class_docstr: Find closest anchors using :class:`scipy.sparse.csgraph`.
class_docstr_tok: ["Find","closest","anchors","using",":","class",":","`","scipy.sparse.csgraph","`","."]
docstr: Return the sorted top-k indices using argsort.
  
  Its complexity is $O(m * n log n)$.
  
  :param array: shape: (n, m)
      the array
  :param k:
      the value of $k$
  
  :return: shape: (m, k)
      the indices of the $k$ smallest values sorted in descending order
docstr_tok: ["Return","the","sorted","top-k","indices","using","argsort",".","Its","complexity","is","$","O","(","m","*","n","log","n",")","$",".",":","param","array",":","shape",":","(","n",",","m",")","the","array",":","param","k",":","the","value","of","$","k","$",":","return",":","shape",":","(","m",",","k",")","the","indices","of","the","$","k","$","smallest","values","sorted","in","descending","order"]
code: def topk_argsort(array: numpy.ndarray, k: int) -> numpy.ndarray:
      """Return the sorted top-k indices using argsort.
  
      Its complexity is $O(m * n log n)$.
  
      :param array: shape: (n, m)
          the array
      :param k:
          the value of $k$
  
      :return: shape: (m, k)
          the indices of the $k$ smallest values sorted in descending order
      """
      return numpy.argsort(array, axis=0)[:k, :]
  
imports: import logging
  from abc import ABC, abstractmethod
  from typing import Iterable, Optional
  import numpy
  import scipy.sparse
  import torch
  from class_resolver import ClassResolver, OptionalKwargs
  from torch_ppr import page_rank
  from torch_ppr.utils import edge_index_to_sparse_matrix, prepare_page_rank_adjacency, prepare_x0
  from tqdm.auto import tqdm
  from .utils import ensure_num_entities
  from ...typing import DeviceHint
  from ...utils import ExtraReprMixin, format_relative_comparison, resolve_device
label_desc: Use image node_id 1 for calling the CSGraphAnchorSearcher obj's underlying member method code with example usage: obj.topk_argsort(array, k) and returns: numpy