code: def smooth_l1_distance(labels, preds, delta=1.0):
      
      with tf.variable_scope("smooth_l1"):
          return tf.reduce_sum(tf.losses.huber_loss(
              labels=labels,
              predictions=preds,
              delta=delta,
              loss_collection=None,
              reduction=tf.losses.Reduction.NONE
          ), axis=-1)
docs: Compute the smooth l1_distance.
  
      :param labels: A float tensor of shape [batch_size, ..., X] representing the labels.
      :param preds: A float tensor of shape [batch_size, ..., X] representing the predictions.
      :param delta: `float`, the point where the huber loss function changes from a quadratic to linear.
      :return: A float tensor of shape [batch_size, ...] representing the smooth l1 distance.