code: def test_actual_results_random_indices(params):
      """Test the actual results with random indices."""
      fe = WindowFeatureExtractor(**params).fit(X_ones)
  
      n_windows = fe.get_params()["n_windows"]
      if isinstance(n_windows, (float, np.floating)):
          n_windows = int(np.ceil(n_windows * n_timestamps))
  
      min_window_size = fe.get_params()["min_window_size"]
      if isinstance(min_window_size, (float, np.floating)):
          min_window_size = int(np.ceil(min_window_size * n_timestamps))
  
      # With an array full of ones
      arr_actual = fe.transform(X_ones)
      arr_desired = np.tile(
          np.c_[np.ones(n_samples), np.zeros((n_samples, 2))], n_windows
      )
      np.testing.assert_allclose(
          arr_actual, arr_desired, atol=1e-5, rtol=0
      )
  
      # With an array arange-like, test the slope
      arr_actual = fe.transform(X_arange)
      if min_window_size > 1:
          np.testing.assert_allclose(
              arr_actual[:, 2::3], 1, atol=1e-5, rtol=0
          )
      else:
          assert np.all(np.isin(arr_actual[:, 2::3], [0, 1]))
  
file_path: pyts/pyts/classification/tests/test_time_series_forest.py
imports: import numpy
  import pytest
  import re
  from pyts.classification import TimeSeriesForest
  from pyts.classification.time_series_forest import WindowFeatureExtractor
label_desc: Use image node_id 3 for calling a global function with example usage: test_actual_results_random_indices(params) without return types