<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def _build_update_ops(self, mean, variance, is_training):
 """Builds the moving average update ops when using moving variance. Args: mean: The mean value to update with. variance: The variance value to update with. is_training: Boolean Tensor to indicate if we're currently in training mode. Returns: Tuple of `(update_mean_op, update_variance_op)` when `is_training` is or could be `True`. Returns `None` when `is_training=False`. """


    def build_update_ops():
      """Builds the exponential moving average update ops."""

      update_mean_op = moving_averages.assign_moving_average(
          variable=self._moving_mean,
          value=tf.reshape(mean, (self._num_channels,)),
          decay=self._decay_rate,
          zero_debias=False,
          name="update_moving_mean").op

      update_variance_op = moving_averages.assign_moving_average(
          variable=self._moving_variance,
          value=tf.reshape(variance, (self._num_channels,)),
          decay=self._decay_rate,
          zero_debias=False,
          name="update_moving_variance").op

      return update_mean_op, update_variance_op

    def build_no_ops():
      return (tf.no_op(), tf.no_op())

    # Only make the ops if we know that `is_training=True`, or the value of
    # `is_training` is unknown.
    is_training_const = utils.constant_value(is_training)
    if is_training_const is None or is_training_const:
      update_mean_op, update_variance_op = utils.smart_cond(
          is_training,
          build_update_ops,
          build_no_ops,
      )
      return (update_mean_op, update_variance_op)
    else:
      return None