code: def test_disable_file_output(self):
      queue_mock = unittest.mock.Mock()
  
      rs = np.random.RandomState(1)
  
      ae = AbstractEvaluator(
          backend=self.backend_mock,
          queue=queue_mock,
          disable_file_output=True,
          metrics=[accuracy],
          port=self.port,
          additional_components=dict(),
      )
  
      predictions_ensemble = rs.rand(33, 3)
      predictions_test = rs.rand(25, 3)
  
      loss_, additional_run_info_ = ae.file_output(
          predictions_ensemble,
          predictions_test,
      )
  
      self.assertIsNone(loss_)
      self.assertEqual(additional_run_info_, {})
      # This function is never called as there is a return before
      self.assertEqual(
          self.backend_mock.save_numrun_to_dir.call_count, 0
      )
  
      for call_count, disable in enumerate(
          ["model", "cv_model"], start=1
      ):
          ae = AbstractEvaluator(
              backend=self.backend_mock,
              output_y_hat_optimization=False,
              queue=queue_mock,
              disable_file_output=[disable],
              metrics=[accuracy],
              port=self.port,
              additional_components=dict(),
          )
          ae.Y_optimization = predictions_ensemble
          ae.model = unittest.mock.Mock()
          ae.models = [unittest.mock.Mock()]
  
          loss_, additional_run_info_ = ae.file_output(
              predictions_ensemble,
              predictions_test,
          )
  
          self.assertIsNone(loss_)
          self.assertEqual(additional_run_info_, {})
          self.assertEqual(
              self.backend_mock.save_numrun_to_dir.call_count,
              call_count,
          )
          if disable == "model":
              self.assertIsNone(
                  self.backend_mock.save_numrun_to_dir.call_args_list[
                      -1
                  ][1]["model"]
              )
              self.assertIsNotNone(
                  self.backend_mock.save_numrun_to_dir.call_args_list[
                      -1
                  ][1]["cv_model"]
              )
          else:
              self.assertIsNotNone(
                  self.backend_mock.save_numrun_to_dir.call_args_list[
                      -1
                  ][1]["model"]
              )
              self.assertIsNone(
                  self.backend_mock.save_numrun_to_dir.call_args_list[
                      -1
                  ][1]["cv_model"]
              )
          self.assertIsNotNone(
              self.backend_mock.save_numrun_to_dir.call_args_list[-1][
                  1
              ]["ensemble_predictions"]
          )
          self.assertIsNotNone(
              self.backend_mock.save_numrun_to_dir.call_args_list[-1][
                  1
              ]["test_predictions"]
          )
  
      ae = AbstractEvaluator(
          backend=self.backend_mock,
          output_y_hat_optimization=False,
          queue=queue_mock,
          metrics=[accuracy],
          disable_file_output=["y_optimization"],
          port=self.port,
          additional_components=dict(),
      )
      ae.Y_optimization = predictions_ensemble
      ae.model = "model"
      ae.models = [unittest.mock.Mock()]
  
      loss_, additional_run_info_ = ae.file_output(
          predictions_ensemble,
          predictions_test,
      )
  
      self.assertIsNone(loss_)
      self.assertEqual(additional_run_info_, {})
  
      self.assertIsNone(
          self.backend_mock.save_numrun_to_dir.call_args_list[-1][1][
              "ensemble_predictions"
          ]
      )
      self.assertIsNotNone(
          self.backend_mock.save_numrun_to_dir.call_args_list[-1][1][
              "test_predictions"
          ]
      )
  
imports: import logging.handlers
  import os
  import shutil
  import sys
  import tempfile
  import numpy
  import sklearn.dummy
  from smac.tae import StatusType
  from autosklearn.automl_common.common.utils.backend import Backend, BackendContext
  from autosklearn.evaluation.abstract_evaluator import AbstractEvaluator
  from autosklearn.metrics import accuracy
  from autosklearn.pipeline.components.base import _addons
  import unittest
  import unittest.mock
  from evaluation_util import get_multiclass_classification_datamanager
label_desc: Use image node_id 4 for calling the AbstractEvaluatorTest obj's underlying member method code with example usage: obj.test_disable_file_output() without return types