def get_config(self):
config = {<EOL>'<STR_LIT>': self.units,<EOL>'<STR_LIT>': (tf.keras.activations.serialize(self.activation)<EOL>if self.activation else None),<EOL>'<STR_LIT>':<EOL>tf.keras.initializers.serialize(self.activity_regularizer),<EOL>}<EOL>function_keys = [<EOL>'<STR_LIT>',<EOL>'<STR_LIT>',<EOL>'<STR_LIT>',<EOL>'<STR_LIT>',<EOL>'<STR_LIT>',<EOL>'<STR_LIT>',<EOL>'<STR_LIT>',<EOL>'<STR_LIT>',<EOL>]<EOL>for function_key in function_keys:<EOL><INDENT>function = getattr(self, function_key)<EOL>if function is None:<EOL><INDENT>function_name = None<EOL>function_type = None<EOL><DEDENT>else:<EOL><INDENT>function_name, function_type = tfp_layers_util.serialize_function(<EOL>function)<EOL><DEDENT>config[function_key] = function_name<EOL>config[function_key + '<STR_LIT>'] = function_type<EOL><DEDENT>base_config = super(_DenseVariational, self).get_config()<EOL>return dict(list(base_config.items()) + list(config.items()))<EOL>
Returns the config of the layer.

        A layer config is a Python dictionary (serializable) containing the
        configuration of a layer. The same layer can be reinstantiated later
        (without its trained weights) from this configuration.

        Returns:
          config: A Python dictionary of class keyword arguments and their
            serialized values.