code: def init_pp(X, n_clusters, random_state):
      """K-means initialization using k-means++
  
      This uses scikit-learn's implementation.
      """
      x_squared_norms = row_norms(X, squared=True).compute()
      logger.info("Initializing with k-means++")
      with _timer(
          "initialization of %2d centers" % n_clusters, _logger=logger
      ):
          # XXX: Using a private scikit-learn API
          centers = _kmeans_plusplus(
              # sklearn 0.24 requires the compute. Unclear if earlier versions
              # just implicitly computed.
              X.compute(),
              n_clusters,
              random_state=random_state,
              x_squared_norms=x_squared_norms,
          )
          centers, _ = centers
  
      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 4 for calling a global function with example usage: init_pp(X, n_clusters, random_state) and returns: centers