class_docstr: A wrapper around an instance of [`~datasets.Datasets`]. The dataset and the index are both loaded from the
  indicated paths on disk.
  
  Args:
      vector_size (`int`): the dimension of the passages embeddings used by the index
      dataset_path (`str`):
          The path to the serialized dataset on disk. The dataset should have 3 columns: title (str), text (str) and
          embeddings (arrays of dimension vector_size)
      index_path (`str`)
          The path to the serialized faiss index on disk.
code: def init_index(self):
      if not self.is_initialized():
          logger.info(f"Loading index from {self.index_path}")
          self.dataset.load_faiss_index(
              "embeddings", file=self.index_path
          )
          self._index_initialized = True
  
imports: import os
  import pickle
  import time
  from typing import Iterable, List, Optional, Tuple
  import numpy
  from ...tokenization_utils import PreTrainedTokenizer
  from ...tokenization_utils_base import BatchEncoding
  from ...utils import cached_file, is_datasets_available, is_faiss_available, logging, requires_backends
  from .configuration_rag import RagConfig
  from .tokenization_rag import RagTokenizer
label_desc: Use image node_id 3 for calling the CustomHFIndex obj's underlying member method code with example usage: obj.init_index() without return types