code: def _call(
      self,
      prompt: str,
      stop: Optional[List[str]] = None,
      run_manager: Optional[CallbackManagerForLLMRun] = None,
      **kwargs: Any,
  ) -> str:
      assert self.tokenizer is not None
      from h2oai_pipeline import H2OTextGenerationPipeline
  
      (
          prompt,
          num_prompt_tokens,
      ) = H2OTextGenerationPipeline.limit_prompt(prompt, self.tokenizer)
  
      # NOTE: TGI server does not add prompting, so must do here
      data_point = dict(
          context=self.context, instruction=prompt, input=self.iinput
      )
      prompt = self.prompter.generate_prompt(data_point)
  
      text = ""
      for text1 in self.stream(
          prompt=prompt, stop=stop, run_manager=run_manager
      ):
          text = text1
      return text
  
imports: from functools import partial
  from langchain.llms.base import LLM
  from langchain.callbacks.manager import CallbackManagerForLLMRun
  from typing import Any, Dict, List, Optional
  from exllama.model import ExLlama, ExLlamaCache, ExLlamaConfig
  from exllama.tokenizer import ExLlamaTokenizer
  from exllama.generator import ExLlamaGenerator
  from exllama.lora import ExLlamaLora
  import os, glob
  from pydantic.v1 import root_validator
label_desc: Use image node_id 7 for calling the Exllama obj's underlying member method code with example usage: obj._call(prompt, stop, run_manager) and returns: text