docstr: Returns the standardized centers for the KMeans model.
  
  :examples:
  
  >>> from h2o.estimators import H2OKMeansEstimator
  >>> from h2o.grid.grid_search import H2OGridSearch
  >>> iris = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/iris/iris_train.csv")
  >>> hyper_parameters = {'k': [2,3,4], 'init': "random"}
  >>> gs = H2OGridSearch(H2OKMeansEstimator(), hyper_parameters)
  >>> gs.train(x=list(range(4)), training_frame=iris)
  >>> gs.centers_std()
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code: def centers_std(self):
      """Returns the standardized centers for the KMeans model.
  
      :examples:
  
      >>> from h2o.estimators import H2OKMeansEstimator
      >>> from h2o.grid.grid_search import H2OGridSearch
      >>> iris = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/iris/iris_train.csv")
      >>> hyper_parameters = {'k': [2,3,4], 'init': "random"}
      >>> gs = H2OGridSearch(H2OKMeansEstimator(), hyper_parameters)
      >>> gs.train(x=list(range(4)), training_frame=iris)
      >>> gs.centers_std()
      """
      return {
          model.model_id: model.centers_std() for model in self.models
      }
  
label_desc: Use image node_id 9 for calling the H2OClusteringGridSearch obj's underlying member method code with example usage: obj.centers_std() and returns: unknown