class_docstr: Random retrieval model.
  
  Gets k random indices from the dataset regardless of the query.
code: def save_index(self, name: str, cache_directory: str):
      index_file_path = os.path.join(cache_directory, name + ".index")
      # open file to prevent using the .npy extension
      # https://numpy.org/doc/stable/reference/generated/numpy.save.html
      with open(index_file_path, "wb") as f:
          np.save(f, self.index)
  
      index_data_file_path = os.path.join(
          cache_directory, name + "_data.csv"
      )
      self.index_data.to_csv(index_data_file_path, index=False)
  
imports: import hashlib
  import json
  import os
  from abc import ABC, abstractmethod
  from typing import Any, Callable, Dict, List, Optional, Type, TYPE_CHECKING, Union
  import numpy
  import pandas
  from tqdm import tqdm
  from ludwig.vector_index import FAISS, get_vector_index_cls
  from ludwig.vector_index.base import VectorIndex
  from ludwig.utils.batch_size_tuner import BatchSizeEvaluator
  from ludwig.utils.torch_utils import get_torch_device
label_desc: Use image node_id 4 for calling the RandomRetrieval obj's underlying member method code with example usage: obj.save_index(name, cache_directory) without return types