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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 simulate_roi(self, name=None, randomize=True, restore=False):
 """Generate a simulation of the ROI using the current best-fit model and replace the data counts cube with this simulation. The simulation is created by generating an array of Poisson random numbers with expectation values drawn from the model cube of the binned analysis instance. This function will update the counts cube both in memory and in the source map file. The counts cube can be restored to its original state by calling this method with ``restore`` = True. Parameters name : str Name of the model component to be simulated. If None then the whole ROI will be simulated. restore : bool Restore the data counts cube to its original state. """


        self.logger.info('Simulating ROI')

        self._fitcache = None

        if restore:
            self.logger.info('Restoring')
            self._restore_counts_maps()
            self.logger.info('Finished')
            return

        for c in self.components:
            c.simulate_roi(name=name, clear=True, randomize=randomize)

        if hasattr(self.like.components[0].logLike, 'setCountsMap'):
            self._init_roi_model()
        else:
            self.write_xml('tmp')
            self._like = SummedLikelihood()
            for i, c in enumerate(self._components):
                c._create_binned_analysis('tmp.xml')
                self._like.addComponent(c.like)
            self._init_roi_model()
            self.load_xml('tmp')

        self.logger.info('Finished')