code: def postprocess_output(
      self, cfg, df: pd.DataFrame, output: Dict
  ) -> Dict:
      if cfg.dataset.num_classes == 1:
          preds = output["logits"]
          preds = (
              np.array((preds > 0.0))
              .astype(int)
              .astype(str)
              .reshape(-1)
          )
      else:
          preds = output["logits"]
          preds = (
              np.array(torch.argmax(preds, dim=1))  # type: ignore[arg-type]
              .astype(str)
              .reshape(-1)
          )
      output["predicted_text"] = preds
      return super().postprocess_output(cfg, df, output)
  
file_path: h2o-llmstudio/llm_studio/src/datasets/text_causal_classification_ds.py
imports: import logging
  from typing import Any, Dict
  import numpy
  import pandas
  import torch
  from llm_studio.src.datasets.text_causal_language_modeling_ds import CustomDataset
  from llm_studio.src.utils.exceptions import LLMDataException
label_desc: Use image node_id 3 for calling the CustomDataset obj's underlying member method code with example usage: obj.postprocess_output(cfg, df, output) and returns: super