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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 multiSMC(nruns=10, nprocs=0, out_func=None, **args):
 """Run SMC algorithms in parallel, for different combinations of parameters. `multiSMC` relies on the `multiplexer` utility, and obeys the same logic. A basic usage is:: results = multiSMC(fk=my_fk_model, N=100, nruns=20, nprocs=0) This runs the same SMC algorithm 20 times, using all available CPU cores. The output, ``results``, is a list of 20 dictionaries; a given dict corresponds to a single run, and contains the following (key, value) pairs: + ``'run'``: a run identifier (a number between 0 and nruns-1) + ``'output'``: the corresponding SMC object (once method run was completed) Since a `SMC` object may take a lot of space in memory (especially when the option ``store_history`` is set to True), it is possible to require `multiSMC` to store only some chosen summary of the SMC runs, using option `out_func`. For instance, if we only want to store the estimate of the log-likelihood of the model obtained from each particle filter:: of = lambda pf: pf.logLt results = multiSMC(fk=my_fk_model, N=100, nruns=20, out_func=of) It is also possible to vary the parameters. Say:: results = multiSMC(fk=my_fk_model, N=[100, 500, 1000]) will run the same SMC algorithm 30 times: 10 times for N=100, 10 times for N=500, and 10 times for N=1000. The number 10 comes from the fact that we did not specify nruns, and its default value is 10. The 30 dictionaries obtained in results will then contain an extra (key, value) pair that will give the value of N for which the run was performed. It is possible to vary several arguments. Each time a list must be provided. The end result will amount to take a *cartesian product* of the arguments:: results = multiSMC(fk=my_fk_model, N=[100, 1000], resampling=['multinomial', 'residual'], nruns=20) In that case we run our algorithm 80 times: 20 times with N=100 and resampling set to multinomial, 20 times with N=100 and resampling set to residual and so on. Parameters * nruns: int, optional number of runs (default is 10) * nprocs: int, optional number of processors to use; if negative, number of cores not to use. Default value is 1 (no multiprocessing) * out_func: callable, optional function to transform the output of each SMC run. (If not given, output will be the complete SMC object). * args: dict arguments passed to SMC class Returns ------- A list of dicts See also -------- `utils.multiplexer`: for more details on the syntax. """

    def f(**args):
        pf = SMC(**args) 
        pf.run()
        return out_func(pf)

    if out_func is None:
        out_func = lambda x: x
    return utils.multiplexer(f=f, nruns=nruns, nprocs=nprocs, seeding=True, 
                             **args)