code: def pca_car():
      num_runs = 10
      run_time_c = []
  
      car = h2o.import_file(
          path=pyunit_utils.locate("smalldata/pca_test/car.arff.txt")
      )  # Nidhi: import may not work
      for run_index in range(
          num_runs
      ):  # multiple runs to get an idea of run time info
          carPCA = H2OPCA(k=car.ncols, transform="STANDARDIZE")
          carPCA.train(x=list(range(0, car.ncols)), training_frame=car)
          run_time_c.append(
              carPCA._model_json["output"]["end_time"]
              - carPCA._model_json["output"]["start_time"]
          )
          print(
              "PCA model training time with car.arff.txt data in ms is {0}".format(
                  run_time_c[run_index]
              )
          )
  
          h2o.remove(carPCA)
  
      assert (
          max(run_time_c)
      ) < 60000, "PCA runs for car.arff.txt take too much time!"
  
imports: 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_car() without return types