_var_ops.append(avg_op)
    with tf.control_dependencies([avg_op]):
      self._grad_avg = [self._moving_averager.average(val)
                        for val in tensor_to_avg]
      self._grad_avg_squared = [tf.square(val) for val in self._grad_avg]

    # Compute Variance
    self._grad_var = tf.maximum(
        tf.constant(1e-6, dtype=self._grad_norm_squared_avg.dtype),
        self._grad_norm_squared_avg
        - tf.add_n([tf.reduce_sum(val) for val in self._grad_avg_squared]))
    if self._sparsity_debias:
      self._grad_var *= self._sparsity_avg
    return grad_var_ops
Estimate of gradient Variance.

    Returns:
      C_t ops.