code: def validator_iteration(
      data: pd.DataFrame,
      train_index: pd.Index,
      test_indexes: pd.Index,
      fold_num: int,
      train_fn: LearnerFnType,
      eval_fn: EvalFnType,
      predict_oof: bool = False,
      return_eval_logs_on_train: bool = False,
      verbose: bool = False,
  ) -> LogType:
      """
      Perform an iteration of train test split, training and evaluation.
  
      Parameters
      ----------
      data : pandas.DataFrame
          A Pandas' DataFrame with training and testing subsets
  
      train_index : numpy.Array
          The index of the training subset of `data`.
  
      test_indexes : list of numpy.Array
          A list of indexes of the testing subsets of `data`.
  
      fold_num : int
          The number of the fold in the current iteration
  
      train_fn : function pandas.DataFrame -> prediction_function, predictions_dataset, logs
          A partially defined learning function that takes a training set and
          returns a predict function, a dataset with training predictions and training
          logs.
  
      eval_fn : function pandas.DataFrame -> dict
          A partially defined evaluation function that takes a dataset with prediction and
          returns the evaluation logs.
  
      predict_oof : bool
          Whether to return out of fold predictions on the logs
  
      return_eval_logs_on_train : bool
          Whether to apply eval_fn to the training set and return the resulting logs in the train log
  
      verbose : bool
          Whether to print the starting status of the fold to be evaluated.
  
      Returns
      ----------
      A log-like dictionary evaluations.
      """
  
      train_data = data.iloc[train_index]
  
      empty_set_warn = (
          "Splitter on validator_iteration in generating an empty training dataset. train_data.shape is %s"
          % str(train_data.shape)
      )
      warnings.warn(empty_set_warn) if train_data.shape[0] == 0 else None  # type: ignore
  
      predict_fn, train_out, train_log = train_fn(train_data)
      if return_eval_logs_on_train:
          train_log = assoc(
              train_log, "eval_results", eval_fn(train_out)
          )
  
      eval_results = []
      oof_predictions = []
  
      if verbose:
          print(f"Running validation for {fold_num} fold.")
      for test_index in tqdm(test_indexes) if verbose else test_indexes:
          test_predictions = predict_fn(data.iloc[test_index])
          eval_results.append(eval_fn(test_predictions))
          if predict_oof:
              oof_predictions.append(test_predictions)
  
      logs = {
          "fold_num": fold_num,
          "train_log": train_log,
          "eval_results": eval_results,
      }
  
      return (
          assoc(logs, "oof_predictions", oof_predictions)
          if predict_oof
          else logs
      )
  
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 1 for calling a global function with example usage: validator_iteration(data, train_index, test_indexes, fold_num, train_fn, eval_fn, predict_oof, return_eval_logs_on_train, verbose) and returns: unknown