code: def precision_ia(
      labels, predictions, weights=None, topn=None, name=None
  ):
      """Computes Intent-Aware Precision as weighted average of relevant examples.
  
      Args:
        labels: A `Tensor` with shape [batch_size, list_size, subtopic_size]. A
          nonzero value means that the example covers the corresponding subtopic.
        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: A cutoff for how many examples to consider for this metric.
        name: A string used as the name for this metric.
  
      Returns:
        A metric for the weighted precision of the batch.
      """
      metric = metrics_impl.PrecisionIAMetric(name, topn)
      with tf.compat.v1.name_scope(
          metric.name, "precision_ia", (labels, predictions, weights)
      ):
          # TODO: Add mask argument for metric.compute() call
          precision_at_k, per_list_weights = metric.compute(
              labels, predictions, weights
          )
      return tf.compat.v1.metrics.mean(precision_at_k, 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 8 for calling a global function with example usage: precision_ia(labels, predictions, weights, topn, name) and returns: tf