code: def test_prepare_searchlight_mvpa_data():
      images = io.load_images_from_dir(data_dir, suffix=suffix)
      conditions = io.load_labels(epoch_file)
      processed_data, labels = prepare_searchlight_mvpa_data(
          images, conditions
      )
      expected_searchlight_processed_data = np.load(
          expected_dir / "expected_searchlight_processed_data.npy"
      )
      for idx in range(len(processed_data)):
          assert np.allclose(
              processed_data[idx],
              expected_searchlight_processed_data[idx],
          ), "raw data do not match in test_prepare_searchlight_mvpa_data"
      assert np.array_equal(
          labels, expected_labels
      ), "the labels do not match in test_prepare_searchlight_mvpa_data"
      from brainiak.fcma.preprocessing import RandomType
  
      images = io.load_images_from_dir(data_dir, suffix=suffix)
      random_processed_data, _ = prepare_searchlight_mvpa_data(
          images, conditions, random=RandomType.REPRODUCIBLE
      )
      assert len(random_processed_data) == len(
          expected_searchlight_processed_data
      ), "numbers of epochs do not match in test_prepare_searchlight_mvpa_data"
      images = io.load_images_from_dir(data_dir, suffix=suffix)
      random_processed_data, _ = prepare_searchlight_mvpa_data(
          images, conditions, random=RandomType.UNREPRODUCIBLE
      )
      assert len(random_processed_data) == len(
          expected_searchlight_processed_data
      ), "numbers of epochs do not match in test_prepare_searchlight_mvpa_data"
  
imports: from pathlib import Path
  import numpy
  from brainiak.fcma.preprocessing import prepare_fcma_data, prepare_mvpa_data, prepare_searchlight_mvpa_data
  from brainiak import io
label_desc: Use image node_id 3 for calling a global function with example usage: test_prepare_searchlight_mvpa_data() without return types