code: def test_nnModel_set_Preprocessing(self):
      model = Sequential().add(Linear(2, 2))
      criterion = MSECriterion()
      estimator = (
          NNEstimator(model, criterion, [2], [2])
          .setBatchSize(4)
          .setLearningRate(0.2)
          .setMaxEpoch(1)
      )
  
      df = self.get_estimator_df()
      nnModel = estimator.fit(df)
      newTransformer = ChainedPreprocessing(
          [SeqToTensor([2]), TensorToSample()]
      )
      nnModel.setSamplePreprocessing(newTransformer)
  
      res = nnModel.transform(df)
      assert type(res).__name__ == "DataFrame"
      assert res.count() == 4
  
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 23 for calling the TestNNClassifer obj's underlying member method code with example usage: obj.test_nnModel_set_Preprocessing() without return types