class_docstr: Basic weighted, directed graph class
  
  Member variables:
  
  * V (int): the number of vertices
  * E (int): the number of edges
  
  Methods
  
  * vertices (list, type=int, shape=(V,)): vertices id
  * edges (list, type=int, shape=(E,2)): edges as vertices id tuples
  * weights (list, type=int, shape=(E,)): weights / lengths
    of the graph's edges
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docstr: Return left incidence matrix
  
  Returns
  -------
  left_incid: list
      the left incidence matrix of self as a list of lists: i.e. the
      list[[e.0.0, .., e.0.i(0)], .., [e.V.0, E.V.i(V)]] where e.i.j is
      the set of edge indexes so that e.i.j[0] = i
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code: def left_incidence(self):
      """Return left incidence matrix
  
      Returns
      -------
      left_incid: list
          the left incidence matrix of self as a list of lists: i.e. the
          list[[e.0.0, .., e.0.i(0)], .., [e.V.0, E.V.i(V)]] where e.i.j is
          the set of edge indexes so that e.i.j[0] = i
      """
      linc = []
      for i in range(self.V):
          linc.append([])
      for e in range(self.E):
          i = self.edges[e, 0]
          a = linc[i]
          a.append(e)
      return linc
  
imports: import numpy
  from scipy.sparse import coo_matrix
label_desc: Use image node_id 24 for calling the WeightedGraph obj's underlying member method code with example usage: obj.left_incidence() and returns: linc