code: def __init__(self, df: pd.DataFrame, cfg: Any, mode: str = "train"):
      super().__init__(df=df, cfg=cfg, mode=mode)
      check_for_non_int_answers(cfg, df)
      self.answers_int = (
          df[cfg.dataset.answer_column].astype(int).values.tolist()
      )
  
      if 1 < cfg.dataset.num_classes <= max(self.answers_int):
          raise LLMDataException(
              "Number of classes is smaller than max label "
              f"{max(self.answers_int)}. Please increase the setting accordingly."
          )
      elif cfg.dataset.num_classes == 1 and max(self.answers_int) > 1:
          raise LLMDataException(
              "For binary classification, max label should be 1 but is "
              f"{max(self.answers_int)}."
          )
      if min(self.answers_int) < 0:
          raise LLMDataException(
              "Labels should be non-negative but min label is "
              f"{min(self.answers_int)}."
          )
      if (
          min(self.answers_int) != 0
          or max(self.answers_int) != len(set(self.answers_int)) - 1
      ):
          logger.warning(
              "Labels should start at 0 and be continuous but are "
              f"{sorted(set(self.answers_int))}."
          )
  
      if cfg.dataset.parent_id_column != "None":
          raise LLMDataException(
              "Parent ID column is not supported for classification datasets."
          )
  
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 to create a new CustomDataset object from inherited base classes: TextCausalLanguageModelingCustomDataset with example: obj = CustomDataset(df, cfg, mode)