code: def predict_and_loss(
      self, train: bool = False
  ) -> Tuple[Union[Dict[str, float], float], np.array, Any, Any]:
      if train:
          Y_pred = self.predict_function(
              self.X_train, self.model, self.task_type, self.Y_train
          )
          err = calculate_losses(
              solution=self.Y_train,
              prediction=Y_pred,
              task_type=self.task_type,
              metrics=self.metrics,
              scoring_functions=self.scoring_functions,
          )
      else:
          Y_pred = self.predict_function(
              self.X_test, self.model, self.task_type, self.Y_train
          )
          err = calculate_losses(
              solution=self.Y_test,
              prediction=Y_pred,
              task_type=self.task_type,
              metrics=self.metrics,
              scoring_functions=self.scoring_functions,
          )
  
      return err, Y_pred, None, None
  
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 3 for calling the TestEvaluator obj's underlying member method code with example usage: obj.predict_and_loss(train) and returns: err,  Y_pred,  None,  None