code: def detect_corresponding_tracks_plus(
      indices, tracks1, indices2, tracks2
  ):
      """Detect corresponding tracks from 1 to 2 where tracks1 & tracks2 are
      sequences of tracks
  
      Parameters
      ----------
      indices : sequence
              of indices of tracks1 that are to be detected in tracks2
      tracks1 : sequence
              of tracks as arrays, shape (N1,3) .. (Nm,3)
      indices2 : sequence
              of indices of tracks2 in the initial brain
      tracks2 : sequence
              of tracks as arrays, shape (M1,3) .. (Mm,3)
  
      Returns
      -------
      track2track : array (N,2) where N is len(indices)
         of int showing the correspondence in th following way
         the first column is the current index of tracks1
         the second column is the corresponding index in tracks2
  
      Examples
      --------
      >>> import numpy as np
      >>> import dipy.tracking.learning as tl
      >>> A = np.array([[0, 0, 0], [1, 1, 1], [2, 2, 2]])
      >>> B = np.array([[1, 0, 0], [2, 0, 0], [3, 0, 0]])
      >>> C = np.array([[0, 0, -1], [0, 0, -2], [0, 0, -3]])
      >>> bundle1 = [A, B, C]
      >>> bundle2 = [B, A]
      >>> indices = [0, 1]
      >>> indices2 = indices
      >>> arr = tl.detect_corresponding_tracks_plus(indices, bundle1, indices2, bundle2)
  
      Notes
      -----
      To find the corresponding tracks we use mam_distances with 'avg' option.
      Then we calculate the argmin of all the calculated distances and return it
      for every index. (See 3rd column of arr in the example given below.)
  
  
      See Also
      --------
      distances.mam_distances
  
      """
      li = len(indices)
      track2track = np.zeros((li, 2))
      cnt = 0
      for i in indices:
          rt = [pf.mam_distances(tracks1[i], t, "avg") for t in tracks2]
          rt = np.array(rt)
          track2track[cnt] = np.array([i, indices2[rt.argmin()]])
          cnt += 1
      return track2track.astype(int)
  
label_desc: Use image node_id 2 for calling a global function with example usage: detect_corresponding_tracks_plus(indices, tracks1, indices2, tracks2) and returns: track2track