code: def k_means(self, io, k, dry_run):
      logger.info(f"k_means {k} {self}")
      kmeans_vectors = DatasetDescriptor(
          tablename=f"{self.get_filename()}kmeans_{k}.npy"
      )
      meta_filename = kmeans_vectors.tablename + ".json"
      if not io.file_exist(
          kmeans_vectors.tablename
      ) or not io.file_exist(meta_filename):
          if dry_run:
              return None, None, kmeans_vectors.tablename
          x = io.get_dataset(self)
          kmeans = faiss.Kmeans(d=x.shape[1], k=k, gpu=True)
          _, t, _ = timer("k_means", lambda: kmeans.train(x))
          io.write_nparray(kmeans.centroids, kmeans_vectors.tablename)
          io.write_json({"k_means_time": t}, meta_filename)
      else:
          t = io.read_json(meta_filename)["k_means_time"]
      return kmeans_vectors, t, None
  
file_path: faiss/benchs/bench_fw/descriptors.py
imports: from dataclasses import dataclass
  import logging
  from typing import Any, Dict, List, Optional
  import faiss
  from .utils import timer
label_desc: Use image node_id 3 for calling the DatasetDescriptor obj's underlying member method code with example usage: obj.k_means(io, k, dry_run) and returns: kmeans_vectors,  t,  None, None,  None,  kmeans_vectors