original_docstring: 
          run Qcover code to solve the given problem
          Args:
              node_num: nodes number in the graphical representation of the problem
              edge_num: edges number in the graphical representation of the problem
              is_parallel: run programs in parallel or not
          Returns:
              results of the problem, which including the optimal parameters of the circuit model,
              the optimal expectation value and the number of times the optimizer iterates
          
docstring: run Qcover code to solve the given problem
docstring_tokens: run Qcover code to solve the given problem
code: def run(self, node_num=None, edge_num=None, is_parallel=False):
          """
          run Qcover code to solve the given problem
          Args:
              node_num: nodes number in the graphical representation of the problem
              edge_num: edges number in the graphical representation of the problem
              is_parallel: run programs in parallel or not
          Returns:
              results of the problem, which including the optimal parameters of the circuit model,
              the optimal expectation value and the number of times the optimizer iterates
          """
          if self.simple_graph is None:
              if node_num is None or edge_num is None:
                  print("Error: the graph to be solved is not specified, "
                        "arguments of form (node_number, edge_number) should be given")
                  return None
              self.simple_graph(node_num, edge_num)
  
          if self._nodes_weight == [] or self._edges_weight == []:
              self._nodes_weight, self._edges_weight = self.get_graph_weights()
  
          self._backend._nodes_weight = self._nodes_weight
          self._backend._edges_weight = self._edges_weight
          self._backend._is_parallel = is_parallel
  
          x, fun, nfev = self._optimizer.optimize(objective_function=self.calculate)
          res = {"Optimal parameter value": x, "Expectation of Hamiltonian": fun, "Total iterations": nfev}
          return res
code_tokens: def run ( self , node_num = None , edge_num = None , is_parallel = False ) : if self . simple_graph is None : if node_num is None or edge_num is None : print ( "Error: the graph to be solved is not specified, " "arguments of form (node_number, edge_number) should be given" ) return None self . simple_graph ( node_num , edge_num ) if self . _nodes_weight == [ ] or self . _edges_weight == [ ] : self . _nodes_weight , self . _edges_weight = self . get_graph_weights ( ) self . _backend . _nodes_weight = self . _nodes_weight self . _backend . _edges_weight = self . _edges_weight self . _backend . _is_parallel = is_parallel x , fun , nfev = self . _optimizer . optimize ( objective_function = self . calculate ) res = { "Optimal parameter value" : x , "Expectation of Hamiltonian" : fun , "Total iterations" : nfev } return res
short_docstring: run Qcover code to solve the given problem
short_docstring_tokens: run Qcover code to solve the given problem
comment: """
          run Qcover code to solve the given problem
          Args:
              node_num: nodes number in the graphical representation of the problem
              edge_num: edges number in the graphical representation of the problem
              is_parallel: run programs in parallel or not
          Returns:
              results of the problem, which including the optimal parameters of the circuit model,
              the optimal expectation value and the number of times the optimizer iterates
          """