code: def test_spike():
      """Test if a convolution with a delta spike is equal to the kernel
      saved in the lookup table."""
  
      # create kernel
      D33 = 1.0
      D44 = 0.04
      t = 1
      num_orientations = 5
      k = EnhancementKernel(
          D33,
          D44,
          t,
          orientations=num_orientations,
          force_recompute=True,
      )
  
      # create a delta spike
      numorientations = k.get_orientations().shape[0]
      spike = np.zeros((7, 7, 7, numorientations), dtype=np.float64)
      spike[3, 3, 3, 0] = 1
  
      # convolve kernel with delta spike
      csd_enh = convolve_sf(spike, k, test_mode=True, normalize=False)
  
      # check if kernel matches with the convolved delta spike
      totalsum = 0.0
      for i in range(0, numorientations):
          totalsum += np.sum(
              np.array(k.get_lookup_table())[i, 0, :, :, :]
              - np.array(csd_enh)[:, :, :, i]
          )
      npt.assert_equal(totalsum, 0.0)
  
file_path: dipy/dipy/denoise/tests/test_kernel.py
imports: import warnings
  from dipy.denoise.enhancement_kernel import EnhancementKernel
  from dipy.denoise.shift_twist_convolution import convolve, convolve_sf
  from dipy.reconst.shm import sh_to_sf, sf_to_sh, descoteaux07_legacy_msg
  from dipy.core.sphere import Sphere
  import numpy
  import numpy.testing
label_desc: Use image node_id 2 for calling a global function with example usage: test_spike() without return types