code: def test_haar_like_feature_precomputed(feature_type):
      img = np.ones((5, 5), dtype=np.int8)
      img_ii = integral_image(img)
      if isinstance(feature_type, list):
          # shuffle the index of the feature to be sure that we are output
          # the features in the same order
          shuffle(feature_type)
          feat_coord, feat_type = zip(
              *[
                  haar_like_feature_coord(5, 5, feat_t)
                  for feat_t in feature_type
              ]
          )
          feat_coord = np.concatenate(feat_coord)
          feat_type = np.concatenate(feat_type)
      else:
          feat_coord, feat_type = haar_like_feature_coord(
              5, 5, feature_type
          )
      haar_feature_precomputed = haar_like_feature(
          img_ii,
          0,
          0,
          5,
          5,
          feature_type=feat_type,
          feature_coord=feat_coord,
      )
      haar_feature = haar_like_feature(img_ii, 0, 0, 5, 5, feature_type)
      assert_array_equal(haar_feature_precomputed, haar_feature)
  
file_path: scikit-image/skimage/feature/tests/test_haar.py
imports: from random import shuffle
  import pytest
  import numpy
  from numpy.testing import assert_allclose
  from numpy.testing import assert_array_equal
  from skimage.transform import integral_image
  from skimage.feature import haar_like_feature
  from skimage.feature import haar_like_feature_coord
  from skimage.feature import draw_haar_like_feature
label_desc: Use image node_id 5 for calling a global function with example usage: test_haar_like_feature_precomputed(feature_type) without return types