code: def test_nnclassifier_in_pipeline(self):
      if self.sc.version.startswith("1"):
          from pyspark.mllib.linalg import Vectors
  
          df = self.sqlContext.createDataFrame(
              [
                  (Vectors.dense([2.0, 1.0]), 1.0),
                  (Vectors.dense([1.0, 2.0]), 2.0),
                  (Vectors.dense([2.0, 1.0]), 1.0),
                  (Vectors.dense([1.0, 2.0]), 2.0),
              ],
              ["features", "label"],
          )
  
          scaler = (
              MinMaxScaler()
              .setInputCol("features")
              .setOutputCol("scaled")
          )
          model = Sequential().add(Linear(2, 2))
          criterion = ClassNLLCriterion()
          classifier = (
              NNClassifier(model, criterion)
              .setBatchSize(4)
              .setLearningRate(0.01)
              .setMaxEpoch(1)
              .setFeaturesCol("scaled")
          )
  
          pipeline = Pipeline(stages=[scaler, classifier])
  
          pipelineModel = pipeline.fit(df)
  
          res = pipelineModel.transform(df)
          assert type(res).__name__ == "DataFrame"
  
imports: import errno
  import shutil
  import pytest
  from bigdl.nn.criterion import *
  from bigdl.nn.layer import *
  from bigdl.optim.optimizer import *
  from numpy.testing import assert_allclose
  from pyspark.ml import Pipeline, PipelineModel
  from pyspark.ml.feature import MinMaxScaler
  from pyspark.ml.tuning import ParamGridBuilder
  from pyspark.sql.types import *
  from zoo.common.nncontext import *
  from zoo.feature.common import *
  from zoo.feature.image import *
  from zoo.pipeline.api.keras import layers
  from zoo.pipeline.api.keras.models import Model
  from zoo.pipeline.api.keras.optimizers import Adam
  from zoo.pipeline.nnframes import *
  from zoo.util.tf import *
label_desc: Use image node_id 34 for calling the TestNNClassifer obj's underlying member method code with example usage: obj.test_nnclassifier_in_pipeline() without return types