code: def weights_to_creation_sequence(
      weights, threshold=1, with_labels=False, compact=False
  ):
      """
      Returns a creation sequence for a threshold graph
      determined by the weights and threshold given as input.
      If the sum of two node weights is greater than the
      threshold value, an edge is created between these nodes.
  
      The creation sequence is a list of single characters 'd'
      or 'i': 'd' for dominating or 'i' for isolated vertices.
      Dominating vertices are connected to all vertices present
      when it is added.  The first node added is by convention 'd'.
  
      If with_labels==True:
      Returns a list of 2-tuples containing the vertex number
      and a character 'd' or 'i' which describes the type of vertex.
  
      If compact==True:
      Returns the creation sequence in a compact form that is the number
      of 'i's and 'd's alternating.
      Examples:
      [1,2,2,3] represents d,i,i,d,d,i,i,i
      [3,1,2] represents d,d,d,i,d,d
  
      Notice that the first number is the first vertex to be used for
      construction and so is always 'd'.
  
      with_labels and compact cannot both be True.
      """
      if with_labels and compact:
          raise ValueError("compact sequences cannot be labeled")
  
      # make an indexed copy
      if isinstance(weights, dict):  # labeled weights
          wseq = [[w, label] for (label, w) in weights.items()]
      else:
          wseq = [[w, i] for i, w in enumerate(weights)]
      wseq.sort()
      cs = []  # creation sequence
      cutoff = threshold - wseq[-1][0]
      while wseq:
          if wseq[0][0] < cutoff:  # isolated node
              (w, label) = wseq.pop(0)
              cs.append((label, "i"))
          else:
              (w, label) = wseq.pop()
              cs.append((label, "d"))
              cutoff = threshold - wseq[-1][0]
          if len(wseq) == 1:  # make sure we start with a d
              (w, label) = wseq.pop()
              cs.append((label, "d"))
      # put in correct order
      cs.reverse()
  
      if with_labels:
          return cs
      if compact:
          return make_compact(cs)
      return [v[1] for v in cs]
  
imports: from math import sqrt
  import networkx
  from networkx.utils import py_random_state
label_desc: Use image node_id 7 for calling a global function with example usage: weights_to_creation_sequence(weights, threshold, with_labels, compact) and returns: unknown, cs, make_compact