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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 Ts2(self, srcName, reoptimize=False, approx=True, tol=None, MaxIterations=10, verbosity=0):
 """Computes the TS value for a source indicated by "srcName." If "reoptimize=True" is selected this function will reoptimize the model up to "MaxIterations" given the tolerance "tol" (default is the tolerance selected for the overall fit). If "appox=True" is selected (the default) it will renormalize the model (see _renorm). """


        saved_state = LikelihoodState(self)
        if verbosity > 0:
            print("*** Start Ts_dl ***")
        source_attributes = self.getExtraSourceAttributes()
        self.logLike.syncParams()
        src = self.logLike.getSource(srcName)
        self._ts_src = src
        freeParams = pyLike.DoubleVector()
        self.logLike.getFreeParamValues(freeParams)
        logLike1 = self.logLike.value()
        self.scaleSource(srcName, 1E-10)
        logLike0 = self.logLike.value()
        if tol is None:
            tol = self.tol
        if reoptimize:
            if verbosity > 0:
                print("** Do reoptimize")
            optFactory = pyLike.OptimizerFactory_instance()
            myOpt = optFactory.create(self.optimizer, self.logLike)
            Niter = 1
            while Niter <= MaxIterations:
                try:
                    myOpt.find_min(0, tol)
                    break
                except RuntimeError as e:
                    print(e)
                if verbosity > 0:
                    print("** Iteration :", Niter)
                Niter += 1
        else:
            if approx:
                try:
                    self._renorm()
                except ZeroDivisionError:
                    pass
        self.logLike.syncParams()
        logLike0 = max(self.logLike.value(), logLike0)
        Ts_value = 2 * (logLike1 - logLike0)
        self.scaleSource(srcName, 1E10)

        self.logLike.setFreeParamValues(freeParams)
        self.model = SourceModel(self.logLike)
        for src in source_attributes:
            self.model[src].__dict__.update(source_attributes[src])
        saved_state.restore()
        self.logLike.value()
        return Ts_value