code: def test_create_backtesting_folds_no_refit_no_initial_train_size_gap(
      capfd, return_all_indexes, expected
  ):
      """
      Test _create_backtesting_folds output when refit is False, gap=5,
      already trained, no remainder, allow_incomplete_fold=True.
      """
      y = pd.Series(np.arange(100))
      initial_train_size = 15  # window_size
      window_size = 15
      gap = 5
      test_size = 10
      refit = False
      externally_fitted = True
  
      folds = _create_backtesting_folds(
          data=y,
          window_size=window_size,
          initial_train_size=initial_train_size,
          test_size=test_size,
          externally_fitted=externally_fitted,
          refit=refit,
          fixed_train_size=False,
          gap=gap,
          allow_incomplete_fold=True,
          return_all_indexes=return_all_indexes,
          verbose=True,
      )
  
      out, _ = capfd.readouterr()
      expected_out = (
          "Information of backtesting process\n"
          "----------------------------------\n"
          "An already trained forecaster is to be used. Window size: 15\n"
          "Number of observations used for backtesting: 85\n"
          "    Number of folds: 8\n"
          "    Number of steps per fold: 10\n"
          "    Number of steps to exclude from the end of each train set before test (gap): 5\n\n"
          "Fold: 0\n"
          "    Validation: 20 -- 29  (n=10)\n"
          "Fold: 1\n"
          "    Validation: 30 -- 39  (n=10)\n"
          "Fold: 2\n"
          "    Validation: 40 -- 49  (n=10)\n"
          "Fold: 3\n"
          "    Validation: 50 -- 59  (n=10)\n"
          "Fold: 4\n"
          "    Validation: 60 -- 69  (n=10)\n"
          "Fold: 5\n"
          "    Validation: 70 -- 79  (n=10)\n"
          "Fold: 6\n"
          "    Validation: 80 -- 89  (n=10)\n"
          "Fold: 7\n"
          "    Validation: 90 -- 99  (n=10)\n\n"
      )
  
      assert out == expected_out
      assert folds == expected
  
imports: import pytest
  import numpy
  import pandas
  from skforecast.model_selection.model_selection import _create_backtesting_folds
label_desc: Use image node_id 4 for calling a global function with example usage: test_create_backtesting_folds_no_refit_no_initial_train_size_gap(capfd, return_all_indexes, expected) without return types