code: def eval_t(
      queue: multiprocessing.Queue,
      config: Union[int, Configuration],
      backend: Backend,
      metrics: Sequence[Scorer],
      seed: int,
      num_run: int,
      instance: Dict[str, Any],
      scoring_functions: Optional[List[Scorer]],
      output_y_hat_optimization: bool,
      include: Optional[List[str]],
      exclude: Optional[List[str]],
      disable_file_output: bool,
      port: Optional[int],
      additional_components: Dict[str, ThirdPartyComponents],
      init_params: Optional[Dict[str, Any]] = None,
      budget: Optional[float] = None,
      budget_type: Optional[str] = None,
  ) -> None:
      evaluator = TestEvaluator(
          configuration=config,
          backend=backend,
          metrics=metrics,
          seed=seed,
          port=port,
          queue=queue,
          scoring_functions=scoring_functions,
          include=include,
          exclude=exclude,
          disable_file_output=disable_file_output,
          additional_components=additional_components,
          init_params=init_params,
      )
  
      evaluator.fit_predict_and_loss()
  
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 1 for calling a global function with example usage: eval_t(queue, config, backend, metrics, seed, num_run, instance, scoring_functions, output_y_hat_optimization, include, exclude, disable_file_output, port, additional_components, init_params, budget, budget_type) without return types