class_docstr: Abstract client class to manage cached state for neighbor examples.
  
  This abstract class will be implemented as a client to lookup and update
  values from a cache service.
docstr: Initializes the `NeighborCacheClient` object.
  
  Args:
    key_feature_name: feature name of the key in the input `tf.Example`
      instances whose value contains neighbor IDs.
docstr_tok: ["Initializes","the","`","NeighborCacheClient","`","object",".","Args",":","key_feature_name",":","feature","name","of","the","key","in","the","input","`","tf.Example","`","instances","whose","value","contains","neighbor","IDs","."]
code: def __init__(self, key_feature_name):
      """Initializes the `NeighborCacheClient` object.
  
      Args:
        key_feature_name: feature name of the key in the input `tf.Example`
          instances whose value contains neighbor IDs.
      """
      self._key_feature_name = key_feature_name
  
file_path: neural-structured-learning/research/carls/neighbor_cache_client.py
imports: import abc
  import six
label_desc: Use image node_id 1 to create a new NeighborCacheClient object from inherited base classes: object with example: obj = NeighborCacheClient(key_feature_name)