def METHOD_NAME(self, single_model_obj):
    LOGGER.info("It's a binary task, start to load single model")
    feature_shape = len(self.header)
    tmp_vars = np.zeros(feature_shape)
    weight_dict = dict(single_model_obj.weight)
    for idx, header_name in enumerate(self.header):
        tmp_vars[idx] = weight_dict.get(header_name)
    if self.fit_intercept:
        tmp_vars = np.append(tmp_vars, single_model_obj.intercept)
    if len(tmp_vars) == 0:
        tmp_vars = [0.]
    self.model_weights = LogisticRegressionWeights(tmp_vars, fit_intercept=self.fit_intercept)
    self.n_iter_ = single_model_obj.iters
    return self