code: def gam_fractional_binomial_mojo_pojo():
      params = set_params()
      train = h2o.import_file(
          pyunit_utils.locate(
              "smalldata/glm_test/fraction_binommialOrig.csv"
          )
      )
      test = h2o.import_file(
          pyunit_utils.locate(
              "smalldata/glm_test/fraction_binommialOrig.csv"
          )
      )
      x = ["log10conc"]
      y = "y"
  
      TMPDIR = tempfile.mkdtemp()
      gamModel = pyunit_utils.build_save_model_generic(
          params, x, train, y, "gam", TMPDIR
      )  # build and save mojo model
      MOJONAME = pyunit_utils.getMojoName(gamModel._id)
  
      h2o.download_csv(
          test[x], os.path.join(TMPDIR, "in.csv")
      )  # save test file, h2o predict/mojo use same file
      pred_h2o, pred_mojo = pyunit_utils.mojo_predict(
          gamModel, TMPDIR, MOJONAME
      )  # load model and perform predict
      h2o.download_csv(pred_h2o, os.path.join(TMPDIR, "h2oPred.csv"))
      print("Comparing mojo predict and h2o predict...")
      pyunit_utils.compare_frames_local(
          pred_h2o, pred_mojo, 0.1, tol=1e-10
      )
  
imports: import sys, os
  import h2o
  from tests import pyunit_utils
  import tempfile
label_desc: Use image node_id 1 for calling a global function with example usage: gam_fractional_binomial_mojo_pojo() without return types