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: Loads a lookup table from index to data example.
docstr_tok: ["Loads","a","lookup","table","from","index","to","data","example","."]
code: def _load_lookup(self, lookup_key):
      """Loads a lookup table from index to data example."""
      lookup_path = self._get_lookup_path(lookup_key)
      if not os.path.exists(lookup_path):
          return {}
      with open(lookup_path, "rb") as f:
          return pickle.load(f)
  
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 7 for calling the Indexer obj's underlying member method code with example usage: obj._load_lookup(lookup_key) and returns: dict, pickle