code: def slr_with_qbx(
      static,
      moving,
      x0="affine",
      rm_small_clusters=50,
      maxiter=100,
      select_random=None,
      verbose=False,
      greater_than=50,
      less_than=250,
      qbx_thr=(40, 30, 20, 15),
      nb_pts=20,
      progressive=True,
      rng=None,
      num_threads=None,
  ):
      """Utility function for registering large tractograms.
  
      For efficiency, we apply the registration on cluster centroids and remove
      small clusters.
  
      Parameters
      ----------
      static : Streamlines
      moving : Streamlines
  
      x0 : str, optional.
          rigid, similarity or affine transformation model (default affine)
  
      rm_small_clusters : int, optional
          Remove clusters that have less than `rm_small_clusters`
  
      maxiter : int, optional
          Maximum number of iterations to perform.
  
      select_random : int, optional.
          If not, None selects a random number of streamlines to apply clustering
          Default None.
  
      verbose : bool, optional
          If True, logs information about optimization. Default: False
  
      greater_than : int, optional
              Keep streamlines that have length greater than
              this value (default 50)
  
      less_than : int, optional
              Keep streamlines have length less than this value (default 250)
  
      qbx_thr : variable int
              Thresholds for QuickBundlesX (default [40, 30, 20, 15])
  
      nb_pts : int, optional
              Number of points for discretizing each streamline (default 20)
  
      progressive : boolean, optional
              (default True)
  
      rng : np.random.Generator
          If None creates random generator in function.
  
      num_threads : int, optional
          Number of threads to be used for OpenMP parallelization. If None
          (default) the value of OMP_NUM_THREADS environment variable is used
          if it is set, otherwise all available threads are used. If < 0 the
          maximal number of threads minus |num_threads + 1| is used (enter -1 to
          use as many threads as possible). 0 raises an error. Only metrics
          using OpenMP will use this variable.
  
      Notes
      -----
      The order of operations is the following. First short or long streamlines
      are removed. Second, the tractogram or a random selection of the tractogram
      is clustered with QuickBundles. Then SLR [Garyfallidis15]_ is applied.
  
      References
      ----------
      .. [Garyfallidis15] Garyfallidis et al. "Robust and efficient linear
      registration of white-matter fascicles in the space of streamlines",
      NeuroImage, 117, 124--140, 2015
      .. [Garyfallidis14] Garyfallidis et al., "Direct native-space fiber
              bundle alignment for group comparisons", ISMRM, 2014.
      .. [Garyfallidis17] Garyfallidis et al. Recognition of white matter
      bundles using local and global streamline-based registration and
      clustering, Neuroimage, 2017.
  
      """
      if rng is None:
          rng = np.random.default_rng()
  
      if verbose:
          logger.info("Static streamlines size {}".format(len(static)))
          logger.info("Moving streamlines size {}".format(len(moving)))
  
      def check_range(streamline, gt=greater_than, lt=less_than):
          if (length(streamline) > gt) & (length(streamline) < lt):
              return True
          else:
              return False
  
      streamlines1 = Streamlines(
          static[np.array([check_range(s) for s in static])]
      )
      streamlines2 = Streamlines(
          moving[np.array([check_range(s) for s in moving])]
      )
      if verbose:
          logger.info(
              "Static streamlines after length reduction {}".format(
                  len(streamlines1)
              )
          )
          logger.info(
              "Moving streamlines after length reduction {}".format(
                  len(streamlines2)
              )
          )
  
      if select_random is not None:
          rstreamlines1 = select_random_set_of_streamlines(
              streamlines1, select_random, rng=rng
          )
      else:
          rstreamlines1 = streamlines1
  
      rstreamlines1 = set_number_of_points(rstreamlines1, nb_pts)
  
      rstreamlines1._data.astype("f4")
  
      cluster_map1 = qbx_and_merge(
          rstreamlines1, thresholds=qbx_thr, rng=rng
      )
      qb_centroids1 = remove_clusters_by_size(
          cluster_map1, rm_small_clusters
      )
  
      if select_random is not None:
          rstreamlines2 = select_random_set_of_streamlines(
              streamlines2, select_random, rng=rng
          )
      else:
          rstreamlines2 = streamlines2
  
      rstreamlines2 = set_number_of_points(rstreamlines2, nb_pts)
      rstreamlines2._data.astype("f4")
  
      cluster_map2 = qbx_and_merge(
          rstreamlines2, thresholds=qbx_thr, rng=rng
      )
  
      qb_centroids2 = remove_clusters_by_size(
          cluster_map2, rm_small_clusters
      )
  
      if verbose:
          t = time()
  
      if not len(qb_centroids1):
          msg = "No cluster centroids found in Static Streamlines. Please "
          msg += "decrease  the value of rm_small_clusters."
          raise ValueError(msg)
      if not len(qb_centroids2):
          msg = "No cluster centroids found in Moving Streamlines. Please "
          msg += "decrease the value of rm_small_clusters."
          raise ValueError(msg)
  
      if not progressive:
          slr = StreamlineLinearRegistration(
              x0=x0,
              options={"maxiter": maxiter},
              num_threads=num_threads,
          )
          slm = slr.optimize(qb_centroids1, qb_centroids2)
      else:
          bounds = DEFAULT_BOUNDS
  
          slm = progressive_slr(
              qb_centroids1,
              qb_centroids2,
              x0=x0,
              metric=None,
              bounds=bounds,
              num_threads=num_threads,
          )
  
      if verbose:
          logger.info(
              "QB static centroids size %d"
              % len(
                  qb_centroids1,
              )
          )
          logger.info(
              "QB moving centroids size %d"
              % len(
                  qb_centroids2,
              )
          )
          duration = time() - t
          logger.info("SLR finished in  %0.3f seconds." % (duration,))
          if slm.iterations is not None:
              logger.info("SLR iterations: %d " % (slm.iterations,))
  
      moved = slm.transform(moving)
  
      return moved, slm.matrix, qb_centroids1, qb_centroids2
  
imports: import logging
  import abc
  from itertools import combinations
  import numpy
  from dipy.core.optimize import Optimizer
  from dipy.align.bundlemin import _bundle_minimum_distance, _bundle_minimum_distance_asymmetric, distance_matrix_mdf
  from dipy.tracking.streamline import transform_streamlines, unlist_streamlines, center_streamlines, set_number_of_points, select_random_set_of_streamlines, length, Streamlines
  from dipy.segment.clustering import qbx_and_merge
  from dipy.core.geometry import compose_transformations, compose_matrix, decompose_matrix
  from time import time
label_desc: Use image node_id 7 for calling a global function with example usage: slr_with_qbx(static, moving, x0, rm_small_clusters, maxiter, select_random, verbose, greater_than, less_than, qbx_thr, nb_pts, progressive, rng, num_threads) and returns: moved,  slm,  qb_centroids1,  qb_centroids2, True, False