class_docstr: Represents a feature column abstraction.
  
  WARNING: Do not subclass this layer unless you know what you are doing:
  the API is subject to future changes.
  
  To distinguish between the concept of a feature family and a specific binary
  feature within a family, we refer to a feature family like "country" as a
  feature column. For example, we can have a feature in a `tf.Example` format:
    {key: "country",  value: [ "US" ]}
  In this example the value of feature is "US" and "country" refers to the
  column of the feature.
  
  This class is an abstract class. Users should not create instances of this.
docstr: Creates a FeatureColumn from its config.
  
  This method should be the reverse of `get_config`, capable of instantiating
  the same FeatureColumn from the config dictionary. See `get_config` for an
  example of common (de)serialization practices followed in this file.
  
  TODO(b/118939620): This is a private method until consensus is reached on
  supporting object deserialization deduping within Keras.
  
  Args:
    config: A Dict config acquired with `get_config`.
    custom_objects: Optional dictionary mapping names (strings) to custom
      classes or functions to be considered during deserialization.
    columns_by_name: A Dict[String, FeatureColumn] of existing columns in
      order to avoid duplication. Should be passed to any calls to
      deserialize_feature_column().
  
  Returns:
    A FeatureColumn for the input config.
docstr_tok: ["Creates","a","FeatureColumn","from","its","config",".","This","method","should","be","the","reverse","of","`","get_config","`",",","capable","of","instantiating","the","same","FeatureColumn","from","the","config","dictionary",".","See","`","get_config","`","for","an","example","of","common","(","de",")","serialization","practices","followed","in","this","file",".","TODO","(","b\/118939620",")",":","This","is","a","private","method","until","consensus","is","reached","on","supporting","object","deserialization","deduping","within","Keras",".","Args",":","config",":","A","Dict","config","acquired","with","`","get_config","`",".","custom_objects",":","Optional","dictionary","mapping","names","(","strings",")","to","custom","classes","or","functions","to","be","considered","during","deserialization",".","columns_by_name",":","A","Dict","[","String",",","FeatureColumn","]","of","existing","columns","in","order","to","avoid","duplication",".","Should","be","passed","to","any","calls","to","deserialize_feature_column","(",")",".","Returns",":","A","FeatureColumn","for","the","input","config","."]
code: def from_config(
      cls, config, custom_objects=None, columns_by_name=None
  ):
      """Creates a FeatureColumn from its config.
  
      This method should be the reverse of `get_config`, capable of instantiating
      the same FeatureColumn from the config dictionary. See `get_config` for an
      example of common (de)serialization practices followed in this file.
  
      TODO(b/118939620): This is a private method until consensus is reached on
      supporting object deserialization deduping within Keras.
  
      Args:
        config: A Dict config acquired with `get_config`.
        custom_objects: Optional dictionary mapping names (strings) to custom
          classes or functions to be considered during deserialization.
        columns_by_name: A Dict[String, FeatureColumn] of existing columns in
          order to avoid duplication. Should be passed to any calls to
          deserialize_feature_column().
  
      Returns:
        A FeatureColumn for the input config.
      """
      return cls._from_config(config, custom_objects, columns_by_name)
  
file_path: tensorflow/tensorflow/python/feature_column/feature_column_v2_types.py
imports: import abc
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 11 for calling the FeatureColumn obj's underlying member method code with example usage: obj.from_config(cls, config, custom_objects, columns_by_name) and returns: cls