code: def pwa(labels, predictions, weights=None, topn=None, name=None):
      """Computes PWA.
  
      Args:
        labels: A `Tensor` of the same shape as `predictions`. A value >= 1 means a
          relevant example.
        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.
        topn: An integer cutoff specifying how many examples to consider for this
          metric. If None, the whole list is considered.
        name: A string used as the name for this metric.
  
      Returns:
        A metric for the Position Weighted Average of the batch.
      """
      metric = metrics_impl.PWAMetric(name, topn)
      with tf.compat.v1.name_scope(
          metric.name, "pwa", (labels, predictions, weights)
      ):
          pwa_value, per_list_weights = metric.compute(
              labels, predictions, weights
          )
          return tf.compat.v1.metrics.mean(pwa_value, 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 16 for calling a global function with example usage: pwa(labels, predictions, weights, topn, name) and returns: tf