code: def eigenvectors(creation_sequence):
      """
      Return a 2-tuple of Laplacian eigenvalues and eigenvectors
      for the threshold network with creation_sequence.
      The first value is a list of eigenvalues.
      The second value is a list of eigenvectors.
      The lists are in the same order so corresponding eigenvectors
      and eigenvalues are in the same position in the two lists.
  
      Notice that the order of the eigenvalues returned by eigenvalues(cs)
      may not correspond to the order of these eigenvectors.
      """
      ccs = make_compact(creation_sequence)
      N = sum(ccs)
      vec = [0] * N
      val = vec[:]
      # get number of type d nodes to the right (all for first node)
      dr = sum(ccs[::2])
  
      nn = ccs[0]
      vec[0] = [1.0 / sqrt(N)] * N
      val[0] = 0
      e = dr
      dr -= nn
      type_d = True
      i = 1
      dd = 1
      while dd < nn:
          scale = 1.0 / sqrt(dd * dd + i)
          vec[i] = i * [-scale] + [dd * scale] + [0] * (N - i - 1)
          val[i] = e
          i += 1
          dd += 1
      if len(ccs) == 1:
          return (val, vec)
      for nn in ccs[1:]:
          scale = 1.0 / sqrt(nn * i * (i + nn))
          vec[i] = (
              i * [-nn * scale] + nn * [i * scale] + [0] * (N - i - nn)
          )
          # find eigenvalue
          type_d = not type_d
          if type_d:
              e = i + dr
              dr -= nn
          else:
              e = dr
          val[i] = e
          st = i
          i += 1
          dd = 1
          while dd < nn:
              scale = 1.0 / sqrt(i - st + dd * dd)
              vec[i] = (
                  [0] * st
                  + (i - st) * [-scale]
                  + [dd * scale]
                  + [0] * (N - i - 1)
              )
              val[i] = e
              i += 1
              dd += 1
      return (val, vec)
  
imports: from math import sqrt
  import networkx
  from networkx.utils import py_random_state
label_desc: Use image node_id 21 for calling a global function with example usage: eigenvectors(creation_sequence) and returns: val,  vec, val,  vec