code: def test_define_dataset():
      family = "gaussian"  # can be any valid GLM families
      nrow = 10000
      ncol = 10
      realFrac = 0.5
      intFrac = 0
      enumFrac = 0.5
      missing_fraction = 0
      factorRange = 5
      numericRange = 10
      targetFactor = 4
      glmDataSet = generate_dataset(
          family,
          nrow,
          ncol,
          realFrac,
          intFrac,
          enumFrac,
          missing_fraction,
          factorRange,
          numericRange,
          targetFactor,
      )
      # h2o.download_csv(gamDataSet, "/Users/wendycwong/temp/dataset.csv") # save dataset
      # np.save('/Users/wendycwong/temp/datasetTypes.npy', gamDataSet.types)
      assert (
          glmDataSet.nrow == nrow
      ), "Dataset number of row: {0}, expected number of row: {1}".format(
          glmDataSet.nrow, nrow
      )
      assert glmDataSet.ncol == (1 + ncol), (
          "Dataset number of row: {0}, expected number of row: "
          "{1}".format(glmDataSet.ncol, (1 + ncol))
      )
  
imports: import sys
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.glm import H2OGeneralizedLinearEstimator
  import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: test_define_dataset() without return types