code: def test_indices_window_feature_extractor(params):
      """Test that the indices are compatible with the input parameters."""
      fe = WindowFeatureExtractor(**params).fit(X_arange)
      indices = fe.indices_
  
      min_window_size = fe.get_params()["min_window_size"]
      if isinstance(min_window_size, (float, np.floating)):
          min_window_size = np.ceil(min_window_size * n_timestamps)
  
      np.testing.assert_array_less(-1, indices[:, 0])
      np.testing.assert_array_less(indices[:, 1], n_timestamps + 1)
      np.testing.assert_array_less(
          min_window_size, indices[:, 1] - indices[:, 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 2 for calling a global function with example usage: test_indices_window_feature_extractor(params) without return types