code: def generate_dataset(
      family,
      nrow,
      ncol,
      ntrees,
      max_depth,
      realFrac,
      intFrac,
      enumFrac,
      missingFrac,
      factorRange,
      numericRange,
      targetFactor,
  ):
      if family == "bernoulli":
          responseFactor = 2
      elif family == "gaussian":
          responseFactor = 1
      else:
          responseFactor = targetFactor
  
      trainData = random_dataset(
          nrow,
          ncol,
          realFrac=realFrac,
          intFrac=intFrac,
          enumFrac=enumFrac,
          factorR=factorRange,
          integerR=numericRange,
          responseFactor=responseFactor,
          misFrac=missingFrac,
      )
  
      myX = trainData.names
      myY = "response"
      myX.remove(myY)
      m = H2OGradientBoostingEstimator(
          distribution=family, ntrees=ntrees, max_depth=max_depth
      )
      m.train(training_frame=trainData, x=myX, y=myY)
      f2 = m.predict(trainData)
  
      finalDataset = trainData[myX]
      finalDataset = finalDataset.cbind(f2[0])
      finalDataset.set_name(col=finalDataset.ncols - 1, name="response")
  
      return finalDataset
  
imports: import sys
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.gbm import H2OGradientBoostingEstimator
  import numpy
label_desc: Use image node_id 2 for calling a global function with example usage: generate_dataset(family, nrow, ncol, ntrees, max_depth, realFrac, intFrac, enumFrac, missingFrac, factorRange, numericRange, targetFactor) and returns: finalDataset