class_docstr: Base class for ranking networks in Keras.
class_docstr_tok: ["Base","class","for","ranking","networks","in","Keras","."]
docstr: Creates a RankingNetwork layer from its config.
  
  Args:
    config: (dict) Layer configuration, typically the output of `get_config`.
    custom_objects: (dict) Optional dictionary mapping names to custom classes
      or functions to be considered during deserialization.
  
  Returns:
    A RankingNetwork layer.
docstr_tok: ["Creates","a","RankingNetwork","layer","from","its","config",".","Args",":","config",":","(","dict",")","Layer","configuration",",","typically","the","output","of","`","get_config","`",".","custom_objects",":","(","dict",")","Optional","dictionary","mapping","names","to","custom","classes","or","functions","to","be","considered","during","deserialization",".","Returns",":","A","RankingNetwork","layer","."]
code: def from_config(cls, config, custom_objects=None):
      """Creates a RankingNetwork layer from its config.
  
      Args:
        config: (dict) Layer configuration, typically the output of `get_config`.
        custom_objects: (dict) Optional dictionary mapping names to custom classes
          or functions to be considered during deserialization.
  
      Returns:
        A RankingNetwork layer.
      """
      config_cp = config.copy()
      config_cp[
          "context_feature_columns"
      ] = feature.deserialize_feature_columns(
          config_cp["context_feature_columns"],
          custom_objects=custom_objects,
      )
      config_cp[
          "example_feature_columns"
      ] = feature.deserialize_feature_columns(
          config_cp["example_feature_columns"],
          custom_objects=custom_objects,
      )
  
      return cls(**config_cp)
  
imports: import abc
  import six
  import tensorflow.compat.v2
  from tensorflow_ranking.python import utils
  from tensorflow_ranking.python.keras import feature
label_desc: Use image node_id 8 for calling the RankingNetwork obj's underlying member method code with example usage: obj.from_config(cls, config, custom_objects) and returns: cls