class_docstr: Implementation of the NeighborCacheClient with DynamicEmbedding.
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.
  
  Returns:
    A `Tensor` of shape [batch_size, config.embedding_dimension].
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.
  
      Returns:
        A `Tensor` of shape [batch_size, config.embedding_dimension].
      """
      return de_ops.dynamic_embedding_update(
          neighbor_ids,
          neighbor_state,
          self._config,
          self._key_feature_name,
          self._service_address,
          self._timeout_ms,
      )
  
file_path: neural-structured-learning/research/carls/dynamic_embedding_neighbor_cache.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import typing
  from research.carls import dynamic_embedding_config_pb2
  from research.carls import dynamic_embedding_ops
  from research.carls import neighbor_cache_client
label_desc: Use image node_id 3 for calling the DynamicEmbeddingNeighborCache obj's underlying member method code with example usage: obj.update(neighbor_ids, neighbor_state) and returns: de_ops