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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 binary_quadratic_model_sampler(which_args):
 """Decorator to validate sampler arguments. Parameters which_args : int or sequence of ints Location of the sampler arguments of the input function in the form `function_name(args, *kw)`. If more than one sampler is allowed, can be a list of locations. Returns ------- _binary_quadratic_model_sampler : function Caller function that validates the sampler format. A sampler is expected to have `sample_qubo` and `sample_ising` methods. Alternatively, if no sampler is provided (or sampler is None), the sampler set by the `set_default_sampler` function is provided to the function. Examples -------- Decorate functions like this:: @binary_quadratic_model_sampler(1) def maximal_matching(G, sampler, **sampler_args):
 pass This example validates two placeholder samplers, which return a correct response only in the case of finding an independent set on a complete graph (where one node is always an independent set), the first valid, the second missing a method. [0] TypeError Traceback (most recent call last) <ipython-input-35-670b71b268c7> in <module>() ----> 1 independent_set(G, IllDefinedSampler) <decorator-gen-628> in independent_set(G, sampler, **sampler_args) /usr/local/lib/python2.7/dist-packages/dwave_networkx/utils/decorators.pyc in _binary_quadratic_model_sampler(f, *args, **kw) 61 62 if not hasattr(sampler, "sample_qubo") or not callable(sampler.sample_qubo):
 ---> 63 raise TypeError("expected sampler to have a 'sample_qubo' method") 64 if not hasattr(sampler, "sample_ising") or not callable(sampler.sample_ising):
 65 raise TypeError("expected sampler to have a 'sample_ising' method") TypeError: expected sampler to have a 'sample_qubo' method """

    @decorator
    def _binary_quadratic_model_sampler(f, *args, **kw):
        # convert into a sequence if necessary
        if isinstance(which_args, int):
            iter_args = (which_args,)
        else:
            iter_args = iter(which_args)

        # check each sampler for the correct methods
        new_args = [arg for arg in args]
        for idx in iter_args:
            sampler = args[idx]

            # if no sampler is provided, get the default sampler if it has
            # been set
            if sampler is None:
                # this sampler has already been vetted
                default_sampler = dnx.get_default_sampler()
                if default_sampler is None:
                    raise dnx.DWaveNetworkXMissingSampler('no default sampler set')
                new_args[idx] = default_sampler
                continue

            if not hasattr(sampler, "sample_qubo") or not callable(sampler.sample_qubo):
                raise TypeError("expected sampler to have a 'sample_qubo' method")
            if not hasattr(sampler, "sample_ising") or not callable(sampler.sample_ising):
                raise TypeError("expected sampler to have a 'sample_ising' method")

        # now run the function and return the results
        return f(*new_args, **kw)
    return _binary_quadratic_model_sampler