code: def get_km_model(
      n_clusters,
      init,
      max_iter,
      batch_size,
      tol,
      max_no_improvement,
      n_init,
      reassignment_ratio,
      seed,
  ):
      return MiniBatchKMeans(
          n_clusters=n_clusters,
          init=init,
          max_iter=max_iter,
          batch_size=batch_size,
          verbose=1,
          compute_labels=False,
          tol=tol,
          max_no_improvement=max_no_improvement,
          init_size=None,
          n_init=n_init,
          reassignment_ratio=reassignment_ratio,
          random_state=seed,
      )
  
file_path: espnet/egs2/libritts/tts1/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 2 for calling a global function with example usage: get_km_model(n_clusters, init, max_iter, batch_size, tol, max_no_improvement, n_init, reassignment_ratio, seed) and returns: MiniBatchKMeans