code: def postprocess_batch_predictions(self, output: Dict) -> Dict:
      if "predicted_answer_ids" in output.keys():
          predicted_text = [
              self.tokenizer.decode(
                  ids, skip_special_tokens=True
              ).strip()
              for ids in output["predicted_answer_ids"]
          ]
  
          output["predicted_text"] = np.array(predicted_text)
          output["predicted_answer_ids"] = output[
              "predicted_answer_ids"
          ].detach()
      return output
  
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 5 for calling the CustomDataset obj's underlying member method code with example usage: obj.postprocess_batch_predictions(output) and returns: output