class_docstr: Dataset for Causal Language modeling.
code: def postprocess_output(
      self, cfg, df: pd.DataFrame, output: Dict
  ) -> Dict:
      if not cfg.prediction.metric == "Perplexity":
          output = self.clean_output(output, cfg)
  
      output["target_text"] = self.conversation_chain_handler.answers
  
      metric_func, _, _ = cfg.prediction.metric_class.get(
          cfg.prediction.metric
      )
  
      if "GPT" in cfg.prediction.metric:
          metrics, explanations = metric_func(
              cfg,
              output,
              df,
              raw_results=True,
          )
          output["explanations"] = explanations
      else:
          metrics = metric_func(
              cfg,
              output,
              df,
          )
      output["metrics"] = metrics
  
      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 12 for calling the CustomDataset obj's underlying member method code with example usage: obj.postprocess_output(cfg, df, output) and returns: output