class_docstr: Implementation of the NeighborCacheClient with DynamicEmbedding.
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.
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.
      """
      return de_ops.dynamic_embedding_lookup(
          neighbor_ids,
          self._config,
          self._key_feature_name,
          self._service_address,
          skip_gradient_update=True,
          timeout_ms=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 2 for calling the DynamicEmbeddingNeighborCache obj's underlying member method code with example usage: obj.lookup(neighbor_ids) and returns: de_ops