code: def test_combine_inputs_floats_ints(self):
      data = [[0, 0.0], [0, 0.0], [1, 1.0], [1, 1.0]]
      scaler = StandardScaler()
      scaler.fit(data)
      model = Pipeline([("scaler1", scaler), ("scaler2", scaler)])
  
      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 4 for calling the TestSklearnPipeline obj's underlying member method code with example usage: obj.test_combine_inputs_floats_ints() without return types