code: def __call__(self, prediction, gt, sample):
      indices = sample["indices"]
      b, n = tf.shape(prediction)[0], tf.shape(prediction)[-1]
      mask = tf.tile(
          tf.expand_dims(sample["valid_boxes_mask"], axis=-1), (1, 1, n)
      )
      mask = tf.cast(mask, tf.float32)
      if self.sparse:
          prediction_centers = prediction
          # permutation = tf.argsort(indices)
          # gt = tf.gather(gt, permutation, batch_dims=1)
      else:
          prediction_centers = tf.gather(
              tf.reshape(prediction, [b, -1, n]), indices, batch_dims=1
          )
      loss = tf.math.abs((gt * mask) - (prediction_centers * mask))
      if self.reduce_batch:
          loss = tf.reduce_sum(loss) / (tf.reduce_sum(mask) + 1e-4)
      else:
          loss = tf.reduce_sum(loss, axis=[1, 2]) / (
              tf.reduce_sum(mask, axis=[1, 2]) + 1e-4
          )
      return loss
  
label_desc: Use image node_id 2 for calling the RegL1Loss obj's underlying member method code with example usage: obj.__call__(prediction, gt, sample) and returns: loss