code: def file_output(
      self,
      Y_optimization_pred: np.ndarray,
      Y_test_pred: np.ndarray,
  ) -> tuple[float | None, dict[str, Any]]:
      # Abort if self.Y_optimization is None
      # self.Y_optimization can be None if we use partial-cv, then,
      # obviously no output should be saved.
      if self.Y_optimization is None:
          return None, {}
  
      # Abort in case of shape misalignment
      if (
          np.shape(self.Y_optimization)[0]
          != Y_optimization_pred.shape[0]
      ):
          return (
              1.0,
              {
                  "error": "Targets %s and prediction %s don't have "
                  "the same length. Probably training didn't "
                  "finish"
                  % (
                      np.shape(self.Y_optimization),
                      Y_optimization_pred.shape,
                  )
              },
          )
  
      # Abort if predictions contain NaNs
      for y, s in [
          (Y_optimization_pred, "optimization"),
          (Y_test_pred, "test"),
      ]:
          if y is not None and not np.all(np.isfinite(y)):
              return (
                  1.0,
                  {
                      "error": "Model predictions for %s set contains NaNs."
                      % s
                  },
              )
  
      # Abort if we don't want to output anything.
      # Since disable_file_output can also be a list, we have to explicitly
      # compare it with True.
      if self.disable_file_output is True:
          return None, {}
  
      # Notice that disable_file_output==False and disable_file_output==[]
      # means the same thing here.
      if self.disable_file_output is False:
          self.disable_file_output = []
  
      # Here onwards, the self.disable_file_output can be treated as a list
      self.disable_file_output = cast(List, self.disable_file_output)
  
      # This file can be written independently of the others down bellow
      if "y_optimization" not in self.disable_file_output:
          if self.output_y_hat_optimization:
              self.backend.save_additional_data(
                  self.Y_optimization, what="targets_ensemble"
              )
              self.backend.save_additional_data(
                  self.X_optimization, what="input_ensemble"
              )
  
      models: Optional[BaseEstimator] = None
      if hasattr(self, "models"):
          if len(self.models) > 0 and self.models[0] is not None:
              if "models" not in self.disable_file_output:
                  if self.task_type in CLASSIFICATION_TASKS:
                      models = VotingClassifier(
                          estimators=None,
                          voting="soft",
                      )
                  else:
                      models = VotingRegressor(estimators=None)
                  # Mypy cannot understand hasattr yet
                  models.estimators_ = self.models  # type: ignore[attr-defined]
  
      self.backend.save_numrun_to_dir(
          seed=self.seed,
          idx=self.num_run,
          budget=self.budget,
          model=self.model
          if "model" not in self.disable_file_output
          else None,
          cv_model=models
          if "cv_model" not in self.disable_file_output
          else None,
          # TODO: below line needs to be deleted once backend is updated
          valid_predictions=None,
          ensemble_predictions=(
              Y_optimization_pred
              if "y_optimization" not in self.disable_file_output
              else None
          ),
          test_predictions=(
              Y_test_pred
              if "y_test" not in self.disable_file_output
              else None
          ),
      )
  
      return None, {}
  
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 6 for calling the AbstractEvaluator obj's underlying member method code with example usage: obj.file_output(Y_optimization_pred, Y_test_pred) and returns: None,  dict, None,  dict, int,  dict, None,  dict, int,  dict