code: def get_labels(self, prompt_encodings, answer_encodings):
      if (
          self.mode == "train"
      ):  # no labels required for RLHF during training
          return dict()
      else:
          return super().get_labels(prompt_encodings, answer_encodings)
  
file_path: h2o-llmstudio/llm_studio/src/datasets/text_rlhf_modeling_ds.py
imports: import logging
  from typing import Any, Dict, List
  import numpy
  import pandas
  import torch
  from llm_studio.src.datasets.text_causal_language_modeling_ds import CustomDataset
  from llm_studio.src.datasets.text_utils import TEXT_SEPARATOR
label_desc: Use image node_id 3 for calling the CustomDataset obj's underlying member method code with example usage: obj.get_labels(prompt_encodings, answer_encodings) and returns: dict, super