class_docstr: Top-Level Indexer.
  
  Provides an interface for interacting with FAISS Indexes.
docstr: Deserialize index from file.
  
  :param file:
      input file
  :param emb_path:
      optional path to embeddings
docstr_tok: ["Deserialize","index","from","file",".",":","param","file",":","input","file",":","param","emb_path",":","optional","path","to","embeddings"]
code: def deserialize_from(self, file: str, emb_path: Optional[str] = None):
      """
      Deserialize index from file.
  
      :param file:
          input file
      :param emb_path:
          optional path to embeddings
      """
      logging.info(f"Loading index from {file}")
  
      if os.path.isdir(file):
          index_file = os.path.join(file, "index")
          meta_file = os.path.join(file, "index_meta")
      elif not file.endswith(".index"):
          index_file = f"{file}.index"
          meta_file = f"{file}.index_meta"
      else:
          index_file = file
          meta_file = f"{index_file}_meta"
  
      self.index = self.faiss.read_index(index_file)
      logging.info(
          f"Loaded index of type {self.index} and size {self.index.ntotal}"
      )
  
      if os.path.exists(meta_file):
          self.index_id_to_db_id = torch.load(meta_file)
      else:
          index_dir = (
              os.path.split(file)[0] if emb_path is None else emb_path
          )
          if not os.path.isdir(index_dir):
              # if emb_path has the embeddings name in there, need to split.
              index_dir = os.path.split(index_dir)[0]
          meta_files = [
              f for f in os.listdir(index_dir) if f.startswith("ids_")
          ]
          meta_files = sorted(
              meta_files, key=lambda x: int(x.split("_")[-1])
          )
          for f in meta_files:
              ids = torch.load(os.path.join(index_dir, f))
              self.index_id_to_db_id.extend(ids)
          torch.save(self.index_id_to_db_id, meta_file)
      assert (
          len(self.index_id_to_db_id) == self.index.ntotal
      ), "Deserialized index_id_to_db_id should match faiss index size "
      f"{len(self.index_id_to_db_id)} != {self.index.ntotal}"
  
imports: from parlai.core.build_data import modelzoo_path
  from parlai.core.opt import Opt
  import parlai.utils.logging
  from abc import ABC, abstractmethod
  import math
  import numpy
  import os
  import random
  import time
  import torch
  import torch.cuda
  from typing import List, Tuple, Optional
  from parlai.agents.rag.args import WIKIPEDIA_COMPRESSED_INDEX, WIKIPEDIA_EXACT_INDEX
label_desc: Use image node_id 6 for calling the BaseIndexer obj's underlying member method code with example usage: obj.deserialize_from(file, emb_path) without return types