code: def _config_model(self, data):
      self.params["max_depth"] = self.max_depth
      self.params["learning_rate"] = self.learning_rate
      self.params["min_split_loss"] = self.min_split_loss
      self.params["min_child_weight"] = self.min_weight
      self.params["alpha"] = self.L1_reg
      self.params["lambda"] = self.L2_reg
      self.params["debug_verbose"] = self.debug_verose
      self.params["max_bin"] = self.max_bin
  
      if self.use_gpu:
          self.params["tree_method"] = (
              "gpu_exact" if self.use_exact else "gpu_hist"
          )
          self.params["n_gpus"] = 1
      else:
          self.params["nthread"] = 20
          self.params["tree_method"] = (
              "exact" if self.use_exact else "hist"
          )
  
      self.params["predictor"] = "gpu_predictor"
      if data.task == "Regression":
          self.params["objective"] = "reg:squarederror"
      elif data.task == "Multiclass classification":
          self.params["objective"] = "multi:softprob"
          self.params["num_class"] = int(np.max(data.y_test) + 1)
      elif data.task == "Classification":
          self.params["objective"] = "binary:logistic"
      elif data.task == "Ranking":
          self.params["objective"] = "rank:ndcg"
      else:
          raise ValueError("Unknown task: " + data.task)
  
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 2 for calling the XGboostModel obj's underlying member method code with example usage: obj._config_model(data) without return types