code: def test_pipeline(self):
      dataset = datasets.load_diabetes()
      target_scaler = preprocessing.MinMaxScaler()
      target = dataset.target.reshape(-1, 1)
  
      x_train, x_test, y_train, y_test = train_test_split(
          asfloat(dataset.data),
          asfloat(target_scaler.fit_transform(target)),
          test_size=0.15,
      )
  
      network = algorithms.GradientDescent(
          network=[
              layers.Input(10),
              layers.Sigmoid(25),
              layers.Sigmoid(1),
          ],
          batch_size=None,
          show_epoch=100,
          verbose=False,
      )
      pipeline = Pipeline(
          [
              ("min_max_scaler", preprocessing.MinMaxScaler()),
              ("gd", network),
          ]
      )
      pipeline.fit(x_train, y_train, gd__epochs=50)
      y_predict = pipeline.predict(x_test)
  
      error = objectives.rmsle(
          target_scaler.inverse_transform(y_test),
          target_scaler.inverse_transform(y_predict).round(),
      )
      error = self.eval(error)
      self.assertGreater(0.5, error)
  
file_path: neupy/tests/compatibilities/test_sklearn_compatibility.py
imports: import numpy
  from sklearn import datasets, preprocessing, model_selection
  from sklearn.model_selection import train_test_split
  from sklearn.pipeline import Pipeline
  from neupy import algorithms, layers
  from neupy.utils import asfloat
  from neupy.algorithms.gd import objectives
  from base import BaseTestCase
label_desc: Use image node_id 1 for calling the SklearnCompatibilityTestCase obj's underlying member method code with example usage: obj.test_pipeline() without return types