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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 generate_parameters(self, parameter_id):
 """Generate next parameter for trial If the number of trial result is lower than cold start number, metis will first random generate some parameters. Otherwise, metis will choose the parameters by the Gussian Process Model and the Gussian Mixture Model. Parameters parameter_id : int Returns ------- result : dict """

        if len(self.samples_x) < self.cold_start_num:
            init_parameter = _rand_init(self.x_bounds, self.x_types, 1)[0]
            results = self._pack_output(init_parameter)
        else:
            self.minimize_starting_points = _rand_init(self.x_bounds, self.x_types, \
                                                       self.selection_num_starting_points)
            results = self._selection(self.samples_x, self.samples_y_aggregation, self.samples_y,
                                      self.x_bounds, self.x_types,
                                      threshold_samplessize_resampling=(None if self.no_resampling is True else 50),
                                      no_candidates=self.no_candidates,
                                      minimize_starting_points=self.minimize_starting_points,
                                      minimize_constraints_fun=self.minimize_constraints_fun)

        logger.info("Generate paramageters:\n" + str(results))
        return results