code: def predict(self, data, output_margin=False, ntree_limit=None, validate_features=True):
          
          # pylint: disable=missing-docstring,invalid-name
          test_dmatrix = DMatrix(data, missing=self.missing, nthread=self.n_jobs)
          # get ntree_limit to use - if none specified, default to
          # best_ntree_limit if defined, otherwise 0.
          if ntree_limit is None:
              ntree_limit = getattr(self, "best_ntree_limit", 0)
          return self.get_booster().predict(test_dmatrix,
                                            output_margin=output_margin,
                                            ntree_limit=ntree_limit,
                                            validate_features=validate_features)
docs: Predict with `data`.
  
          .. note:: This function is not thread safe.
  
            For each booster object, predict can only be called from one thread.
            If you want to run prediction using multiple thread, call ``xgb.copy()`` to make copies
            of model object and then call ``predict()``.
  
          .. note:: Using ``predict()`` with DART booster
  
            If the booster object is DART type, ``predict()`` will perform dropouts, i.e. only
            some of the trees will be evaluated. This will produce incorrect results if ``data`` is
            not the training data. To obtain correct results on test sets, set ``ntree_limit`` to
            a nonzero value, e.g.
  
            .. code-block:: python
  
              preds = bst.predict(dtest, ntree_limit=num_round)
  
          Parameters
          ----------
          data : DMatrix
              The dmatrix storing the input.
          output_margin : bool
              Whether to output the raw untransformed margin value.
          ntree_limit : int
              Limit number of trees in the prediction; defaults to best_ntree_limit if defined
              (i.e. it has been trained with early stopping), otherwise 0 (use all trees).
          validate_features : bool
              When this is True, validate that the Booster's and data's feature_names are identical.
              Otherwise, it is assumed that the feature_names are the same.
          Returns
          -------
          prediction : numpy array