code: def __call__(self, prediction, gt, sample):
      loss = tf.norm(prediction - gt, ord=self.ord, axis=3)
  
      if self.reduce_batch:
          loss = tf.reduce_mean(loss)
      else:
          loss = tf.reduce_mean(loss, axis=[1, 2])
      return loss
  
label_desc: Use image node_id 2 for calling the HuberLoss obj's underlying member method code with example usage: obj.__call__(prediction, gt, sample) and returns: loss