code: def evaluate_cost(X, centers):
      # type: (da.Array, np.array) -> float
      # parallel for dask arrays
      return (pairwise_distances(X, centers).min(1) ** 2).sum()
  
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 7 for calling a global function with example usage: evaluate_cost(X, centers) and returns: unknown