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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 fit(self, data='obsData', model_config='ModelConfig', param_const=None, param_values=None, param_ranges=None, poi_const=False, poi_value=None, poi_range=None, extended=False, num_cpu=1, process_strategy=0, offset=False, print_level=None, return_nll=False, **kwargs):
 """ Fit a pdf to data in a workspace Parameters workspace : RooWorkspace The workspace data : str or RooAbsData, optional (default='obsData') The name of the data or a RooAbsData instance. model_config : str or ModelConfig, optional (default='ModelConfig') The name of the ModelConfig in the workspace or a ModelConfig instance. param_const : dict, optional (default=None) A dict mapping parameter names to booleans setting the const state of the parameter param_values : dict, optional (default=None) A dict mapping parameter names to values param_ranges : dict, optional (default=None) A dict mapping parameter names to 2-tuples defining the ranges poi_const : bool, optional (default=False) If True, then make the parameter of interest (POI) constant poi_value : float, optional (default=None) If not None, then set the POI to this value poi_range : tuple, optional (default=None) If not None, then set the range of the POI with this 2-tuple extended : bool, optional (default=False) If True, add extended likelihood term (False by default) num_cpu : int, optional (default=1) Parallelize NLL calculation on multiple CPU cores. If negative then use all CPU cores. By default use only one CPU core. process_strategy : int, optional (default=0) **Strategy 0:** Divide events into N equal chunks. **Strategy 1:** Process event i%N in process N. Recommended for binned data with a substantial number of zero-bins, which will be distributed across processes more equitably in this strategy. **Strategy 2:** Process each component likelihood of a RooSimultaneous fully in a single process and distribute components over processes. This approach can be benificial if normalization calculation time dominates the total computation time of a component (since the normalization calculation must be performed in each process in strategies 0 and 1. However beware that if the RooSimultaneous components do not share many parameters this strategy is inefficient: as most minuit-induced likelihood calculations involve changing a single parameter, only 1 of the N processes will be active most of the time if RooSimultaneous components do not share many parameters. **Strategy 3:** Follow strategy 0 for all RooSimultaneous components, except those with less than 30 dataset entries, for which strategy 2 is followed. offset : bool, optional (default=False) Offset likelihood by initial value (so that starting value of FCN in minuit is zero). This can improve numeric stability in simultaneously fits with components with large likelihood values. print_level : int, optional (default=None) The verbosity level for the minimizer algorithm. If None (the default) then use the global default print level. If negative then all non-fatal messages will be suppressed. return_nll : bool, optional (default=False) If True then also return the RooAbsReal NLL function that was minimized. kwargs : dict, optional Remaining keyword arguments are passed to the minimize function Returns ------- result : RooFitResult The fit result. func : RooAbsReal If return_nll is True, the NLL function is also returned. See Also -------- minimize """

        if isinstance(model_config, string_types):
            model_config = self.obj(
                model_config, cls=ROOT.RooStats.ModelConfig)
        if isinstance(data, string_types):
            data = self.data(data)
        pdf = model_config.GetPdf()

        pois = model_config.GetParametersOfInterest()
        if pois.getSize() > 0:
            poi = pois.first()
            poi.setConstant(poi_const)
            if poi_value is not None:
                poi.setVal(poi_value)
            if poi_range is not None:
                poi.setRange(*poi_range)

        if param_const is not None:
            for param_name, const in param_const.items():
                var = self.var(param_name)
                var.setConstant(const)
        if param_values is not None:
            for param_name, param_value in param_values.items():
                var = self.var(param_name)
                var.setVal(param_value)
        if param_ranges is not None:
            for param_name, param_range in param_ranges.items():
                var = self.var(param_name)
                var.setRange(*param_range)

        if print_level < 0:
            msg_service = ROOT.RooMsgService.instance()
            msg_level = msg_service.globalKillBelow()
            msg_service.setGlobalKillBelow(ROOT.RooFit.FATAL)

        args = [
            ROOT.RooFit.Constrain(model_config.GetNuisanceParameters()),
            ROOT.RooFit.GlobalObservables(model_config.GetGlobalObservables())]
        if extended:
            args.append(ROOT.RooFit.Extended(True))
        if offset:
            args.append(ROOT.RooFit.Offset(True))
        if num_cpu != 1:
            if num_cpu == 0:
                raise ValueError("num_cpu must be non-zero")
            if num_cpu < 0:
                num_cpu = NCPU
            args.append(ROOT.RooFit.NumCPU(num_cpu, process_strategy))

        func = pdf.createNLL(data, *args)

        if print_level < 0:
            msg_service.setGlobalKillBelow(msg_level)

        result = minimize(func, print_level=print_level, **kwargs)

        if return_nll:
            return result, func
        return result