code: def _call(
      self,
      prompt: str,
      stop: Optional[List[str]] = None,
      run_manager: Optional[CallbackManagerForLLMRun] = None,
      **kwargs,
  ) -> str:
      # Roughly 4 chars per token if natural language
      n_ctx = 2048
      prompt = prompt[-self.max_tokens * 4 :]
  
      # use instruct prompting
      data_point = dict(
          context=self.context, instruction=prompt, input=self.iinput
      )
      prompt = self.prompter.generate_prompt(data_point)
  
      verbose = False
      if verbose:
          print("_call prompt: %s" % prompt, flush=True)
      # FIXME: GPT4ALl doesn't support yield during generate, so cannot support streaming except via itself to stdout
      return super()._call(prompt, stop=stop, run_manager=run_manager)
  
imports: import inspect
  import os
  from typing import Dict, Any, Optional, List, Iterator
  from langchain.callbacks.manager import CallbackManagerForLLMRun
  from langchain.schema.output import GenerationChunk
  from langchain.llms import gpt4all
  from pydantic.v1 import root_validator
  from utils import FakeTokenizer, url_alive, download_simple, clear_torch_cache, n_gpus_global
  from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
  from langchain.llms import LlamaCpp
label_desc: Use image node_id 2 for calling the H2OGPT4All obj's underlying member method code with example usage: obj._call(prompt, stop, run_manager) and returns: super