code: def deserialize_feature_columns(
      feature_column_configs, custom_objects=None
  ):
      """Deserializes dict of feature column configs.
  
      Args:
        feature_column_configs: (dict) A dict mapping feature names to Keras feature
          column config, could be generated using `serialize_feature_columns`.
        custom_objects: (dict) Optional dictionary mapping names to custom classes
          or functions to be considered during deserialization.
  
      Returns:
        A dict mapping feature names to feature columns.
      """
      if not feature_column_configs:
          return {}
  
      feature_columns = {}
      sorted_fc_configs = sorted(six.iteritems(feature_column_configs))
      sorted_names, sorted_configs = zip(*sorted_fc_configs)
  
      sorted_feature_columns = fc.deserialize_feature_columns(
          sorted_configs, custom_objects=custom_objects
      )
  
      feature_columns = dict(zip(sorted_names, sorted_feature_columns))
      return feature_columns
  
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 2 for calling a global function with example usage: deserialize_feature_columns(feature_column_configs, custom_objects) and returns: feature_columns, dict