code: def mean_average_precision(
      labels, predictions, weights=None, topn=None, name=None
  ):
      """Computes mean average precision (MAP).
  
      The implementation of MAP is based on Equation (1.7) in the following:
      Liu, T-Y "Learning to Rank for Information Retrieval" found at
      https://www.nowpublishers.com/article/DownloadSummary/INR-016
  
      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: 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 mean average precision.
      """
      metric = metrics_impl.MeanAveragePrecisionMetric(name, topn)
      with tf.compat.v1.name_scope(
          metric.name,
          "mean_average_precision",
          (labels, predictions, weights),
      ):
          # TODO: Add mask argument for metric.compute() call
          per_list_map, per_list_weights = metric.compute(
              labels, predictions, weights
          )
      return tf.compat.v1.metrics.mean(per_list_map, 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 7 for calling a global function with example usage: mean_average_precision(labels, predictions, weights, topn, name) and returns: tf