code: def parallel_validator_iteration(
      train_data: pd.DataFrame,
      fold: Tuple[int, Tuple[pd.Index, pd.Index]],
      train_fn: LearnerFnType,
      eval_fn: EvalFnType,
      predict_oof: bool,
      return_eval_logs_on_train: bool = False,
      verbose: bool = False,
  ) -> LogType:
      (fold_num, (train_index, test_indexes)) = fold
      return validator_iteration(
          train_data,
          train_index,
          test_indexes,
          fold_num,
          train_fn,
          eval_fn,
          predict_oof,
          return_eval_logs_on_train,
          verbose,
      )
  
file_path: fklearn/src/fklearn/validation/validator.py
imports: import gc
  import inspect
  import warnings
  from typing import Dict, Tuple, List
  import pandas
  from joblib import Parallel, delayed
  from toolz import compose
  from toolz.curried import assoc, curry, dissoc, first, map, partial, pipe
  from toolz.functoolz import identity
  from tqdm import tqdm
  from fklearn.types import EvalFnType, LearnerFnType, LogType
  from fklearn.types import SplitterFnType, ValidatorReturnType, PerturbFnType
label_desc: Use image node_id 3 for calling a global function with example usage: parallel_validator_iteration(train_data, fold, train_fn, eval_fn, predict_oof, return_eval_logs_on_train, verbose) and returns: validator_iteration