code: def learn_kmeans(
      rspecifier,
      in_filetype,
      km_path,
      n_clusters,
      seed,
      percent,
      init,
      max_iter,
      batch_size,
      tol,
      n_init,
      reassignment_ratio,
      max_no_improvement,
  ):
      np.random.seed(seed)
      feat = load_feature(rspecifier, in_filetype, percent)
      km_model = get_km_model(
          n_clusters,
          init,
          max_iter,
          batch_size,
          tol,
          max_no_improvement,
          n_init,
          reassignment_ratio,
          seed,
      )
      km_model.fit(feat)
      joblib.dump(km_model, km_path)
  
      inertia = -km_model.score(feat) / len(feat)
      logger.info("total intertia: %.5f", inertia)
      logger.info("finished successfully")
  
file_path: espnet/egs2/hub4_spanish/asr1/pyscripts/utils/learn_kmeans.py
imports: import argparse
  import logging
  import os
  import random
  import sys
  import joblib
  import numpy
  from sklearn.cluster import MiniBatchKMeans
  from espnet.utils.cli_readers import file_reader_helper
label_desc: Use image node_id 5 for calling a global function with example usage: learn_kmeans(rspecifier, in_filetype, km_path, n_clusters, seed, percent, init, max_iter, batch_size, tol, n_init, reassignment_ratio, max_no_improvement) without return types