code: def setup_module():
      """Module-level setup"""
      global image_gibbs, image_gt, image_cor, Nre
  
      # Produce a 2D image
      Nori = 32
      image = np.zeros((6 * Nori, 6 * Nori))
      image[Nori : 2 * Nori, Nori : 2 * Nori] = 1
      image[Nori : 2 * Nori, 4 * Nori : 5 * Nori] = 1
      image[2 * Nori : 3 * Nori, Nori : 3 * Nori] = 1
      image[3 * Nori : 4 * Nori, 2 * Nori : 3 * Nori] = 2
      image[3 * Nori : 4 * Nori, 4 * Nori : 5 * Nori] = 1
      image[4 * Nori : 5 * Nori, 3 * Nori : 5 * Nori] = 3
  
      # Corrupt image with gibbs ringing
      c = np.fft.fft2(image)
      c = np.fft.fftshift(c)
      c_crop = c[48:144, 48:144]
      image_gibbs = abs(np.fft.ifft2(c_crop) / 4)
  
      # Produce ground truth
      Nre = 16
      image_gt = np.zeros((6 * Nre, 6 * Nre))
      image_gt[Nre : 2 * Nre, Nre : 2 * Nre] = 1
      image_gt[Nre : 2 * Nre, 4 * Nre : 5 * Nre] = 1
      image_gt[2 * Nre : 3 * Nre, Nre : 3 * Nre] = 1
      image_gt[3 * Nre : 4 * Nre, 2 * Nre : 3 * Nre] = 2
      image_gt[3 * Nre : 4 * Nre, 4 * Nre : 5 * Nre] = 1
      image_gt[4 * Nre : 5 * Nre, 3 * Nre : 5 * Nre] = 3
  
      # Suppressing gibbs artefacts
      image_cor = _gibbs_removal_2d(image_gibbs)
  
file_path: dipy/dipy/denoise/tests/test_gibbs.py
imports: import numpy
  from dipy.denoise.gibbs import _gibbs_removal_1d, _gibbs_removal_2d, gibbs_removal, _image_tv
  from numpy.testing import assert_, assert_array_almost_equal, assert_raises
label_desc: Use image node_id 1 for calling a global function with example usage: setup_module() without return types