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.
docstr: Decides if the feature specified by `name` should be a model input.
docstr_tok: ["Decides","if","the","feature","specified","by","`","name","`","should","be","a","model","input","."]
code: def _include_feature(self, name):
      """Decides if the feature specified by `name` should be a model input."""
      return (
          self._feature_names is None
          or name in self._feature_names
          or name.startswith(self._neighbor_config.prefix)
      )
  
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 3 for calling the NeighborFeatures obj's underlying member method code with example usage: obj._include_feature(name) and returns: unknown