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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 multiplexer(f=None, nruns=1, nprocs=1, seeding=None, **args):
 """Evaluate a function for different parameters, optionally in parallel. Parameters f: function function f to evaluate, must take only kw arguments as inputs nruns: int number of evaluations of f for each set of arguments nprocs: int + if <=0, set to actual number of physical processors plus nprocs (i.e. -1 => number of cpus on your machine minus one) Default is 1, which means no multiprocessing seeding: bool (default: True if nruns > 1, False otherwise) whether we need to provide different seeds for RNGS **args: keyword arguments for function f. Note ---- see documentation of `utils` """

    if not callable(f):
        raise ValueError('multiplexer: function f missing, or not callable')
    if seeding is None:
        seeding = (nruns > 1)
    # extra arguments (meant to be arguments for f)
    fixedargs, listargs, dictargs = {}, {}, {}
    listargs['run'] = list(range(nruns))
    for k, v in args.items():
        if isinstance(v, list):
            listargs[k] = v
        elif isinstance(v, dict):
            dictargs[k] = v
        else:
            fixedargs[k] = v
    # cartesian product
    inputs, outputs = cartesian_args(fixedargs, listargs, dictargs)
    for ip in inputs:
        ip.pop('run')  # run is not an argument of f, just an id for output
    # distributing different seeds
    if seeding:
        seeds = distinct_seeds(len(inputs))
        for ip, op, s in zip(inputs, outputs, seeds):
            ip['seed'] = s
            op['seed'] = s
    # the actual work happens here
    return distribute_work(f, inputs, outputs, nprocs=nprocs)