docstr: build the models and initialize.
  :param config: hyper parameters for building the model
  :return:
docstr_tok: ["build","the","models","and","initialize",".",":","param","config",":","hyper","parameters","for","building","the","model",":","return",":"]
code: def _build(self, **config):
      """
      build the models and initialize.
      :param config: hyper parameters for building the model
      :return:
      """
      self.set_params(**config)
      if self.model_type == "regressor":
          self.model = XGBRegressor(
              n_estimators=self.n_estimators,
              max_depth=self.max_depth,
              n_jobs=self.n_jobs,
              tree_method=self.tree_method,
              random_state=self.random_state,
              learning_rate=self.learning_rate,
              min_child_weight=self.min_child_weight,
              seed=self.seed,
              subsample=self.subsample,
              colsample_bytree=self.colsample_bytree,
              gamma=self.gamma,
              reg_alpha=self.reg_alpha,
              reg_lambda=self.reg_lambda,
              verbosity=self.verbosity,
          )
      elif self.model_type == "classifier":
          self.model = XGBClassifier(
              n_estimators=self.n_estimators,
              max_depth=self.max_depth,
              n_jobs=self.n_jobs,
              tree_method=self.tree_method,
              random_state=self.random_state,
              learning_rate=self.learning_rate,
              min_child_weight=self.min_child_weight,
              seed=self.seed,
              subsample=self.subsample,
              colsample_bytree=self.colsample_bytree,
              gamma=self.gamma,
              reg_alpha=self.reg_alpha,
              objective="binary:logistic",
              reg_lambda=self.reg_lambda,
              verbosity=self.verbosity,
          )
      else:
          invalidInputError(
              False,
              'model_type can only be "regressor" or "classifier"',
          )
  
      self.model_init = True
  
imports: from bigdl.orca.common import SafePickle
  import pandas
  from xgboost.sklearn import XGBRegressor
  from xgboost.sklearn import XGBClassifier
  from bigdl.orca.automl.metrics import Evaluator
  from bigdl.orca.automl.model.abstract import BaseModel, ModelBuilder
  from bigdl.dllib.utils.log4Error import invalidInputError
  import logging
label_desc: Use image node_id 3 for calling the XGBoost obj's underlying member method code with example usage: obj._build() without return types