code: def test_combine_inputs_union_in_pipeline(self):
      from sklearn.preprocessing import StandardScaler
      from sklearn.pipeline import Pipeline
  
      data = np.array(
          [[0.0, 0.0], [0.0, 0.0], [1.0, 1.0], [1.0, 1.0]],
          dtype=np.float32,
      )
      model = Pipeline(
          [
              ("scaler1", StandardScaler()),
              (
                  "union",
                  FeatureUnion(
                      [
                          ("scaler2", StandardScaler()),
                          ("scaler3", MinMaxScaler()),
                      ]
                  ),
              ),
          ]
      )
      model.fit(data)
  
      torch_model = hummingbird.ml.convert(model, "torch")
  
      self.assertTrue(torch_model is not None)
  
      np.testing.assert_allclose(
          model.transform(data),
          torch_model.transform(data),
          rtol=1e-06,
          atol=1e-06,
      )
  
imports: import unittest
  import numpy
  from sklearn import datasets
  from sklearn.compose import ColumnTransformer
  from sklearn.datasets import load_iris, load_diabetes
  from sklearn.svm import LinearSVC, LinearSVR
  from sklearn.datasets import make_regression
  from sklearn.decomposition import PCA
  from sklearn.linear_model import LogisticRegression, RidgeCV
  from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
  from sklearn.model_selection import train_test_split
  from sklearn.pipeline import Pipeline, FeatureUnion, make_pipeline
  from sklearn.preprocessing import OneHotEncoder, StandardScaler, MinMaxScaler
  import hummingbird.ml
  from hummingbird.ml._utils import pandas_installed, onnx_runtime_installed
  from hummingbird.ml import constants
  from onnxconverter_common.data_types import FloatTensorType, Int64TensorType, StringTensorType
label_desc: Use image node_id 3 for calling the TestSklearnPipeline obj's underlying member method code with example usage: obj.test_combine_inputs_union_in_pipeline() without return types