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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def min_weighted_vertex_cover(G, weight=None, sampler=None, **sampler_args):
 """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]_ """

    indep_nodes = set(maximum_weighted_independent_set(G, weight, sampler, **sampler_args))
    return [v for v in G if v not in indep_nodes]