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: Updates the neighbor cache with the new state of neighbor examples.
  
  Args:
    neighbor_ids: a string Tensor of shape [batch_size] representing the ids
      of a neighborhood.
    neighbor_state: a Tensor of shape [batch_size, ...] representing newly
      computed neighbor state(e.g. embeddings, logits) that should be stored
      in the neighbor cache.
docstr_tok: ["Updates","the","neighbor","cache","with","the","new","state","of","neighbor","examples",".","Args",":","neighbor_ids",":","a","string","Tensor","of","shape","[","batch_size","]","representing","the","ids","of","a","neighborhood",".","neighbor_state",":","a","Tensor","of","shape","[","batch_size",",","...","]","representing","newly","computed","neighbor","state","(","e.g",".","embeddings",",","logits",")","that","should","be","stored","in","the","neighbor","cache","."]
code: def update(self, neighbor_ids, neighbor_state):
      """Updates the neighbor cache with the new state of neighbor examples.
  
      Args:
        neighbor_ids: a string Tensor of shape [batch_size] representing the ids
          of a neighborhood.
        neighbor_state: a Tensor of shape [batch_size, ...] representing newly
          computed neighbor state(e.g. embeddings, logits) that should be stored
          in the neighbor cache.
      """
      raise NotImplementedError
  
file_path: neural-structured-learning/research/carls/neighbor_cache_client.py
imports: import abc
  import six
label_desc: Use image node_id 3 for calling the NeighborCacheClient obj's underlying member method code with example usage: obj.update(neighbor_ids, neighbor_state) without return types