code: def test_validator_iteration(data):
      train_index = [0, 1]
      test_indexes = [[2, 3]]
  
      result = validator_iteration(
          data, train_index, test_indexes, 1, train_fn, eval_fn
      )
  
      assert result["fold_num"] == 1
      assert result["train_log"]["xgb_classification_learner"][
          "features"
      ] == ["f1"]
      assert result["eval_results"][0]["some_score"] == 1.2
  
      # test return_eval_fn_on_train=True
      result = validator_iteration(
          data,
          train_index,
          test_indexes,
          1,
          train_fn,
          eval_fn,
          False,
          True,
      )
      assert result["train_log"]["eval_results"]["some_score"] == 1.2
  
      # test empty dataset warning
      with warnings.catch_warnings(record=True) as w:
          warnings.simplefilter("always")
          validator_iteration(
              data, [], test_indexes, 1, train_fn, eval_fn
          )
          assert len(w) == 1
          assert "train_data.shape is (0, 1)" in str(w[-1].message)
  
file_path: fklearn/tests/validation/test_validator.py
imports: import warnings
  from datetime import datetime, timedelta
  import pandas
  from toolz.functoolz import identity
  from fklearn.training.classification import lgbm_classification_learner
  from fklearn.validation import splitters, evaluators
  from fklearn.validation.validator import validator_iteration, validator, parallel_validator
  from fklearn.validation.perturbators import perturbator, nullify
  import pytest
label_desc: Use image node_id 5 for calling a global function with example usage: test_validator_iteration(data) without return types