code: def average_link_graph_segment(G, stop=0, qmax=1, verbose=False):
      """Agglomerative function based on a (hopefully sparse) similarity graph
  
      Parameters
      ----------
      G the input graph
      stop: float
          the stopping criterion
      qmax: int, optional
          the number of desired clusters (in the limit of the stopping criterion)
      verbose : bool, optional
          If True, print diagnostic information
  
      Returns
      -------
      u: array of shape (G.V)
         a labelling of the graph vertices according to the criterion
      cost: array of shape (G.V (?))
            the cost of each merge step during the clustering procedure
      """
      warn(
          "Function average_link_graph_segment deprecated, will be removed",
          FutureWarning,
          stacklevel=2,
      )
  
      # prepare a graph with twice the number of vertices
      n = G.V
      if qmax == -1:
          qmax = n
      qmax = int(np.minimum(qmax, n))
  
      t = average_link_graph(G)
  
      if verbose:
          t.plot()
  
      u1 = np.zeros(n, np.int_)
      u2 = np.zeros(n, np.int_)
      if stop >= 0:
          u1 = t.partition(-stop)
      if qmax > 0:
          u2 = t.split(qmax)
  
      if u1.max() < u2.max():
          u = u2
      else:
          u = u1
  
      cost = -t.get_height()
      cost = cost[t.isleaf() == False]
  
      return u, cost
  
imports: from warnings import warn
  import numpy
  from ..graph.forest import Forest
  from ..graph.graph import WeightedGraph
label_desc: Use image node_id 3 for calling a global function with example usage: average_link_graph_segment(G, stop, qmax, verbose) and returns: u,  cost