code: def test_dataset_prompt_ids_are_the_same_as_for_causal_language_modeling(
      df_single_prompt,
  ):
      """
      DPO model should generate the same prompts as causal language modeling
      """
      generated_text_causal_lm = generate_causal_lm_model_input_ids(
          df_single_prompt
      )
  
      cfg = ConfigProblemBase(
          llm_backbone="h2oai/h2ogpt-4096-llama2-7b",
          dataset=ConfigDPODataset(
              system_column="system",
              prompt_column=("prompt",),
              answer_column="answer",
              rejected_answer_column="rejected_answer",
          ),
          tokenizer=ConfigNLPCausalLMTokenizer(
              max_length_prompt=256,
              max_length_answer=256,
              max_length=512,
          ),
      )
      dataset = CustomDataset(df_single_prompt, cfg, mode="train")
      generated_text = dataset[0]
  
      for key in ["prompt_input_ids", "prompt_attention_mask"]:
          assert torch.all(
              generated_text_causal_lm[key] == generated_text[key]
          ), f"{key} is not the same"
  
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 9 for calling a global function with example usage: test_dataset_prompt_ids_are_the_same_as_for_causal_language_modeling(df_single_prompt) without return types