code: def generate_causal_lm_model_input_ids(df):
      from llm_studio.python_configs.text_causal_language_modeling_config import (
          ConfigNLPCausalLMDataset,
      )
      from llm_studio.python_configs.text_causal_language_modeling_config import (
          ConfigProblemBase as ConfigCausalLMProblemBase,
      )
      from llm_studio.src.datasets.text_causal_language_modeling_ds import (
          CustomDataset as CausalLMCustomDataset,
      )
  
      cfg = ConfigCausalLMProblemBase(
          llm_backbone="h2oai/h2ogpt-4096-llama2-7b",
          dataset=ConfigNLPCausalLMDataset(
              system_column="system",
              prompt_column=("prompt",),
              answer_column="answer",
          ),
          tokenizer=ConfigNLPCausalLMTokenizer(
              max_length_prompt=256,
              max_length_answer=256,
              max_length=512,
          ),
      )
      dataset = CausalLMCustomDataset(df, cfg, mode="train")
      return dataset[0]
  
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 8 for calling a global function with example usage: generate_causal_lm_model_input_ids(df) and returns: dataset