code: def progressive_slr(
      static,
      moving,
      metric,
      x0,
      bounds,
      method="L-BFGS-B",
      verbose=False,
      num_threads=None,
  ):
      """Progressive SLR.
  
      This is an utility function that allows for example to do affine
      registration using Streamline-based Linear Registration (SLR)
      [Garyfallidis15]_ by starting with translation first, then rigid,
      then similarity, scaling and finally affine.
  
      Similarly, if for example, you want to perform rigid then you start with
      translation first. This progressive strategy can helps with finding the
      optimal parameters of the final transformation.
  
      Parameters
      ----------
      static : Streamlines
      moving : Streamlines
      metric : StreamlineDistanceMetric
      x0 : string
          Could be any of 'translation', 'rigid', 'similarity', 'scaling',
          'affine'
      bounds : array
          Boundaries of registration parameters. See variable `DEFAULT_BOUNDS`
          for example.
      method : string
          L_BFGS_B' or 'Powell' optimizers can be used. Default is 'L_BFGS_B'.
      verbose :  bool, optional.
          If True, log messages. Default:
      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.
  
      References
      ----------
      .. [Garyfallidis15] Garyfallidis et al. "Robust and efficient linear
          registration of white-matter fascicles in the space of streamlines",
          NeuroImage, 117, 124--140, 2015
  
      """
      if verbose:
          logger.info("Progressive Registration is Enabled")
  
      if x0 in (
          "translation",
          "rigid",
          "similarity",
          "scaling",
          "affine",
      ):
          if verbose:
              logger.info(" Translation  (3 parameters)...")
          slr_t = StreamlineLinearRegistration(
              metric=metric,
              x0="translation",
              bounds=bounds[:3],
              method=method,
          )
  
          slm_t = slr_t.optimize(static, moving)
  
      if x0 in ("rigid", "similarity", "scaling", "affine"):
          x_translation = slm_t.xopt
          x = np.zeros(6)
          x[:3] = x_translation
          if verbose:
              logger.info(" Rigid  (6 parameters) ...")
          slr_r = StreamlineLinearRegistration(
              metric=metric, x0=x, bounds=bounds[:6], method=method
          )
          slm_r = slr_r.optimize(static, moving)
  
      if x0 in ("similarity", "scaling", "affine"):
          x_rigid = slm_r.xopt
          x = np.zeros(7)
          x[:6] = x_rigid
          x[6] = 1.0
          if verbose:
              logger.info(" Similarity (7 parameters) ...")
          slr_s = StreamlineLinearRegistration(
              metric=metric, x0=x, bounds=bounds[:7], method=method
          )
          slm_s = slr_s.optimize(static, moving)
  
      if x0 in ("scaling", "affine"):
          x_similarity = slm_s.xopt
          x = np.zeros(9)
          x[:6] = x_similarity[:6]
          x[6:] = np.array((x_similarity[6],) * 3)
          if verbose:
              logger.info(" Scaling (9 parameters) ...")
  
          slr_c = StreamlineLinearRegistration(
              metric=metric, x0=x, bounds=bounds[:9], method=method
          )
          slm_c = slr_c.optimize(static, moving)
  
      if x0 == "affine":
          x_scaling = slm_c.xopt
          x = np.zeros(12)
          x[:9] = x_scaling[:9]
          x[9:] = np.zeros(3)
          if verbose:
              logger.info(" Affine (12 parameters) ...")
  
          slr_a = StreamlineLinearRegistration(
              metric=metric, x0=x, bounds=bounds[:12], method=method
          )
          slm_a = slr_a.optimize(static, moving)
  
      if x0 == "translation":
          slm = slm_t
      elif x0 == "rigid":
          slm = slm_r
      elif x0 == "similarity":
          slm = slm_s
      elif x0 == "scaling":
          slm = slm_c
      elif x0 == "affine":
          slm = slm_a
      else:
          raise ValueError("Incorrect SLR transform")
  
      return slm
  
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 6 for calling a global function with example usage: progressive_slr(static, moving, metric, x0, bounds, method, verbose, num_threads) and returns: slm