code: def test_conformal_with_hierarchical():
      """Test ConformalIntervals with a wrapped estimator of hierarchical mtype.
  
      Failure case of bug #5092.
      """
      from sklearn.linear_model import LinearRegression
  
      from sktime.forecasting.compose import ForecastX, make_reduction
      from sktime.split import temporal_train_test_split
      from sktime.utils._testing.hierarchical import _make_hierarchical
  
      df = _make_hierarchical(
          hierarchy_levels=(2, 3),
          max_timepoints=25,
          min_timepoints=25,
          n_columns=4,
          all_positive=True,
          random_state=0,
      )
  
      y = df[["c0", "c1"]]
      X = df[["c2", "c3"]]
  
      y_train, y_test, X_train, X_test = temporal_train_test_split(
          y, X=X, test_size=5
      )
  
      regressor = LinearRegression()
      endogenous_model = make_reduction(regressor, pooling="global")
  
      exogenous_model = NaiveForecaster()
  
      forecaster = ConformalIntervals(
          ForecastX(endogenous_model.clone(), exogenous_model.clone()),
          initial_window=15,
      )
  
      forecaster.fit(y_train, X=X_train, fh=range(1, 3))
  
      forecaster.predict(X=X_test)
      forecaster.predict_interval(X=X_test)
  
file_path: sktime/sktime/forecasting/tests/test_conformal.py
imports: import pytest
  from sktime.datasets import load_airline
  from sktime.datatypes import check_is_mtype
  from sktime.forecasting.conformal import ConformalIntervals
  from sktime.forecasting.naive import NaiveForecaster
  from sktime.tests.test_switch import run_test_for_class
label_desc: Use image node_id 3 for calling a global function with example usage: test_conformal_with_hierarchical() without return types