class_docstr: Random retrieval model.
  
  Gets k random indices from the dataset regardless of the query.
code: def create_dataset_index(
      self,
      df: pd.DataFrame,
      backend: "Backend",
      columns_to_index: Optional[List[str]] = None,
  ):
      self.index = np.array(range(len(df)))
      self.index_data = df
  
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 2 for calling the RandomRetrieval obj's underlying member method code with example usage: obj.create_dataset_index(df, backend, columns_to_index) without return types