code: def get_encodings(self, input_text_dict):
      (
          system_encoding,
          prompt_encodings,
          answer_encodings,
      ) = super().get_encodings(input_text_dict)
      # remove last ground truth answer,
      # as RLHF will generate the answer from the prompt
      answer_encodings[-1] = torch.empty(0)
      return system_encoding, 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 4 for calling the CustomDataset obj's underlying member method code with example usage: obj.get_encodings(input_text_dict) and returns: system_encoding,  prompt_encodings,  answer_encodings