code: def preprocess(
      self,
      prompt_text,
      prefix="",
      handle_long_generation=None,
      **generate_kwargs,
  ):
      (
          prompt_text,
          num_prompt_tokens,
      ) = H2OTextGenerationPipeline.limit_prompt(
          prompt_text, self.tokenizer
      )
  
      data_point = dict(
          context=self.context,
          instruction=prompt_text,
          input=self.iinput,
      )
      if self.prompter is not None:
          prompt_text = self.prompter.generate_prompt(data_point)
      self.prompt_text = prompt_text
      self.prompts.append(prompt_text)
      if handle_long_generation is None:
          # forces truncation of inputs to avoid critical failure
          handle_long_generation = None  # disable with new approaches
      return super().preprocess(
          prompt_text,
          prefix=prefix,
          handle_long_generation=handle_long_generation,
          **generate_kwargs,
      )
  
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 4 for calling the H2OTextGenerationPipeline obj's underlying member method code with example usage: obj.preprocess(prompt_text, prefix, handle_long_generation) and returns: super