class_docstr: Forest structure, i.e. a set of trees
  
  The nodes can be segmented into trees.
  
  Within each tree a node has one parent and children
  that describe the associated hierarchical structure.
  Some of the nodes can be viewed as leaves, other as roots
  The edges within a tree are associated with a weight:
  
  * +1 from child to parent
  * -1 from parent to child
  
  Attributes
  ----------
  V : int
      int > 0, the number of vertices
  E : int
      the number of edges
  parents : (self.V,) array
      the parent array
  edges : (self.E, 2) array
      representing pairwise neighbors
  weights : (self.E,) array
      +1/-1 for ascending/descending links
  children: list
      list of arrays that represents the children any node
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docstr: returns the nodes that are children of v as a list
  
  Parameters
  ----------
  v: int, a node index
  
  Returns
  -------
  desc: list of int, the list of all descendant of the input node
docstr_tok: ["returns","the","nodes","that","are","children","of","v","as","a","list","Parameters","--","--","--","--","--","v",":","int",",","a","node","index","Returns","--","--","--","-","desc",":","list","of","int",",","the","list","of","all","descendant","of","the","input","node"]
code: def get_descendants(self, v, exclude_self=False):
      """returns the nodes that are children of v as a list
  
      Parameters
      ----------
      v: int, a node index
  
      Returns
      -------
      desc: list of int, the list of all descendant of the input node
      """
      v = int(v)
      if v < 0:
          raise ValueError("the given node index is too low")
      if v > self.V - 1:
          raise ValueError("the given node index is too high")
      if self.children == []:
          self.compute_children()
      if len(self.children[v]) == 0:
          return [v]
      else:
          desc = [v]
          for w in self.children[v]:
              temp = self.get_descendants(w)
              for q in temp:
                  desc.append(q)
      desc.sort()
      if exclude_self and v in desc:
          desc = [i for i in desc if i != v]
      return desc
  
imports: import numpy
  from .graph import WeightedGraph
label_desc: Use image node_id 5 for calling the Forest obj's underlying member method code with example usage: obj.get_descendants(v, exclude_) and returns: desc, list