class_docstr: Random retrieval model.
  
  Gets k random indices from the dataset regardless of the query.
code: def search(
      self,
      df,
      backend: "Backend",
      k: int = 10,
      return_data: bool = False,
  ) -> Union[List[int], List[Dict[str, Any]]]:
      results = []
      for _ in tqdm(range(len(df))):
          indices = np.random.choice(self.index, k, replace=False)
  
          if return_data:
              result = self.index_data.iloc[indices].to_dict(
                  orient="records"
              )
          else:
              result = indices
          results.append(result)
      return results
  
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 3 for calling the RandomRetrieval obj's underlying member method code with example usage: obj.search(df, backend, k, return_data) and returns: results