code: def test_ColumnTransformer_pipeline():
      """Test pipeline with ColumnTransformer."""
      X_train, y_train = load_basic_motions(
          split="train", return_X_y=True
      )
      X_test, y_test = load_basic_motions(split="test", return_X_y=True)
  
      # using Identity function transformations (transform series to series)
      def id_func(X):
          return X
  
      column_transformer = ColumnTransformer(
          [
              (
                  "id0",
                  FunctionTransformer(func=id_func, validate=False),
                  ["dim_0"],
              ),
              (
                  "id1",
                  FunctionTransformer(func=id_func, validate=False),
                  ["dim_1"],
              ),
          ]
      )
      steps = [
          ("extract", column_transformer),
          ("tabularise", Tabularizer()),
          (
              "classify",
              RandomForestClassifier(n_estimators=2, random_state=1),
          ),
      ]
      model = Pipeline(steps=steps)
      model.fit(X_train, y_train)
      y_pred = model.predict(X_test)
      assert y_pred.shape[0] == y_test.shape[0]
      np.testing.assert_array_equal(
          np.unique(y_pred), np.unique(y_test)
      )
  
imports: import numpy
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import FunctionTransformer
  from sktime.datasets import load_basic_motions
  from sktime.transformations.panel.compose import ColumnTransformer
  from sktime.transformations.panel.reduce import Tabularizer
label_desc: Use image node_id 1 for calling a global function with example usage: test_ColumnTransformer_pipeline() and returns: X