code: def test_pipeline_column_transformer_passthrough_slice(self):
      iris = datasets.load_iris()
      X = iris.data[:, :3]
      y = iris.target
      X_train = pandas.DataFrame(X, columns=["vA", "vB", "vC"])
      X_train["vcat"] = X_train["vA"].apply(
          lambda x: 1 if x > 0.5 else 2
      )
      X_train["vcat2"] = X_train["vB"].apply(
          lambda x: 3 if x > 0.5 else 4
      )
      y_train = y % 2
      numeric_features = slice(0, 1)  # ["vA", "vB"]
      categorical_features = slice(3, 4)  # ["vcat", "vcat2"]
  
      classifier = LogisticRegression(
          C=0.01,
          class_weight=dict(zip([False, True], [0.2, 0.8])),
          n_jobs=1,
          max_iter=10,
          solver="liblinear",
          tol=1e-3,
      )
  
      numeric_transformer = Pipeline(
          steps=[("scaler", StandardScaler())]
      )
  
      categorical_transformer = Pipeline(
          steps=[
              (
                  "onehot",
                  OneHotEncoder(sparse=True, handle_unknown="ignore"),
              )
          ]
      )
  
      preprocessor = ColumnTransformer(
          transformers=[
              ("num", numeric_transformer, numeric_features),
              ("cat", categorical_transformer, categorical_features),
          ],
          transformer_weights={"num": 2, "cat": 3},
          remainder="passthrough",
      )
  
      model = Pipeline(
          steps=[
              ("precprocessor", preprocessor),
              ("classifier", classifier),
          ]
      )
  
      model.fit(X_train, y_train)
  
      X_test = X_train[:11]
  
      torch_model = hummingbird.ml.convert(model, "torch")
  
      self.assertTrue(torch_model is not None)
  
      np.testing.assert_allclose(
          model.predict_proba(X_test),
          torch_model.predict_proba(X_test.values),
          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 16 for calling the TestSklearnPipeline obj's underlying member method code with example usage: obj.test_pipeline_column_transformer_passthrough_slice() without return types