code: def pca_arrests():
      print("Importing USArrests.csv data...")
      arrestsH2O = h2o.upload_file(
          pyunit_utils.locate("smalldata/pca_test/USArrests.csv")
      )
  
      print(
          "Testing to see whether the trained PCA are essentially the same using different implementation..."
      )
  
      eigenvector_standard = None
      for impl in [
          "MTJ_EVD_DENSEMATRIX",
          "MTJ_EVD_SYMMMATRIX",
          "MTJ_SVD_DENSEMATRIX",
          "JAMA",
      ]:
          print("Run PCA with implementation: " + impl)
          model = H2OPCA(k=4, pca_impl=impl, seed=1234)
          model.train(x=list(range(4)), training_frame=arrestsH2O)
          eigenvectors = model._model_json["output"]["eigenvectors"]
          if eigenvector_standard is not None:
              # Compare to see if they are fundamentally the same
              pyunit_utils.assert_H2OTwoDimTable_equal(
                  eigenvector_standard,
                  eigenvectors,
                  model._model_json["output"]["names"],
                  tolerance=1e-6,
                  check_sign=True,
                  check_all=False,
              )
          else:
              eigenvector_standard = eigenvectors
  
imports: from builtins import str
  from builtins import range
  import sys
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.pca import H2OPrincipalComponentAnalysisEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: pca_arrests() without return types