code: def scale_svd_rf_pipe():
      from h2o.transforms.decomposition import H2OSVD
  
      print("Importing USArrests.csv data...")
      arrests = h2o.upload_file(
          pyunit_utils.locate("smalldata/pca_test/USArrests.csv")
      )
  
      # build  transformation pipeline using sklearn's Pipeline and H2O transforms
      pipe = Pipeline(
          [
              ("standardize", H2OScaler()),
              ("svd", H2OSVD(nv=3)),
              ("rf", H2ORandomForestEstimator(seed=42, ntrees=50)),
          ]
      )
  
      pipe.fit(arrests[1:], arrests[0])
      print(pipe)
  
imports: import sys
  import h2o
  from tests import pyunit_utils
  from h2o.transforms.preprocessing import H2OScaler
  from h2o.estimators.random_forest import H2ORandomForestEstimator
  from sklearn.pipeline import Pipeline
label_desc: Use image node_id 1 for calling a global function with example usage: scale_svd_rf_pipe() without return types