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: Returns the key of an index, added to avoid collisions.
docstr_tok: ["Returns","the","key","of","an","index",",","added","to","avoid","collisions","."]
code: def _get_index_key(self, model_name, dataset_name, embedding_name):
      """Returns the key of an index, added to avoid collisions."""
      index_key = f"{dataset_name}:{model_name}:{embedding_name}"
      return index_key
  
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 2 for calling the Indexer obj's underlying member method code with example usage: obj._get_index_key(model_name, dataset_name, embedding_name) and returns: index_key