old_contents: # Third-party modules
  from xgboost import XGBRegressor
  # Hand-made modules
  from .base import BloscpackMixin, MyEstimatorBase, PickleMixin
  
  MACHINE_LEARNING_TECHNIQUE_NAME = "xgb"
  PICKLE_EXTENSION = "pkl"
  SERIALIZE_FILEPATH_PREFIX = "fit_model"
  
  
  class MyXGBRegressor(MyEstimatorBase, BloscpackMixin, PickleMixin):
      def __init__(self, model_name: str, params: dict):
          super().__init__(model_name, params)
          self.MACHINE_LEARNING_TECHNIQUE_NAME = MACHINE_LEARNING_TECHNIQUE_NAME
          self.SERIALIZE_FILEPATH_PREFIX = SERIALIZE_FILEPATH_PREFIX
          self.regr = self.get_model_instance()
  
      def get_model_instance(self):
          return XGBRegressor(**self.params)
  
      def fit(self, X, y):
          fit_model = self.regr.fit(X, y, eval_metric="mae")
  
          pkl_path = self.gen_abspath(
              self.path.join(
                  self.MODELS_SERIALIZING_BASEPATH,
                  self.MACHINE_LEARNING_TECHNIQUE_NAME,
                  self.gen_serialize_filepath(self.SERIALIZE_FILEPATH_PREFIX,
                                              PICKLE_EXTENSION)
              )
          )
          self.to_pkl(fit_model, pkl_path)
  
          return fit_model
  
      def predict(self, X):
          fit_model = self.read_pkl(
              self.path.join(
                  self.MODELS_SERIALIZING_BASEPATH,
                  MACHINE_LEARNING_TECHNIQUE_NAME,
                  self.gen_serialize_filepath(
                      "fit_model",
                      PICKLE_EXTENSION
                  )
              )
          )
  
          return fit_model.predict(X)
  
  
  if __name__ == '__main__':
      print("My eXtra Gradient Boosting trees !")
  
new_contents: # Third-party modules
  from xgboost import XGBRegressor
  # Hand-made modules
  from .base import BloscpackMixin, MyEstimatorBase, PickleMixin
  
  MACHINE_LEARNING_TECHNIQUE_NAME = "xgb"
  PICKLE_EXTENSION = "pkl"
  SERIALIZE_FILENAME_PREFIX = "fit_model"
  
  
  class MyXGBRegressor(MyEstimatorBase, BloscpackMixin, PickleMixin):
      def __init__(self, model_name: str, params: dict):
          super().__init__(model_name, params)
          self.regr = self.get_model_instance()
          self.MODELS_SERIALIZING_BASEPATH = self.path.join(self.MODELS_SERIALIZING_BASEPATH,
                                                            MACHINE_LEARNING_TECHNIQUE_NAME)
          self.SERIALIZE_FILENAME_PREFIX = SERIALIZE_FILENAME_PREFIX
  
      def get_model_instance(self):
          return XGBRegressor(**self.params)
  
      def fit(self, X, y):
          fit_model = self.regr.fit(X, y, eval_metric="mae")
  
          pkl_path = self.gen_abspath(
              self.path.join(
                  self.MODELS_SERIALIZING_BASEPATH,
                  self.gen_serialize_filepath(self.SERIALIZE_FILENAME_PREFIX, PICKLE_EXTENSION)
              )
          )
          self.to_pkl(fit_model, pkl_path)
  
          return fit_model
  
      def predict(self, X):
          fit_model = self.read_pkl(
              self.path.join(
                  self.MODELS_SERIALIZING_BASEPATH,
                  self.gen_serialize_filepath(self.SERIALIZE_FILENAME_PREFIX, PICKLE_EXTENSION)
              )
          )
  
          return fit_model.predict(X)
  
  
  if __name__ == '__main__':
      print("My eXtra Gradient Boosting trees !")
  
subject: Modify pathname