code: def scale_pca_rf_pipe():
      from h2o.transforms.preprocessing import H2OScaler
      from h2o.transforms.decomposition import H2OPCA
      from h2o.estimators.random_forest import H2ORandomForestEstimator
      from sklearn.pipeline import Pipeline
  
      iris = h2o.import_file(
          path=pyunit_utils.locate("smalldata/iris/iris_wheader.csv")
      )
  
      # build transformation pipeline using sklearn's Pipeline and H2O transforms
      pipe = Pipeline(
          [
              ("standardize", H2OScaler()),
              ("pca", H2OPCA(k=2)),
              ("rf", H2ORandomForestEstimator(seed=42, ntrees=50)),
          ]
      )
      pipe.fit(iris[:4], iris[4])
      pca = pipe.named_steps["pca"]
      assert pca.model_id == pca._delegate.model_id
      assert pca._model_json == pca._delegate._model_json
      pca.download_pojo()
  
imports: import sys
  import h2o
  from tests import pyunit_utils
label_desc: Use image node_id 1 for calling a global function with example usage: scale_pca_rf_pipe() without return types