code: def fit_predict_and_loss(self) -> None:
      _fit_and_suppress_warnings(
          self.logger, self.model, self.X_train, self.Y_train
      )
      loss, Y_pred, _, _ = self.predict_and_loss()
      self.finish_up(
          loss=loss,
          train_loss=None,
          opt_pred=Y_pred,
          test_pred=None,
          file_output=False,
          final_call=True,
          additional_run_info=None,
          status=StatusType.SUCCESS,
      )
  
file_path: auto-sklearn/autosklearn/evaluation/test_evaluator.py
imports: from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
  import multiprocessing
  import numpy
  from ConfigSpace import Configuration
  from smac.tae import StatusType
  from autosklearn.automl_common.common.utils.backend import Backend
  from autosklearn.evaluation.abstract_evaluator import AbstractEvaluator, _fit_and_suppress_warnings
  from autosklearn.metrics import Scorer, calculate_losses
  from autosklearn.pipeline.components.base import ThirdPartyComponents
label_desc: Use image node_id 2 for calling the TestEvaluator obj's underlying member method code with example usage: obj.fit_predict_and_loss() without return types