code: def pca_pubdev_4314():
      print("Importing prostate_cat.csv data...\n")
      prostate = h2o.upload_file(
          pyunit_utils.locate("smalldata/prostate/prostate_cat.csv")
      )
      prostate.describe()
      print("PCA with k = 3, retx = FALSE, transform = 'STANDARDIZE'")
      fitPCA = H2OPCA(
          k=3, transform="StANDARDIZE", pca_method="GramSVD"
      )
      fitPCA.train(x=list(range(0, 8)), training_frame=prostate)
      print(fitPCA.summary())
      varimpPandas = fitPCA.varimp(use_pandas=True)
      assert_is_type(varimpPandas, DataFrame)
      varimpList = fitPCA.varimp()
      print(varimpList)
      assert_is_type(varimpList, list)
      sys.stdout.flush()
  
imports: import sys
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.pca import H2OPrincipalComponentAnalysisEstimator
  from h2o.utils.typechecks import assert_is_type
  from pandas import DataFrame
label_desc: Use image node_id 1 for calling a global function with example usage: pca_pubdev_4314() without return types