code: def min_weighted_vertex_cover(G, weight=None, sampler=None, **sampler_args):
      
      indep_nodes = set(maximum_weighted_independent_set(G, weight, sampler, **sampler_args))
      return [v for v in G if v not in indep_nodes]
docs: Returns an approximate minimum weighted vertex cover.
  
      Defines a QUBO with ground states corresponding to a minimum weighted
      vertex cover and uses the sampler to sample from it.
  
      A vertex cover is a set of vertices such that each edge of the graph
      is incident with at least one vertex in the set. A minimum weighted
      vertex cover is the vertex cover of minimum total node weight.
  
      Parameters
      ----------
      G : NetworkX graph
  
      weight : string, optional (default None)
          If None, every node has equal weight. If a string, use this node
          attribute as the node weight. A node without this attribute is
          assumed to have max weight.
  
      sampler
          A binary quadratic model sampler. A sampler is a process that
          samples from low energy states in models defined by an Ising
          equation or a Quadratic Unconstrained Binary Optimization
          Problem (QUBO). A sampler is expected to have a 'sample_qubo'
          and 'sample_ising' method. A sampler is expected to return an
          iterable of samples, in order of increasing energy. If no
          sampler is provided, one must be provided using the
          `set_default_sampler` function.
  
      sampler_args
          Additional keyword parameters are passed to the sampler.
  
      Returns
      -------
      vertex_cover : list
         List of nodes that the form a the minimum weighted vertex cover, as
         determined by the given sampler.
  
      Notes
      -----
      Samplers by their nature may not return the optimal solution. This
      function does not attempt to confirm the quality of the returned
      sample.
  
      https://en.wikipedia.org/wiki/Vertex_cover
  
      https://en.wikipedia.org/wiki/Quadratic_unconstrained_binary_optimization
  
      References
      ----------
      Based on the formulation presented in [AL]_