code: def test_empy_answer_dataset_throws_no_error(df):
      cfg = ConfigProblemBase(
          dataset=ConfigDPODataset(
              prompt_column=("prompt_column",),
              answer_column="answer_column",
              rejected_answer_column="rejected_answer_column",
              add_eos_token_to_answer=False,
              add_eos_token_to_prompt=False,
              add_eos_token_to_system=False,
          ),
      )
      for column in [
          "prompt_column",
          "answer_column",
          "rejected_answer_column",
      ]:
          values = df[column].values
          df[column] = ""
          dataset = CustomDataset(df, cfg, mode="train")
          [dataset[i] for i in range(len(dataset))]
          df[column] = values
  
file_path: h2o-llmstudio/tests/src/datasets/test_text_dpo_modeling_ds.py
imports: import numpy
  import pandas
  import pytest
  import torch
  from tqdm import tqdm
  from llm_studio.python_configs.text_causal_language_modeling_config import ConfigNLPCausalLMTokenizer
  from llm_studio.python_configs.text_dpo_modeling_config import ConfigDPODataset, ConfigProblemBase
  from llm_studio.src.datasets.text_dpo_modeling_ds import CustomDataset
label_desc: Use image node_id 6 for calling a global function with example usage: test_empy_answer_dataset_throws_no_error(df) without return types