code: def _postprocess(
      self,
      model_outputs,
      return_type=ReturnType.FULL_TEXT,
      clean_up_tokenization_spaces=True,
      conditional_type=False,
  ):
      generated_sequence = model_outputs["generated_sequence"][0]
      input_ids = model_outputs["input_ids"]
      prompt_text = model_outputs["prompt_text"]
      generated_sequence = generated_sequence.numpy().tolist()
      records = []
      for sequence in generated_sequence:
          if return_type == ReturnType.TENSORS:
              record = {"generated_token_ids": sequence}
          elif return_type in {
              ReturnType.NEW_TEXT,
              ReturnType.FULL_TEXT,
          }:
              # Decode text
              text = self.tokenizer.decode(
                  sequence,
                  skip_special_tokens=True,
                  clean_up_tokenization_spaces=clean_up_tokenization_spaces,
              )
              if conditional_type:
                  all_text = text
              else:
                  # Remove PADDING prompt of the sequence if XLNet or Transfo-XL model is used
                  if input_ids is None:
                      prompt_length = 0
                  else:
                      prompt_length = len(
                          self.tokenizer.decode(
                              input_ids[0],
                              skip_special_tokens=True,
                              clean_up_tokenization_spaces=clean_up_tokenization_spaces,
                          )
                      )
  
                  if return_type == ReturnType.FULL_TEXT:
                      all_text = prompt_text + text[prompt_length:]
                  else:
                      all_text = text[prompt_length:]
  
              record = {"generated_text": all_text}
          records.append(record)
  
      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 5 for calling the H2OTextGenerationPipeline obj's underlying member method code with example usage: obj._postprocess(model_outputs, return_type, clean_up_tokenization_spaces, conditional_type) and returns: records