code: def check_for_non_int_answers(cfg, df):
      answers_non_int = [
          x
          for x in df[cfg.dataset.answer_column].values
          if not is_castable_to_int(x)
      ]
      if len(answers_non_int) > 0:
          raise LLMDataException(
              f"Column {cfg.dataset.answer_column} contains non int items. "
              f"Sample values: {answers_non_int[:5]}."
          )
  
file_path: h2o-llmstudio/llm_studio/src/datasets/text_causal_classification_ds.py
imports: import logging
  from typing import Any, Dict
  import numpy
  import pandas
  import torch
  from llm_studio.src.datasets.text_causal_language_modeling_ds import CustomDataset
  from llm_studio.src.utils.exceptions import LLMDataException
label_desc: Use image node_id 1 for calling a global function with example usage: check_for_non_int_answers(cfg, df) without return types