code: def default_keras_metrics(**kwargs) -> List[tf.keras.metrics.Metric]:
      """Returns a list of ranking metrics.
  
      Args:
        **kwargs: Additional kwargs to pass to each keras metric.
  
      Returns:
        A list of metrics of type `tf.keras.metrics.Metric`.
      """
      list_kwargs = [
          dict(
              key="ndcg",
              topn=topn,
              name="metric/ndcg_{}".format(topn),
              **kwargs,
          )
          for topn in [1, 3, 5, 10]
      ] + [
          dict(key="arp", name="metric/arp", **kwargs),
          dict(
              key="ordered_pair_accuracy",
              name="metric/ordered_pair_accuracy",
              **kwargs,
          ),
          dict(key="mrr", name="metric/mrr", **kwargs),
          dict(key="precision", name="metric/precision", **kwargs),
          dict(key="map", name="metric/map", **kwargs),
          dict(key="dcg", name="metric/dcg", **kwargs),
          dict(key="ndcg", name="metric/ndcg", **kwargs),
      ]
      return [get(**kwargs) for kwargs in list_kwargs]
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from typing import Any, Dict, List, Optional
  import tensorflow.compat.v2
  from tensorflow_ranking.python import metrics_impl
  from tensorflow_ranking.python.keras import utils
label_desc: Use image node_id 2 for calling a global function with example usage: default_keras_metrics() and returns: unknown