code: def predict(self, data, num_iteration=None,
                  raw_score=False, pred_leaf=False, pred_contrib=False,
                  data_has_header=False, is_reshape=True, **kwargs):
          
          predictor = self._to_predictor(copy.deepcopy(kwargs))
          if num_iteration is None:
              num_iteration = self.best_iteration
          return predictor.predict(data, num_iteration,
                                   raw_score, pred_leaf, pred_contrib,
                                   data_has_header, is_reshape)
docs: Make a prediction.
  
          Parameters
          ----------
          data : string, numpy array, pandas DataFrame, H2O DataTable's Frame or scipy.sparse
              Data source for prediction.
              If string, it represents the path to txt file.
          num_iteration : int or None, optional (default=None)
              Limit number of iterations in the prediction.
              If None, if the best iteration exists, it is used; otherwise, all iterations are used.
              If <= 0, all iterations are used (no limits).
          raw_score : bool, optional (default=False)
              Whether to predict raw scores.
          pred_leaf : bool, optional (default=False)
              Whether to predict leaf index.
          pred_contrib : bool, optional (default=False)
              Whether to predict feature contributions.
  
              Note
              ----
              If you want to get more explanations for your model's predictions using SHAP values,
              like SHAP interaction values,
              you can install the shap package (https://github.com/slundberg/shap).
              Note that unlike the shap package, with ``pred_contrib`` we return a matrix with an extra
              column, where the last column is the expected value.
  
          data_has_header : bool, optional (default=False)
              Whether the data has header.
              Used only if data is string.
          is_reshape : bool, optional (default=True)
              If True, result is reshaped to [nrow, ncol].
          **kwargs
              Other parameters for the prediction.
  
          Returns
          -------
          result : numpy array
              Prediction result.