code: def _centers_dense(X, labels, n_clusters):
      n_samples = X.shape[0]
      n_features = X.shape[1]
      centers = np.zeros((n_clusters, n_features), dtype=np.float64)
  
      for i in range(n_samples):
          for j in range(n_features):
              centers[labels[i], j] += X[i, j]
  
      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 10 for calling a global function with example usage: _centers_dense(X, labels, n_clusters) and returns: centers