code: def class_extensions():
      def init_for_pipeline(self):
          """
          Returns H2OSVD object which implements fit and transform method to be used in sklearn.Pipeline properly.
          All parameters defined in self.__params, should be input parameters in H2OSVD.__init__ method.
  
          :returns: H2OSVD object
  
          :examples:
  
          >>> from h2o.transforms.preprocessing import H2OScaler
          >>> from h2o.estimators import H2ORandomForestEstimator
          >>> from h2o.estimators import H2OSingularValueDecompositionEstimator
          >>> from sklearn.pipeline import Pipeline
          >>> arrests = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/USArrests.csv")
          >>> pipe = Pipeline([("standardize", H2OScaler()),
          ...                  ("svd", H2OSingularValueDecompositionEstimator(nv=3).init_for_pipeline()),
          ...                  ("rf", H2ORandomForestEstimator(seed=42,ntrees=50))])
          >>> pipe.fit(arrests[1:], arrests[0])
          """
          import inspect
          from h2o.transforms.decomposition import H2OSVD
  
          # check which parameters can be passed to H2OSVD init
          var_names = list(
              dict(inspect.getmembers(H2OSVD.__init__.__code__))[
                  "co_varnames"
              ]
          )
          parameters = {
              k: v for k, v in self._parms.items() if k in var_names
          }
          return H2OSVD(**parameters)
  
label_desc: Use image node_id 1 for calling a global function with example usage: class_extensions() and returns: H2OSVD