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: propagates from leaves to roots some binary property of the nodes
  so that prop[parents] = logical_and(prop[children])
  
  Parameters
  ----------
  prop, array of shape(self.V), the input property
  
  Returns
  -------
  prop, array of shape(self.V), the output property field
docstr_tok: ["propagates","from","leaves","to","roots","some","binary","property","of","the","nodes","so","that","prop","[","parents","]","=","logical_and","(","prop","[","children","]",")","Parameters","--","--","--","--","--","prop",",","array","of","shape","(","self.V",")",",","the","input","property","Returns","--","--","--","-","prop",",","array","of","shape","(","self.V",")",",","the","output","property","field"]
code: def propagate_upward_and(self, prop):
      """propagates from leaves to roots some binary property of the nodes
      so that prop[parents] = logical_and(prop[children])
  
      Parameters
      ----------
      prop, array of shape(self.V), the input property
  
      Returns
      -------
      prop, array of shape(self.V), the output property field
      """
      prop = np.asanyarray(prop).copy()
      if np.size(prop) != self.V:
          raise ValueError("incoherent size for prop")
  
      prop[self.isleaf() == False] = True
  
      for j in range(self.tree_depth()):
          for i in range(self.V):
              if prop[i] == False:
                  prop[self.parents[i]] = False
  
      return prop
  
imports: import numpy
  from .graph import WeightedGraph
label_desc: Use image node_id 16 for calling the Forest obj's underlying member method code with example usage: obj.propagate_upward_and(prop) and returns: prop