code: def filter_top_k_numpy(data, k, replace=None, ascending=False):
      idxes = np.argpartition(data, -k)[-k:]
  
      if replace:
          replace_idxes = np.argpartition(data, len(data) - k)[
              : len(data) - k
          ]
          _data = data.copy()
          _data[replace_idxes] = replace
          return _data, idxes
  
      if not ascending:
          idxes = idxes[::-1]
  
      return data[tuple([idxes])], idxes
  
imports: import numpy
label_desc: Use image node_id 3 for calling a global function with example usage: filter_top_k_numpy(data, k, replace, ascending) and returns: data,  idxes, _data,  idxes