code: def go_prepare_offline(*args, **kwargs):
      kwargs0 = kwargs["kwargs"]
      # gen.py steps should have already obtained:
      #   model+tokenizers from base_model or model_lock if required
      #   tokenizers, including tokenizers for model_lock if using inference servers even if no LLM locally
      #   score_model or reward model
      #
      # Additional steps are related to document Q/A:
      # For simplicity use gradio functions,
      #  but not API calls that would require actual gradio app up and API usage that might have issues
  
      kwargs["max_quality"] = True
      embed = True
      h2ogpt_key = ""
      file_list = [
          "tests/driverslicense.jpeg",
          "tests/CityofTshwaneWater.pdf",
          "tests/example.xlsx",
      ]
  
      inputs2 = [
          kwargs["my_db_state0"],
          kwargs["selection_docs_state0"],
          kwargs["requests_state0"],
          kwargs0["langchain_mode"],
          kwargs0["chunk"],
          kwargs0["chunk_size"],
          embed,
          kwargs["image_audio_loaders_options"],
          kwargs["pdf_loaders_options"],
          kwargs["url_loaders_options"],
          kwargs["jq_schema0"],
          kwargs["extract_frames"],
          h2ogpt_key,
      ]
  
      for fileup_output in file_list:
          inputs1 = [fileup_output]
          add_file_kwargs = dict(
              fn=kwargs["update_db_func"], inputs=inputs1 + inputs2
          )
          add_file_kwargs["fn"](*tuple(add_file_kwargs["inputs"]))
  
          # ensure normal blip (not 2) obtained
          blip2 = "CaptionBlip2"
          if blip2 in kwargs["image_audio_loaders_options"]:
              image_audio_loaders_options = kwargs[
                  "image_audio_loaders_options"
              ].copy()
              image_audio_loaders_options.remove(blip2)
  
          # ensure normal asr (not asrlarge) obtained
          asrlarge = "ASRLarge"
          if asrlarge in kwargs["image_audio_loaders_options"]:
              image_audio_loaders_options = kwargs[
                  "image_audio_loaders_options"
              ].copy()
              image_audio_loaders_options.remove(asrlarge)
  
          inputs2[8] = kwargs["image_audio_loaders_options"]
          add_file_kwargs = dict(
              fn=kwargs["update_db_func"], inputs=inputs1 + inputs2
          )
          add_file_kwargs["fn"](*tuple(add_file_kwargs["inputs"]))
  
      # FakeTokenizer etc. needs tiktoken for general tasks
      import tiktoken
  
      encoding = tiktoken.get_encoding("cl100k_base")
      assert encoding
      encoding = tiktoken.encoding_for_model("gpt-3.5-turbo")
      assert encoding
  
      # sometimes summarization needs gpt2 still
      from transformers import AutoTokenizer
  
      model_name = "gpt2"
      tokenizer = AutoTokenizer.from_pretrained(model_name)
      assert tokenizer
  
label_desc: Use image node_id 2 for calling a global function with example usage: go_prepare_offline() without return types