code: def test_pipeline_many_inputs(self):
      n_features = 18
      X = np.random.rand(100, n_features)
      y = np.random.randint(1000, size=100)
  
      scaler_transformer = Pipeline(
          steps=[("scaler", StandardScaler())]
      )
      preprocessor = ColumnTransformer(
          transformers=[
              ("scaling", scaler_transformer, list(range(n_features)))
          ]
      )
      model = RandomForestRegressor(n_estimators=10, max_depth=9)
      pipeline = Pipeline(
          steps=[("preprocessor", preprocessor), ("model", model)]
      )
  
      pipeline.fit(X, y)
  
      X_test = tuple(np.split(X, n_features, axis=1))
  
      hb_model = hummingbird.ml.convert(pipeline, "onnx", X_test)
  
      assert len(hb_model.model.graph.input) == n_features
  
      np.testing.assert_allclose(
          pipeline.predict(X),
          np.array(hb_model.predict(X_test)).flatten(),
          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 18 for calling the TestSklearnPipeline obj's underlying member method code with example usage: obj.test_pipeline_many_inputs() without return types