code: def random_dataset(
      nrow,
      ncol,
      realFrac=0.4,
      intFrac=0.3,
      enumFrac=0.3,
      factorR=10,
      integerR=100,
      responseFactor=1,
      misFrac=0.01,
      randSeed=None,
  ):
      fractions = dict()
      fractions[
          "real_fraction"
      ] = realFrac  # Right now we are dropping string columns, so no point in having them.
      fractions["categorical_fraction"] = enumFrac
      fractions["integer_fraction"] = intFrac
      fractions["time_fraction"] = 0
      fractions[
          "string_fraction"
      ] = 0  # Right now we are dropping string columns, so no point in having them.
      fractions["binary_fraction"] = 0
  
      df = h2o.create_frame(
          rows=nrow,
          cols=ncol,
          missing_fraction=misFrac,
          has_response=True,
          response_factors=responseFactor,
          factors=factorR,
          integer_range=integerR,
          real_range=integerR,
          seed=randSeed,
          **fractions,
      )
      print(df.types)
      return df
  
imports: import sys
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.glm import H2OGeneralizedLinearEstimator
  import numpy
label_desc: Use image node_id 3 for calling a global function with example usage: random_dataset(nrow, ncol, realFrac, intFrac, enumFrac, factorR, integerR, responseFactor, misFrac, randSeed) and returns: df