code: def get_cv_indices(folds, cv_params, input_data, target_data):
      """Produce iterables of cross validation indices in the shape of (n_repeats, n_folds)
  
      Parameters
      ----------
      folds: Instance of `cv_type`
          Cross validation folds object, whose :meth:`split` receives `input_data` and `target_data`
      cv_params: Dict
          Parameters given to instantiate `folds`. Must contain `n_splits`. May contain `n_repeats`
      input_data: pandas.DataFrame
          Input data to be split by `folds`, to which yielded indices will correspond
      target_data: pandas.DataFrame
          Target data to be split by `folds`, to which yielded indices will correspond
  
      Yields
      ------
      Generator
          Cross validation indices in shape of (<n_repeats or 1>, <n_splits>)
      """
      indices = folds.split(input_data, target_data)
  
      for rep in range(cv_params.get("n_repeats", 1)):
          yield (next(indices) for _ in range(cv_params["n_splits"]))
  
imports: from hyperparameter_hunter.algorithm_handlers import identify_algorithm, identify_algorithm_hyperparameters
  from hyperparameter_hunter.data import TrainDataset, OOFDataset, HoldoutDataset, TestDataset
  from hyperparameter_hunter.experiment_core import ExperimentMeta
  from hyperparameter_hunter.feature_engineering import FeatureEngineer
  from hyperparameter_hunter.i_o.exceptions import EnvironmentInactiveError, EnvironmentInvalidError, RepeatedExperimentError
  from hyperparameter_hunter.i_o.recorders import RecorderList
  from hyperparameter_hunter.keys.makers import HyperparameterKeyMaker
  from hyperparameter_hunter.metrics import ScoringMixIn, get_formatted_target_metric
  from hyperparameter_hunter.models import model_selector
  from hyperparameter_hunter.settings import G
  from hyperparameter_hunter.utils.file_utils import RetryMakeDirs
  from abc import abstractmethod
  from copy import deepcopy
  from inspect import isclass
  import numpy
  import pandas
  import random
  import shutil
  from sys import exc_info
  from uuid import uuid4
  import warnings
  import sklearn.utils
label_desc: Use image node_id 1 for calling a global function with example usage: get_cv_indices(folds, cv_params, input_data, target_data) without return types