class_docstr: Dataset for Causal Language modeling.
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)
          del output["predicted_answer_ids"]
      return output
  
imports: import codecs
  import collections.abc
  import logging
  from typing import Any, Dict, List, Tuple, Union
  import numpy
  import pandas
  import torch
  from torch.utils.data import Dataset
  from llm_studio.src.datasets.conversation_chain_handler import ConversationChainHandler
  from llm_studio.src.datasets.text_utils import get_tokenizer
label_desc: Use image node_id 10 for calling the CustomDataset obj's underlying member method code with example usage: obj.postprocess_batch_predictions(output) and returns: output