code: def calculate_auxiliary_losses(
      self,
      Y_test_pred: np.ndarray | None,
  ) -> float | dict[str, float] | None:
      if Y_test_pred is None or self.y_test is None:
          return None
  
      test_loss = self._loss(self.y_test, Y_test_pred)
      if len(self.metrics) == 1:
          test_loss = test_loss[self.metrics[0].name]
  
      return test_loss
  
imports: from __future__ import annotations
  from typing import Any, Dict, List, Optional, Sequence, TextIO, Tuple, Type, Union, cast
  import logging
  import multiprocessing
  import time
  import warnings
  import numpy
  from ConfigSpace import Configuration
  from sklearn.base import BaseEstimator
  from sklearn.dummy import DummyClassifier, DummyRegressor
  from sklearn.ensemble import VotingClassifier, VotingRegressor
  from smac.tae import StatusType
  from threadpoolctl import threadpool_limits
  import autosklearn.pipeline.classification
  import autosklearn.pipeline.regression
  from autosklearn.askl_typing import FEAT_TYPE_TYPE
  from autosklearn.automl_common.common.utils.backend import Backend
  from autosklearn.constants import CLASSIFICATION_TASKS, MULTICLASS_CLASSIFICATION, MULTILABEL_CLASSIFICATION, MULTIOUTPUT_REGRESSION, REGRESSION_TASKS
  from autosklearn.data.target_validator import SUPPORTED_TARGET_TYPES, SUPPORTED_XDATA_TYPES
  from autosklearn.metrics import Scorer, calculate_losses
  from autosklearn.pipeline.components.base import ThirdPartyComponents, _addons
  from autosklearn.pipeline.implementations.util import convert_multioutput_multiclass_to_multilabel
  from autosklearn.util.logging_ import PicklableClientLogger, get_named_client_logger
label_desc: Use image node_id 5 for calling the AbstractEvaluator obj's underlying member method code with example usage: obj.calculate_auxiliary_losses(Y_test_pred) and returns: test_loss, None