code: def fit(
      self,
      x,
      y,
      validation_x=None,
      validation_y=None,
      patience=0,
      verbose=None,
      **xgboost_kwargs,
  ):
      eval_set = [(x, y)]
      if validation_x is not None and validation_y is not None:
          eval_set += [(validation_x, validation_y)]
      if verbose is None:
          verbose = (
              True if lore.env.NAME == lore.env.DEVELOPMENT else False
          )
      try:
          super(Base, self).fit(
              X=x,
              y=y,
              eval_set=eval_set,
              eval_metric=self.eval_metric,
              verbose=verbose,
              early_stopping_rounds=patience,
              **xgboost_kwargs,
          )
      except KeyboardInterrupt:
          logger.warning("Caught SIGINT. Training aborted.")
  
      evals = super(Base, self).evals_result()
  
      if self.scoring_metric is None:
          self.scoring_metric = self.eval_metric
  
      results = {
          "eval_metric": self.eval_metric,
          "train": evals["validation_0"][self.eval_metric][
              self.best_iteration
          ],
          "best_iteration": self.best_iteration,
      }
      if validation_x is not None:
          results["validate"] = evals["validation_1"][self.eval_metric][
              self.best_iteration
          ]
      return results
  
imports: from __future__ import absolute_import
  import inspect
  import logging
  import warnings
  import threading
  import lore.env
  import lore.estimators
  from lore.util import timed, before_after_callbacks
  import xgboost
label_desc: Use image node_id 4 for calling the Base obj's underlying member method code with example usage: obj.fit(x, y, validation_x, validation_y, patience, verbose) and returns: results