code: def test_haar_like_feature_fused_type(dtype, feature_type):
      # check that the input type is kept
      img = np.ones((5, 5), dtype=dtype)
      img_ii = integral_image(img)
      expected_dtype = img_ii.dtype
      # to avoid overflow, unsigned type are converted to signed
      if "uint" in expected_dtype.name:
          expected_dtype = np.dtype(
              expected_dtype.name.replace("u", "")
          )
      haar_feature = haar_like_feature(
          img_ii, 0, 0, 5, 5, feature_type=feature_type
      )
      assert haar_feature.dtype == expected_dtype
  
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 3 for calling a global function with example usage: test_haar_like_feature_fused_type(dtype, feature_type) without return types