code: def generate_trainingFrame():
      nrows = 1000000  # per nidhi request
      trainGroup = pyunit_utils.random_dataset_enums_only(
          nrows, randint(1, 3), randint(2, 10)
      )
      trainEnums = pyunit_utils.random_dataset_numeric_only(
          nrows, randint(1, 3), randint(20, 100)
      )  # columns to sort
      sortColumnsNames = ["sort0", "sort1", "sort2"]
      trainEnums.set_names(sortColumnsNames[0 : trainEnums.ncols])
      groupNames = ["GroupByCols0", "GroupByCols1", "GroupByCols2"]
      trainGroup.set_names(groupNames[0 : trainGroup.ncols])
      finalTrain = trainGroup.cbind(
          trainEnums
      )  # this will be the training frame
      return finalTrain, trainGroup.names, trainEnums.names
  
imports: import sys, os
  import h2o
  from tests import pyunit_utils
  from random import randint
  import random
label_desc: Use image node_id 2 for calling a global function with example usage: generate_trainingFrame() and returns: finalTrain,  trainGroup,  trainEnums