code: def h2oassembly_download_mojo_documented_pipeline():
      frame1 = h2o.import_file(
          path=pyunit_utils.locate("smalldata/iris/iris_train.csv")
      )
      frame2 = h2o.import_file(
          path=pyunit_utils.locate("smalldata/iris/iris_train.csv")
      )
      assembly = H2OAssembly(
          steps=[
              (
                  "col_select",
                  H2OColSelect(["sepal_len", "petal_len", "species"]),
              ),
              (
                  "cos_Sepal.Length",
                  H2OColOp(
                      op=H2OFrame.cos, col="sepal_len", inplace=True
                  ),
              ),
              (
                  "str_cnt_Species",
                  H2OColOp(
                      op=H2OFrame.countmatches,
                      col="species",
                      inplace=False,
                      pattern="s",
                  ),
              ),
          ]
      )
  
      expected = assembly.fit(frame1)
      assert_is_type(expected, H2OFrame)
  
      results_dir = os.path.join(os.getcwd(), "results")
      file_name = "h2oassembly_download_mojo_documented_pipeline"
      path = os.path.join(results_dir, file_name + ".mojo")
  
      mojo_file = assembly.download_mojo(file_name=file_name, path=path)
      assert os.path.exists(mojo_file)
  
      pipeline = H2OMojoPipeline(mojo_path=mojo_file)
      result = pipeline.transform(frame2)
      print(result)
      assert_is_type(result, H2OFrame)
      pyunit_utils.compare_frames(
          expected, result, expected.nrows, tol_numeric=1e-5
      )
  
imports: import sys
  from tests import pyunit_utils
  from h2o.assembly import *
  from h2o.transforms.preprocessing import *
  from h2o.pipeline import H2OMojoPipeline
label_desc: Use image node_id 1 for calling a global function with example usage: h2oassembly_download_mojo_documented_pipeline() without return types