code: def postprocess(
      self,
      model_outputs,
      return_type=ReturnType.FULL_TEXT,
      clean_up_tokenization_spaces=True,
  ):
      conditional_type = (
          hasattr(self.model, "conditional_type")
          and self.model.conditional_type
      )
      records = self._postprocess(
          model_outputs,
          return_type=return_type,
          clean_up_tokenization_spaces=clean_up_tokenization_spaces,
          conditional_type=conditional_type,
      )
      key = "generated_text"
      for rec in records:
          if self.use_prompter:
              outputs = rec[key]
              if return_type == ReturnType.NEW_TEXT:
                  output_with_prompt = outputs
                  prompt = None
                  only_new_text = True
              elif conditional_type:
                  if self.prompter.botstr:
                      prompt = self.prompter.botstr
                      output_with_prompt = prompt + outputs
                      only_new_text = False
                  else:
                      prompt = None
                      output_with_prompt = outputs
                      only_new_text = True
              else:
                  output_with_prompt = outputs
                  prompt = self.prompt_text
                  only_new_text = False
              outputs = self.prompter.get_response(
                  output_with_prompt,
                  prompt=prompt,
                  only_new_text=only_new_text,
                  sanitize_bot_response=self.sanitize_bot_response,
              )
          elif self.bot in rec[key]:
              if self.human:
                  outputs = (
                      rec[key].split(self.bot)[-1].split(self.human)[0]
                  )
              else:
                  outputs = (
                      rec[key].split(self.bot)[-1].split(self.bot)[0]
                  )
          else:
              outputs = rec[key]
          rec[key] = outputs
          if self.debug:
              print(
                  "prompt: %s\noutputs: %s\n\n"
                  % (self.prompt_text, outputs),
                  flush=True,
              )
      return records
  
file_path: h2ogpt/src/h2oai_pipeline.py
imports: import os
  from transformers import TextGenerationPipeline
  from transformers.pipelines.text_generation import ReturnType
  from stopping import get_stopping
  from prompter import Prompter
label_desc: Use image node_id 6 for calling the H2OTextGenerationPipeline obj's underlying member method code with example usage: obj.postprocess(model_outputs, return_type, clean_up_tokenization_spaces) and returns: records