class_docstr: Path to the pre-trained GPT4All model file.
class_docstr_tok: ["Path","to","the","pre-trained","GPT4All","model","file","."]
code: def _stream(
      self,
      prompt: str,
      stop: Optional[List[str]] = None,
      run_manager: Optional[CallbackManagerForLLMRun] = None,
      **kwargs: Any,
  ) -> Iterator[GenerationChunk]:
      # parent expects only see actual new tokens, not prompt too
      total_text = ""
      for chunk in super()._stream(
          prompt, stop=stop, run_manager=run_manager, **kwargs
      ):
          # remove stop sequences from the end of the generated text
          total_text += chunk.text
          got_stop = False
          if stop:
              for stop_seq in stop:
                  if stop_seq in total_text:
                      got_stop = True
          if not got_stop:
              yield chunk
  
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 4 for calling the H2OLlamaCpp obj's underlying member method code with example usage: obj._stream(prompt, stop, run_manager) without return types