code: def generate_dataset(
      family,
      nrow,
      ncol,
      realFrac,
      intFrac,
      enumFrac,
      missingFrac,
      factorRange,
      numericRange,
      targetFactor,
  ):
      if family == "binomial":
          responseFactor = 2
      elif family == "multinomial" or family == "ordinal":
          responseFactor = targetFactor
      else:
          responseFactor = 1
  
      trainData = random_dataset(
          nrow,
          ncol,
          realFrac=realFrac,
          intFrac=intFrac,
          enumFrac=enumFrac,
          factorR=factorRange,
          integerR=numericRange,
          responseFactor=responseFactor,
          misFrac=missingFrac,
      )
      if family == "poisson":
          trainData["response"] = trainData["response"] + numericRange
      myX = trainData.names
      myY = "response"
      myX.remove(myY)
  
      m = glm(
          family=family,
          max_iterations=1,
          interactions=["C1", "C2"],
          tweedie_link_power=2,
          tweedie_variance_power=0.4,
      )
      m.train(training_frame=trainData, x=myX, y=myY)
      r = glm.getGLMRegularizationPath(m)
      coeffDict = r["coefficients"][0]
      coeffLen = len(coeffDict)
      randCoeffVals = np.random.uniform(
          low=-3, high=3, size=coeffLen
      ).tolist()
      keyset = coeffDict.keys()
      count = 0
      for key in keyset:
          coeffDict[key] = randCoeffVals[count]
          count = count + 1
  
      m2 = glm.makeGLMModel(
          model=m, coefs=coeffDict
      )  # model generated from setting coefficients to model
      f2 = m2.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.glm import H2OGeneralizedLinearEstimator
  import numpy
label_desc: Use image node_id 2 for calling a global function with example usage: generate_dataset(family, nrow, ncol, realFrac, intFrac, enumFrac, missingFrac, factorRange, numericRange, targetFactor) and returns: finalDataset