code: def get_params(self, deep=False):
          
          params = super(XGBModel, self).get_params(deep=deep)
          if isinstance(self.kwargs, dict):  # if kwargs is a dict, update params accordingly
              params.update(self.kwargs)
          if params['missing'] is np.nan:
              params['missing'] = None  # sklearn doesn't handle nan. see #4725
          if not params.get('eval_metric', True):
              del params['eval_metric']  # don't give as None param to Booster
          return params
docs: Get parameters.