code: def _create_hierarchical_data(n_columns=1):
      y = _make_hierarchical(
          random_state=TEST_RANDOM_SEEDS[0],
          hierarchy_levels=(2, 2),
          min_timepoints=15,
          max_timepoints=15,
          n_columns=n_columns,
      )
      X = _make_hierarchical(
          random_state=TEST_RANDOM_SEEDS[1],
          hierarchy_levels=(2, 2),
          min_timepoints=15,
          max_timepoints=15,
      )
      return y, X
  
imports: import numpy
  import pytest
  from sklearn.model_selection import ParameterGrid, ParameterSampler
  from sktime.datasets import load_airline, load_longley
  from sktime.forecasting.arima import ARIMA
  from sktime.forecasting.compose import TransformedTargetForecaster
  from sktime.forecasting.exp_smoothing import ExponentialSmoothing
  from sktime.forecasting.model_evaluation import evaluate
  from sktime.forecasting.model_selection import ForecastingGridSearchCV, ForecastingRandomizedSearchCV, ForecastingSkoptSearchCV
  from sktime.forecasting.model_selection._tune import BaseGridSearch
  from sktime.forecasting.naive import NaiveForecaster
  from sktime.forecasting.tests._config import TEST_N_ITERS, TEST_OOS_FHS, TEST_RANDOM_SEEDS, TEST_WINDOW_LENGTHS_INT
  from sktime.forecasting.trend import PolynomialTrendForecaster
  from sktime.performance_metrics.forecasting import MeanAbsolutePercentageError, MeanSquaredError
  from sktime.performance_metrics.forecasting.probabilistic import CRPS, PinballLoss
  from sktime.split import SingleWindowSplitter, SlidingWindowSplitter
  from sktime.tests.test_switch import run_test_for_class
  from sktime.transformations.series.detrend import Detrender
  from sktime.transformations.series.impute import Imputer
  from sktime.utils._testing.hierarchical import _make_hierarchical
  from sktime.utils.parallel import _get_parallel_test_fixtures
label_desc: Use image node_id 4 for calling a global function with example usage: _create_hierarchical_data(n_columns) and returns: y,  X