class_docstr: Path to the pre-trained GPT4All model file.
class_docstr_tok: ["Path","to","the","pre-trained","GPT4All","model","file","."]
code: def _call(
      self,
      prompt: str,
      stop: Optional[List[str]] = None,
      run_manager: Optional[CallbackManagerForLLMRun] = None,
      **kwargs,
  ) -> str:
      verbose = False
  
      inner_tokenizer = FakeTokenizer(
          tokenizer=self.client,
          is_llama_cpp=True,
          model_max_length=self.n_ctx,
      )
      assert inner_tokenizer is not None
      from h2oai_pipeline import H2OTextGenerationPipeline
  
      (
          prompt,
          num_prompt_tokens,
      ) = H2OTextGenerationPipeline.limit_prompt(
          prompt, inner_tokenizer, max_prompt_length=self.n_ctx
      )
  
      # use instruct prompting
      data_point = dict(
          context=self.context, instruction=prompt, input=self.iinput
      )
      prompt = self.prompter.generate_prompt(data_point)
      self.count_input_tokens += self.get_num_tokens(prompt)
      self.prompts.append(prompt)
      if stop is None:
          stop = []
      stop.extend(self.prompter.stop_sequences)
  
      if verbose:
          print("_call prompt: %s" % prompt, flush=True)
  
      if self.streaming:
          # parent handler of streamer expects to see prompt first else output="" and lose if prompt=None in prompter
          text = ""
          for token in self.stream(input=prompt, stop=stop):
              # for token in self.stream(input=prompt, stop=stop, run_manager=run_manager):
              text_chunk = token  # ["choices"][0]["text"]
              text += text_chunk
          self.count_output_tokens += self.get_num_tokens(text)
          text = self.remove_stop_text(text, stop=stop)
          return text
      else:
          params = self._get_parameters(stop)
          params = {**params, **kwargs}
          result = self.client(prompt=prompt, **params)
          text = result["choices"][0]["text"]
          self.count_output_tokens += self.get_num_tokens(text)
          text = self.remove_stop_text(text, stop=stop)
          return text
  
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 H2OLlamaCpp obj's underlying member method code with example usage: obj._call(prompt, stop, run_manager) and returns: text, text