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 from_config(cls, config):
      return cls(
          configs.GraphNeighborConfig(**config.pop("neighbor_config")),
          **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 6 for calling the NeighborFeatures obj's underlying member method code with example usage: obj.from_config(cls, config) and returns: cls