code: def fetch_kmeans_model(
      n_clusters,
      init,
      max_iter,
      batch_size,
      tol,
      max_no_improvement,
      n_init,
      reassignment_ratio,
      random_state,
  ):
      """Return a k-means clustering model with specified parameters."""
      return MiniBatchKMeans(
          n_clusters=n_clusters,
          init=init,
          max_iter=max_iter,
          batch_size=batch_size,
          tol=tol,
          max_no_improvement=max_no_improvement,
          n_init=n_init,
          reassignment_ratio=reassignment_ratio,
          random_state=random_state,
          verbose=1,
          compute_labels=True,
          init_size=None,
      )
  
imports: import sys
  import logging
  import time
  import random
  import itertools
  import pathlib
  import joblib
  import torch
  import torchaudio
  import tqdm
  import numpy
  from sklearn.cluster import MiniBatchKMeans
  from hyperpyyaml import load_hyperpyyaml
  import speechbrain
  from ljspeech_prepare import prepare_ljspeech
  from speechbrain.lobes.models.huggingface_wav2vec import HuggingFaceWav2Vec2
label_desc: Use image node_id 6 for calling a global function with example usage: fetch_kmeans_model(n_clusters, init, max_iter, batch_size, tol, max_no_improvement, n_init, reassignment_ratio, random_state) and returns: MiniBatchKMeans