code: def nativeCliqueEmbed(self, width):
          
          def f(x):
              return x.nativeCliqueEmbed(width)
          objective = self._objective_bestscore
          return self._translate(self._map_to_processors(f, objective))
docs: Compute a maximum-sized native clique embedding in an induced
          subgraph of chimera with chainsize ``width+1``.  If possible,
          returns a uniform choice among all largest cliques.
  
          INPUTS:
              width: width of the squares to search, also `chainlength-1`
  
          OUTPUT:
              clique: a list containing lists of qubits, each associated
              to a chain.  These lists of qubits are carefully
              arranged so that
  
              >>> [zip(chain,chain[1:]) for chain in clique]
  
              is a list of valid couplers.
  
          Note: this fails to return a uniform choice if there are broken
          intra-cell couplers between working qubits. (the choice is
          uniform on a particular subprocessor)