code: def get_model_kmeans():
      print("Importing benign.csv data...")
      benign_h2o = h2o.import_file(
          path=pyunit_utils.locate("smalldata/logreg/benign.csv")
      )
  
      benign_sci = np.genfromtxt(
          pyunit_utils.locate("smalldata/logreg/benign.csv"),
          delimiter=",",
      )
      # Impute missing values with column mean
      imp = SimpleImputer(missing_values=np.nan, strategy="mean")
      benign_sci = imp.fit_transform(benign_sci)
  
      for i in range(2, 7):
          km_h2o = H2OKMeansEstimator(k=i)
          km_h2o.train(
              x=list(range(benign_h2o.ncol)), training_frame=benign_h2o
          )
          km_h2o.show()
          model = h2o.get_model(km_h2o._id)
          model.show()
  
          km_sci = KMeans(n_clusters=i, init="k-means++", n_init=1)
          km_sci.fit(benign_sci)
          print("sckit centers")
          print(km_sci.cluster_centers_)
  
file_path: h2o-3/h2o-py/tests/testdir_algos/kmeans/pyunit_get_modelKmeans.py
imports: from builtins import range
  import sys
  import h2o
  from tests import pyunit_utils
  import numpy
  from sklearn.cluster import KMeans
  from sklearn.impute import SimpleImputer
  from h2o.estimators.kmeans import H2OKMeansEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: get_model_kmeans() without return types