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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 suggest_charges(self, tolerance=0.1):
 """ Suggest possible charges for defects to computee based on proximity of known transitions from entires to VBM and CBM Args: tolerance (float):
 tolerance with respect to the VBM and CBM to ` continue to compute new charges """

        recommendations = {}

        for def_type in self.defect_types:
            test_charges = np.arange(
                np.min(self.stable_charges[def_type]) - 1,
                np.max(self.stable_charges[def_type]) + 2)
            test_charges = [charge for charge in test_charges if charge not in self.finished_charges[def_type]]

            if len(self.transition_level_map[def_type].keys()):
                # More positive charges will shift the minimum transition level down
                # Max charge is limited by this if its transition level is close to VBM
                min_tl = min(self.transition_level_map[def_type].keys())
                if min_tl < tolerance:
                    max_charge = max(self.transition_level_map[def_type][min_tl])
                    test_charges = [charge for charge in test_charges if charge < max_charge]

                # More negative charges will shift the maximum transition level up
                # Minimum charge is limited by this if transition level is near CBM
                max_tl = max(self.transition_level_map[def_type].keys())
                if max_tl > (self.band_gap - tolerance):
                    min_charge = min(self.transition_level_map[def_type][max_tl])
                    test_charges = [charge for charge in test_charges if charge > min_charge]
            else:
                test_charges = [charge for charge in test_charges if charge not in self.stable_charges[def_type]]

            recommendations[def_type] = test_charges

        return recommendations