code: def _register_neighb_to_model(
      self,
      model_bundle,
      neighb_streamlines,
      metric=None,
      x0=None,
      bounds=None,
      select_model=400,
      select_target=600,
      method="L-BFGS-B",
      nb_pts=20,
      num_threads=None,
  ):
      if self.verbose:
          logger.info("# Local SLR of neighb_streamlines to model")
          t = time()
  
      if metric is None or metric == "symmetric":
          metric = BundleMinDistanceMetric(num_threads=num_threads)
      if metric == "asymmetric":
          metric = BundleMinDistanceAsymmetricMetric()
      if metric == "diagonal":
          metric = BundleSumDistanceMatrixMetric()
  
      if x0 is None:
          x0 = "similarity"
  
      if bounds is None:
          bounds = [
              (-30, 30),
              (-30, 30),
              (-30, 30),
              (-45, 45),
              (-45, 45),
              (-45, 45),
              (0.8, 1.2),
          ]
  
      # TODO this can be speeded up by using directly the centroids
      static = select_random_set_of_streamlines(
          model_bundle, select_model, rng=self.rng
      )
      moving = select_random_set_of_streamlines(
          neighb_streamlines, select_target, rng=self.rng
      )
  
      static = set_number_of_points(static, nb_pts)
      moving = set_number_of_points(moving, nb_pts)
  
      slr = StreamlineLinearRegistration(
          metric=metric, x0=x0, bounds=bounds, method=method
      )
      slm = slr.optimize(static, moving)
  
      transf_streamlines = neighb_streamlines.copy()
      transf_streamlines._data = apply_affine(
          slm.matrix, transf_streamlines._data
      )
  
      transf_matrix = slm.matrix
      slr_bmd = slm.fopt
      slr_iterations = slm.iterations
  
      if self.verbose:
          logger.info(f" Square-root of BMD is {np.sqrt(slr_bmd):.3f}")
          if slr_iterations is not None:
              logger.info(f" Number of iterations {slr_iterations}")
          logger.info(f" Matrix size {slm.matrix.shape}")
          original = np.get_printoptions()
          np.set_printoptions(3, suppress=True)
          logger.info(transf_matrix)
          logger.info(slm.xopt)
          np.set_printoptions(**original)
  
          logger.info(f" Duration {time() - t:0.3f} s\n")
  
      return transf_streamlines, slr_bmd
  
imports: from time import time
  from itertools import chain
  import logging
  import numpy
  from dipy.tracking.streamline import set_number_of_points, nbytes, select_random_set_of_streamlines
  from dipy.segment.clustering import qbx_and_merge
  from dipy.tracking.distances import bundles_distances_mdf, bundles_distances_mam
  from dipy.align.streamlinear import StreamlineLinearRegistration, BundleMinDistanceMetric, BundleSumDistanceMatrixMetric, BundleMinDistanceAsymmetricMetric
  from dipy.tracking.streamline import Streamlines, length
  from dipy.utils.deprecator import deprecated_params
  from nibabel.affines import apply_affine
label_desc: Use image node_id 8 for calling the RecoBundles obj's underlying member method code with example usage: obj._register_neighb_to_model(model_bundle, neighb_streamlines, metric, x0, bounds, select_model, select_target, method, nb_pts, num_threads) and returns: transf_streamlines,  slr_bmd