code: def test_evaluate_hierarchical_unequal_X_y():
      """Test evaluate with hierarchical X and y where X is larger.
  
      Tests failure case in bug report #4842.
      """
      from sktime.transformations.hierarchical.aggregate import (
          Aggregator,
      )
  
      # hierarchical/panel with 2-level pd.MultiIndex,
      # level 0 with "A", and "B", dates from 2020-01-01 to 2020-01-10
      df = pd.DataFrame(
          index=pd.MultiIndex.from_product(
              [
                  ["A", "B"],
                  pd.date_range("2020-01-01", "2020-01-10").to_period(
                      "D"
                  ),
              ]
          ),
          data={"target": np.arange(20) % 10},
      ).sort_index()
      df = Aggregator().fit_transform(df)
  
      y = df[df.index.get_level_values(-1) < "2020-01-08"]
      X = df.copy()
      cv = ExpandingWindowSplitter(
          initial_window=2, fh=[1], step_length=1
      )
  
      f = NaiveForecaster()
  
      # this fails in the case of #4842 as y and X have different length
      res = evaluate(f, cv, y, X, error_score="raise")
  
      # further sanity checks to pin down deterministic properties of return
      assert isinstance(res, pd.DataFrame)
      assert res.shape == (5, 5)
  
      expected_cols = np.array([1 / 2, 1 / 3, 1 / 4, 1 / 5, 1 / 6])
      output_metrics = res.loc[
          :, "test_MeanAbsolutePercentageError"
      ].values
      _assert_array_almost_equal(output_metrics, expected_cols)
  
imports: import numpy
  import pandas
  import pytest
  from sklearn.linear_model import LinearRegression
  from sktime.datasets import load_airline, load_longley
  from sktime.exceptions import FitFailedWarning
  from sktime.forecasting.arima import ARIMA, AutoARIMA
  from sktime.forecasting.compose._reduce import DirectReductionForecaster
  from sktime.forecasting.exp_smoothing import ExponentialSmoothing
  from sktime.forecasting.model_evaluation import evaluate
  from sktime.forecasting.model_evaluation._functions import _check_scores, _get_column_order_and_datatype
  from sktime.forecasting.naive import NaiveForecaster
  from sktime.forecasting.tests._config import TEST_FHS, TEST_STEP_LENGTHS_INT
  from sktime.performance_metrics.forecasting import MeanAbsoluteError, MeanAbsolutePercentageError, MeanAbsoluteScaledError
  from sktime.performance_metrics.forecasting.probabilistic import CRPS, EmpiricalCoverage, LogLoss, PinballLoss
  from sktime.split import ExpandingWindowSplitter, SlidingWindowSplitter
  from sktime.tests.test_switch import run_test_for_class
  from sktime.utils._testing.estimator_checks import _assert_array_almost_equal
  from sktime.utils._testing.forecasting import make_forecasting_problem
  from sktime.utils._testing.hierarchical import _make_hierarchical
  from sktime.utils._testing.series import _make_series
  from sktime.utils.parallel import _get_parallel_test_fixtures
  from sktime.utils.validation._dependencies import _check_soft_dependencies
label_desc: Use image node_id 10 for calling a global function with example usage: test_evaluate_hierarchical_unequal_X_y() without return types