code: def test_col_vectorization_correct_col_order():
      """Test that forecaster vectorization preserves column index ordering.
  
      Failure case is as in issue #4683 where the column index is correct,
      but the values are in fact coming from forecasters in jumbled order.
      """
      cols = [
          "realgdp",
          "realcons",
          "realinv",
          "realgovt",
          "realdpi",
          "cpi",
          "m1",
      ]
      vals = np.random.rand(5, 7)
      y = pd.DataFrame(vals, columns=cols)
  
      f = NaiveForecaster()
      # force univariate tag to trigger vectorization over columns for sure
      f.set_tags(**{"scitype:y": "univariate"})
  
      f.fit(y=y, fh=[1])
      y_pred = f.predict()
  
      # last value, so entries of last y column and y_pred should all be exactly equal
      # if they were jumbled, as in #4683 by lexicographic column name order,
      # this assertion would fail since the values are all different
      assert (y_pred == y.iloc[4]).all().all()
  
imports: from functools import reduce
  from operator import mul
  import numpy
  import pandas
  import pytest
  from pandas.testing import assert_series_equal
  from sktime.datatypes import check_is_mtype, convert
  from sktime.datatypes._utilities import get_cutoff, get_window
  from sktime.forecasting.arima import ARIMA
  from sktime.forecasting.naive import NaiveForecaster
  from sktime.forecasting.theta import ThetaForecaster
  from sktime.forecasting.var import VAR
  from sktime.utils._testing.hierarchical import _make_hierarchical
  from sktime.utils._testing.panel import _make_panel
  from sktime.utils._testing.series import _make_series
  from sktime.utils.parallel import _get_parallel_test_fixtures
  from sktime.utils.validation._dependencies import _check_estimator_deps
label_desc: Use image node_id 7 for calling a global function with example usage: test_col_vectorization_correct_col_order() without return types