code: def subsegment(streamlines, max_segment_length):
      """Split the segments of the streamlines into small segments.
  
      Replaces each segment of each of the streamlines with the smallest possible
      number of equally sized smaller segments such that no segment is longer
      than max_segment_length. Among other things, this can useful for getting
      streamline counts on a grid that is smaller than the length of the
      streamline segments.
  
      Parameters
      ----------
      streamlines : sequence of ndarrays
          The streamlines to be subsegmented.
      max_segment_length : float
          The longest allowable segment length.
  
      Returns
      -------
      output_streamlines : generator
          A set of streamlines.
  
      Notes
      -----
      Segments of 0 length are removed. If unchanged
  
      Examples
      --------
      >>> streamlines = [np.array([[0,0,0],[2,0,0],[5,0,0]])]
      >>> list(subsegment(streamlines, 3.))
      [array([[ 0.,  0.,  0.],
             [ 2.,  0.,  0.],
             [ 5.,  0.,  0.]])]
      >>> list(subsegment(streamlines, 1))
      [array([[ 0.,  0.,  0.],
             [ 1.,  0.,  0.],
             [ 2.,  0.,  0.],
             [ 3.,  0.,  0.],
             [ 4.,  0.,  0.],
             [ 5.,  0.,  0.]])]
      >>> list(subsegment(streamlines, 1.6))
      [array([[ 0. ,  0. ,  0. ],
             [ 1. ,  0. ,  0. ],
             [ 2. ,  0. ,  0. ],
             [ 3.5,  0. ,  0. ],
             [ 5. ,  0. ,  0. ]])]
  
      """
      for sl in streamlines:
          diff = sl[1:] - sl[:-1]
          dist = np.sqrt((diff * diff).sum(-1))
          num_segments = np.ceil(dist / max_segment_length).astype(
              "int"
          )
  
          output_sl = np.empty((num_segments.sum() + 1, 3), "float")
          output_sl[0] = sl[0]
  
          count = 1
          for ii in range(len(num_segments)):
              ns = num_segments[ii]
              if ns == 1:
                  output_sl[count] = sl[ii + 1]
                  count += 1
              elif ns > 1:
                  small_d = diff[ii] / ns
                  point = sl[ii]
                  for _ in range(ns):
                      point = point + small_d
                      output_sl[count] = point
                      count += 1
              elif ns == 0:
                  pass
                  # repeated point
              else:
                  # this should never happen because ns should be a positive
                  # int
                  assert ns >= 0
          yield output_sl
  
imports: from collections import defaultdict, OrderedDict
  from functools import wraps
  from itertools import combinations, groupby
  from warnings import warn
  import numpy
  from nibabel.affines import apply_affine
  from scipy.spatial.distance import cdist
  from dipy.core.geometry import dist_to_corner
  from dipy.tracking import metrics
  from dipy.tracking.vox2track import _streamlines_in_mask
  from dipy.tracking._utils import _mapping_to_voxel, _to_voxel_coordinates
label_desc: Use image node_id 5 for calling a global function with example usage: subsegment(streamlines, max_segment_length) without return types