class_docstr: Langchain LLM wrapper for a local LLM
class_docstr_tok: ["Langchain","LLM","wrapper","for","a","local","LLM"]
code: def _call(
      self,
      prompt: str,
      stop: Optional[List[str]] = None,
      run_manager: Optional[CallbackManagerForLLMRun] = None,
      **kwargs: Any,
  ) -> str:
      logger.info(
          f"kwargs:{kwargs}\nstop:{stop}\nprompt:{prompt}",
          verbose=self.verbose,
      )
      for k in self.kwargs:
          if k not in kwargs:
              kwargs[k] = self.kwargs[k]
  
      generate_args = {
          k: kwargs[k] for k in kwargs if k not in ["stop", "n"]
      }
      out = self.api.generate(prompt, **generate_args)
      if isinstance(stop, list) and len(stop) != 0:
          for stopping_words in stop:
              if stopping_words in out:
                  out = out.split(stopping_words)[0]
      logger.info(f"{prompt}{out}", verbose=self.verbose)
      return out
  
imports: from typing import Any, List, Mapping, Optional
  import torch
  from colossalqa.local.utils import get_response, post_http_request
  from colossalqa.mylogging import get_logger
  from langchain.callbacks.manager import CallbackManagerForLLMRun
  from langchain.llms.base import LLM
  from transformers import AutoModelForCausalLM, AutoTokenizer
label_desc: Use image node_id 2 for calling the ColossalLLM obj's underlying member method code with example usage: obj._call(prompt, stop, run_manager) and returns: out