code: def main(readcsv=pd_read_csv):
      data_path = Path(__file__).parent / "data" / "batch"
      infile = data_path / "stump_train.csv"
      testfile = data_path / "stump_test.csv"
  
      # Configure a stump classification training object
      train_algo = d4p.stump_classification_training(
          splitCriterion="gini"
      )
  
      # Read data. Let's have 20 independent,
      # and 1 dependent variable (for each observation)
      indep_data = readcsv(infile, range(20))
      dep_data = readcsv(infile, range(20, 21))
      # Now train/compute, the result provides the model for prediction
      train_result = train_algo.compute(indep_data, dep_data)
  
      # Now let's do some prediction
      predict_algo = d4p.stump_classification_prediction()
      # read test data (with same #features)
      pdata = readcsv(testfile, range(20))
      # now predict using the model from the training above
      predict_result = predict_algo.compute(pdata, train_result.model)
  
      # The prediction result provides prediction
      assert predict_result.prediction.shape == (
          pdata.shape[0],
          dep_data.shape[1],
      )
      ptdata = np.loadtxt(
          testfile, usecols=range(20, 21), delimiter=",", ndmin=2
      )
      assert np.allclose(predict_result.prediction, ptdata)
      return (train_result, predict_result, ptdata)
  
imports: from pathlib import Path
  import numpy
  from readcsv import pd_read_csv
  import daal4py
label_desc: Use image node_id 1 for calling a global function with example usage: main(readcsv) and returns: train_result,  predict_result,  ptdata