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 load_from_disk(cls, vector_size, dataset_path, index_path):
      logger.info(f"Loading passages from {dataset_path}")
      if dataset_path is None or index_path is None:
          raise ValueError(
              "Please provide `dataset_path` and `index_path` after calling `dataset.save_to_disk(dataset_path)` "
              "and `dataset.get_index('embeddings').save(index_path)`."
          )
      dataset = load_from_disk(dataset_path)
      return cls(
          vector_size=vector_size,
          dataset=dataset,
          index_path=index_path,
      )
  
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 2 for calling the CustomHFIndex obj's underlying member method code with example usage: obj.load_from_disk(cls, vector_size, dataset_path, index_path) and returns: cls