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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 load_roi(self, infile, reload_sources=False, params=None, mask=None):
 """This function reloads the analysis state from a previously saved instance generated with `~fermipy.gtanalysis.GTAnalysis.write_roi`. Parameters infile : str reload_sources : bool Regenerate source maps for non-diffuse sources. params : str Path to a yaml file with updated parameter values mask : str Path to a fits file with an updated mask """


        infile = utils.resolve_path(infile, workdir=self.workdir)
        roi_file, roi_data = utils.load_data(infile, workdir=self.workdir)

        self.logger.info('Loading ROI file: %s', roi_file)

        key_map = {'dfde': 'dnde',
                   'dfde100': 'dnde100',
                   'dfde1000': 'dnde1000',
                   'dfde10000': 'dnde10000',
                   'dfde_index': 'dnde_index',
                   'dfde100_index': 'dnde100_index',
                   'dfde1000_index': 'dnde1000_index',
                   'dfde10000_index': 'dnde10000_index',
                   'e2dfde': 'e2dnde',
                   'e2dfde100': 'e2dnde100',
                   'e2dfde1000': 'e2dnde1000',
                   'e2dfde10000': 'e2dnde10000',
                   'Npred': 'npred',
                   'Npred_wt': 'npred_wt',
                   'logLike': 'loglike',
                   'dlogLike': 'dloglike',
                   'emin': 'e_min',
                   'ectr': 'e_ctr',
                   'emax': 'e_max',
                   'logemin': 'loge_min',
                   'logectr': 'loge_ctr',
                   'logemax': 'loge_max',
                   'ref_dfde': 'ref_dnde',
                   'ref_e2dfde': 'ref_e2dnde',
                   'ref_dfde_emin': 'ref_dnde_e_min',
                   'ref_dfde_emax': 'ref_dnde_e_max',
                   }

        self._roi_data = utils.update_keys(roi_data['roi'], key_map)

        if 'erange' in self._roi_data:
            self._roi_data['loge_bounds'] = self._roi_data.pop('erange')

        self._loge_bounds = self._roi_data.setdefault('loge_bounds',
                                                      self.loge_bounds)

        sources = roi_data.pop('sources')
        sources = utils.update_keys(sources, key_map)
        for k0, v0 in sources.items():
            for k, v in defaults.source_flux_output.items():
                if k not in v0:
                    continue
                if v[2] == float and isinstance(v0[k], np.ndarray):
                    sources[k0][k], sources[k0][k + '_err'] \
                        = v0[k][0], v0[k][1]

        self.roi.load_sources(sources.values())
        for i, c in enumerate(self.components):
            if 'src_expscale' in self._roi_data['components'][i]:
                c._src_expscale = copy.deepcopy(self._roi_data['components']
                                                [i]['src_expscale'])

        self._create_likelihood(infile)
        self.set_energy_range(self.loge_bounds[0], self.loge_bounds[1])

        if params is not None:
            self.load_parameters_from_yaml(params)

        if mask is not None:
            self.set_weights_map(mask, update_roi=False)

        if reload_sources:
            names = [s.name for s in self.roi.sources if not s.diffuse]
            self.reload_sources(names, False)

        self.logger.info('Finished Loading ROI')