code: def bundle_min_distance_fast(
      t, static, moving, block_size, num_threads=None
  ):
      """MDF-based pairwise distance optimization function (MIN).
  
      We minimize the distance between moving streamlines as they align
      with the static streamlines.
  
      Parameters
      ----------
      t : array
          1D array. t is a vector of affine transformation parameters with
          size at least 6.
          If the size is 6, t is interpreted as translation + rotation.
          If the size is 7, t is interpreted as translation + rotation +
          isotropic scaling.
          If size is 12, t is interpreted as translation + rotation +
          scaling + shearing.
  
      static : array
          N*M x 3 array. All the points of the static streamlines. With order of
          streamlines intact. Where N is the number of streamlines and M
          is the number of points per streamline.
  
      moving : array
          K*M x 3 array. All the points of the moving streamlines. With order of
          streamlines intact. Where K is the number of streamlines and M
          is the number of points per streamline.
  
      block_size : int
          Number of points per streamline. All streamlines in static and moving
          should have the same number of points M.
  
      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.
  
      Returns
      -------
      cost: float
  
      Notes
      -----
      This is a faster implementation of ``bundle_min_distance``, which requires
      that all the points of each streamline are allocated into an ndarray
      (of shape N*M by 3, with N the number of points per streamline and M the
      number of streamlines). This can be done by calling
      `dipy.tracking.streamlines.unlist_streamlines`.
  
      """
  
      aff = compose_matrix44(t)
      moving = np.dot(aff[:3, :3], moving.T).T + aff[:3, 3]
      moving = np.ascontiguousarray(moving, dtype=np.float64)
  
      rows = static.shape[0] // block_size
      cols = moving.shape[0] // block_size
  
      return _bundle_minimum_distance(
          static, moving, rows, cols, block_size, num_threads
      )
  
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 3 for calling a global function with example usage: bundle_min_distance_fast(t, static, moving, block_size, num_threads) and returns: _bundle_minimum_distance