code: def eval_iterative_cv(
      queue: multiprocessing.Queue,
      config: Union[int, Configuration],
      backend: Backend,
      resampling_strategy: Union[
          str, BaseCrossValidator, _RepeatedSplits, BaseShuffleSplit
      ],
      resampling_strategy_args: Dict[
          str, Optional[Union[float, int, str]]
      ],
      metrics: Sequence[Scorer],
      seed: int,
      num_run: int,
      instance: str,
      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,
      iterative: bool = True,
  ) -> None:
      eval_cv(
          backend=backend,
          queue=queue,
          metrics=metrics,
          config=config,
          seed=seed,
          num_run=num_run,
          resampling_strategy=resampling_strategy,
          resampling_strategy_args=resampling_strategy_args,
          scoring_functions=scoring_functions,
          output_y_hat_optimization=output_y_hat_optimization,
          include=include,
          exclude=exclude,
          disable_file_output=disable_file_output,
          port=port,
          additional_components=additional_components,
          init_params=init_params,
          budget=budget,
          budget_type=budget_type,
          iterative=iterative,
          instance=instance,
      )
  
imports: from typing import Any, Dict, List, Optional, Sequence, Tuple, TypeVar, Union, cast
  import copy
  import json
  import logging
  import multiprocessing
  import warnings
  import numpy
  import pandas
  import pandas
  import scipy.sparse
  from ConfigSpace import Configuration
  from sklearn.base import BaseEstimator
  from sklearn.model_selection import BaseCrossValidator, KFold, PredefinedSplit, ShuffleSplit, StratifiedKFold, StratifiedShuffleSplit, train_test_split
  from sklearn.model_selection._split import BaseShuffleSplit, _RepeatedSplits
  from smac.tae import StatusType, TAEAbortException
  from autosklearn.automl_common.common.utils.backend import Backend
  from autosklearn.constants import CLASSIFICATION_TASKS, MULTILABEL_CLASSIFICATION, MULTIOUTPUT_REGRESSION, REGRESSION_TASKS
  from autosklearn.data.abstract_data_manager import AbstractDataManager
  from autosklearn.data.validation import SUPPORTED_FEAT_TYPES, SUPPORTED_TARGET_TYPES
  from autosklearn.evaluation.abstract_evaluator import TYPE_ADDITIONAL_INFO, AbstractEvaluator, _fit_and_suppress_warnings
  from autosklearn.evaluation.splitter import CustomStratifiedKFold, CustomStratifiedShuffleSplit
  from autosklearn.metrics import Scorer
  from autosklearn.pipeline.base import PIPELINE_DATA_DTYPE
  from autosklearn.pipeline.components.base import IterativeComponent, ThirdPartyComponents
  from autosklearn.util.logging_ import PicklableClientLogger
label_desc: Use image node_id 11 for calling a global function with example usage: eval_iterative_cv(queue, config, backend, resampling_strategy, resampling_strategy_args, metrics, seed, num_run, instance, scoring_functions, output_y_hat_optimization, include, exclude, disable_file_output, port, additional_components, init_params, budget, budget_type, iterative) without return types