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: Returns the config of the feature column.
  
  A FeatureColumn config is a Python dictionary (serializable) containing the
  configuration of a FeatureColumn. The same FeatureColumn can be
  reinstantiated later from this configuration.
  
  The config of a feature column does not include information about feature
  columns depending on it nor the FeatureColumn class name.
  
  Example with (de)serialization practices followed in this file:
  ```python
  class SerializationExampleFeatureColumn(
      FeatureColumn, collections.namedtuple(
          'SerializationExampleFeatureColumn',
          ('dimension', 'parent', 'dtype', 'normalizer_fn'))):
  
    def get_config(self):
      # Create a dict from the namedtuple.
      # Python attribute literals can be directly copied from / to the config.
      # For example 'dimension', assuming it is an integer literal.
      config = dict(zip(self._fields, self))
  
      # (De)serialization of parent FeatureColumns should use the provided
      # (de)serialize_feature_column() methods that take care of de-duping.
      config['parent'] = serialize_feature_column(self.parent)
  
      # Many objects provide custom (de)serialization e.g: for tf.DType
      # tf.DType.name, tf.as_dtype() can be used.
      config['dtype'] = self.dtype.name
  
      # Non-trivial dependencies should be Keras-(de)serializable.
      config['normalizer_fn'] = generic_utils.serialize_keras_object(
          self.normalizer_fn)
  
      return config
  
    @classmethod
    def from_config(cls, config, custom_objects=None, columns_by_name=None):
      # This should do the inverse transform from `get_config` and construct
      # the namedtuple.
      kwargs = config.copy()
      kwargs['parent'] = deserialize_feature_column(
          config['parent'], custom_objects, columns_by_name)
      kwargs['dtype'] = dtypes.as_dtype(config['dtype'])
      kwargs['normalizer_fn'] = generic_utils.deserialize_keras_object(
        config['normalizer_fn'], custom_objects=custom_objects)
      return cls(**kwargs)
  
  ```
  Returns:
    A serializable Dict that can be used to deserialize the object with
    from_config.
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code: def get_config(self):
      """Returns the config of the feature column.
  
      A FeatureColumn config is a Python dictionary (serializable) containing the
      configuration of a FeatureColumn. The same FeatureColumn can be
      reinstantiated later from this configuration.
  
      The config of a feature column does not include information about feature
      columns depending on it nor the FeatureColumn class name.
  
      Example with (de)serialization practices followed in this file:
      ```python
      class SerializationExampleFeatureColumn(
          FeatureColumn, collections.namedtuple(
              'SerializationExampleFeatureColumn',
              ('dimension', 'parent', 'dtype', 'normalizer_fn'))):
  
        def get_config(self):
          # Create a dict from the namedtuple.
          # Python attribute literals can be directly copied from / to the config.
          # For example 'dimension', assuming it is an integer literal.
          config = dict(zip(self._fields, self))
  
          # (De)serialization of parent FeatureColumns should use the provided
          # (de)serialize_feature_column() methods that take care of de-duping.
          config['parent'] = serialize_feature_column(self.parent)
  
          # Many objects provide custom (de)serialization e.g: for tf.DType
          # tf.DType.name, tf.as_dtype() can be used.
          config['dtype'] = self.dtype.name
  
          # Non-trivial dependencies should be Keras-(de)serializable.
          config['normalizer_fn'] = generic_utils.serialize_keras_object(
              self.normalizer_fn)
  
          return config
  
        @classmethod
        def from_config(cls, config, custom_objects=None, columns_by_name=None):
          # This should do the inverse transform from `get_config` and construct
          # the namedtuple.
          kwargs = config.copy()
          kwargs['parent'] = deserialize_feature_column(
              config['parent'], custom_objects, columns_by_name)
          kwargs['dtype'] = dtypes.as_dtype(config['dtype'])
          kwargs['normalizer_fn'] = generic_utils.deserialize_keras_object(
            config['normalizer_fn'], custom_objects=custom_objects)
          return cls(**kwargs)
  
      ```
      Returns:
        A serializable Dict that can be used to deserialize the object with
        from_config.
      """
      return self._get_config()
  
file_path: tensorflow-upstream/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 9 for calling the FeatureColumn obj's underlying member method code with example usage: obj.get_config() and returns: self