code: def init_random(X, n_clusters, random_state):
      """K-means initialization using randomly chosen points"""
      logger.info("Initializing randomly")
      idx = sorted(draw_seed(random_state, 0, len(X), size=n_clusters))
      centers = X[idx].compute()
      return centers
  
imports: import logging
  from multiprocessing import cpu_count
  from numbers import Integral
  import dask.array
  import dask.dataframe
  import numpy
  import pandas
  import sklearn.cluster
  from dask import compute
  from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.utils.extmath import squared_norm
  from sklearn.utils.validation import check_is_fitted
  from .._compat import blockwise
  from .._utils import draw_seed
  from ..metrics import euclidean_distances, pairwise_distances, pairwise_distances_argmin_min
  from ..utils import _timed, _timer, check_array, row_norms
  from ._compat import _kmeans_plusplus
  import numba
label_desc: Use image node_id 5 for calling a global function with example usage: init_random(X, n_clusters, random_state) and returns: centers