code: def pca_pubdev_4167_OOM():
      """
      This pyunit is written to make sure PCA works with customer data.  It is mainly used by customer to verify
      PCA operations and not to be used as a regular test since I do not want to expose customer data.
      """
      h2o.remove_all()
      transform_types = [
          "NONE",
          "STANDARDIZE",
          "NORMALIZE",
          "DEMEAN",
          "DESCALE",
      ]  # make sure we check all tranforms
      transformN = transform_types[randint(0, len(transform_types) - 1)]
      print("transform used on dataset is {0}.\n".format(transformN))
  
      training_data = h2o.import_file(
          path=pyunit_utils.locate(
              "/Users/wendycwong/gitBackup/SDatasets/pubdev_4167_Avkash/m120K.tar"
          )
      )  # Nidhi: import may not work
  
      gramSVDPCA = H2OPCA(k=training_data.ncols, transform=transformN)
      gramSVDPCA.train(
          x=list(range(0, training_data.ncols)),
          training_frame=training_data,
      )
  
      powerSVDPCA = H2OPCA(
          k=training_data.ncols,
          transform=transformN,
          pca_method="Power",
      )
      powerSVDPCA.train(
          x=list(range(0, training_data.ncols)),
          training_frame=training_data,
      )
  
      # compare singular values and stuff between power and GramSVD methods
      print(
          "@@@@@@  Comparing eigenvalues between GramSVD and Power...\n"
      )
      pyunit_utils.assert_H2OTwoDimTable_equal(
          gramSVDPCA._model_json["output"]["importance"],
          powerSVDPCA._model_json["output"]["importance"],
          [
              "Standard deviation",
              "Cumulative Proportion",
              "Cumulative Proportion",
          ],
          tolerance=1e-5,
          check_all=False,
      )
      print(
          "@@@@@@  Comparing eigenvectors between GramSVD and Power...\n"
      )
      # compare singular vectors
      pyunit_utils.assert_H2OTwoDimTable_equal(
          gramSVDPCA._model_json["output"]["eigenvectors"],
          powerSVDPCA._model_json["output"]["eigenvectors"],
          powerSVDPCA._model_json["output"]["names"],
          tolerance=1e-1,
          check_sign=True,
      )
  
imports: from builtins import range
  import sys
  import h2o
  from tests import pyunit_utils
  from random import randint
  from h2o.estimators.pca import H2OPrincipalComponentAnalysisEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: pca_pubdev_4167_OOM() without return types