code: def get_model(with_pipeline=False):
      
      model = NeuralNetClassifier(MLPClassifier)
      if with_pipeline:
          model = Pipeline([
              ('scale', FeatureUnion([
                  ('minmax', MinMaxScaler()),
                  ('normalize', Normalizer()),
              ])),
              ('select', SelectKBest(k=N_FEATURES)),  # keep input size constant
              ('net', model),
          ])
      return model
docs: Get a multi-layer perceptron model.
  
      Optionally, put it in a pipeline that scales the data.