code: def ward(G, feature, verbose=False):
      """
      Agglomerative function based on a topology-defining graph
      and a feature matrix.
  
      Parameters
      ----------
      G : graph
          the input graph (a topological graph essentially)
      feature : array of shape (G.V,dim_feature)
          vectorial information related to the graph vertices
      verbose : bool, optional
          If True, print diagnostic information
  
      Returns
      --------
      t : ``WeightedForest`` instance
          structure that represents the dendrogram
  
      Notes
      -----
      When G has more than 1 connected component, t is no longer a tree.  This
      case is handled cleanly now
      """
      warn(
          "Function ward from "
          + "nipy.algorithms.clustering.hierrachical_clustering "
          + "deprecated, will be removed",
          FutureWarning,
          stacklevel=2,
      )
  
      # basic check
      if feature.ndim == 1:
          feature = np.reshape(feature, (-1, 1))
  
      if feature.shape[0] != G.V:
          raise ValueError(
              "Incompatible dimension for the feature matrix and the graph"
          )
  
      Features = [
          np.ones(2 * G.V),
          np.zeros((2 * G.V, feature.shape[1])),
          np.zeros((2 * G.V, feature.shape[1])),
      ]
      Features[1][: G.V] = feature
      Features[2][: G.V] = feature**2
  
      # prepare a graph with twice the number of vertices
      # this graph will contain the connectivity information
      # along the merges.
      n = G.V
      nbcc = G.cc().max() + 1
      K = _auxiliary_graph(G, Features)
  
      # prepare some variables that are useful tp speed up the algorithm
      parent = np.arange(2 * n - nbcc).astype(np.int_)
      height = np.zeros(2 * n - nbcc)
      linc = K.left_incidence()
      rinc = K.right_incidence()
  
      # iteratively merge clusters
      for q in range(n - nbcc):
          # 1. find the lightest edge
          m = (K.weights).argmin()
          cost = K.weights[m]
          k = q + n
          i = K.edges[m, 0]
          j = K.edges[m, 1]
          height[k] = cost
          if verbose:
              print(q, i, j, m, cost)
  
          # 2. remove the current edge
          K.edges[m] = -1
          K.weights[m] = np.inf
          linc[i].remove(m)
          rinc[j].remove(m)
  
          ml = linc[j]
          if np.sum(K.edges[ml, 1] == i) > 0:
              m = ml[int(np.flatnonzero(K.edges[ml, 1] == i))]
              K.edges[m] = -1
              K.weights[m] = np.inf
              linc[j].remove(m)
              rinc[i].remove(m)
  
          # 3. merge the edges with third part edges
          parent[i] = k
          parent[j] = k
          for p in range(3):
              Features[p][k] = Features[p][i] + Features[p][j]
  
          linc, rinc = _remap(K, i, j, k, Features, linc, rinc)
  
      # build a tree to encode the results
      t = WeightedForest(2 * n - nbcc, parent, height)
      return t
  
imports: from warnings import warn
  import numpy
  from ..graph.forest import Forest
  from ..graph.graph import WeightedGraph
label_desc: Use image node_id 13 for calling a global function with example usage: ward(G, feature, verbose) and returns: t