code: def glm_binomial_mojo_pojo():
      h2o.remove_all()
      NTESTROWS = 200  # number of test dataset rows
      PROBLEM = "binomial"
      params = set_params()  # set deeplearning model parameters
      df = pyunit_utils.random_dataset(
          PROBLEM
      )  # generate random dataset
      train = df[NTESTROWS:, :]
      test = df[:NTESTROWS, :]
      x = list(set(df.names) - {"response"})
      TMPDIR = tempfile.mkdtemp()
      glmBinomialModel = pyunit_utils.build_save_model_generic(
          params, x, train, "response", "glm", TMPDIR
      )  # build and save mojo model
      MOJONAME = pyunit_utils.getMojoName(glmBinomialModel._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(
          glmBinomialModel, TMPDIR, MOJONAME
      )  # load model and perform predict
      h2o.download_csv(pred_h2o, os.path.join(TMPDIR, "h2oPred.csv"))
      pred_pojo = pyunit_utils.pojo_predict(
          glmBinomialModel, TMPDIR, MOJONAME
      )
      print("Comparing mojo predict and h2o predict...")
      pyunit_utils.compare_frames_local(
          pred_h2o, pred_mojo, 0.1, tol=1e-10
      )  # make sure operation sequence is preserved from Tomk        h2o.save_model(glmOrdinalModel, path=TMPDIR, force=True)  # save model for debugging
      print("Comparing pojo predict and h2o predict...")
      pyunit_utils.compare_frames_local(
          pred_mojo, pred_pojo, 0.1, tol=1e-10
      )
  
imports: import sys, os
  import h2o
  from tests import pyunit_utils
  from random import randint
  import tempfile
label_desc: Use image node_id 1 for calling a global function with example usage: glm_binomial_mojo_pojo() without return types