code: def check_eval(test_input, expected):
      """Test which columns are returned for different arguments."""
      if test_input is not None:
          assert len(test_input) == len(expected)
          assert all([a == b for a, b in zip(test_input, expected)])
      else:
          assert expected is None
  
imports: from sktime.tests.test_switch import run_test_for_class
  from sktime.utils.validation._dependencies import _check_soft_dependencies
  import random
  import numpy
  import pandas
  import pytest
  from sklearn.ensemble import GradientBoostingRegressor, HistGradientBoostingRegressor, RandomForestRegressor
  from sklearn.linear_model import LinearRegression
  from sklearn.pipeline import make_pipeline
  from sktime.datasets import load_airline, load_solar
  from sktime.datatypes import get_examples
  from sktime.forecasting.base import ForecastingHorizon
  from sktime.forecasting.compose import make_reduction
  from sktime.forecasting.compose._reduce import _DirectReducer, _RecursiveReducer
  from sktime.performance_metrics.forecasting import mean_absolute_percentage_error
  from sktime.split import temporal_train_test_split
  from sktime.transformations.series.summarize import WindowSummarizer
  from sktime.utils._testing.hierarchical import _make_hierarchical
label_desc: Use image node_id 2 for calling a global function with example usage: check_eval(test_input, expected) without return types