code: def parse_partitionBy():
      """
      Tests Parquet parser by comparing the summary of the original csv frame with the h2o parsed Parquet frame.
      Basic use case of importing files with auto-detection of column types.
      :return: None if passed.  Otherwise, an exception will be thrown.
      """
      parquet = h2o.import_file(
          path=pyunit_utils.locate(
              "smalldata/partitioned/partitioned_arilines/"
          ),
          partition_by=["Year", "IsArrDelayed"],
      )
      print(parquet.as_data_frame())
      csv = h2o.import_file(
          path=pyunit_utils.locate(
              "smalldata/partitioned/partitioned_arilines_csv/"
          ),
          partition_by=["Year", "IsArrDelayed"],
      )
      original = h2o.import_file(
          path=pyunit_utils.locate(
              "smalldata/airlines/modified_airlines.csv"
          )
      )
      print(original.as_data_frame())
  
      assert original.nrows == parquet.nrows == csv.nrows
      assert original.ncols == parquet.ncols == csv.ncols
  
      # Column names must remain the same, order might be different (partitioned columns are append at the end).
      for i in range(0, original.ncols):
          assert original.names[i] in parquet.names
          assert original.names[i] in csv.names
  
      assert parquet.type("Year") == "enum"
      assert parquet.type("IsArrDelayed") == "enum"
      assert csv.type("Year") == "enum"
      assert csv.type("IsArrDelayed") == "enum"
  
label_desc: Use image node_id 1 for calling a global function with example usage: parse_partitionBy() without return types