class_docstr: Class to build and search annoy indices.
  
  TODO(b/162421415): Split the managing of indices and the index logic.
  
  Upon instantiation of this class, it will first create empty indices for
  every model/embedding/dataset triple. It will then search for existing indices
  in the data directory and fill those. If the flag `initialize_new_indices` is
  set, it will fill the remaining indices by iterating over the data.
  During the saving process, both the index and a mapping from index-row to
  example are saved. These are used during the nearest neighbor lookup to return
  the closest example.
  
  Attributes:
     models: specification akin to the LIT server.
     datasets: specification akin to the LIT server.
     data_dir: path for (de-)serialization of indices.
     initialize_new_indices: whether to build new indices or simply load
       existing ones.
docstr: Iterate over indices and lookup tables and save them.
docstr_tok: ["Iterate","over","indices","and","lookup","tables","and","save","them","."]
code: def _save_lookups(self):
      """Iterate over indices and lookup tables and save them."""
      # Save the lookup tables.
      for lookup_key, lookup_table in self._example_lookup.items():
          file_path = self._get_lookup_path(lookup_key)
          with open(file_path, "wb") as f:
              pickle.dump(lookup_table, f, pickle.HIGHEST_PROTOCOL)
  
imports: import collections
  from collections.abc import Mapping
  import os
  import pickle
  from typing import Optional
  from absl import logging
  import annoy
  from lit_nlp.api import dataset
  from lit_nlp.api import model
  from lit_nlp.api import types
  from lit_nlp.lib import utils
label_desc: Use image node_id 11 for calling the Indexer obj's underlying member method code with example usage: obj._save_lookups() without return types