code: def test_eval_test_all_loss_functions(self):
      eval_t(
          queue=self.queue,
          backend=self.backend,
          config=self.configuration,
          metrics=[accuracy],
          seed=1,
          num_run=1,
          scoring_functions=SCORER_LIST,
          output_y_hat_optimization=False,
          include=None,
          exclude=None,
          disable_file_output=False,
          instance=self.dataset_name,
          port=self.port,
          additional_components=dict(),
      )
      return_value = read_queue(self.queue)
      self.assertEqual(len(return_value), 1)
  
      # Note: All metric here should be minimized
      fixture = {
          "accuracy": 0.040000000000000036,
          "balanced_accuracy": 0.02777777777777779,
          "f1_macro": 0.0341005967604433,
          "f1_micro": 0.040000000000000036,
          "f1_weighted": 0.039693094629155934,
          "log_loss": 0.13966929787769913,
          "precision_macro": 0.03703703703703709,
          "precision_micro": 0.040000000000000036,
          "precision_weighted": 0.03555555555555556,
          "recall_macro": 0.02777777777777779,
          "recall_micro": 0.040000000000000036,
          "recall_weighted": 0.040000000000000036,
          "num_run": -1,
      }
  
      additional_run_info = return_value[0]["additional_run_info"]
      for key, value in fixture.items():
          self.assertAlmostEqual(
              additional_run_info[key], fixture[key], msg=key
          )
      self.assertEqual(
          len(additional_run_info),
          len(fixture) + 1,
          msg=sorted(additional_run_info.items()),
      )
      self.assertIn("duration", additional_run_info)
      self.assertAlmostEqual(
          return_value[0]["loss"], 0.040000000000000036
      )
      self.assertEqual(return_value[0]["status"], StatusType.SUCCESS)
  
imports: import copy
  import json
  import logging.handlers
  import multiprocessing
  import os
  import shutil
  import sys
  import tempfile
  import numpy
  from smac.tae import StatusType
  from autosklearn.automl_common.common.utils.backend import Backend
  from autosklearn.constants import BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION, MULTILABEL_CLASSIFICATION, REGRESSION
  from autosklearn.evaluation.test_evaluator import TestEvaluator, eval_t
  from autosklearn.evaluation.util import read_queue
  from autosklearn.metrics import accuracy, balanced_accuracy, f1_macro, r2
  from autosklearn.util.pipeline import get_configuration_space
  import unittest
  import unittest.mock
  import test.conftest
  from evaluation_util import SCORER_LIST, BaseEvaluatorTest, get_dataset_getters, get_evaluation_backend, get_multiclass_classification_datamanager
label_desc: Use image node_id 5 for calling the FunctionsTest obj's underlying member method code with example usage: obj.test_eval_test_all_loss_functions() without return types