code: def serialize_feature_columns(feature_columns):
      """Serializes feature columns to a dict of class name and config.
  
      This serialization is required to support for SavedModel using model.save()
      in Keras. The serialization is similar to that of
      `tf.keras.layers.DenseFeatures`, which also has feature columns in it's
      constructor.
  
      Args:
        feature_columns: (dict) feature names to feature columns.
  
      Returns:
        A dict mapping feature names to serialized feature columns.
      """
      if not feature_columns:
          return {}
  
      feature_column_configs = {}
      sorted_name_to_feature_columns = sorted(
          six.iteritems(feature_columns)
      )
      sorted_names, sorted_feature_columns = zip(
          *sorted_name_to_feature_columns
      )
      sorted_configs = fc.serialize_feature_columns(
          sorted_feature_columns
      )
      feature_column_configs = dict(zip(sorted_names, sorted_configs))
      return feature_column_configs
  
imports: import six
  import tensorflow.compat.v2
  from tensorflow.python.feature_column import feature_column_lib
  from tensorflow_ranking.python import utils
label_desc: Use image node_id 1 for calling a global function with example usage: serialize_feature_columns(feature_columns) and returns: feature_column_configs, dict