class_docstr: Base class for indexing
class_docstr_tok: ["Base","class","for","indexing"]
docstr: Find the nearest examples indices to the query.
  
  Args:
      queries (`Union[List[str], np.ndarray]`): The queries as a list of strings if `column` is a text index or as a numpy array if `column` is a vector index.
      k (`int`): The number of examples to retrieve per query.
  
  Ouput:
      total_scores (`List[List[float]`): The retrieval scores of the retrieved examples per query.
      total_indices (`List[List[int]]`): The indices of the retrieved examples per query.
docstr_tok: ["Find","the","nearest","examples","indices","to","the","query",".","Args",":","queries","(","`","Union","[","List","[","str","]",",","np.ndarray","]","`",")",":","The","queries","as","a","list","of","strings","if","`","column","`","is","a","text","index","or","as","a","numpy","array","if","`","column","`","is","a","vector","index",".","k","(","`","int","`",")",":","The","number","of","examples","to","retrieve","per","query",".","Ouput",":","total_scores","(","`","List","[","List","[","float","]","`",")",":","The","retrieval","scores","of","the","retrieved","examples","per","query",".","total_indices","(","`","List","[","List","[","int","]","]","`",")",":","The","indices","of","the","retrieved","examples","per","query","."]
code: def search_batch(
      self, queries, k: int = 10, **kwargs
  ) -> BatchedSearchResults:
      """Find the nearest examples indices to the query.
  
      Args:
          queries (`Union[List[str], np.ndarray]`): The queries as a list of strings if `column` is a text index or as a numpy array if `column` is a vector index.
          k (`int`): The number of examples to retrieve per query.
  
      Ouput:
          total_scores (`List[List[float]`): The retrieval scores of the retrieved examples per query.
          total_indices (`List[List[int]]`): The indices of the retrieved examples per query.
      """
      total_scores, total_indices = [], []
      for query in queries:
          scores, indices = self.search(query, k)
          total_scores.append(scores)
          total_indices.append(indices)
      return BatchedSearchResults(total_scores, total_indices)
  
imports: import importlib.util
  import os
  import tempfile
  from pathlib import PurePath
  from typing import TYPE_CHECKING, Dict, List, NamedTuple, Optional, Union
  import fsspec
  import numpy
  from .utils import logging
  from .utils import tqdm
label_desc: Use image node_id 2 for calling the BaseIndex obj's underlying member method code with example usage: obj.search_batch(queries, k) and returns: BatchedSearchResults