code: def LR_deconv(prop, psf, numit=5, acc_factor=1):
      r"""
      Perform Lucy-Richardson deconvolution algorithm on a 3D array.
  
      Parameters
      ----------
      prop : 3-D ndarray of dtype float
          The 3D volume to be deconvolve
      psf : 3-D ndarray of dtype float
          The filter that will be used for the deconvolution.
      numit : int
          Number of Lucy-Richardson iteration to perform.
      acc_factor : float
          Exponential acceleration factor as in [1]_.
  
      References
      ----------
      .. [1] Biggs David S.C. et al., "Acceleration of Iterative Image
         Restoration Algorithms", Applied Optics, vol. 36, No. 8, p. 1766-1775,
         1997.
  
      """
  
      eps = 1e-16
      # Create the otf of the same size as prop
      otf = np.zeros_like(prop)
      # prop.ndim==3
      otf[
          otf.shape[0] // 2
          - psf.shape[0] // 2 : otf.shape[0] // 2
          + psf.shape[0] // 2
          + 1,
          otf.shape[1] // 2
          - psf.shape[1] // 2 : otf.shape[1] // 2
          + psf.shape[1] // 2
          + 1,
          otf.shape[2] // 2
          - psf.shape[2] // 2 : otf.shape[2] // 2
          + psf.shape[2] // 2
          + 1,
      ] = psf
      otf = np.real(np.fft.fftn(np.fft.ifftshift(otf)))
      # Enforce Positivity
      prop = np.clip(prop, 0, np.inf)
      prop_deconv = prop.copy()
      for it in range(numit):
          # Blur the estimate
          reBlurred = np.real(
              np.fft.ifftn(otf * np.fft.fftn(prop_deconv))
          )
          reBlurred[reBlurred < eps] = eps
          # Update the estimate
          prop_deconv = (
              prop_deconv
              * (
                  np.real(
                      np.fft.ifftn(
                          otf * np.fft.fftn((prop / reBlurred) + eps)
                      )
                  )
              )
              ** acc_factor
          )
          # Enforce positivity
          prop_deconv = np.clip(prop_deconv, 0, np.inf)
      return prop_deconv / prop_deconv.sum()
  
imports: import numpy
  from scipy.ndimage import map_coordinates
  from scipy.fftpack import fftn, fftshift, ifftshift
  from dipy.reconst.odf import OdfModel, OdfFit
  from dipy.reconst.cache import Cache
  from dipy.reconst.multi_voxel import multi_voxel_fit
label_desc: Use image node_id 10 for calling a global function with example usage: LR_deconv(prop, psf, numit, acc_factor) and returns: unknown