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: Looks up neighbor state in the neighbor cache.
  
  Args:
    neighbor_ids: a string Tensor of shape [batch_size] representing the ids
      of a neighborhood.
  
  Returns:
    Cached state of neighbor examples; `None` if it doesn't exist.
docstr_tok: ["Looks","up","neighbor","state","in","the","neighbor","cache",".","Args",":","neighbor_ids",":","a","string","Tensor","of","shape","[","batch_size","]","representing","the","ids","of","a","neighborhood",".","Returns",":","Cached","state","of","neighbor","examples",";","`","None","`","if","it","does","n't","exist","."]
code: def lookup(self, neighbor_ids):
      """Looks up neighbor state in the neighbor cache.
  
      Args:
        neighbor_ids: a string Tensor of shape [batch_size] representing the ids
          of a neighborhood.
  
      Returns:
        Cached state of neighbor examples; `None` if it doesn't exist.
      """
      # TODO(thunderfyc): provide decoding utils for None in batched input.
      raise NotImplementedError
  
file_path: neural-structured-learning/research/carls/neighbor_cache_client.py
imports: import abc
  import six
label_desc: Use image node_id 2 for calling the NeighborCacheClient obj's underlying member method code with example usage: obj.lookup(neighbor_ids) without return types