def METHOD_NAME(self, model_dict):
    result_obj = list(model_dict.get('model').values())[0].get(
        self.model_param_name)
    meta_obj = list(model_dict.get('model').values())[0].get(self.model_meta_name)
    fit_intercept = meta_obj.fit_intercept
    self.exposure_colname = meta_obj.exposure_colname
    self.header = list(result_obj.header)
    # For poisson regression arbiter predict function
    if self.header is None:
        return
    feature_shape = len(self.header)
    tmp_vars = np.zeros(feature_shape)
    weight_dict = dict(result_obj.weight)
    self.intercept_ = result_obj.intercept
    for idx, header_name in enumerate(self.header):
        tmp_vars[idx] = weight_dict.get(header_name)
    if fit_intercept:
        tmp_vars = np.append(tmp_vars, result_obj.intercept)
    self.model_weights = PoissonRegressionWeights(l=tmp_vars,
                                                  fit_intercept=fit_intercept,
                                                  raise_overflow_error=False)
    self.n_iter_ = result_obj.iters