code: def scale_svd_rf_pipe_new_import():
      from h2o.estimators.svd import (
          H2OSingularValueDecompositionEstimator,
      )
  
      print("Importing USArrests.csv data...")
      arrests = h2o.upload_file(
          pyunit_utils.locate("smalldata/pca_test/USArrests.csv")
      )
  
      print("Compare with SVD")
  
      # build transformation pipeline using sklearn's Pipeline and H2O estimators without H2OSingularValueDecompositionEstimator.init_for_pipeline()
      # it should fail
      # Note: if you use SVD algo in a different combination of pipeline tasks, it could not fail, for example
      #     if you comment line with H2ORandomForestEstimator task, the fit method doesn't fail because the pipeline doesn't
      #     use _fit_transform_one method thus does not use HH2OSingularValueDecompositionEstimator.transform method
      try:
          pipe = Pipeline(
              [
                  ("standardize", H2OScaler()),
                  ("svd", H2OSingularValueDecompositionEstimator(nv=3)),
                  ("rf", H2ORandomForestEstimator(seed=42, ntrees=50)),
              ]
          )
  
          pipe.fit(arrests[1:], arrests[0])
          assert (
              False
          ), "Pipeline should fail without using H2OSingularValueDecompositionEstimator.init_for_pipeline()"
      except TypeError:
          pass
  
      # build transformation pipeline using sklearn's Pipeline and H2O estimators with H2OSingularValueDecompositionEstimator.init_for_pipeline()
      pipe = Pipeline(
          [
              ("standardize", H2OScaler()),
              (
                  "svd",
                  H2OSingularValueDecompositionEstimator(
                      nv=3
                  ).init_for_pipeline(),
              ),
              ("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 2 for calling a global function with example usage: scale_svd_rf_pipe_new_import() without return types