code: def iris_h2o_vs_sciKmeans():
      # Connect to a pre-existing cluster
      # connect to localhost:54321
  
      iris_h2o = h2o.import_file(
          path=pyunit_utils.locate("smalldata/iris/iris.csv")
      )
      iris_sci = np.genfromtxt(
          pyunit_utils.locate("smalldata/iris/iris.csv"), delimiter=","
      )
      iris_sci = iris_sci[:, 0:4]
  
      s = [
          [4.9, 3.0, 1.4, 0.2],
          [5.6, 2.5, 3.9, 1.1],
          [6.5, 3.0, 5.2, 2.0],
      ]
  
      start = h2o.H2OFrame(s)
  
      h2o_km = H2OKMeansEstimator(
          k=3, user_points=start, standardize=False
      )
      h2o_km.train(x=list(range(4)), training_frame=iris_h2o)
  
      sci_km = KMeans(n_clusters=3, init=np.asarray(s), n_init=1)
      sci_km.fit(iris_sci)
  
      # Log.info("Cluster centers from H2O:")
      print("Cluster centers from H2O:")
      h2o_centers = h2o_km.centers()
      print(h2o_centers)
  
      # Log.info("Cluster centers from scikit:")
      print("Cluster centers from scikit:")
      sci_centers = sci_km.cluster_centers_.tolist()
  
      for hcenter, scenter in zip(h2o_centers, sci_centers):
          for hpoint, spoint in zip(hcenter, scenter):
              assert (
                  hpoint - spoint
              ) < 1e-10, "expected centers to be the same"
  
file_path: h2o-3/h2o-py/tests/testdir_algos/kmeans/pyunit_iris_h2o_vs_sciKmeans.py
imports: from builtins import zip
  from builtins import range
  import sys
  import h2o
  from tests import pyunit_utils
  import numpy
  from sklearn.cluster import KMeans
  from h2o.estimators.kmeans import H2OKMeansEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: iris_h2o_vs_sciKmeans() without return types