code: def average_relevance_position(
      labels, predictions, weights=None, name=None
  ):
      """Computes average relevance position (ARP).
  
      This can also be named as average_relevance_rank, but this can be confusing
      with mean_reciprocal_rank in acronyms. This name is more distinguishing and
      has been used historically for binary relevance as average_click_position.
  
      Args:
        labels: A `Tensor` of the same shape as `predictions`.
        predictions: A `Tensor` with shape [batch_size, list_size]. Each value is
          the ranking score of the corresponding example.
        weights: A `Tensor` of the same shape of predictions or [batch_size, 1]. The
          former case is per-example and the latter case is per-list.
        name: A string used as the name for this metric.
  
      Returns:
        A metric for the weighted average relevance position.
      """
      metric = metrics_impl.ARPMetric(name)
      with tf.compat.v1.name_scope(
          metric.name,
          "average_relevance_position",
          (labels, predictions, weights),
      ):
          # TODO: Add mask argument for metric.compute() call
          per_list_arp, per_list_weights = metric.compute(
              labels, predictions, weights
          )
      return tf.compat.v1.metrics.mean(per_list_arp, per_list_weights)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import inspect
  import tensorflow
  from tensorflow_ranking.python import metrics_impl
  from tensorflow_ranking.python import utils
label_desc: Use image node_id 4 for calling a global function with example usage: average_relevance_position(labels, predictions, weights, name) and returns: tf