code: def odf_deconv(odf_sh, R, B_reg, lambda_=1.0, tau=0.1, r2_term=False):
      r"""ODF constrained-regularized spherical deconvolution using
      the Sharpening Deconvolution Transform (SDT) [1]_, [2]_.
  
      Parameters
      ----------
      odf_sh : ndarray (``(sh_order + 1)*(sh_order + 2)/2``,)
           ndarray of SH coefficients for the ODF spherical function to be
           deconvolved
      R : ndarray (``(sh_order + 1)(sh_order + 2)/2``,
           ``(sh_order + 1)(sh_order + 2)/2``)
           SDT matrix in SH basis
      B_reg : ndarray (``(sh_order + 1)(sh_order + 2)/2``,
           ``(sh_order + 1)(sh_order + 2)/2``)
           SH basis matrix used for deconvolution
      lambda_ : float
           lambda parameter in minimization equation (default 1.0)
      tau : float
           threshold (tau *max(fODF)) controlling the amplitude below
           which the corresponding fODF is assumed to be zero.
      r2_term : bool
           True if ODF is computed from model that uses the $r^2$ term in the
           integral.  Recall that Tuch's ODF (used in Q-ball Imaging [1]_) and
           the true normalized ODF definition differ from a $r^2$ term in the ODF
           integral. The original Sharpening Deconvolution Transform (SDT)
           technique [2]_ is expecting Tuch's ODF without the $r^2$ (see [3]_ for
           the mathematical details).  Now, this function supports ODF that have
           been computed using the $r^2$ term because the proper analytical
           response function has be derived.  For example, models such as DSI,
           GQI, SHORE, CSA, Tensor, Multi-tensor ODFs, should now be deconvolved
           with the r2_term=True.
  
      Returns
      -------
      fodf_sh : ndarray (``(sh_order + 1)(sh_order + 2)/2``,)
           Spherical harmonics coefficients of the constrained-regularized fiber
           ODF
      num_it : int
           Number of iterations in the constrained-regularization used for
           convergence
  
      References
      ----------
      .. [1] Tuch, D. MRM 2004. Q-Ball Imaging.
      .. [2] Descoteaux, M., et al. IEEE TMI 2009. Deterministic and
             Probabilistic Tractography Based on Complex Fibre Orientation
             Distributions
      .. [3] Descoteaux, M, PhD thesis, INRIA Sophia-Antipolis, 2008.
      """
      # In ConstrainedSDTModel.fit, odf_sh is divided by its norm (Z) and
      # sometimes the norm is 0 which creates NaNs.
      if np.any(np.isnan(odf_sh)):
          return np.zeros_like(odf_sh), 0
  
      # Generate initial fODF estimate, which is the ODF truncated at SH order 4
      fodf_sh = np.linalg.lstsq(R, odf_sh, rcond=-1)[0]
      fodf_sh[15:] = 0
  
      fodf = np.dot(B_reg, fodf_sh)
  
      # if sharpening a q-ball odf (it is NOT properly normalized), we need to
      # force normalization otherwise, for DSI, CSA, SHORE, Tensor odfs, they are
      # normalized by construction
      if not r2_term:
          Z = np.linalg.norm(fodf)
          fodf_sh /= Z
  
      threshold = tau * np.max(np.dot(B_reg, fodf_sh))
  
      k = np.empty([])
      convergence = 50
      for num_it in range(1, convergence + 1):
          A = np.dot(B_reg, fodf_sh)
          k2 = np.nonzero(A < threshold)[0]
  
          if (k2.shape[0] + R.shape[0]) < B_reg.shape[1]:
              warnings.warn(
                  "too few negative directions identified - failed to converge"
              )
              return fodf_sh, num_it
  
          if num_it > 1 and k.shape[0] == k2.shape[0]:
              if (k == k2).all():
                  return fodf_sh, num_it
  
          k = k2
          M = np.concatenate((R, lambda_ * B_reg[k, :]))
          ODF = np.concatenate((odf_sh, np.zeros(k.shape)))
          try:
              fodf_sh = np.linalg.lstsq(M, ODF, rcond=-1)[0]
          except np.linalg.LinAlgError:
              # SVD did not converge in Linear Least Squares in current
              # voxel. Proceeding with initial SH estimate for this voxel.
              pass
  
      warnings.warn(
          "maximum number of iterations exceeded - failed to converge"
      )
      return fodf_sh, num_it
  
imports: import warnings
  import numpy
  import numbers
  from scipy.integrate import quad
  from scipy.special import lpn, gamma
  import scipy.linalg
  import scipy.linalg.lapack
  from dipy.data import small_sphere, get_sphere, default_sphere
  from dipy.core.geometry import cart2sphere
  from dipy.core.ndindex import ndindex
  from dipy.sims.voxel import single_tensor
  from dipy.reconst.multi_voxel import multi_voxel_fit
  from dipy.reconst.dti import TensorModel, fractional_anisotropy
  from dipy.reconst.shm import sph_harm_ind_list, real_sh_descoteaux_from_index, sph_harm_lookup, lazy_index, SphHarmFit, real_sh_descoteaux, sh_to_rh, forward_sdeconv_mat, SphHarmModel
  from dipy.reconst.utils import _roi_in_volume, _mask_from_roi
  from dipy.direction.peaks import peaks_from_model
  from dipy.core.geometry import vec2vec_rotmat
  from dipy.utils.deprecator import deprecate_with_version, deprecated_params
label_desc: Use image node_id 7 for calling a global function with example usage: odf_deconv(odf_sh, R, B_reg, lambda_, tau, r2_term) and returns: fodf_sh,  num_it, np,  int, fodf_sh,  num_it, fodf_sh,  num_it