code: def _make_y_X(n_timepoints, n_variables):
      # We generate y and X values so that the y should always be greater
      # than its lagged values and the lagged and contemporaneous values of the
      # exogenous variables X
      assert n_variables < 10
      base = np.arange(n_timepoints)
      y = pd.Series(base + n_variables / 10)
  
      if n_variables > 1:
          X = np.column_stack(
              [base + i / 10 for i in range(1, n_variables)]
          )
          X = pd.DataFrame(X)
      else:
          X = None
  
      return y, X
  
imports: import numpy
  import pandas
  import pytest
  from sklearn.base import BaseEstimator, RegressorMixin
  from sklearn.dummy import DummyRegressor
  from sklearn.linear_model import LinearRegression
  from sklearn.pipeline import make_pipeline
  from sktime.datasets import load_airline
  from sktime.forecasting.base import ForecastingHorizon
  from sktime.forecasting.compose import DirectTabularRegressionForecaster, DirectTimeSeriesRegressionForecaster, DirRecTabularRegressionForecaster, DirRecTimeSeriesRegressionForecaster, MultioutputTabularRegressionForecaster, MultioutputTimeSeriesRegressionForecaster, RecursiveTabularRegressionForecaster, RecursiveTimeSeriesRegressionForecaster, make_reduction
  from sktime.forecasting.compose._reduce import _sliding_window_transform
  from sktime.forecasting.tests._config import TEST_OOS_FHS, TEST_WINDOW_LENGTHS_INT
  from sktime.performance_metrics.forecasting import mean_absolute_percentage_error
  from sktime.regression.base import BaseRegressor
  from sktime.regression.interval_based import TimeSeriesForestRegressor
  from sktime.split import SlidingWindowSplitter, temporal_train_test_split
  from sktime.split.tests.test_split import _get_windows
  from sktime.transformations.panel.reduce import Tabularizer
  from sktime.utils._testing.forecasting import make_forecasting_problem
  from sktime.utils.validation.forecasting import check_fh
label_desc: Use image node_id 2 for calling a global function with example usage: _make_y_X(n_timepoints, n_variables) and returns: y,  X