code: def main(readcsv=pd_read_csv):
      data_path = (
          Path(__file__).parent.parent / "daal4py" / "data" / "batch"
      )
      train_file = data_path / "df_classification_train.csv"
      test_file = data_path / "df_classification_test.csv"
  
      # Data reading
      X_train = readcsv(train_file, usecols=range(3), dtype=np.float32)
      y_train = readcsv(
          train_file, usecols=range(3, 4), dtype=np.float32
      )
      X_test = readcsv(test_file, usecols=range(3), dtype=np.float32)
      y_test = readcsv(test_file, usecols=range(3, 4), dtype=np.float32)
  
      # Datasets creation
      cb_train = cb.Pool(X_train, label=np.array(y_train))
      cb_test = cb.Pool(X_test, label=np.array(y_test))
  
      # training parameters setting
      params = {
          "reg_lambda": 1,
          "max_depth": 6,
          "num_leaves": 2**6,
          "verbose": 0,
          "objective": "MultiClass",
          "learning_rate": 0.3,
          "n_estimators": 25,
          "classes_count": 5,
      }
  
      # Training
      cb_model = cb.CatBoost(params)
      cb_model.fit(cb_train)
  
      # Catboost prediction
      cb_prediction = cb_model.predict(
          cb_test, prediction_type="Class"
      ).T[0]
      cb_errors_count = np.count_nonzero(
          cb_prediction - np.ravel(y_test)
      )
  
      # Conversion to daal4py
      daal_model = d4p.mb.convert_model(cb_model)
  
      # daal4py prediction
      daal_prediction = daal_model.predict(X_test)
      daal_errors_count = np.count_nonzero(
          daal_prediction - np.ravel(y_test)
      )
      assert np.absolute(cb_errors_count - daal_errors_count) == 0
  
      return (
          cb_prediction,
          cb_errors_count,
          daal_prediction,
          daal_errors_count,
          np.ravel(y_test),
      )
  
imports: from pathlib import Path
  import catboost
  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: cb_prediction,  cb_errors_count,  daal_prediction,  daal_errors_count,  np