class_docstr: A layer to unpack a dictionary of sample features and neighbor features.
  
  Missing neighbor inputs will also be created for the functional API.
code: def get_config(self):
      config = super(NeighborFeatures, self).get_config()
      config["neighbor_config"] = attr.asdict(self._neighbor_config)
      config["feature_names"] = (
          list(self._feature_names)
          if self._feature_names is not None
          else None
      )
      return config
  
file_path: neural-structured-learning/neural_structured_learning/keras/layers/neighbor_features.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import attr
  import neural_structured_learning.configs
  from neural_structured_learning.lib import utils
  import tensorflow
label_desc: Use image node_id 5 for calling the NeighborFeatures obj's underlying member method code with example usage: obj.get_config() and returns: config