code: def test_daisy_normalization():
      img = img_as_float(data.astronaut()[:64, :64].mean(axis=2))
  
      descs = daisy(img, normalization="l1")
      for i in range(descs.shape[0]):
          for j in range(descs.shape[1]):
              assert_almost_equal(np.sum(descs[i, j, :]), 1)
      descs_ = daisy(img)
      assert_almost_equal(descs, descs_)
  
      descs = daisy(img, normalization="l2")
      for i in range(descs.shape[0]):
          for j in range(descs.shape[1]):
              assert_almost_equal(sqrt(np.sum(descs[i, j, :] ** 2)), 1)
  
      orientations = 8
      descs = daisy(
          img, orientations=orientations, normalization="daisy"
      )
      desc_dims = descs.shape[2]
      for i in range(descs.shape[0]):
          for j in range(descs.shape[1]):
              for k in range(0, desc_dims, orientations):
                  assert_almost_equal(
                      sqrt(
                          np.sum(descs[i, j, k : k + orientations] ** 2)
                      ),
                      1,
                  )
  
      img = np.zeros((50, 50))
      descs = daisy(img, normalization="off")
      for i in range(descs.shape[0]):
          for j in range(descs.shape[1]):
              assert_almost_equal(np.sum(descs[i, j, :]), 0)
  
      with pytest.raises(ValueError):
          daisy(img, normalization="does_not_exist")
  
file_path: scikit-image/skimage/feature/tests/test_daisy.py
imports: import numpy
  import pytest
  from numpy import sqrt, ceil
  from numpy.testing import assert_almost_equal
  from skimage import data
  from skimage import img_as_float
  from skimage.feature import daisy
label_desc: Use image node_id 6 for calling a global function with example usage: test_daisy_normalization() without return types