code: def _predict(self, data):
      dtest = xgb.DMatrix(data.X_test, data.y_test)
      if data.task == "Ranking":
          dtest.set_group(data.groups)
      pred = self.model.predict(dtest)
      metric = self.eval(data=data, pred=pred)
  
      return metric
  
file_path: thundergbm/python/benchmarks/model/xgboost_model.py
imports: from model.base_model import BaseModel
  import numpy
  import xgboost
  import time
  import utils.data_utils
  from model.datasets import Dataset
label_desc: Use image node_id 4 for calling the XGboostModel obj's underlying member method code with example usage: obj._predict(data) and returns: metric